COMPARISON OF DIFFERENT CONJOINT APPROACHES · reference analysis and discuss which method’s...

BUNDLING OF DIGITAL INFORMATION GOODS:

A COMPARISON OF DIFFERENT CONJOINT APPROACHES

MASTER’S THESIS

MATTHIAS LAUBE

Thesis Supervisor: PROF. DR. FLORIAN STAHL

Chair: Quantitative Marketing

Faculty: Department of Business Administration

Institution: University of Zurich

Submission Date: August 21, 2013

Author Details

Full Name: Matthias Andreas Laube Matriculation No. 06-914-543

Email: [email protected] Mobile No. +4176 479 4544

II

ABSTRACT

This master’s thesis compares the results of a Choice-Based Conjoint Analysis (CBCA) and a

Menu-Based Conjoint Analysis (MBCA) study who investigated the same pricing issue for a

European newspaper company. En route, I compile an exhaustive overview on the MBCA

literature and assemble the biggest theory reading on MBCA to date. The two investigated studies

find different bundling strategies to be optimal and I explain why this is the case and outline how

the results can be compared. Specifically, I identify important differences in the market

simulations of the bundling scenarios which can explain the different strategy proposals. Since

the two methods also delivered different levels of maximum revenue forecasts, I conduct a cross-

reference analysis and discuss which method’s forecasts should be trusted more. I deduce four

hypotheses in the theory part which I subsequently test in Chapter 4. The most fascinating

hypothesis-driven finding concerns price sensitivity. While MBCA is expected to yield higher

price sensitivity, this turns out to be untrue when using the slope of the demand curve as measure

for price sensitivity. But when looking at the quantity demanded at the same price level, MBCA

is in fact more price sensitive. Finally, I draw four managerial implications from my findings,

where the most important one addresses the hypothetical nature of both CBCA and MBCA.

Acknowledgements

First and foremost, I would like to thank my supervisor, Professor Stahl, for his guidance and

support. I am also grateful to Katja Werder and Robert Ung who both kindly took time to answer

my questions regarding study analysis details. I am further obliged to Bryan Orme and Brian

McEwan from Sawtooth Software, who answered my questions in the Sawtooth Software forum

on minimum sample sizes for HB estimation and attribute coding in CBCA (cf. References).

Additionally, I would like to express my gratitude towards Bryan Orme for sending me a copy of

the 2003 ART Forum slides of Bakken and Bremer.

III

TABLE OF CONTENTS

1 INTRODUCTION ........................................................................................................ 1

2 LITERATURE REVIEW ............................................................................................. 2

3 THEORETICAL FOUNDATIONS ................................................................................. 4

3.1 Bundling .................................................................................................................................... 4

3.2 Measuring the Willingness-to-Pay .......................................................................................... 7

3.3 Conjoint Analysis: An Overview ............................................................................................ 9

3.3.1 Definitions ......................................................................................................................... 10

3.3.2 A Brief History of Conjoint Analysis ............................................................................... 11

3.3.3 Which Method Should Be Used? ...................................................................................... 13

3.4 Choice-Based Conjoint Analysis ........................................................................................... 13

3.4.1 CBC Methodology ............................................................................................................ 13

3.4.2 Advantages of CBCA ........................................................................................................ 16

3.4.3 Disadvantages of CBCA ................................................................................................... 16

3.4.4 Summary ........................................................................................................................... 17

3.5 Menu-Based Conjoint Analysis ............................................................................................. 18

3.5.1 MBC Methodology ........................................................................................................... 18

3.5.2 Conceptual Differences between CBCA and MBCA ....................................................... 25

3.5.3 Advantages of MBCA ....................................................................................................... 26

3.5.4 Disadvantages of MBCA .................................................................................................. 28

3.5.5 Comparing the Simple and Extended Menu Approach ..................................................... 30

3.5.6 Summary and Hypotheses ................................................................................................. 30

4 COMPARISON BETWEEN CBC AND MBC STUDY RESULTS................................. 32

4.1 Study Overview ...................................................................................................................... 32

4.2 Product and Bundle Overview .............................................................................................. 33

4.3 Result Comparison ................................................................................................................. 35

4.3.1 Overview and Bundling Strategy ...................................................................................... 36

IV

4.3.2 Cross-Reference Analysis ................................................................................................. 38

4.3.3 Cannibalization Potential and Substitutability .................................................................. 42

4.3.4 Content and Form Utility .................................................................................................. 46

4.3.5 Price Sensitivity ................................................................................................................. 48

4.3.6 Negatively Valued Attributes ............................................................................................ 52

5 DISCUSSION ........................................................................................................... 54

5.1 Differences in the Study Designs ........................................................................................... 54

5.2 Differences in the Study Analyses ......................................................................................... 61

5.3 Theoretical Differences .......................................................................................................... 66

5.4 Conclusion ............................................................................................................................... 69

6 MANAGERIAL IMPLICATIONS ............................................................................... 69

7 CONCLUDING REMARKS ....................................................................................... 72

8 REFERENCES ......................................................................................................... 75

APPENDIX A.............................................................................................................. 87

APPENDIX B .............................................................................................................. 97

B.1 Price ........................................................................................................................................ 97

B.2 Revenue ................................................................................................................................... 98

APPENDIX C............................................................................................................ 103

APPENDIX D............................................................................................................ 107

APPENDIX E ............................................................................................................ 110

V

LIST OF TABLES

Table 1: Advantages and Drawbacks of State-of-the-Art CBCA .................................................. 18

Table 2: Overview of MBC Data Analysis Methodologies ........................................................... 24

Table 3: Advantages and Drawbacks of MBCA ............................................................................ 31

Table 4: Product Overview and Categorization ............................................................................. 34

Table 5: Bundle Overview ............................................................................................................. 35

Table 6: Overview on the Revenue Maximizing Cases ................................................................. 36

Table 7: Overview on the Revenue Maximizing Cases (Normalized) ........................................... 36

Table 8: Prices and Normalized Revenues for Bundle 1 Components .......................................... 40

Table 9: Prices and Normalized Revenues for Bundle 6 Components .......................................... 40

Table 10: Summary of the Cross-Reference Analysis ................................................................... 41

Table 11: Difference Between Stand-Alone Estimation Approaches – Bundles 1, 2 & 3 ............. 43

Table 12: Difference Between Stand-Alone Estimation Approaches – Bundles 4, 8 & 9 ............. 43

Table 13: Pure Stand Alone Revenues and Added Value of Additional Products ........................ 45

Table 14: Added Value of Season Pass .......................................................................................... 45

Table 15: Revenue Differences between MBC Pure Bundles and Their Highest Valued Part...... 45

Table 16: Pure Single Product Revenue for CBC and MBC Methods .......................................... 47

Table 17: Slopes of Pure Product and Pure Bundling Scenarios ................................................... 51

Table 18: CBC Pure Bundling – Possibly Negative Valued Attributes ......................................... 53

Table 19: Summary of (Quality) Indicators and Their Impact ....................................................... 61

Table 20: Summary of the Analysis Indicators .............................................................................. 66

Table 21: Summary of the Theoretical Differences Applicable in This Study .............................. 68

Table 22: Exhaustive Overview on MBCA Literature ................................................................... 87

Table 23: Comprehensive Literature Overview on Choice Modeling Across Multiple Categories

........................................................................................................................................................ 92

Table 24: Calculating Weighted Prices (Bundling Scenario 1, Stand Alone Case CBC) .............. 98

Table 25: Comparing Weighted Prices (Bundling Scenario 8, Stand Alone Case) ....................... 98

Table 26: Comparing MBC and CBC Mixed Bundling Results .................................................. 101

Table 27: Comparing Mixed Bundling Cases A and D in Bundle Scenario 2 ............................. 102

Table 28: Prices and Normalized Revenue for Bundle 2 Components ........................................ 103


VI


Table 31: Prices and Normalized Revenues for Bundle 5 Components ...................................... 104

Table 32: Prices and Normalized Revenues for Bundle 7 Components ...................................... 105



Table 35: Prices and Normalized Revenues for Bundle 10 Components .................................... 106

Table 36: Preference Shares of the CBC and MBC Methods for Pure Bundling Cases 3, 5, 6 & 7

...................................................................................................................................................... 107

Table 37: Preference Shares of the CBC and MBC Methods for Pure Bundling Cases 1, 2, 4, 8, 9

& 10 .............................................................................................................................................. 108

Table 38: Preference Shares of the CBC and MBC Methods for Pure Single Product Cases ..... 108

Table 39: CBCA Pure Single Product Revenues ......................................................................... 109

Table 40: MBCA Pure Single Product Revenues ........................................................................ 109

Table 41: List of CBC Product Attributes and Their Attribute Levels ........................................ 110

Table 42: List of CBC Price Attributes and Their Attribute Levels ............................................ 111

Table 43: List of MBC Attributes and Their Price Levels ........................................................... 111

VII

LIST OF FIGURES

Figure 1: Example of a CBC Task ................................................................................................. 14

Figure 2: Example of an MBC Task .............................................................................................. 19

Figure 3: Illustration of an Extended Menu Approach .................................................................. 20

Figure 4: Typology of Market Research Methods ......................................................................... 25

Figure 5: Example of Reasonably Well Behaving Curves ............................................................. 49

Figure 6: Example of an Erratic Behaving Curve .......................................................................... 49

VIII

ABBREVIATIONS

ABYO Adaptive Build Your Own

ACA Adaptive Conjoint Analysis

ACBC Adaptive Choice-Based Conjoint

AL Auto-Logistic

APT Alternatives per Task

B Browser

BYO Build Your Own

CVA Conjoint Value Analysis

CBC Choice-Based Conjoint

CBCA Choice-Based Conjoint Analysis

CSS Choice Set Sampling

DCM Discrete Choice Modeling

DP Day Pass

DYOP Design Your Own Product

EA Exhaustive Alternatives

EBA Elimination by Aspects

EMA Extended Menu Approach

GP Game Pass

HB Hierarchical Bayes

ICBC Incentive-aligned Choice-Based Conjoint

ICE Individual Choice Estimation

IIA Independence of Irrelevant Alternatives

ISC Independent Serial Choice

LC Latent Class

MBC Menu-Based Conjoint (also: Menu-Based Choice)

MBCA Menu-Based Conjoint Analysis (also: Menu-Based Choice Analysis)

IX

MCM Menu Choice Modeling

MNL Multinomial Logit

MNP Multinomial Probit

MVL Multivariate Logit

MVP Multivariate Probit

N Newspaper

NA Newspaper App

n/a not available

P Print

PDF ePaper (PDF)

RFC Randomized First Choice

SA Smartphone App

SCE Serial Cross-Effects

SEM Self-Explication Method

SL Sports League

SMA Simple Menu Approach

SP Season Pass

TA Tablet App

TPR Tasks per Respondent

VCM Volumetric CBC Model

W Website

WTP Willingness-to-Pay

1

1 INTRODUCTION

Bundling of digital information goods has been a topic of academic interest since 1995 (Stahl,

Schäfer, & Maass, 2004). While most of the research in the bundling literature is understandably

concerned with optimal bundling strategies, this master’s thesis is mainly concerned with how to

obtain the willingness-to-pay (WTP) estimates which are needed as input into such calculations.

More pronouncedly, I am going to compare two conjoint methods, Choice-Based Conjoint

Analysis (CBCA) and Menu-Based Conjoint Analysis (MBCA). CBCA is also often referred to

as discrete choice modeling (DCM) and MBCA is also often referred to as Menu-Based Choice

Analysis or menu choice modeling (MCM). While CBCA is considered the industry standard in

conjoint analysis (Orme, 2010a), MBCA is a quite recent development but already being praised

as the next big innovation in conjoint analysis (Cordella, Borghi, van der Wagt, & Loosschilder,

2012b; Huisman, 2011; Orme, 2013a). So far, there are only four studies comparing these two

methods and all of them have the same big limitation, namely just one menu task per respondent

(cf. Chapter 2). Therefore, my thesis is the first attempt to compare CBCA to MBCA without that

limitation. En route, I will first review the theory behind both methods, compiling the up-to-date

most complete theoretical description available for MBCA.

Coming back to the topic of bundling, the comparison between the two methods, which have

been analyzed in the scope of two separate master’s theses, yielded different results regarding the

optimal bundling strategy. I am therefore going to investigate why, looking at the issue from

three angles: Differences in study design, differences in the study analysis, and theoretical

differences. My imperative goal is to point out which result variations occur due to the differing

theoretical frameworks behind both methods, therefore contributing to the literature of MBCA.

But I will also tackle this issue from a pragmatic point of view and provide some valuable

insights to facilitate the ultimate pricing decision.

The rest of this master’s thesis is structured as follows: In Chapter 2 I am going to quickly review

the literature and point out the unique contribution of my thesis to the conjoint literature. In

Chapter 3, I am looking at theory which bears importance to this topic. Section 3.1 quickly

reviews important bundling papers and deduces two hypotheses. Section 3.2 explains the

hypothetical bias and why conjoint methods provide value over self-explicated WTP elicitation

methods. Section 3.3 defines conjoint analysis and provides a quick overview on its history.

2

Section 3.4 reviews the CBCA theory and compares CBCA to the traditional conjoint analysis.

Finally, Section 3.5 exhaustively looks at MBCA theory and modeling techniques and compares

MBCA to CBCA on a theoretical basis. Section 3.6 then infers two more hypotheses based on

theoretical expectations regarding MBCA. Chapter 4 then introduces, compares and comments

the results obtained in the CBCA and MBCA studies. It also investigates the four hypotheses

derived in the theory part, among some other interesting issues. In Chapter 5, I will discuss the

study design differences, study analysis differences and theoretical differences and conclude the

findings. Chapter 6 then draws some managerial implications and Chapter 7 concludes the thesis.

2 LITERATURE REVIEW

As mentioned in the introduction, the added value from this master’s thesis stems from

comparing two studies which applied different conjoint approaches, specifically CBCA and

MBCA. Sometimes, the distinction between discrete choice and menu choice can be blurred, as

in Jedidi, Jagpal and Manchanda’s (2003) model, where they analyze a bundle situation with two

goods. Only when they extend their model to n > 2, the discrete choice character will become

clear. Also, Orme (2010c) applies a study design which potentially combines discrete and menu

choice (although in his case, the menu character was prevalent). In most cases though, these two

approaches are quite distinct from each other, even implying different choice behavior as I will

show in Section 3.5. While there is ample literature on CBCA and DCM, not many papers have

been written on MBCA and MCM. Regarding the former, I would like to refer to the works of

Green, Krieger, and Wind (2001) and Hensher, Rose and Greene (2005) for an overview.

Regarding the latter, I compiled an exhaustive overview in Appendix A, which includes

methodologies, contributions and limitations of each paper and study. I divided Appendix A into

two tables, Table 22 summarizes MCM in the context of conjoint analysis, so henceforth MBCA

will refer to only that particular stream of MCM, while I will use the term MCM to refer to other

menu choice literature. Table 23 summarizes MCM in the contexts of bundling (e.g. Bradlow &

Rao, 2000; Chung & Rao, 2003) and shopping baskets (e.g. Russell & Petersen, 2000; Song &

Chintagunta, 2006, 2007). This second stream of menu choice literature is not directly relevant to

conjoint theory, but provides interesting insights on menu modeling techniques and their

dis/advantages.

3

In this chapter, I’d like to focus on the literature comparing CBCA to MBCA. As to date, there

seem to be only four attempts for a direct comparison. The first one has been conducted by

Bakken and Bayer (2001). Their respondents answered several Choice-Based Conjoint (CBC)

tasks and one Menu-Based Conjoint (MBC) task each, hence they only compared price

sensitivity and (predicted) preference shares. As a result, they found respondents to be more price

sensitive in MBC tasks. In both studies they looked at in their 2001 paper, Bakken and Bayer had

a high sample size (n=967 and n=1170) but asked only one MBC task per respondent. Bakken &

Bremer (2003) conducted study with CBCA, and added a self-explication task and an MBC task.

They were then able to estimate utilities and preference shares from the MBC data using

Bayesian modeling techniques. CBCA and MBCA share predictions differed, but not much

explication was offered in the slides. In 2006, Rice and Bakken refined that comparison approach.

They apparently used the same data as Bakken & Bremer, but a different estimation technique.

Panelists had to answer a self-explication task, 25 CBC tasks and one MBC task. Rice and

Bakken then hypothesized a conditional decision process for each attribute and panelist, allowing

them to split the MBC task into a series of attribute-specific tasks. They then estimated these

binary models with CBC/HB from Sawtooth Software. Again, they found MBCA to deliver more

price sensitive results than CBCA. Furthermore, MBCA predicted preference shares which are

highly different from the CBCA predictions for two out of four products. They had

approximately n=500 respondents which again answered only one MBC task each. Finally,

Johnson, Orme and Pinnell (2006) conducted a study with n=605 respondents, asking each

respondent to complete four CBC and one MBC task. They looked at different ways to code the

MBC data which yielded interesting insights (cf. Section 3.5.1). In their CBCA to MBCA

comparison, Johnson et al. found context effects to be present in the MBC data (respondents

tended to avoid the lowest and highest attribute levels), and found that both methods might

measure different things (cf. Section 3.5.3). Furthermore, both methods were measuring price

sensitivity poorly, which Johnson et al. explained with low sample size, the CBC questionnaire

design (untypical for CBCA, prices were shown for every attribute) and that MBC data was an

across-respondents treatment, not within-respondents treatment. With regard to hold-out task

predictions the result was sobering, which Johnson et al. explain with the CBC questionnaire

design and the fact that only CBC tasks were used as hold-out tasks, a potential disadvantage for

MBCA.

4

Therefore, this master’s thesis is the first one to compare CBCA and MBCA with more than just

one MBC task per respondent. It also includes the highest number of respondents so far, namely

n=1388 and n=1462, respectively. In order to not overburden test subjects, the idea of test

subjects answering both CBC and MBC questionnaires was abandoned. Unlike in the three

studies introduced above, the CBC and MBC data originates from different test subjects.

3 THEORETICAL FOUNDATIONS

In this chapter I am going to present the most important theoretical foundations needed to

understand and compare the studies conducted by Werder (2013) and Ung (2012). The most

important facts on bundling will be summarized in Section 3.1, culminating in a hypothesis on

the expected results of the two studies examined in Chapter 4. Stated preference data is discussed

in Section 3.2 and conjoint analysis is looked at to a bigger extent in Sections 3.3 to 3.5 because

one aim of this thesis is to advance the literature comparing CBCA to MBCA. Section 3.3 is

going to provide a quick overview on the various conjoint methods in a chronological fashion and

outline the most important forms. Since there already are comprehensive overviews on the

traditional forms of conjoint analysis and their derivatives, sections 3.4 and 3.5 will therefore

focus on CBCA and MBCA, respectively. In Section 3.5, I am also going to postulate two

hypotheses which will subsequently be tested in Chapter 4.

3.1 Bundling

The main focus of this Master’s thesis is to compare two pre-analyzed studies which used

different marketing research methodologies. So naturally, the main focus will be on those

methodologies. However, as they both investigated three forms of bundling and came to

diverging results regarding which form of bundling yields maximum revenues, an introduction to

this topic is warranted.

Bundling has been defined in narrower and broader ways. For this master’s thesis, I prefer an

encompassing definition as used by Guiltinan (1987) and endorsed by Yadav and Monroe (1993):

“Bundling is the practice of marketing two or more products and/or services in a single ‘package’

for a special price” (Guiltinan 1987, p. 74). Traditionally, three forms of bundling have been

distinguished: no bundling (also known as unbundled sales, unbundling or stand-alone sales),

pure bundling (only bundles are sold, their components aren’t available separately) and mixed

5

bundling (bundles and their components are sold jointly). Hitt and Chen (2005) also introduced

the term customized bundling, which refers to a situation where the customer has the right to buy

a predefined amount of goods out of a (much) larger pool of goods for a fixed price. Although it

has some interesting properties,1 it won’t be investigated here since it hasn’t been analyzed by

Werder (2013) or Ung (2012). Yet another form of bundling is called rebundling, which refers to

offline content that has been split apart and recomposed for online channels, e.g. article dossiers

or music playlists (Stahl et al., 2004). Bundling has been identified as a useful strategy for several

reasons. For example, Koukova, Kannan and Ratchford (2008) identify the following demand

side reasons: negatively correlated reservation prices among customers,2

goods which are

complementary in consumption and uncertainty in the valuation of a good’s quality. Supply side

reasons include cost saving via economies of scale or scope (Chuang & Sirbu, 1999), economies

of aggregation (Bakos & Brynjolfsson, 2000) and network externalities (Arthur, 1996; Bakos &

Brynjolfsson, 2000).

In the context of this master’s thesis, the interesting part will be the performance of the three

traditional forms of bundling compared against one another. Schmalensee (1984) and McAfee,

McMillan and Whinston (1989) showed that in a monopoly setting with two goods, pure

bundling and unbundling generally are weakly dominated by mixed bundling. McAfee et al. also

showed the conditions under which their results extend to an oligopoly case. Chuang and Sirbu

(1999) analyzed an n-good model and also find mixed bundling to be the dominant strategy, with

pure bundling and unbundling only being boundary cases. Li, Feng, Chen and Kou (2013) found

partial mixed bundling to be optimal, i.e. offering bundles which don’t include all products.

Kopalle, Krishna and Assunção (1999) investigated competitive environments (with

simultaneous decisions) and found that with decreasing scope for market expansion, the sub-

game perfect Nash equilibrium shifts from mixed bundling to unbundling, while pure bundling is

never an equilibrium strategy. Jedidi et al. (2003) conducted two empirical studies, considering

competition via a none-option. They find mixed bundling to be the optimal strategy in every

scenario.

1 Hitt and Chen (2005) find the mathematical formulation of customized bundling to be identical to nonlinear pricing.

Hence it is useful as a price discrimination tool. 2 I use the standard economic definition of reservation price as e.g. in Frank (2006): The price which makes an

individual indifferent between paying and not paying for a good or service.

6

There is also literature specifically considering distribution channels, which is one of the insights

the client of the study underlying this master’s thesis aims to gain. Venkatesh and Chatterjee

(2006) suggested that unbundling of online content (e.g. single articles) could be useful to skim

consumers who are not interested in the full print product. Such a pricing model could even be

combined with customized bundling. Koukova et al. (2008) distinguished between content utility

and form utility of a good. For example, an article will have the same content utility, regardless if

published online or offline. However, form utility will differ, depending on the usage situation

like the ability to search for keywords or reading while traveling (Koukova et al., 2008).

Therefore, Koukova et al. showed that content substitutes can become form complements,

especially if the different usage situations are emphasized. Furthermore, they found that discounts

play a crucial role for consumers to buy a bundle of content-substitute-form-complement goods.

Finally, Ben-Akiva and Gershenfeld (1998) and Bakken and Bond (2004) pointed out that

bundling can also be viewed from the consumer’s perspective, i.e. that bundling can simplify the

decision process by saving time and effort to evaluate other combinations. Similarly, Koukova,

Kannan and Kirmani (2012) found in three experiments that if two otherwise similar products

each dominate in a different salient attribute, consumers have to resolve a tie. This induces a

significant number of test-subjects to counter-intuitively buying the bundle which consists of

these two products (Koukova et al., 2012). Also related to this topic, Agarwal and Chatterjee

(2003) discovered in their studies that as more products have to be evaluated per bundle, the

higher the chance that the prospect will defer her decision. Finally, Myung and Mattila’s (2010)

study showed that consumers are more probable to choose a bundle which provides the highest

savings, i.e. the difference between the bundle price and the sum of its components. In

accordance with Thaler’s (1985) mental accounting theory, this finding stresses the importance of

reference prices – and hence highlights another advantage of mixed bundling.

From the bundling literature reviewed above, I infer two hypotheses:

Hypothesis 1a: Pure bundling is never an optimal strategy, because it is weakly dominated

by mixed bundling.

Hypothesis 1b: Mixed bundling will be the optimal strategy. Even though the industry

under consideration does not have much scope for market expansion, there is a certain

degree of monopolistic competition. Also, even if stand-alone products are not often

7

chosen, they likely enhance the perceived value of a customer who buys the bundle (cf.

Myung & Mattila, 2010). Furthermore, online pay walls are a relatively new phenomenon

in this industry and thus might even be seen as a quality signal by consumers, because the

firm feels confident enough to erect one.

3.2 Measuring the Willingness-to-Pay

In order to price its product, a firm needs to know what a customer wants and how much she is

willing to pay for it. So why not simply ask? Because there are several problems with stated

preference data3 to measure the willingness-to-pay:

4

Indifference Problem. Since hypothetical scenarios don’t affect the respondent’s welfare,

the respondent may be so uninterested or careless that he or she might make irrational

decisions (Morikawa, 1989).

Policy-response bias. This bias arises if the respondent believes that he or she will benefit

from answering in a certain way (Morikawa, 1989).5

Justification bias. The extent to which respondents feel that they must justify past

behavior by responding in a similar way during a survey (Morikawa, 1989).6

Price-bargaining bias. This can occur if a respondent feels that by rejecting a higher-

priced alternative he or she can influence the firm to charge a lower price (Ben-Akiva &

Gershenfeld, 1998).

Warm glow bias. In hypothetical settings, some respondents are more prone to support

good causes and follow social norms than in reality (Diamond & Hausman, 1994; Ding,

Grewal, & Liechty, 2005).

Risk bias. Respondents are likely to behave less risk-averse in hypothetical settings (Ding

et al., 2005).

Budget bias. Participants discount budget constraints in hypothetical situations (Diamond

& Hausman, 1994; Ding et al., 2005).

3 Stated preferences are derived from hypothetical behaviour, while revealed preferences are derived from actual

behavior. 4 I define the maximum willingness-to-pay in a simple fashion as the reservation price of an agent.

5 For instance, overstating the intention to use a planned good or service in the hope it will be realized.

6 For example, a person downloading copyrighted files from the internet might overstate his or her preference for

sharing.

8

Ding et al. simply used the term hypothetical bias as an umbrella term. One possibility to reduce

the impact of the hypothetical bias is to calibrate models which use stated preference data with

real data, if available (Ben-Akiva & Gershenfeld, 1998). Another possibility is to use traditional

conjoint methods or Choice-Based Conjoint. These methods force respondents to make trade-offs

between attributes and therefore conceal the direct price impact, which should alleviate several of

these biases. Also, it should be noted that except for some street markets, products usually have a

price tag. Hence Ben-Akiva and Gershenfeld’s (1998) statement that “stated preference exercises

which are realistic and meaningful to the respondent tend to elicit responses which are more

commensurate with actual choice behavior” (p. 178) intuitively makes sense and gives additional

support to conjoint analysis as opposed to self-explicated measures such as directly asking for the

willingness to pay. However, Gibson (2001) defended self-explicated measures and points out

that repetitive questioning in conjoint analysis also reveals the purpose of the study to

respondents. Furthermore, the need to limit attributes in many forms of conjoint analysis may

lead to missing out on potentially important information (Gibson, 2001). Green and Srinivasan

(1990) on the other hand point out that self-explication methods usually take less time but

redundancy may lead to double counting, among other problems. Riedesel (2003) tested CBCA

against the ‘Marder style’ self-explication approach and found that CBCA/HB only does

marginally better in hold-out choice predictions but gave a more accurate prediction of preference

shares. More important in the context of this master’s thesis, Jedidi et al. (2003) found in a

bundling study that discrete choice models clearly outperform self-explicated measures (by a

margin of 8% to 43%).

Ding et al. (2005) conducted an experiment in which they compared four methods: Hypothetical

CBC (CBC), a hypothetical self-explication method (SEM7), incentive-aligned CBC (ICBC) and

incentive-aligned self-explication (BDM8). In ICBC, respondents had to purchase their preferred

product/price combinations as calculated by the CBC model. The results clearly showed that

ICBC outperformed the other methods, followed by CBC, then BDM, and finally SEM. Ding et

al. found respondents to be more price sensitive (budget bias), more risk averse and caring less

about social norms in the incentive aligned versions than in the hypothetical counterparts. Miller,

7 Lieb (2013a) defines a self-explicated measure as “when a respondent give[s] a specific value for an attribute” (p.

4-3) as opposed to distributing points, ranking or choosing between items. 8 The BDM mechanism is described in Becker, DeGroot, and Marschak (1964). A price will be randomly determined.

If it is below the respondents stated WTP, he or she must buy the product for that random price. If the random price

is above their stated WTP, they will not be able to buy the product.

9

Hofstetter, Krohmer, and Zhang (2011) also compared above four methods and additionally used

a simulated onlineshop (REAL) to emulate a buying situation as realistic as possible (although

they admit some biases which might have remained). Using REAL as benchmark, Miller et al.

found that BDM predicts WTP the best, followed by ICBC, SEM and lastly CBC. When it comes

to forecasting pricing decisions though, CBC slightly outperformed SE. Another interesting result

they found was that much more respondents used the none-option in ICBC (19%) as opposed to

CBC (5%). Finally, in line with Ding et al., Miller et al. report price sensitivity to be the highest

for the two incentive-aligned methods, followed by the hypothetical methods and, surprisingly,

the REAL setting as least price sensitive. It needs to be mentioned that their REAL benchmark

was still an experimental setting, in addition to the fact that the product was new to the market,

hence respondents didn’t have any price experience. Also, the poor performance of CBC and

ICBC might partially be explained with their small sample size and only five tasks per respondent

to estimate the model.

In summary, the literature available so far suggests that CBC outperforms SEM and incentive-

aligned methods outperform hypothetical methods. The two pre-conducted market research

experiments which will be analyzed here use CBCA and MBCA, both hypothetical measures.

The managerial advantage of hypothetical measures is, that they cost much less than incentive-

aligned ones. More importantly, I am confident that CBCA and MBCA can outperform self-

explicated measures “because consumer WTP is a context-sensitive construct (Thaler 1985),

[therefore] the suitability of a WTP measurement method can depend on how well such a method

approximates the actual purchasing context of the underlying product and/or category” (Miller et

al., 2011, p. 182) and because Ding and Huber (2009) made a persuading argument that CBCA

can alleviate hypothetical biases like social desirability.

3.3 Conjoint Analysis: An Overview

This section will provide a general definition of conjoint analysis, explain its usefulness and

review its origin and development to date. The aim is to familiarize the reader with the most

important terms, forms, and concepts of the huge strand of literature concerned with conjoint

analysis. For readers interested in the history of conjoint analysis and other conjoint methods,

detailed overviews with references to influential papers can be found, for example, in the works

of Green and Srinivasan (1990), Green, Krieger and Wind (2001), Lieb (2013a) or Gustafsson,

Herrmann and Huber (2000).

10

3.3.1 Definitions

A clear-cut definition for the term conjoint analysis is hard to find in the literature. In most newer

papers, the term is only described and explained instead of clearly defined, whereas older

definitions often don’t encompass the whole scope of conjoint analysis.9 Drawing on Mohr,

Sengupta and Slater (2010), “conjoint analysis is a survey research tool that can statistically

predict which combination of product attributes across various brands and prices customers will

prefer to buy” (p. 193). They outlined the key mechanism of conjoint analysis as observing the

trade-offs respondents are making between different combinations of product attributes in order

to determine the importance and value of each attribute. Orme (2010a) identified the key

characteristic of conjoint analysis as “respondents evaluat[ing] product profiles composed of

multiple conjoined elements” (p. 29) and clarifies the widespread misconception that the term

‘conjoint analysis’ is not an abbreviation of ‘considered jointly’, but rather descends from the

verb ‘to conjoin’.

In order to collect conjoint data, a researcher has to find respondents who need to complete an

experimental design, often referred to as survey or interview. An experimental design consists of

one or several tasks. For example, the full-profile conjoint analysis typically consists of one task,

which is to sort a number of alternatives from most to least preferred. Choice-Based Conjoint

Analysis on the other hand consists of several tasks, and in each task respondents have to choose

one of several alternatives. An alternative, sometimes also referred to as a concept or profile, can

be a bundle, a product profile, a partial product profile or a none-option. Each alternative is

composed of several attributes. Attributes are sometimes also referred to as features or items.

Typical attributes are brand, size, color and price. In the case of bundles, an attribute usually

equals a single product. Finally, each attribute has one or several levels. For example, the

attribute color could have the five levels blue, red, silver, black and yellow or the attribute air

conditioning could exhibit the two levels ‘included’ and ‘not included’. In Menu-Based Conjoint

Analysis, tasks aren’t consisted of alternatives, instead respondents can directly choose their

preferred attributes and/or attribute levels in each task, therefore creating their own alternative

(usually a product or bundle).

9 E.g. Green and Srinivasan (1978) or Morikawa (1989) include ‘decompositional method’ as a key word in their

broad definitions. However, MBCA is a compositional method.

11

3.3.2 A Brief History of Conjoint Analysis

Many authors such as Hauser and Rao (2004); Green, Krieger and Wind (2001); and Orme

(2010a) recognized the work of Luce and Tukey (1964), published in the Journal of Mathematical

Psychology, as the birth of conjoint analysis. From that point, it took seven more years until the

seminal paper of Green and Rao (1971) introduced conjoint analysis to the field of marketing.

This early form of conjoint analysis is also known as traditional conjoint analysis, full-profile

conjoint analysis, card-sort conjoint analysis or Conjoint Value Analysis (CVA). It was initially

based on respondents sorting a set of cards with product profiles printed on them from best to

worst. Later improvements included to ask respondents to rate each card, for example on a ten-

point scale (Orme, 2010a).

In the late sixties, Johnson (1974) independently came up with the idea of working with trade-off

matrices which simplified the tasks for respondents significantly, as they only had to choose

between two partial profile cards at a time (Orme, 2010a). With the rise of personal computers,

Johnson founded Sawtooth Software and refined his trade-off analysis by exploiting the

flexibility offered by personal computers (Orme, 2010a). He wrote a program where respondents

first had to execute a self-explication task, making it a hybrid method (Green et al., 2001). The

program could then adapt the trade-off survey in real time, presenting only the most relevant

trade-off problems (Orme, 2010a). This more user friendly conjoint method encouraged more

realistic responses (Orme, 2010a) and became later known as Adaptive Conjoint Analysis (ACA).

In 1985, not only the ACA software was released but also a CVA software package; and both

included market simulation tools. Subsequently, debates on which method is the better one

ensued between the ACA and CVA camps which sparked research but also dampened

practitioners’ enthusiasm (Orme, 2010a).

The whole debate between the CVA and ACA camps became increasingly obsolete with the

appearance of discrete choice models also known as Choice-Based Conjoint Analysis (CBCA).

CBCA dates back to the precursory paper written by the econometrician McFadden (1974), using

a multinomial logit model. His approach has then been extended by Louviere and Woodworth

(1983), a paper which spawned subsequent research (Green et al., 2001). In CBCA, respondents

don’t need to rate or rank anything, instead they just have to make a choice between a set of

available alternatives. While making choices seems more natural and realistic to respondents, it is

an inefficient way to ask questions since a choice doesn’t indicate the strength of preference

12

(Orme, 2010a). Hence, initially there was typically not enough data to model individual

preferences but aggregated preferences were subject to various problems, such as independence

of irrelevant alternatives (IIA)10

and ignorance of separate preferences for subgroups (Orme,

2010a). However, the development and introduction of latent class models (segmenting

respondents into relatively homogenous preference groups) and Hierarchical Bayes (HB) models

(estimating individual level preferences from discrete choice data) helped to alleviate those

problems and by the late 1990s CBCA emerged as the most accepted conjoint method and

became the industry standard (Orme, 2010a). A good part of CBCA’s success is attributed to the

availability of commercial software tools (Orme, 2010a) which were introduced by Sawtooth

Software in 1993.

One approach to make choice tasks more realistic and enjoyable for test-subjects is called design

your own product (DYOP), which is also known as build your own (BYO). Bakken and Bayer

(2001) made the proposition to increase the value of CBCA with a BYO element; this idea was

later refined into the Adaptive Choice-Based Conjoint (ACBC) analysis, a hybrid conjoint method

developed by Sawtooth Software and launched in 2009. This method asks the respondents a BYO

task first, then follows up with screening tasks and in a last step applies the CBC questionnaire

(Johnson & Orme, 2007).

Another attempt to improve choice tasks by making them more realistic and enjoyable

incorporates the BYO approach as well, but was developed into another direction which later

became known as Menu-Based Conjoint Analysis (MBCA) or Menu-Based Choice Analysis. The

idea has first been introduced by Ben-Akiva and Gershenfeld (1998) and was based on the

observation that consumers nowadays have lots of bundle choices, often with the additional

option(s) to mix them with à la carte items. Like CBCA, demand can be modeled using discrete

choice modeling techniques (Ben-Akiva & Gershenfeld, 1998) in the sense that picking or not

picking an option is the discrete choice space. Up to date, MBCA is in high demand and even

praised as the next big trend in conjoint analysis (Huisman, 2011; Orme, 2013a; Cordella et al.,

2012b). Since early 2012 there is a commercial software package available, and if one keeps in

mind that CBCA only took off after commercial software packages have been introduced to the

market, the future of MBCA might look bright indeed.

10

This is a problem because when for example Pepsi is added to a market consisting of Coke, Sprite and hot

chocolate, it would take the same share proportion from all existing products, even though that seems illogical.

13

3.3.3 Which Method Should Be Used?

In light of all those methods, the question which one should be applied quickly arises. Important

factors are the goal of a study, the possible sample size and the number of attributes. Since the

two studies discussed in this paper have already been designed and conducted by a marketing

research company and analyzed within the scope of two other master’s theses, I couldn’t take any

influence. So the question changes to: ‘Were the chosen methods appropriate?’

According to a Sawtooth Software (2007) technical paper, ACA has an advantage in being able

to handle large amounts of attributes but has shown weaknesses in pricing research, often

underestimating the importance of the price. Since the goal of the underlying study was to gain

pricing information, ACA would not have been a wise choice. ACBC would not have been

necessary either, since the number of attributes is only four (Newspaper, Website, Sports League

and Price) in the CBC part of the underlying study, while the sample size is big enough for CBC

data to be analyzed in a meaningful fashion. Even though ACBC interviews are more engaging to

respondents, they take two to three times longer than similar CBC interviews (Sawtooth Software,

2013). And since CBCA enjoys many advantages over CVA, as outlined in the next section, the

above stated question can be answered with ‘yes’.

3.4 Choice-Based Conjoint Analysis

In this section, Choice-Based Conjoint Analysis (CBCA) will be described in more detail. Since

CBCA historically emerged from and mostly replaced the traditional forms of conjoint analysis

(CVA, ACA and their derivatives) this section focuses on pointing out advantages and

shortcomings related to the ‘older’ forms of conjoint analysis, while pros and cons regarding

Menu-Based Conjoint Analysis (MBCA) will be described in the next section.

3.4.1 CBC Methodology

As briefly outlined in the last section, CBC tasks are structured in such a way that respondents

have to choose one of several pre-designed alternatives. This induces them to make trade-offs

between the different attribute levels the presented alternatives exhibit. Balderjahn, Hedergott,

and Peyer (2009) point out that CBC analyzes discrete choices instead of preference judgments

like in CVA. Therefore, CBCA is technically a discrete choice analysis employed on a conjoint

design (Cohen, 1997). Figure 1 depicts how the CBC tasks in the study later discussed in this

thesis looked like. In every task, exactly one alternative had to be chosen.

14

Figure 1: Example of a CBC Task

Which offer would you choose?

Offer 1 Offer 2 Offer 3 Offer 4

Website

tablet app

Website

browser access

smartphone app

tablet app

Website

smartphone app

tablet app

Website

smartphone app

tablet app

I would

not choose

any of

these

offers.

Newspaper

as animated tablet app

as ePaper (PDF)

as printed edition

Newspaper


as printed edition

Newspaper


Inclusive: All videos of

one season

Inclusive: Video of a

single game per match

day

Add-on option: Video

for a single game for

0.99 € per game

Monthly

Fee 22.99 € 6.99 € 26.99 € 17.99 €

Source: Own illustration.

With respect to questionnaire design, there are four major methods for Choice-Based Conjoint

experiments. Chrzan and Orme (2000) investigated those four methods along with several

others11

in terms of relative efficiency and give some guidance which approach could be used in

which situation; however those results should be used with caution considering new estimation

techniques such as HB. The first two major methods are complete enumeration and the shortcut

method. Both are randomized designs but coded to produce near-orthogonal12

designs for each

respondent while displaying minimal overlap13

and level balance14

(Sawtooth Software, 2013).

While the high quality complete enumeration considers all possible alternatives, the faster

shortcut method attempts to build alternatives by using attribute levels least frequently shown

previously to the same respondent (Sawtooth Software, 2013). The random method utilizes

11

Such as modified versions of fractional factorial design plans (which are manually generated and hence not

randomized among respondents) and the SPSS computer optimization method. 12

Orthogonality: Attribute levels are chosen independently from other attribute levels (Sawtooth Software, 2013). 13

Minimal overlap: Each attribute level is shown as few times as possible in a single task (Sawtooth Software, 2013). 14

Level balance: Shows each attribute level an approximately equal number of times (Sawtooth Software, 2013).

Website

Logo

News-

paper

Logo

Sports

League

Logo

15

random sampling with replacement for choosing alternatives, therefore allowing attribute level

overlap within tasks (Sawtooth Software, 2013). Finally, the balanced overlap method combines

elements of the complete enumeration and random method, permitting roughly half the overlap of

the random method (Sawtooth Software, 2013). According to Sawtooth Software (2013), the

complete enumeration method estimates main effects best, but doesn’t perform as well with

interaction effects; the random method estimates interaction effects best, but is the least efficient

with main effects; the balanced overlap model is nearly as efficient as complete enumeration in

estimating main effects while measurably better with interaction effects.

With respect to data analysis, there are five major methods. The simplest one is counting choices,

also known as counts. It just divides ‘total number of times attribute level X has been chosen’ by

‘total appearance of attribute level X’. According to Sawtooth Software (2013), aggregating those

counts and taking their log should come close to the aggregate logit solutions. This intuitively

makes sense, as logit is nothing else than log-odds. However, since the logit analysis iteratively

finds the maximum likelihood solution for fitting a multinomial logit model to the data, it is

slightly more accurate than counting choices (Sawtooth Software, 2013). One should be aware

that aggregate logit still suffers from biases, for example the IIA property (Sawtooth Software,

2013) and the ‘fictitious average consumer’ bias, e.g. if 50% of respondents highly like an

attribute and 50% highly dislike it, aggregate analysis will yield a mid-level preference

(Baumgartner & Steiner, 2009). Over the years, researchers developed several methods to

overcome these shortcomings. The first is Latent Class (LC) analysis, which assumes that

respondents can be clustered into homogenous segments. Segment maximum likelihood solutions

are computed, as well as a probability estimate for each respondent regarding to which segment

he or she belongs (Huber, 1998). This allows to estimate expected individual part-worths as

probability weighted combinations of the segment part-worths (Huber, 1998).15

Sawtooth

Software refined this approach in a method called Individual Choice Estimation (ICE), by not

constraining those weights to be positive and therefore allowing more deviations of the individual

values from the segments (Huber, 1998). But with the emergence of more potent computers,

Hierarchical Bayes became the predominant method. HB can provide estimates of individual

part-worths with only a few choices per individual by ‘borrowing’ information from the

population data (Sawtooth Software, 2009b). Huber (1998) tested those three methods, finding

15

‘part-worth’ refers to attribute level utility

16

that LC has the worst performance in all three experiments, while HB outperforms ICE in one

and ties with ICE in the two others. However, he underlines the theoretical superiority of HB

over ICE and Orme (2005) finds that HB is more stable than ICE and can deliver more effective

estimates, the fewer respondent choices are available. Orme (2010b) points out that all of these

advanced techniques are multinomial logit estimations as they all employ the logit rule.16

It

should be noted, that even in HB, the choices at the individual level are still described by a

multinomial logit (MNL) model (Orme, 2005), this is at least the way it is implemented in the

Sawtooth Software CBC/HB module. Theoretically though, there are other possibilities available

like multinomial probit (MNP), hybrid logit and non-parametrical methods. But since discussing

these methods for CBC modeling would go beyond the scope of this master’s thesis, I refer to

Ben-Akiva et al. (1997).

3.4.2 Advantages of CBCA

Moore (2010) identifies the main advantage of CBCA as the fact that respondents perceive it as a

much simpler task then older forms of conjoint analysis. But there are a number of other reasons,

why CBC is considered to be a superior approach to CVA and ACA (Johnson & Orme, 2003;

Orme, 2009; Sawtooth Software 2007, 2013): First, it includes a none-option and second, it

presents more realistic tasks. In real-world situations, customers either choose a product or don’t

buy anything, as opposed to ranking or rating different products. Specifically, the none-option is

helpful for volume estimations, rather than just share-estimations (Sawtooth Software, 2007,

2013). Also, the none-option can be used to model the constant alternative, i.e. reflecting the

continuance of a current situation (Moore, 2010). Third, because CBC analysis can be done for

groups, sufficient data is available to support measuring interaction effects, which is a concern in

many pricing studies (Sawtooth Software, 2007, 2013). Fourth, CBC allows for product- or

alternative-specific attribute levels (Sawtooth Software, 2013), which allows to study two

different products at the same time. Fifth, shares-of-choice can be calculated directly, instead of

needing to stipulate a decision rule as in the case of CVA (Balderjahn et al., 2009).

3.4.3 Disadvantages of CBCA

But as it is often the case, these advantages come at a cost. Drawbacks of CBC are that it presents

lots of data to test-subjects and hence should not include more than six attributes (Sawtooth

16

Basically, the logit rule says that using the anti-log of the utility of alternatives is proportional to the choice

probabilities of those alternatives.

17

Software, 2007, 2013). But worse than that, it is not a very efficient method of data collection. In

every task, all alternatives have to be evaluated by the test-subjects but just one will be chosen

per task. In a CVA setting, each alternative will be ranked or rated and hence much more

information is provided per test-subject (Sawtooth Software, 2013). A third drawback was the

lack of individual-level analysis and its biases which aggregate logit models bring along,

however, this has largely been remedied with the introduction of the HB method (Sawtooth

Software, 2013). Though Lieb (2013a) critically stated that “using a market specific technique,

such as Choice-Based-Conjoint produces inaccurate or at least questionable individual response

measurements” (p. 4–5), because Bayesian procedures rely on prior distributions obtained from

market or segment logit models. In a practical approach, Moore, Gray-Lee and Louviere (1998)

compared the prediction powers of several CVA and CBC methods. Their results show that

individual-level estimates using CBC/HB yields better predictions than any CVA method tested

in that study, but they mention that their study design might have given CBC an advantage.

Another concern is that respondents, especially online, tend to answer choice tasks extremely

quick (12 to 15 seconds per task); hence they likely simplify their choice procedures (Sawtooth

Software, 2009a). Lastly, Balderjahn et al. (2009) pointed out that including a none-option might

introduce the problem of decision avoidance. Carson et al. (1994) stated that decision avoidance

is likely a function of respondent fatigue, task difficulty and respondent characteristics. Johnson

and Orme (1996) found that respondents are indeed more likely to use the none-option in later

tasks, but they are unsure if this is due to respondent fatigue or the fact, that respondents might be

reluctant to choose mediocre product profiles after having seen superior ones. They test the

decision avoidance hypothesis and find evidence against it, concluding that the use of the none-

option is usually a rational decision. Sawtooth Software (2009a) even states that respondents tend

to avoid the none-option.

3.4.4 Summary

In the early days of CBCA, the drawbacks weighted quite heavy. However, over the years many

of them could be resolved with the availability of more powerful computers and new

developments in data analysis techniques. It has been shown that CBC/HB is the best method as

of yet to analyze CBC data. A synoptic overview on the pros and cons of state-of-the-art CBCA

versus CVA and ACA are presented in Table 1.

18

Table 1: Advantages and Drawbacks of State-of-the-Art CBCA

Advantages Disadvantages

tasks are more realistic needs larger sample size

includes a none-option ACA can handle more attributes

able to measure interaction effects respondents simplify their choice procedures

alternative specific designs possible

no need to hypothesize a decision rule

Source: Own Illustration.

3.5 Menu-Based Conjoint Analysis

In this section, Menu-Based Conjoint Analysis (MBCA) is presented in more detail. Since its

main competitor is CBCA, their relative advantages and shortcomings are outlined and discussed

here.

Judging from the literature (Bakken & Bayer, 2001; Ben-Akiva & Gershenfeld, 1998; Liechty,

Ramaswamy, & Cohen, 2001; Moore, 2010; Orme, 2010b) the idea of MBC was inspired by the

marketplace. When the mass customization trend emerged, academics and market researchers

noticed that CBCA wasn’t fully adequate to model the consumer choice behavior for those menus.

For example, Cohen and Liechty (2007) commented that the complexity of a menu situation

makes traditional conjoint analysis approaches completely inadequate. Ben-Akiva and

Gershenfeld (1998) have been the first researchers to link menu choices with conjoint analysis

and to provide an analytical approach to solve this type of problem. Predestined fields of

application for MBCA include mass customization (a base model with additional options, e.g.

cars), build your own (e.g. Dell Computers, mySwissChocolate), bundle vs. à la carte choice

situations (e.g. cell phone contracts, fast food restaurants) and picking items from a menu (e.g.

restaurants). Consumers assembling their shopping basket by picking items from a store would be

a more complex application.

3.5.1 MBC Methodology

MBCA is not a conjoint method in the strict definition of e.g. Morikawa (1989) although

included in more encompassing ones like Mohr et al. (2010). The same way CBCA is about

discrete choice modeling, MBCA is about menu choice modeling. Ben-Akiva and Gershenfeld

(1998) distinguish two types of menu modeling, the simple menu approach (SMA) and the

19

extended menu approach (EMA). The simple menu approach is pretty much the same as a BYO

exercise: A list of bundle or product features is provided and the respondent can choose a

particular attribute level for every feature on the list. Attributes could be binary (included/not

included) or manifold (e.g. choosing a product color). In an extended menu approach, bundles of

features are added to the list of features, often at a discount. So this approach comes closest to

simulating mixed bundling offers. In the pre-designed and pre-analyzed study discussed in this

master’s thesis, a simple menu approach was chosen for the MBC tasks as depicted in Figure 2.

An example of an extended menu approach is provided in Figure 3.

Figure 2: Example of an MBC Task

Please compile an offer which you would purchase. You are free to choose none, one or several of the represented products.

The total price of your selection will be shown below.

Website Newspaper Sports League

browser access

€ 4.99 Newspaper as

ePaper (PDF)

€ 1.99 season pass for all

videos

€ 6.99

smartphone app

€ 5.99

Newspaper as

animated tablet app

€ 1.99

videos of one match

day

€ 0.99 price per match day

tablet app

€ 0.99

Newspaper as

printed edition

* .

video of a single

game

€ 0.99

price per single game

*price varies depending on selection and will be

shown in the total price

Total Price: € / Month I would not choose any of these

options + € / sports league access


(picture)

(picture)

(picture) (picture) (picture)

(picture) (picture)

(picture) (picture)

20

Figure 3: Illustration of an Extended Menu Approach

Source: Orme (2013a), p. 7.

With respect to questionnaire design, I was unable to find specific literature on it. Kamakura

and Kwak (2012) encountered the same problem and identified this as a possible area for future

research. Only a few pointers can be found scattered across papers. For example, Kamakura and

Kwak reported that respondents get bored or are unwilling to do more than eight tasks with

sixteen attributes each. Orme (2010c) observed that respondents are still fine with up to 16 MBC

tasks. And Orme (2010c) mentioned that if attribute prices are alternated in an uncorrelated

fashion, then the analyst can estimate the price sensitivity of each attribute independent of the

others, while cross-elasticities can be estimated as well. Orme (2013a) recommends using a

purely randomized design, adhering to standard design principles such as excellent level balance

and (near-)orthogonality. This can help reducing order and context effects (Orme, 2013a).

21

Another point Orme (2013a) covered is, that between four to nine price levels should be used per

item. While more price points improve price sensitivity estimation, too many could lead to utility

reversals in adjacent price levels for dummy coded functions (Orme, 2013a).

With respect to data analysis, many kinds of approaches have been tried as an industry standard

has yet to emerge. There are four fundamental ways to model MBC data with a few derivatives

and some less common approaches, as presented below.

As in CBC, counting choices or counts is the simplest form of analysis for MBC data. Johnson et

al. (2006) found that with a balanced design, taking the logs of aggregate counts delivers the

same results as an independent serial choice (ISC) model, which should not surprise as they used

an aggregate logit estimation (cf. Section 3.4.1). The ISC model breaks a menu down into a series

of separate choices, assuming independence among attributes in the menu. A menu with k

attributes would therefore be converted into k separate logit models, which are later merged in

the market simulation (Orme, 2010b). Cohen and Liechty (2007) warned that this approach will

yield incorrect estimations if attributes are correlated and point out that it only predicts whether

each attribute is chosen but does not recognize combinatorial outcomes (i.e. which attributes are

selected together). Hence, Orme (2010b) presented an enhanced version, called serial cross

effects (SCE) model. It is constructed in a similar way as the ISC model but each of the k logit

models predicts the likelihood of selecting that attribute as a function of its inherent desirability,

its price and the prices of all other attributes available in the menu. It can therefore handle

correlated attributes but still doesn’t recognize combinatorial outcomes. Other drawbacks of the

SCE approach are that it should only include significant cross-effects, which are not always easy

to identify and therefore require an experienced analyst (Cordella, Borghi, van der Wagt, &

Loosschilder, 2012a). Moreover, if cross-effects are (not) included, they (don’t) hold for the

entire sample, which might pose a problem in case of heterogeneous or extremely clustered

samples (Cordella et al., 2012a). The main advantage of this model is, that it can break complex

menus up into a series of smaller ones (Orme, 2010b). Bakken and Bremer (2003) and Rice and

Bakken (2006) also used a similar approach to analyze data from a single MBC task per

respondent. Rice and Bakken estimated their k functions for each individual using an attribute’s

intrinsic value, the attribute’s appeal (measured outside the MBC task), the relative price (which

is calculated as the attribute’s price divided by that individual’s total product price) and an error

term.

22

A conceptually different approach is the exhaustive alternatives (EA) model, also known as

single choice modeling. It assumes that the different attribute choices are converted into a single

choice array, and then the model predicts which single array is chosen from all possible arrays

(Cohen & Liechty, 2007). In other words, a respondent considers all possible ways a menu task

could be completed and then chooses his most preferred way (Orme, 2010b). According to Orme,

(2010b) and Cordella et al. (2012a), EA is a more comprehensive model of consumer choice than

SCE, because it recognizes and estimates combinatorial outcomes of all attributes in conjunction.

However, as the number of total arrays grows exponentially with the numbers of attributes, it

makes (especially HB) estimation unfeasible (Cordella et al., 2012a; Orme, 2010b). Other

problems EA encounters are, that it could become quite sparse at the individual level, which

could lead to overfitting (Orme, 2010b) and that correlation among the utilities of arrays, net of

price and intrinsic effects, must be set to a constant, typically zero (Cohen & Liechty, 2007).

Interestingly, Johnson et al. (2006) found the estimated part-worths of the EA and ISC model to

be identical to three decimal places, using an aggregate logit estimation. Orme (2010b) reported

almost identical predictive validity of the EA and SCE model, using aggregate logit, LC and HB

estimation. In fact, Schweidel, Bradlow and Fader (2010) demonstrate that serial choice and EA

models can be formulated equivalently. In order to overcome the computation problem associated

with EA, one can resort to a choice set sampling (CSS) model. Cordella et al. explain that one can

exploit the IIA property of the logit model, which permits consistent estimation with only a

subset of all possible combinations. This subset can be chosen by using random sampling of

alternatives, but this is inefficient, as many combinations are never chosen by respondents

(Cordella et al., 2012a). Hence they recommend the importance sampling of alternatives

technique,17

which has also been used by Ben-Akiva and Gershenfeld (1998).

Another main approach is known as menu modeling, which preserves the individual menu

choices. Liechty et al. (2001) implemented it via a multivariate probit (MVP) model, which is

designed to predict which collection of attributes will be chosen. The MVP model assigns a

distinct latent utility to each attribute as a function of its price, the price of other attributes, other

scenario specific effects and an error term. An attribute is chosen, if its utility is above a certain

threshold and the utility of all attributes is maximized simultaneously (Liechty et al., 2001). The

chief advantages of the MVP model are, that it can reveal natural bundles and that researchers

17

The importance sampling of alternatives technique uses a sub-sample of combinations with a higher probability to

be chosen, e.g. all combinations which were chosen at least once (Cordella et al., 2012a).

23

can access the intrinsic worth of each feature and the price sensitivity of an individual to that

feature (Cohen & Liechty, 2007). Moreover, unlike SCE or EA, the MVP model can also capture

unobserved cross-dependencies by computing the correlations between the errors (Cohen &

Liechty, 2007). That is, it can capture correlations across latent utilities and consumers

(Kamakura & Kwak, 2012). Its biggest drawback is that it implicitly assumes attributes to be

locally independent, i.e. ignoring interactions among attributes within choice tasks (Kamakura &

Kwak, 2012).

In an attempt to overcome the drawback of the MVP model, Kamakura and Kwak (2012)

developed an auto-logistic (AL) model. Their model includes the first-order interactions between

all attributes in the menu. These interactions capture for example how white wine might lose

value and red wine gain value, given a consumer chooses a steak (Kamakura & Kwak, 2012).

The model then calculates the utility of each attribute as the sum of its intrinsic value, all

interactions and an error term. An attribute will be selected by a consumer if that sum is bigger or

equal than the attribute price multiplied by a factor representing the value of a unit of money

spent on the outside good (Kamakura & Kwak, 2012).18

Their model uses a multinomial logit

formulation similar to EA. Since the complete enumeration is computationally unfeasible for

more than four attributes (Kamakura & Kwak, 2012), they use random sampling of alternatives

and represent the pair-wise interactions in a reduced space. Furthermore, they assume interactions

among menu items to be homogeneous among consumers (Kamakura & Kwak, 2012). Especially

this last assumption (not made in the auto-logistic model of e.g. Russell and Petersen, 2000) lets

me question the superiority of their approach over the MVP model. While most people will prefer

to drink red wine instead of white wine when ordering a steak, this certainly doesn’t hold for

everyone. Kamakura and Kwak also omitted individual level estimations, they only estimated

segments. But Liechty et al. among others demonstrated by how much individual level

estimations can improve the predictive power of a model. In fact, it is likely that individual level

estimation can capture a great deal of the attribute interactions because respondents should take

attribute interaction into consideration while making their choices. Kamakura and Kwak’s

approach does seem promising though, because it allows to specifically estimate the (positive or

negative) synergies of choosing items together, but there is scope to implement it in an even more

powerful way. It should also be mentioned that Sawtooth Software’s CBC and MBC modules

18

This factor takes the budget constraint into consideration because it arises from the shadow price associated with it

(Kamakura & Kwak, 2012).

24

allow a user to estimate interaction effects, although it then requires larger sample sizes and runs

the risk of overfitting with an increasing number of parameters (SSI Web Help, n.d.). The exact

estimation procedure is not described, but it seems to work on an aggregate basis while

Kamakura and Kwak specify the interactions on a choice occasion basis.

Lastly, there are some less common approaches. One of them is the volumetric CBC model

(VCM), which basically excludes the none-option in the study design and uses this fact in the

market simulator to code volumes (Orme, 2010b). Its key benefit is, that it can estimate the

likelihood of choosing multiple items on the menu using a single model, while its biggest

drawback is that it doesn’t actually estimate preference shares but volumes, which are not

bounded by zero and one (Orme, 2010b). Another idea is the 2-stage model, which could be used

for constrained choices. If, for example, choice X depends on another choice Y, then the analyst

could first calculate the likelihood of Y being chosen. Then the analyst could filter the tasks for

those including Y and then use any model (like EA or SCE) to estimate X, given Y (Orme,

2010b). Yet another approach is taken by the adaptive build your own (ABYO) method, which

yields a WTP database as output, using a rather simplistic calculation method (Hagens & Johnson,

2011).

It is important to note that many of these methods, such as ISC, SCE, EA, CSS and MVP are

based on random utility theory and can be estimated on an aggregate, segment or individual level.

While serial and single choice models have been described as MNL estimations, Liechty et al.

(2001) used an MNP formulation for their EA model. Hence an analyst has a variety of potential

approaches to choose from. A final overview is presented in Table 2.

Table 2: Overview of MBC Data Analysis Methodologies

Serial Choice Models Single Choice Models Menu Choice Models Other Models

Counts

ISC

SCE

CSS

EA

AL

MVP VCM

2-Stage

ABYO


25

3.5.2 Conceptual Differences between CBCA and MBCA

Although especially Figure 3 looks quite similar to a CBC task, one should keep in mind that

MBCA and CBCA are different methodologies. This can be seen when they are categorized as

shown in Figure 4. A characteristic of compositional methods is, that attribute levels can be

directly evaluated, as opposed to situations where respondents are shown a number of product

profiles with the value of the features decomposed from them (Lieb, 2013a). This is clearly the

case for MBC designs (Liechty et al., 2001; Louviere & Bunch, 1998).

Figure 4: Typology of Market Research Methods

compositional methods decompositional methods

self-explicated measures BYO/SMA

derived measures CVA, CBC

Source: Own representation based on Lieb (2013a, p. 4-4).

It has to be noted that Lieb (2013a) means the simple menu approach when he refers to build

your own. While the extended Menu-Based Conjoint approach is closer in nature to the BYO task,

especially if bundle choices are considered to be attribute levels of a ‘bundle attribute’, these

bundle choices usually are of a decompositional nature. Hence the position of the EMA in Figure

4 isn’t clearly between the boundaries of a cell but presumably southeast to east of the BYO

position while still mostly within the same cell as BYO.

More importantly, however, are the implications regarding the comparisons of the different

conjoint methods. While CVA and CBCA are in the same category, it is easier to conclusively

determine the superiority of a method. But since CBCA and MBCA are categorized differently,

the likely implication is that no method dominates the other, rather the prevalent situation will

determine which method is superior. According to Liechty et al. (2001) and Cohen and Liechty

(2007), CBCA is designed to explain a single choice, i.e. which product will be chosen from the

set of alternatives, given prespecified product profiles. MBCA, on the other hand, is designed to

explain multiple simultaneous choices, i.e. which collection of attribute levels will be chosen,

given attributes and prices (Cohen & Liechty, 2007). Lieb (2013a) got to the heart of it by

declaring that CVA and CBCA “tend to simulate a consumer package purchase” (p. 4-5) while

BYO “tends to simulate a negotiation process” (p. 4-5). These statements, considered together

with Figure 1, Figure 2 and Figure 3 can link specific conjoint methods to bundling theory.

26

Namely, it can be inferred that CBCA emulates pure bundling situations, the EMA emulates

mixed bundling situations and finally the SMA emulates stand-alone situations.

Also related to the different designs is the fact, that respondents may use different choice

heuristics for the different tasks. According to Billings and Marcus (1983) and Johnson et al.

(2006), respondents tend to employ non-compensatory strategies to simplify their choices when

they are burdened with too much information. 19

Bakken and Bayer (2001) likely had a similar

thought in mind when they claimed that respondents may employ a compensatory strategy with

respect to price in BYO exercises while the same respondents might employ non-compensatory

strategies in discrete choice tasks. Gilbride and Allenby (2004) found by analyzing a CBC study

that 92% of respondents applied a two-stage decision process. Respondents first used a

conjunctive screening rule to reduce the choice set before making their final decision (Gilbride &

Allenby, 2004). This is in support of what Green and Srinivasan (1990) reported, namely that

test-subjects behave ‘conjunctive’ in a first stage, eliminating options with one or more

unacceptable attribute levels. In the compensatory second stage, the remaining options are then

traded off on multiple attributes (Green & Srinivasan, 1990).

3.5.3 Advantages of MBCA

Wind and Mahajan (1997) stated that new research methods are needed to properly address mass

customization because the focus is no longer on finding optimal products or product lines; but

rather to identify the attributes and their levels which should be offered, how customers want to

customize their products and the premium they are willing to pay for being able to customize a

product. Regarding such mass customization situations, the probably biggest advantage of

MBCA is that it provides a more realistic task to respondents as it allows them to choose the

attributes by themselves rather than just an inflexible product profile. So there is no need for

attribute trade-offs and compromises like in CBCA, consumers can get whatever they are willing

to pay for, they can create their ideal product (Bakken & Bayer, 2001; Bakken & Bremer, 2003;

19

Billings and Marcus (1983) describe the user of a non-compensatory strategy as someone who examines a variable

amount of information, because a low level in one attribute cannot be compensated by a high level in another

attribute. Compensatory strategies on the other hand imply that a constant number of attributes are considered and

usually refer to a linear model (Billings & Marcus, 1983). Johnson, Meyer, and Ghose (1989) list four non-

compensatory decision strategies: Elimination by Aspect (EBA), Lexicographic, Conjunctive (Satisficing) and

Phased EBA. Apart from information overload, non-compensatory strategies are used if ‘must have’ or ‘must not

have’ features are involved. For example, someone with a peanut allergy won’t choose any kind of food with traces

of peanuts in it, no matter how cheap or delicious it is.

27

Dahan & Hauser, 2002; Rice & Bakken, 2006). Bakken and Bayer (2001) also pointed out that

CBCA is disadvantaged in situations with many attributes, as the possible amount of attribute

combinations to be considered gets very high. Bakken and Bremer (2003), Cohen and Liechty

(2007) and Moore (2010) also made the point that MBCA can reduce the cognitive burden on

respondents because the decision is less complex than in CBC situations. Agarwal and Chatterjee

(2003) found in a bundling study that the more attribute a consumer has to consider, the more

likely he or she will be to defer a decision. This is directly applicable to CBCA, but in MBCA

respondents only have to decide on which attribute level to choose for every attribute. Cohen and

Liechty (2007) further point out that their MVP model is also able to capture correlations

between attributes, hence identifying them as substitutes or complements to each other. This

subsequently makes them able to identify ‘natural bundles’. Orme’s (2010b) SCE model can’t

predict combinatorial choices, but is able to capture cross-price effects and can therefore also

identify substitutes and complements.

Furthermore, MBCA is able to capture the price sensitivity for each attribute on the menu,

independent of other attributes (Orme, 2010c), while Rice and Bakken (2006) reported anecdotal

evidence from their clients which suggests that CBCA underestimates price elasticity for

individual attributes. Likely related to this capability is the finding by Johnson et al. (2006), who

report that the same attribute levels can have dramatically different utilities, depending on the

measurement method. It appears that respondents tend to almost ignore presumably cheap and

unimportant attributes in CBC tasks, which focus on between-attribute trade-offs (Johnson et al.,

2006). Hence the calculated part-worth utility is often close to zero. But when it comes to

attribute-price trade-offs as in BYO, even unimportant attributes can achieve large (positive or

negative) part-worth utilities (Johnson et al., 2006).20

It also seems logical to me, to connect this

finding with the above discussed different choice heuristics. Sawtooth Software (2009a) is

convinced that most respondents employ non-compensatory decision rules in complex conjoint

studies, and Section 3.5.2 presented findings of other authors in support of that, so a CBC study

probably does not even need to be that complex. MBC tasks on the other hand are comparably

simple, as every attribute is traded off against its price, wherefore a compensatory decision

20

As an example, imagine a toothbrush study with the attributes brand, comfort, cleansing ability, color and price. It

is safe to assume that most respondents would not pay much attention to the color in their decision on which

toothbrush to choose in CBC tasks. However, this changes if in a BYO task the base color is given and it would cost

money to choose a different color.

28

making process is much more likely (Bakken & Bayer, 2001). As Bakken and Bremer pointed

out, this compensatory process can operate within the respondent’s self-imposed budget

constraint if total price is displayed, as many MBC tasks do.

Data accuracy tends to decline with the complexity and difficulty of a method (Lieb, 2013a).

Therefore, researchers need to be careful to not ‘tiring out’ respondents. With respect to that,

Rice and Bakken (2006) pointed out that DYOP questions require less interview time than

comparable CBC tasks in most cases, likely due to the fact that they confront respondents with

simpler choices. Johnson et al. (2006) confirmed that finding and Orme (2010c) and Hagens and

Johnson (2011) discovered that respondents find MBC tasks relatively enjoyable – implying high

data quality. With regard to simplifying choice strategies, Orme (2010c) reported that in a study

with eight MBC tasks, only 16% (130 out of 806) respondents gave the same answer all eight

times. Since only 26 of those 130 clicked the none-option all the time, he interprets this as

generally engaged test subjects. On the other hand, the non-compensatory decision heuristics

many respondents apply in CBC tasks are likely to be a disadvantage for CBCA because they are

at odds with the compensatory logit rule and portend to CBC tasks being boring to test subjects –

implying that these simplifying heuristics might not be used in typical real life decision making

(Sawtooth Software, 2009a).

3.5.4 Disadvantages of MBCA

As already discussed in Section 3.5.2, it is quite likely that CBCA and MBCA both have the

potential to outperform each other, depending on the area they are applied in. In Section 3.5.3, it

was argued that MBCA outperforms CBCA in mass customization environments. CBCA, on the

other hand, might have an edge in packaged consumer goods situations. Dahan and Hauser (2002)

reinforced that line of argument by noting that BYO sacrifices the generality of conjoint methods,

i.e. BYO only gathers the ideal feature combinations for each respondent but unlike other

conjoint methods can’t simulate how test subjects will react to any attribute combination.

However, it seems they didn’t consider the application of multiple MBC tasks which can produce

reliable part-worth estimates at the individual level. On the other hand, Lieb’s (2013a) argument

is more convincing, it explains that CVA and CBCA can handle both positive and negative

valued features. The nature of the utility impact doesn’t even have to be known by the study

designer (Lieb, 2013a). BYO on the other hand requires positively valued attributes, or at the

29

very least the knowledge which attributes could have negative utilities (Lieb, 2013a).21

Lieb

(2013a) further stated that BYO is restricted to tangible features, with the exception of possible

surrogates such as brand names or service conditions. While I believe this is a problem which can

be circumvented with some fantasy, it might lead to unnatural choices.22

This critique is also

applicable to the EMA since the bundles presented in these tasks are usually made up of

attributes also available in unbundled form, combined with a bundle discount. Furthermore, Orme

(2010a) pointed out how CBCA can be used to measure brand equity. This might be harder to

realistically implement in MBCA because choosing the brand detached from the whole product

might feel unnatural in many cases, especially when there’s a price tag attached to that decision.

It is quite easy to come up with an example of a negatively valued feature, for example the option

to add meat to a meal when the respondent is a vegetarian. However, sometimes negative

intrinsic attribute values aren’t that obvious and may be dependent on the presence or absence of

other attributes. For example, Ben-Akiva and Gershenfeld (1998) discovered in their research

that “some telephone customers would not take an additional custom-calling feature even if it

were offered for free” (p. 177). As a possible explanation, they mention the fear of usage

complications. Hence it seems that while the feature by itself is positively valued, its combination

with other features decreases its value, sometimes even below zero. Such negative correlations

might also pose a danger to CBC designs. Johnson, Meyer, and Ghose (1989) warned that choice

models (which are compensatory to simulate the trade-offs in choice tasks) may fail when

respondents use non-compensatory decision heuristics and attributes are negatively correlated.

Because it is not possible to estimate individual-level price sensitivity with only one task per

respondent, nor would that be an efficient method of data gathering, it makes sense to present

several MBC tasks with varying prices to interviewees. But Lieb (2013) spotted a face validity

problem if researchers apply such a ‘sequential BYO’ since the explicit form of pricing exposes

this method to manipulation as respondents could “try to game the survey” (p. 4-71). However,

increasing price sensitivity in later BYO tasks as found by Orme (2010c) can’t be used as a

conclusive argument since Johnson and Orme (1996) found the same to be true for CBC. This

rather represents a learning effect and/or a decline in response error, hence repeated tasks should

21

Imagine, for example, that an attribute is never chosen in a MBC exercise. What might its value be? One price

increment below the lowest price asked? Zero? Less than zero? 22

Such as needing to choose inherent features, e.g. service level, atmosphere and food quality in a restaurant study.

30

consistently model search-intensive goods and repeated purchase goods. (Orme, 2010c). Still, it

is very likely that CBCA is able to alleviate more hypothetical biases discussed in Section 3.2

than MBCA, since MBCA is based on self-explication and not a derived measure like CBCA.

Lastly, a concern raised by Johnson et al. (2006) is the possibility of context effects in BYO tasks.

In the analyzed study, they found that respondents had a bias toward ‘middle’ attribute levels, i.e.

they traded up from the base level but avoided top levels. However, Dahan and Hauser (2002)

reported that in a study conducted by them, respondents used the BYO exercise to build a product

which is near-ideal according to the full profile conjoint analysis results, so more research on that

issue is warranted. Alas, my master’s thesis can’t contribute to this issue because the MBC

attributes were of binary nature.

3.5.5 Comparing the Simple and Extended Menu Approach

The simple and extended menu approaches seem quite related, especially when there is some sort

of ‘base product’ involved. In the absence of a base product however, 23

they are actually

comparable to stand-alone situations vs. mixed bundling situations. It is therefore important to

apply the approach which fits a situation the most in order to be as close to reality as possible and

therefore get most reliable results. To apply a SMA where an EMA is warranted or the other way

around could distort WTP measurements. Possible reasons are that consumers prefer the bundle

with the highest savings (Myung & Mattila, 2010) or that bundles could simplify the consumer’s

decision process (Bakken & Bond, 2004; Cordella et al., 2012a).

3.5.6 Summary and Hypotheses

Section 3.5 introduced the MBC method and compared it to the CBC method. Since lots of

arguments have been made for and against each method, Table 3 provides a final overview on pro

and cons of MBCA as compared to CBCA.

23

Examples for base products are laptops or cars, where the menus enable respondents to customize their chosen

base product, but usually can’t order single components. Examples for the absence of base products are (fast food)

restaurant menus or mobile phone service bundles, where customers can get a full menu or just one of the

components, like a drink or a data flat rate.

31

Table 3: Advantages and Drawbacks of MBCA

Advantages Disadvantages

tasks are more realistic in menu situations harder to implement intangible attributes

less vulnerable to simplifying choice strategies more susceptible to some hypothetical biases

able to identify ‘natural bundles’, complements

and substitutes difficulties in handling negative valued attributes

better able to capture real price sensitivity of each

attribute possibility of context effects

experiments take less time and are more enjoyable

to respondents

able to handle a large amount of attributes


From these advantages and disadvantages, I deduce the following two hypotheses:

Hypothesis 2: Respondents exhibit higher price sensitivity24

for the same attributes under

MBCA than under CBCA. I derive this hypothesis from Johnson et al.’s (2006) findings

on different utility measures for the same attributes and Bakken and Bayer’s (2001) claim

that respondents may employ a compensatory strategy with respect to price in BYO

exercises while the same respondents might employ non-compensatory strategies in

discrete choice tasks.

Hypothesis 3: CBCA is better able to identify negatively valued attributes and/or attribute

levels than MBCA. I derive this hypothesis directly from Lieb’s (2013a) statements on

that issue.

24

I prefer a wide definition of the term as e.g. used by Investopedia (n.d.), where price sensitivity is defined as “the

degree to which the price of a product affects consumers purchasing behaviors” (para 1). This avoids the narrow

microeconomic definition of price elasticity without excluding it.

32

4 COMPARISON BETWEEN CBC AND MBC STUDY RESULTS

After the theory has been introduced in Chapter 3, this chapter is going to add some empirical

findings on the topic. Specifically, I am going to compare the results of a market research study

which actually conducted two separate studies, one applied the CBC methodology and the other

one the MBC methodology. Both sub-studies have been conducted by the same marketing

research company during the same time frame to investigate the same issue. Namely, they aimed

to investigate the optimal pricing structure for products and product bundles of a European media

company. This is an explorative endeavor since this company didn’t charge anything for its

online content so far. The data collected for the two studies have subsequently been scrutinized

and processed by two students in the scope of their master’s theses. Robert Ung (2012) analyzed

the MBC data and Katja Werder (2013) the CBC data. In Section 4.1 I will give an overview on

the data collection and the questionnaire design. In Section 4.2 I will provide an overview on the

products and bundling scenarios which Werder and Ung looked at. Section 4.3 then compares the

CBCA and MBCA results to each other and investigates the hypotheses postulated in Chapter 3.

Before starting, there is one caveat I have to make. Namely, I did not have access to the raw data

which has been collected. All my study information originates from a client presentation draft by

the marketing research company, Ung’s (2012) MBC analysis, Werder’s (2013) CBC analysis

and some personal communication with Werder and Ung. For source data, I could only rely on

Ung and Werder’s master’s theses. Unfortunately, I am therefore not always able to make a final

conclusion on some topics investigated in Section 4.3.

4.1 Study Overview

The data for both studies has been pre-collected by a market research company in June 2012. It

was a project for a newspaper and especially the online channels of that newspaper which is

published by a European media company. The survey was conducted online and targeted users

aged between 16 to 59 who have either read the newspaper in the past two weeks, and/or have

used a digital good related to the newspaper in the past two weeks, and/or are interested in

national sports and show a willingness to pay. For respondents not fitting these requirements,

only a short interview has been conducted. For respondents fitting one or several of above criteria,

a full interview has been conducted, including either a CBC or an MBC questionnaire, among

33

other questions. In order to get enough interviews per group, the full interviews were applied

disproportionally.

The CBC study was designed as follows: A total of 1388 online respondents were recruited as

described above. Each respondent had to answer 12 tasks. A task consisted of four alternatives

plus a none-option. Each alternative contained three combinatorial ‘bundling attributes’ plus the

price, making a total of four. However, the three bundle attributes (Newspaper, Website and

National Sports League) consisted of 8, 8 and 10 levels, respectively, where a level could display

between 0 to 3 products. Therefore, respondents were most likely under the impression that

alternatives have between 1 to 7 attributes, plus price. An example of a CBC task can be seen in

Figure 1. In order to have some sort of price logic, an alternative specific design was

implemented (Werder, 2013). Each bundle attribute level was assigned to exactly 1 out of 6 price

attributes, and ‘better’ bundle attribute levels were assigned to price attributes with a higher price

range. All 6 price attributes consisted of 7 price levels. The exact details on the alternative

specific design and their price levels are provided in Appendix E. Finally, a restriction has been

implemented, such that the newspaper attribute level ‘print’ was prohibited to appear alone.25

The MBC study was designed as follows: A total of 1462 online respondents were recruited as

described above. Each respondent had to answer 6 tasks. Every task contained the same 9

attributes plus a none-option. Each attribute had a binary level structure (choose/don’t choose).

Prices were varying from task to task, most attributes had a range of 6 possible prices although

some had fewer. Exact details are presented in Appendix E. Again, a design restriction was

implemented such that the attribute ‘print’ could not be chosen without any other attribute being

chosen first. An example of an MBC task can be seen in Figure 2.

4.2 Product and Bundle Overview

Nine different products have been included in this pricing study which can be assigned to one of

three categories, as presented in Table 4.

25

It could, however, appear together with website and/or sports league attributes which is why ‘print’ appears in the

counting analysis and has an assigned part-worth in the aggregate logit and HB analysis.

34

Table 4: Product Overview and Categorization

Website (W) Newspaper (N) National Sports League (SL)

Browser (B) Print (P) Season Pass (SP)

Smartphone App (SA) ePaper (PDF) Match Day Pass (DP)

Tablet App (TA) Newspaper App (NA) Single Game Pass (GP)


Browser, smartphone app and tablet app each are a subscription giving access to the entire

website of the newspaper, the only difference is the channel through which the content can be

accessed. To respondents, the website category was described as follows: The basic news and the

community are accessible without charge in any case. But the website subscription will add

customer value through exclusive photos, videos, reports, multimedia reports and interviews, 3D

pictures, regional sports content, livestreams and more.26

Print, ePaper and newspaper app each

are a subscription to the actual newspaper content, again with the main difference being the

channel through which the content can be accessed. To respondents, the newspaper category was

described as follows: Print is the daily newspaper of the respondent’s region. The ePaper (PDF)

is the print version as PDF, with the same design and the option to either choose the national or

any regional version. It is specifically mentioned to be accessible on computers, laptops,

smartphones and tablets. The animated newspaper app is marketed as a multimedia version of the

newspaper, additionally including an exclusive evening edition. It is specifically mentioned that

the newspaper app is optimized for tablets and includes lots of multimedia features. Lastly, the

three types of sports league passes give access to watch the online highlight videos for the entire

season, one match day or one game. They have been described to respondents in just that way.

With this description and the overview in Table 4, I think it’s logical to deduce that the three

categories (website, newspaper and sports league) are at least to some degree complementary to

each other, while the products within a category, especially website and sports league, are

perceived as highly substitutable. Since there are numerous ways to arrange those nine products

into bundling scenarios, 27

Ung (2012) and Werder (2013) only looked at ten bundling cases, as

presented in Table 5 below.

26

Undisclosed for anonymity reasons. 27

There are +

+

+

+

+

+

+

+

= 511 bundle possibilities (or 502, if not counting the

9 cases of offering a single product only).

35

Table 5: Bundle Overview

Bundle No. Content Included Categories

1 Browser & Smartphone App W

2 Browser & Tablet App W

3 Browser, Smartphone App & Tablet App W

4 Smartphone App & Tablet App W

5 Smartphone App & Print W/N

6 Browser & Print W/N

7 Browser, Print & Season Pass W/N/SL

8 Browser & Season Pass W/SL

9 Browser, Smartphone App, Tablet App & Season Pass W/SL

10 Browser, Smartphone App, Tablet App, Print & Season

Pass W/N/SL


4.3 Result Comparison

Since Werder (2013) and Ung (2012) were both very diligent in their analyses, I had to think

about which of their computations and results to compare. I could have compare counts data,

since they are closely related to part-worth values (cf. Section 3.4.1). But as explained there, the

aggregate logit computations are more robust. Also, the alternative-specific design in the CBC

study prevents a counts comparison to the generically designed MBC study. The different ways

models are built also makes it hard to compare aggregate logit or HB utilities. First of all, the

MBC Software is built on the SCE design (although data could be recoded into an EA model),

and hence estimated utilities in a different way than the CBC software. Furthermore, attribute

level utilities should not be compared anyways, since these part-worths are interval data and

scaled to an arbitrary additive constant within each attribute, which makes between-attribute

comparisons impossible (Orme, 2010a). So I have no choice but to compare the market simulator

results – which fortunately is the recommended way to observe and interpret conjoint results by

Orme (2010a) anyways. The price ranges are not always exactly the same on the two studies, but

since the preference share calculations depend on prespecified prices, I will instead compare the

revenues, calculated as preference share multiplied with price. Unfortunately, profit can’t be

looked at due to an absence of data on product costs.

36

4.3.1 Overview and Bundling Strategy

First, Table 6 gives an overview on the revenue maximizing case of each bundle scenario. In each

column, the most profitable bundle is marked with bold font.

Table 6: Overview on the Revenue Maximizing Cases

CBCA MBCA

Bundle Pure Bundling Stand Alone Mixed Bundling Pure Bundling Stand Alone Mixed Bundling

1 1.7705 1.3507 1.5594 0.1477 0.7480 2.2512

2 1.7705 1.3206 1.6040 0.0502 0.6687 2.1391

3 1.7346 1.3766 1.5625 0.0511 0.8598 2.3655

4 1.6842 1.1129 1.4176 0.0818 0.3152 2.3253

5 2.5557 n/a n/a 0.1912 1.0191 2.4709

6 3.2086 n/a n/a 0.6855 1.3849 2.1964

7 3.7904 n/a n/a 0.1241 1.6055 2.2464

8 2.5362 1.4356 2.2443 0.1724 0.8036 2.1406

9 2.3398 1.4674 1.9880 0.0156 1.0776 2.3300

10 3.7568 n/a n/a 0.0210 1.9040 2.3300

Source: Own calculations with source data from Ung (2012) and Werder (2013). Unit: Preference share * price in €.

Since these numbers make it hard to recognize any patterns by just glancing over them, I

normalized them. I chose the highest overall revenue to represent 100% and expressed all other

revenues as a fraction of it.

Table 7: Overview on the Revenue Maximizing Cases (Normalized)

CBCA MBCA

Bundle Pure Bundling Stand Alone Mixed Bundling Pure Bundling Stand Alone Mixed Bundling

1 46.71% 35.63% 41.14% 3.90% 19.73% 59.39%

2 46.71% 34.84% 42.32% 1.32% 17.64% 56.43%

3 45.76% 36.32% 41.22% 1.35% 22.68% 62.41%

4 44.43% 29.36% 37.40% 2.16% 8.32% 61.35%

5 67.43% n/a n/a 5.04% 26.89% 65.19%

6 84.65% n/a n/a 18.09% 36.54% 57.95%

7 100.00% n/a n/a 3.27% 42.36% 59.27%

8 66.91% 37.87% 59.21% 4.55% 21.20% 56.47%

9 61.73% 38.71% 52.45% 0.41% 28.43% 61.47%

10 99.11% n/a n/a 0.55% 50.23% 61.47%

Source: Own calculations. Bundles including the same categories are marked with the same background color.

Table 7 strongly facilitates a first comparative impression on the results. It can easily be seen that

pure bundling performs very poorly under MBCA which supports Hypothesis 1a. But pure

37

bundling performs excellent under CBCA. In fact, the highest overall revenue is made under the

CBCA pure bundling regime. This is surprising, and at odds with Hypotheses 1a and 1b. On the

other hand, the MBC regime supports Hypothesis 1b as the mixed bundling case clearly performs

best, whereas under the CBC regime, mixed bundling is the middle case. Moreover, the CBCA

results show a logical revenue pattern. As explained above, I expect within-category products to

be highly substitutable and across category products to be complementary to some degree. The

pure and mixed bundling scenarios nicely outline this fact, as more categories are included in a

bundle, the higher the revenue. In the stand-alone case, this pattern is also present but not very

distinct. Under MBCA, this pattern is much less obvious – most likely because the MBC tasks

didn’t feature any bundles, hence the preference share calculation depended on combinations

actually or very likely to be chosen by respondents. This, of course, severely punishes larger

bundles and bundles containing less popular products. If one considers this revenue penalty for

larger and less popular bundles, the complementary patterns can be recognized within the pure

bundling and stand-alone scenarios.

Regarding the optimal bundling strategy, above results provide the following insights: First of all,

adding complementary products enhances revenue much more than substitutable products. In fact,

adding substitutes can reduce total revenue (see e.g. Bundle No. 1 vs. 3 under both CBCA and

MBCA). Second, these studies unfortunately only have limited conclusive validity regarding

bundling strategies because of the ways the maximum revenues have been calculated, as

explained in Appendix B.2. Under the CBC methodology, there has been no sensitivity analysis

in the mixed bundling case. Hence, it could well be that maximum mixed bundling revenues

could actually be higher when combining different bundle and single product price levels,

especially since the computed ones are not far lower than the corresponding pure bundling cases.

When looking at the MBC methodology, it surprises that mixed bundling delivers higher revenue

in every bundling scenario than both, pure bundling and stand-alone added together! While

consumers might experience a higher utility due to having more options, Myung and Mattila’s

(2010) argument about higher value perception through perceived bundle savings does not hold

since in some cases the above finding even shows when stand-alone price levels are below the

bundle price level (which is simply the added product price levels, as no discounts are offered).

But the explanation of this finding turns out to be rather simple after all. Ung (2012) calculated

his mixed bundling revenues by including the revenue of all nine products, instead of only the

38

products of the bundling scenario. Therefore, true mixed bundling revenues are almost certainly

lower under the MBC methodology. Also, Werder (2013) and Ung used different stand-alone

market simulations, which almost certainly overestimated the MBCA stand-alone revenues.

Considering all this, I would expect Hypothesis 1b to be supported, as the maximum revenues are

very likely to go up if conducting a sensitivity analysis for CBCA mixed bundling, and true

revenues are most likely not corrected downwards strong enough under MBCA. A possible

indicator for higher CBCA mixed bundling revenues can be found in the MBCA mixed bundling

analysis, where revenue maximizing mixed bundles always experience higher single product

price levels than bundling price levels (cf. Ung, 2012). Hypothesis 1a on the other hand is

negated by the CBC data and supported by the MBC data, which is unlikely to change, even if

the MBCA stand-alone market simulations are corrected downwards.

4.3.2 Cross-Reference Analysis

From a managerial perspective, the conflicting results produced by the two methods cause

confusion about the optimal strategy. Potential reasons for which method should be trusted more

are outlined and discussed in Chapter 5. Here, I would like to present a pragmatic cross-reference

analysis between the two methods in order to see how severe the differences in fact are. For this,

I compare the price levels of a certain bundle scenario with their normalized revenues. The

methodological details are described in Appendix B. Table 8, shows the three bundling scenarios

with the components of Bundle 1. The data presented in this table allows for several comparisons.

If for example comparing the revenue directly, it can be seen that under pure bundling, the lowest

CBC revenue is almost six times higher than the highest MBC revenue. Under mixed bundling,

the roles are reversed and the highest CBC revenue is now about 22% lower than the lowest MBC

revenue. In the stand-alone scenario, the highest MBC revenue is about 32% higher than the

lowest CBC revenue, but still 45% lower than the highest CBC revenue. This information is the

same in nature as what we can learn from Table 7. In order to interpret it properly, we need to

know which conjoint method is more appropriate for which case. Therefore it is more interesting

to compare the revenue maximizing prices within each method. In Table 8 under pure bundling

[stand-alone], the price which maximizes the revenue under the CBC regime (9.99) [11.98]

corresponds to the MBC price (9.98) [11.98] which promises 58.77% [94.01%] of the maximum

revenue under the MBC regime. Vice versa, the price which maximizes revenue under the MBC

regime (5.98) [9.98] corresponds to the CBC price (5.99) [9.98] which promises 71.91% [92.36%]

39

of the maximum revenue under the CBC regime. Hence, this analysis can be seen as a ‘worst case’

indicator. For the mixed bundling case, the comparison is not as straight forward because of the

reasons outlined in Appendix B. One point of discussion is about which prices should be

compared. One could argue that the added MBC prices should be compared to the CBC bundle

price because this is what has been done in the pure bundling case. Regarding Table 8, this would

mean that the revenue maximizing prices (9.99 and 9.98) of each method are compared,

coincidentally the same. So at least for this approach, the price decision is straightforward. On the

other hand, if one argues that added prices should be compared to added prices for the sake of

consistency, then the two revenue maximizing prices are 13.98 and 9.98.

In Table 9, I look at the case where MBC performed best in the pure bundling case, namely

Bundling Scenario 6. As it combines browser and print, only the pure bundling case was

estimated with the CBC method. Even here, the highest MBC revenue is still about four times

smaller than the lowest CBC revenue. Unfortunately, prices are difficult to compare because the

lowest CBC price is exactly the highest MBC price. The revenue maximizing price in the MBC

universe (13.99) would be closest to the lowest CBC price (14.99), which delivers 88.19% of the

maximum revenue under the CBC regime. While a 1€ (-7%) difference could be acceptable for

this rather ad-hoc analysis approach, the price maximizing the CBC revenues is 26.99, i.e. 12€

(+80%) more than the highest available price in the MBC universe. Hence, caution is required.

40

Table 8: Prices and Normalized Revenues for Bundle 1 Components

Pure Bundling Stand Alone Mixed Bundling

CBC Price

CBC Revenue

MBC Price

MBC Revenue

MBC Revenue 2

CBC Price 2

CBC Revenue

MBC Price

MBC Revenue

MBC Revenue 2

CBC Price

CBC Price 2

CBC Revenue

CBC Revenue 2

MBC Price

MBC Revenue

2.99 49.88% 1.98 3.24% 38.86% 3.18 41.90% 1.98 22.90% 41.36% 2.99 3.18 33.70% 48.65% 1.98 88.79%

3.99 59.10% 3.98 6.00% 71.97% 3.98 53.71% 3.98 37.91% 68.45% 3.99 3.98 39.65% 57.24% 3.98 93.74%

4.99 66.40% 5.98 8.34% 100.00% 5.98 71.13% 5.98 48.10% 86.86% 4.99 5.98 48.85% 70.52% 5.98 98.79%

5.99 71.91% 7.98 6.90% 82.67% 7.98 81.30% 7.98 54.20% 97.88% 5.99 7.98 56.46% 81.51% 7.98 99.62%

6.99 82.77% 9.98 4.90% 58.77% 9.98 92.36% 9.98 55.38% 100.00% 6.99 9.98 63.57% 91.77% 9.98 100.00%

7.99 86.16% 11.98 1.96% 23.49% 11.98 100.00% 11.98 52.06% 94.01% 7.99 11.98 66.85% 96.51% 11.98 100.00%

9.99 100.00% - - - 13.98 90.60% - - - 9.99 13.98 69.27% 100.00% - -

In bold font: Revenue maximizing and minimizing cases. ‘Revenue 2’ is the revenue of the underperforming method, normalized to 100%. MBC prices are always the added single product prices, as there

are no bundle prices. For CBC, ‘Price’ refers to the bundle price and ‘Price 2’ to the added single product prices. Price unit: €

Source: Own calculations.



CBC

Price

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

MBC

Price

MBC

Revenue

MBC

Price

MBC

Revenue

14.99 88.19% 1.99 4.56% 21.34% 1.99 23.22% 1.99 55.23%

16.99 87.37% 4.99 11.03% 51.61% 4.99 48.39% 4.99 72.61%

18.99 90.40% 7.99 17.31% 81.01% 7.99 66.78% 7.99 81.14%

20.99 92.38% 10.99 19.93% 93.31% 10.99 86.96% 10.99 90.98%

22.99 89.31% 13.99 21.36% 100.00% 13.99 100.00% 13.99 100.00%

24.99 95.39% 14.99 20.00% 93.59% 14.99 98.04% 14.99 99.32%

26.99 100.00% - - - - - - -

In bold font: Revenue maximizing and minimizing cases. ‘Revenue 2’ is the revenue of the underperforming method, normalized to 100%.

MBC prices are always the added single product prices, as there are no bundle prices. Price unit: €


41

To facilitate the overview, Table 10 summarizes the results (the extensive tables for the eight

remaining bundles can be found in Appendix C), also giving the amount of price deviation in

percent in cases where prices didn’t had a direct counterpart. This should give the decision maker

at least a rough idea on how reliable the numbers are. It can be seen that for most bundle

decisions, the two methods aren’t too far apart, expressed as percentage of revenues. For example

in the Bundle Scenario 3 stand-alone case, choosing either revenue maximizing price yields at

least 91% (or 92%, respectively) in terms of revenue from the other method. But since the CBC

methodology forecasts maximum revenues which are almost 40% higher in absolute terms, it is

therefore recommended to use the CBC price as benchmark since there is much more to gain than

to lose. Sometimes though, the decision can be difficult as in the Bundle Scenario 1 pure

bundling case. Here, the wrong decision can cost the manager up to 41% of revenue! All in all, it

has to be noted that even though the cross-reference analysis can give some important clues, it is

a quite simplistic method with many limitations (such as artificial prices for the stand alone and

mixed bundling scenarios). Hence, in Chapter 5 the difference between the CBC and MBC

methods will be discussed in much more detail.

Table 10: Summary of the Cross-Reference Analysis

The revenue maximizing MBC price (benchmark)

corresponds to the price in the CBC universe

which approximately delivers … % revenue of

the maximum CBC revenue.

The revenue maximizing CBC price (benchmark)

corresponds to the price in the MBC universe

which delivers … % revenue of the maximum

MBC revenue.

Bundle

Scenario

Pure

Bundling

Stand

Alone

Mixed

Bundling

Pure

Bundling

Stand

Alone

Mixed

Bundling

1 72 92 100 59 94 100

2 71 90 76 72 95 99

3 100 92 98 (+50) 100 91 95 (-11)

4 100 80 100 100 72 (+17) 100

5 95 (-27) n/a n/a 60 (+40) n/a n/a

6 88 (-7) n/a n/a 94 (+80) n/a n/a

7 97 (-4) n/a n/a 100 (+17) n/a n/a

8 70 99 68 89 100 (+21) 97

9 100 (+100) 83 (-6) 100 (+61) 50 99 97

10 94 (+6) n/a n/a 100 (+6) n/a n/a

If prices don’t have a direct counterpart, the numbers in brackets indicates how many percent lower (-) or higher

(+) the benchmark price is than the one it is compared to. In mixed bundling, CBC bundle prices (instead of added

single prices) are used as benchmark as the bundle is a significant part in mixed bundling revenues.

Source: Own calculations. Unit: %

42

Another interesting fact which can be observed in Table 8, Table 9 and Table 28 to Table 35 is,

that for example in the pure bundling cases, eight out of ten CBCA prices are the maximum

prices within the study (and hence raise the question, if even higher prices could have been

charged as noted by Werder, 2013). In MBCA, this is only the case in three out of ten, whereas

two of these three cases (Pure Bundles 9 & 10) are even questionable with regard to data quality.

When looking at the pure single products case (see Table 39 and Table 40), CBCA prices are at

their highest price level in five out of six cases, whereas MBCA prices are at their highest price

level in six out of nine cases but three out of these six cases might be due to random noise or a

biased pricing structure (because not all products have the same amount of price levels). These

findings are not directly relevant for the cross-reference analysis but will be discussed in more

detail in Section 5.1 in the context of the different pricing structures.

4.3.3 Cannibalization Potential and Substitutability

Werder (2013) conducted market simulations for the stand-alone case with all products from the

respective bundle scenario in the estimation model, but excluding other products. For example, in

Bundle Scenario 1 only browser and smartphone app were used for the market simulations, thus

any potential cannibalization from e.g. tablet app has not been captured. Luckily, Werder did

conduct market simulations for single products by themselves. This means, to take above

example, that I can compare the market simulation scenario in which the shares for browser and

smartphone app are estimated together and compare the resulting revenue to the added revenues

of the market simulation scenarios where browser and smartphone app have been estimated only

by themselves. The difference between those revenues should mostly be due to cannibalization

(although some of it might be due to CBC questionnaire design, as will be explained in Section

4.3.6).

43

Table 11: Difference Between Stand-Alone Estimation Approaches – Bundles 1, 2 & 3

Bundle Scenario 1 – Stand Alone Bundle Scenario 2 – Stand Alone Bundle Scenario 3 – Stand Alone

Original

Estimation

Pure

Single Est. Δ

Original

Estimation

Pure

Single Est. Δ

Original

Estimation

Pure

Single Est. Δ

0.5659 0.9371 0.3712 0.5418 0.8924 0.3505 0.5831 1.3197 0.7366

0.7255 1.2000 0.4746 0.6867 1.1398 0.4531 0.7398 1.6832 0.9434

0.9608 1.5962 0.6355 0.9435 1.5273 0.5838 0.9952 2.2317 1.2365

1.0981 1.8570 0.7589 1.0435 1.7765 0.7330 1.1096 2.6245 1.5149

1.2475 2.1211 0.8736 1.1828 2.0384 0.8556 1.2690 3.0127 1.7437

1.3507 2.3045 0.9538 1.3206 2.2528 0.9322 1.3766 3.2928 1.9162

1.2237 2.2159 0.9922 1.2137 2.1856 0.9720 1.2590 3.2432 1.9842

Source: Own calculations. Unit: Preference share * price in €.

Table 12: Difference Between Stand-Alone Estimation Approaches – Bundles 4, 8 & 9

Bundle Scenario 4 – Stand Alone Bundle Scenario 8 – Stand Alone Bundle Scenario 9 – Stand Alone

Original

Estimation

Pure

Single Est. Δ

Original

Estimation

Pure

Single Est. Δ

Original

Estimation

Pure

Single Est. Δ

0.4628 0.8099 0.3471 0.6990 1.2637 0.5647 0.7089 2.0736 1.3647

0.5821 1.0265 0.4444 0.8020 1.5305 0.7285 0.8333 2.5571 1.7237

0.7626 1.3399 0.5773 1.0502 1.8733 0.8231 1.0960 3.2132 2.1172

0.8883 1.6156 0.7273 1.1717 2.1052 0.9334 1.2190 3.7207 2.5017

1.0318 1.8658 0.8340 1.3039 2.3857 1.0818 1.3670 4.2515 2.8845

1.0961 2.0283 0.9322 1.4272 2.5597 1.1325 1.4674 4.5880 3.1206

1.1129 2.0849 0.9720 1.4356 2.7201 1.2846 1.4276 4.8051 3.3775

Source: Own calculations. Unit: Preference share * price in €.

As one can see in Table 11 and Table 12, there is indeed a significant cannibalization effect.

Since the Bundle Scenarios 1 to 4 only consist of products from the website category, they are

predestined to investigate the nature of the cannibalization effect. Bundle Scenarios 1, 2 and 4,

which only include two products each, experience an almost equal amount of cannibalization

(0.9922, 0.9720 and 0.9720), while Bundle Scenario 3, which includes all three website products,

experiences a much higher cannibalization (1.9842). It is further interesting, that browser and

season pass (Bundling Scenario 8) seem to cannibalize each other, even to a quite high degree.

Budget considerations (i.e. the negative utility impact from price) likely play a role in that. The

high values in Bundling Scenario 9, on the other hand, are less surprising since it basically

combines the results of Bundling Scenarios 3 and 8.

44

In Table 13, I look at cannibalization from another angle. Specifically, I took the difference

between the maximum revenue of a stand-alone scenario as estimated by Werder (2013) and the

product within that mix which delivered the highest revenue as a pure stand-alone product. This

difference then measures the added value of the other products within that mix – and hence is

more a measure of substitutability than cannibalization, because it compares bundles with single

products. For example, Bundling Scenario 1 includes the products browser and smartphone app.

Subtracting the revenue which browser could generate on its own from the pure stand-alone case

yields the additional revenue the media company can obtain by even offering these products. This

is especially important when it comes to cost considerations, something largely abstracted from

in Ung (2012), Werder, and this master’s thesis due to a lack of data. So what does Table 13 tell

us? Apparently, there is not much extra revenue to be made in offering either a smartphone app, a

tablet app, or both in addition to the browser. In the revenue maximizing cases (marked in bold),

the company could achieve between 0.0554 to 0.0863 extra revenue or between 4.25% to 8.15%

in relative terms. This means that website products are strong substitutes indeed. This is most

likely due to the fact, that respondents are mostly aware that they could just use their

smartphone’s or tablet’s browser instead of paying for a dedicated app. From a manager’s

perspective, it could still be interesting to offer the three products together, although not much

extra revenue is to be made, in order to increase the reach and therefore overall market share.

Even more surprising is the fact that the media company would do better to just offer the season

pass as a pure stand-alone product in about half of the price level scenarios, including a revenue

maximizing case. In these cases, offering either browser or browser, smartphone app & tablet app

as additional stand-alone products actually reduces the total revenue. Possible explanations

would be that consumer preferences for a season pass aren’t very strong and therefore they gain

higher utility from the lower priced website alternatives and/or consumers perceive season pass

and the website products as highly substitutable, maybe assuming they can also access e.g.

desired sports league information via the website products and therefore choose them instead of a

season pass. On the other hand, the differences taken between the stand-alone cases of Bundle

Scenario 9 and 3 is always positive (see Table 14), suggesting that a season pass adds value if it

is added as stand-alone product to an existing mix of stand-alone products. Nonetheless, if the

season pass were the only product in the entire market, it would still generate the most revenue, at

least considering the four products analyzed here.

45

Table 13: Pure Stand Alone Revenues and Added Value of Additional Products

Revenue

Browser

Revenue

S. App

Revenue

T. App

Revenue

S. Pass

Revenue

BSa1 – B

Revenue

BS2 – B

Revenue

BS3 – B

Revenue

BS4 – SA

Revenue

BS8 – SP

Revenue

BS9 – SP

0.5098 0.4273 0.3826 0.8466 0.0561 0.0321 0.0733 0.0355 -0.0550 -0.0451

0.6566 0.5434 0.4832 1.0234 0.0688 0.0301 0.0832 0.0387 -0.0718 -0.0405

0.8918 0.7044 0.6355 1.1145 0.0689 0.0517 0.1034 0.0582 0.0687 0.1145

1.0090 0.8480 0.7675 1.2558 0.0891 0.0345 0.1006 0.0402 0.0756 0.1229

1.1469 0.9742 0.8916 1.4453 0.1006 0.0359 0.1222 0.0575 0.0650 0.1281

1.2645 1.0400 0.9883 1.4794 0.0863 0.0561 0.1121 0.0561 0.1320 0.1722

1.1583 1.0576 1.0273 1.7705 0.0654 0.0554 0.1007 0.0554 -0.1263 -0.1343 a BS stands for ‘Bundling Scenario’ and specifically refers to the respective CBC stand-alone cases as originally estimated by Werder (2013).

The revenue maximizing cases are marked in bold. Unit: Preference share * price in €.

Source: Own calculations with source data from Werder (2013).

Table 14: Added Value of Season Pass

Revenue

BS8 – B

Revenue

BS9 – BS3

0.1892 0.1258

0.1454 0.0935

0.1584 0.1008

0.1627 0.1094

0.1570 0.0980

0.1627 0.0908

0.2773 0.1685

Source: Own calculations with source data from Werder (2013).

Within the MBC data, the substitutability is much higher because there are no bundle discounts.

By just looking at the overview Table 6 and Table 7, one can suspect that single products yield

more revenue than forcing consumers to buy a bundle. To proof this, I subtracted the revenue of

the highest valued product of each MBC pure bundle from the respective pure bundle revenue.

As Table 15 shows, the result is negative throughout, which does not surprise.

Table 15: Revenue Differences between MBC Pure Bundles and Their Highest Valued Part

Revenue

PB1 – B

Revenue

PB2 – B

Revenue

PB3 – B

Revenue

PB4 – B

Revenue

PB5 – P

Revenue

PB6 – P

Revenue

PB7 – P

Revenue

PB8 – P

Revenue

PB9 – P

Revenue

PB10 – P

-0.1462 -0.1854 -0.1867 -0.2033 -0.0634 0.0284 -0.0650 -0.1117 -0.1134 -0.1161

-0.2626 -0.3355 -0.3349 -0.3678 -0.1680 0.0526 -0.2262 -0.2786 -0.2958 -

-0.3137 -0.4111 -0.4102 -0.4588 -0.2765 0.0918 -0.3952 -0.4318 -0.4557 -

-0.4138 -0.5031 -0.4999 -0.5347 -0.4773 -0.0289 -0.5566 -0.5937 -0.6583 -

-0.4701 -0.5210 -0.5254 -0.5558 -0.6643 -0.1425 -0.7201 -0.7387 - -

-0.5212 -0.5283 -0.5397 -0.5554 -0.6880 -0.1603 -0.6832 -0.7067 - -

- - - - - - -0.6778 -0.7024 -0.7863 -0.7809


46

4.3.4 Content and Form Utility

As Koukova et al. (2008) showed, products have a content utility and a form utility. Specifically,

Koukova et al. argue and show that content substitutes can be form complements, and that

advertising specific usage situations can induce content substitutes to become form complements.

While the usage situation wasn’t especially advertised in the study underlying this master’s thesis,

the pros and cons of each option have been pointed out in the respondent’s task description. It is

therefore likely that respondents to at least some degree differentiated between these utilities,

especially in the MBC questionnaires, where they had to more explicitly decide on their preferred

channel. The amount of non-cannibalization within a category is one indicator of form utility. As

shown in the previous section for the CBC case with Bundling Scenarios 1 to 4, there is not much

perceived form complementarity (revenue only increases between 4.25% to 8.15%). Another

possible indicator is the pure single product case, as shown in Table 16. In this case, the market

simulations have been conducted with only one product in the simulated market. As established

before, products within one category (website, newspaper or sports league) have the same content

utility, except for the newspaper app, which has additional features compared to print and ePaper.

Hence, if the products within a category have similar (dissimilar) revenues, this means that form

utility is low (high). Hence, the variance of within category revenues is a measure for the form

utility. The CBC and MBC data show that browser indeed dominates the other two website

products, and smartphone app is ahead of the tablet app, although only marginally in the CBC

case. These form utility advantages most likely stem from several sources. First, smartphones and

tablets have a browser as well, hence the browser gives a user more freedom of usage. Second,

smartphones only have small displays and third, tablets have much less market penetration than

smartphones, computers and laptops. In the newspaper category, both CBC and MBC data show

higher overall revenues for the ePaper over the newspaper app. Although the newspaper app has

higher content utility, its limited usability (only available on tablets) together with the low market

penetration of tablets outweighs this advantage. The MBC data further shows that print

dominates within this category. Possible reasons are that the print version can be read while

traveling and that people prefer to read on paper instead of screens. For sports league, the CBC

data is insufficient for drawing any conclusion while the MBC data shows that a season pass is

preferred over a match day pass or one game pass. Again, this likely has more than one possible

explanation. One is form utility: Respondents following the sports league on a regular basis don’t

47

need to buy new day or game passes all the time. The other is (assumed) savings of buying the

season pass instead of a series of day or game passes.

Table 16: Pure Single Product Revenue for CBC and MBC Methods

Product Max. CBC

Revenue

Max. MBC

Revenue

Browser (B) 1.2645 0.5631

Smartphone App (SA) 1.0576 0.1963

Tablet App (TA) 1.0273 0.1189

Print (P) n/a 0.8280

ePaper (PDF) 1.8343 0.1999

Newspaper App (NA) 1.7220 0.1146

Season Pass (SP) 1.5618 0.2412

Day Pass (DP) n/a 0.0742

Game Pass (GP) n/a 0.0694

Source: Own illustration with source data from Ung (2012) and Werder (2013).

Also, the pure bundling cases in Table 6 and Table 7 from Section 4.3.1 can be used as indicators

for form utility as well. First, the CBC data shows that the maximum revenue for Bundle

Scenario 3, which includes all three website products, is less than the maximum revenue of

Bundle Scenario 1 (B & SA) or 2 (B & TA), but more than Bundle Scenario 4 (SA & TA). This

is a clear indication that respondents were afraid to ‘pay double’, or in other words, that form

utility hasn’t been perceived as strong enough to compensate for a presumed higher price, with

the exception of browser. Because when Bundle 3 is created by adding a browser to Bundle 4,

then the maximum possible revenue goes slightly up. Similarly, if comparing Bundles 5 (SA & P)

and 6 (B & P), one can see that the browser adds much more value than the smartphone app. And

when comparing Bundle 7 (B, P & SP) to 10 (B, SA, TA, P & SP) and Bundle 8 (B & SP) to 9 (B,

SA, TA & SP) it can again be seen that adding products which are perceived as redundant

heightens consumer fear of paying too much or paying for products they have no use for (tablet

app). It is also possible that as more content utility is added to a bundle, the less importance is

attributed to form utility. This hypothesis would be an interesting point for future research.

Second, the MBC data shows similar patterns as the CBC data when comparing Bundle 5 to 6, 7

to 10, and 8 to 9. However, Bundles 1 to 4 differ from the CBC data. Out of these four bundles,

Bundle 2 has the lowest revenue, followed by Bundle 3, 4 and 1. In MBCA, every item has a

price attached to it, and Ung (2012) estimated the pure bundling case by estimating the most

likely combinations made by respondents. This, in my view correctly, shows that browser and

48

tablet app are the most substitutable, i.e. experience the lowest amount of form utility. Most

likely, respondents think they can just use the browser on the tablet instead of paying for a

dedicated app. Not surprisingly, this is followed by the three product case, because as I just

established, a tablet app doesn’t add much value to a browser. And that browser and smartphone

app are stronger form complements than smartphone and tablet should not come as a surprise as

well.

4.3.5 Price Sensitivity

In the theory part, I derived Hypothesis 2, which states that respondents are more price sensitive

when answering MBC tasks as when they answer CBC tasks. This Section aims to find support

for this hypothesis.

Hypothesis 2 can be investigated by measuring the slope of the demand curves. By assuming that

respondents only want to buy one of each product per subscription bundle, I can use the

preference shares as proxy for demand. I further assumed only linear models. While this might be

a simplification, the fact that only six to seven data points are available per bundle scenario and

the sometimes erratic behaving preference shares impair the added value of more sophisticated

curve estimation. Figure 5 and Figure 6 give an impression on how the curves compare. I decided

to restrict the price elasticity comparison to the ten pure bundling scenarios and the six out of

nine pure single product cases where data for both methods is available, because I believe the

mixed bundling results are not suited for comparison in light of the different methods applied to

estimate them.28

28

Pure single product cases are those cases, where only a product by itself has entered the market simulations.

Crucial reasons why mixed bundling is not suited for comparison can be found in Appendix B.

49

Figure 5: Example of Reasonably Well Behaving Curves

Source: Own illustration. X-axis depicts preference share, Y-axis depicts price in €. The MBC data point 1.59 has

been interpolated for this graph using the following formula: 0.4 * {0.99 data point} + 0.6 * {1.99 data point}. Note:

X-axis is not linear.

Figure 6: Example of an Erratic Behaving Curve

Source: Own illustration. X-axis depicts preference share, Y-axis depicts price in €. Note: X-axis is not linear.

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

0.99 1.59 1.99 2.99 3.99 4.99 5.99 6.99

B_Share_MBC B_Share_CBC

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

1.99 2.99 3.99 4.99 5.99 6.99 7.99 8.99 9.99 12.99

PDF_Share_MBC PDF_Share_CBC

50

As one can see, the linear case might not fit some of the data best, but it is questionable if another

type of curve, which fits the data better, has any theoretical foundations. Such ‘reversals’ can

happen due to random noise and could be eliminated with monotonicity constraints (Orme, 2003).

However, neither Ung (2012) nor Werder (2013) applied monotonicity constraints, since they

also have drawbacks. Wittink (2000) finds that constraints imposed on estimated parameters may

increase individual choice predictions but reduce preference share forecast accuracy. Orme (2005)

explains that this is because constraints reduce variance at the expense of increasing bias, i.e.

since random noise usually cancels out when taking many individuals into consideration, the

additional bias reduces the preference share forecast accuracy. Keeping this in mind, Table 17

takes a look at the slope estimates, their standard errors (SE) and coefficient of determination

(R2). Fully aware of the shortcomings of the R

2 measure, it is very high in most cases, which in

agreement with the respective graphs indicates that the linear model fits the data fairly well after

all. In fact, all non-linear looking graphs like the one depicted in Figure 6 have a low R2.

Examples of low R2 are the MBC tablet app data, which according to its graph would better be fit

by a quadratic function of the type x – ax2. In my view, this points to the fact that tablet users are

price insensitive along the first four price points. The MBC Bundling Scenario 4 (which includes

smartphone and tablet app) also hints to price inelasticity along the first four price points. This

should not come as a surprise, since the MBC pure bundling scenarios are estimated using

combinatorial choices, i.e. only choices very likely or actually made by respondents. This leads to

the situation that bundles with many products only have a few or even no answers per data point,

which explains the low and even non-existent R2 of the MBC Bundling Scenarios 9 and 10. This

also explains why the four lowest slopes belong to MBC bundling scenarios with three or more

products. In CBC, the four low R2 cases are all associated with graphs similar to the one in Figure

6. In fact, that figure depicts one of these cases. Without any access to the raw data, I am unable

to explain the exact nature of this erratic behavior.

51

Table 17: Slopes of Pure Product and Pure Bundling Scenarios

Scenario Slope

MBC SE

cSlope R

2

Slope

CBC SESlope R

2

PSPa B -0.02263 0.00071 0.99504 -0.02969 0.00197 0.97836 +31.2%

PSP SA -0.01461 0.00145 0.95287 -0.02265 0.00116 0.98713 +55.1%

PSP TA -0.00192 0.00117 0.35040 -0.01798 0.00106 0.98289 +836.5%

PSP NA -0.00360 0.00023 0.98394 -0.00548 0.00228 0.53663 +52.0%

PSP PDF -0.00291 0.00070 0.77477 -0.00695 0.00340 0.45569 +138.5%

PSP SP -0.00669 0.00130 0.84162 -0.01310 0.00221 0.87575 +95.9%

PBb 1 -0.00277 0.00031 0.95351 -0.01641 0.00226 0.91347 +492.5%

PB 2 -0.00076 0.00013 0.90210 -0.01492 0.00227 0.89632 +1862.8%

PB 3 -0.00036 0.00007 0.88285 -0.01645 0.00190 0.93719 +4506.0%

PB 4 -0.00065 0.00032 0.51138 -0.01153 0.00240 0.82143 +1685.1%

PB 5 -0.00154 0.00021 0.93217 -0.00602 0.00055 0.96062 +289.7%

PB 6 -0.00237 0.00038 0.90839 -0.00576 0.00070 0.93187 +143.2%

PB 7 -0.00036 0.00009 0.76153 -0.00570 0.00059 0.94877 +1488.0%

PB 8 -0.00224 0.00033 0.90384 -0.01019 0.00499 0.45451 +353.8%

PB 9 -0.00001 0.00001 0.38942 -0.00859 0.00387 0.49565 +82578%

PB 10 +0.00001 - - -0.00541 0.00077 0.90808 +52210% a PSP stands for ‘Pure Single Product’

b PB stands for ‘Pure Bundling’

c SE stands for ‘Standard Error’


Despite these exceptions, most cases have a stable linear relationship between preference share

and price. So what about Hypothesis 2? Surprisingly, in all cases, the CBC data exhibits a steeper

slope. In Table 17, I calculated the quotient of the slopes minus one to indicate by how many

percent the CBC curves are steeper than the MBC curves. This ranges in between 30% to 500%,

with a few outliers, such as Bundling Scenarios 9 and 10 where almost no MBC data was

available for preference share and slope estimation. These findings are in strong contradiction of

Hypothesis 2. However, I soon realized that I was comparing apples and oranges, because the

MBC data in general has lower shares per price point than the CBC data. This can be seen in

Figure 5 and Figure 6 and in Table 36, Table 37 and Table 38 provided in Appendix D. For every

common price point, the MBC shares are without exception always lower than the CBC shares,

which means that fewer MBC respondents are interested in buying that product or bundle for the

given price, i.e. they are more sensitive to price. Seen from this perspective, the findings are now

in strong support of Hypothesis 2. Summed up, the CBC method exhibits a higher within-method

price sensitivity, while MBC exhibits a higher across-methods price sensitivity. Considering

these results, I strongly suspect that the more price sensitive MBC respondents opted themselves

out via the none-option. But since the none-option usage is comparable between CBCA (70.13%)

52

and MBCA (67.86%), this would mean that some price-sensitive respondents didn’t screen

themselves out in the CBC questionnaire. This seems plausible, since it is very probable that a

greater learning effect is attached to CBCA, where only four concepts with one total price are

shown per task. In this more opaque setting, respondents almost certainly need more time to learn

about the offered price ranges. This argument is supported by the finding of Johnson and Orme

(1996) who find that the none-option usage on average increases during a CBC experiment.

Furthermore, by choosing just one product in the MBC questionnaire, no none-option is chosen.

But in CBCA, in most cases more than one product is chosen when a respondent makes a buy

decision. So I believe there are enough indicators which suggest that, compared to CBC, more

less price sensitive MBC respondents actually remained in the sample, which explains the lower

within-method price sensitivity of MBC! This leads me to the conclusion that actually all my

findings in this section are in support of Hypothesis 2.

4.3.6 Negatively Valued Attributes

Lastly, this section aims to investigate Hypothesis 3, which states that CBCA is better able to

point out and handle negatively valued attributes than MBCA. Unfortunately, investigating this

issue was not a goal of the underlying study. Taken on its own, each product supposedly has a

positive value. However, there are some clues regarding negatively valued attributes, at least in

combination with other attributes. For one, I found in Section 4.3.3 that if season pass is offered

as the only product in the market, it would yield more revenue on three out of seven price points

than if offered together with browser or browser, smartphone app and tablet app. Also, Werder

(2013) made an interesting observation. She noticed that Pure Bundles 1 through 4 are all offered

at Price Range 2, i.e. they cost the same. Hence, Bundle 3 should exhibit higher or at least the

same preference shares as Bundles 1, 2 and 4, because in every case, Bundle 3 offers an

additional product (Werder, 2013). Similar reasoning holds between Bundles 7 & 10 and 8 & 9.

Table 18 shows the shares of the bundles in question. The preference reversals (smaller bundle

has a higher preference share for the same price) are marked in bold.

53

Table 18: CBC Pure Bundling – Possibly Negative Valued Attributes

Bundle

Price

Share

PB1

Share

PB2

Share

PB3

Share

PB4

Bundle

Price

Share

PB7

Share

PB10

Bundle

Price

Share

PB8

Share

PB9

2.99 29.54% 28.31% 29.47% 25.14% 17.98 20.97% 20.61% 4.99 25.36% 23.27%

3.99 26.23% 25.65% 25.87% 22.12% 20.98 17.94% 17.65% 5.99 25.22% 22.55%

4.99 23.56% 22.33% 23.49% 20.17% 23.98 15.35% 14.19% 6.99 29.18% 26.01%

5.99 21.25% 20.97% 21.83% 18.23% 26.98 14.05% 13.40% 7.99 18.80% 18.16%

6.99 20.97% 20.68% 21.11% 18.16% 29.98 12.03% 11.96% 8.99 19.74% 17.94%

7.99 19.09% 18.52% 19.16% 16.86% 32.98 11.02% 10.66% 9.99 19.52% 18.44%

9.99 17.72% 17.72% 17.36% 16.86% 36.98 10.01% 10.16% 12.99 19.52% 18.01%

Source: Own illustration with source data from Werder (2013). Preference reversals are marked in bold font.

Since I don’t own a tablet myself, the first thing coming to my mind when reading Werder’s

findings about these preference reversals was, that respondents without a tablet, or even

smartphone, might be afraid to pay too much. This is because in the CBC tasks, only four

alternatives are shown together and only total price is shown. Hence, respondents most likely

didn’t realize that they could get more for the same price. In other words, the complexity of

different bundle offerings can confuse respondents, especially when they are not directly

comparable. Werder thought of this point as well, and offered two additional explanations. One,

respondents might sometimes simply have given ‘sloppy answers’ and two, they could fear an

information overload. Ben-Akiva and Gershenfeld (1998) also observed that some respondents

would not even want a product for free, although they argued not with information overload but

that usage might get more complicated by owning more products. In the context of this study

though, I have one more explanation to offer. In CBC, bundles can be coded in two ways. One

could code each product as a binary attribute, which is either shown or not shown. Or one could

use combinatorial coding, which codes the whole bundle as one attribute with several levels. This

has been applied in this CBC study, every category has been coded as one attribute.29

But since

the software ‘thinks’ in terms of attribute levels, it cannot recognize that e.g. ‘browser’ is also

present in ‘browser & smartphone app’. Instead, it will treat them the same way as it would e.g.

treat the colors ‘silver’ and ‘blue’ in a color attribute (McEwan, 2013). This surely facilitated the

estimation of mixed bundles, however it might also explain above preference reversal as simple

random noise. The random noise explanation seems especially appealing for Bundles 1 through 4,

where only minor reversals happened. Though it looks less convincing for Bundle 8, where some

reversals are as high as 3.17%!

29

Table 41 in Appendix E provides an overview on how the combinatorial approach has been implemented.

54

On the other hand, I was unable to find any examples within the MBC data which could possibly

indicate negatively valued features (although strong evidence for substitutability within bundles).

But with just one convincing case in the CBC data, I find this inconclusive to either support or

dismiss Hypothesis 3.

5 DISCUSSION

In Chapter 3 I provided a focused overview on the CBCA theory and an extensive overview on

the currently available MBCA theory. I also worked out the most important differences between

these two conjoint approaches. In Chapter 4 I then showed the results of an MBCA and a CBCA

study, which have been conducted to research the same issue, namely the optimal pricing of

digital media products (and the print version). While I often added brief explanations for the

results and why they might differ between methods, this Chapter intends to outline the theoretical

reasons why the results of the two methods differ and which method is likely to deliver more

robust results. Since there are many possible reasons, I will split my line of argumentation into

three sections. In Section 5.1 I will point out the differences between the two study designs and

how they might have affected the results. In Section 5.2 I will look at the differences in the study

analyses of Katja Werder and Robert Ung and the possible impact on the results. In Section 5.3 I

will discuss how the theoretical differences between CBC and MBC could explain the different

study outcomes and Section 5.4 concludes the discussion.

5.1 Differences in the Study Designs

In Chapter 4 I already mentioned my sources regarding the marketing study. Regretfully, there

will be some issues in this section which require information on the study design not available in

any of these sources. Even though my thesis supervisor tried to help me getting in contact with

the marketing research company, our attempts to acquire the missing information were

unsuccessful as the project has been completed a year ago and the staff in charge of the project

was no longer working there. Hence, I will try to ‘solve’ this unsatisfactory situation by providing

conditional answers where needed.

In order to compare the results of the two studies, I will first establish an impression on their

accuracy, reliability and validity. Alas, I do not have access to the raw data, so I was unable to

55

conduct any tests or calculate any statistical measures of these concepts,30

hence a qualitative

comparison has to do. According to Statistics Canada (2009) “the accuracy of statistical

information is the degree to which the information correctly describes the phenomena it was

designed to measure” (p. 7). Therefore, it is important to note that in our context, CBCA and

MBCA are designed to measure the hypothetical WTP. Traditionally, accuracy is decomposed

into bias (systematic error) and variance (random error) (Statistics Canada, 2009). With regard to

conjoint data, Orme (2010a) lists two kinds or error influencing accuracy: Sampling error is

related to the size and randomness of a sample while measurement error is related to the number

of tasks answered per respondent. Additionally, the number of attributes likely influences the

quality of the answers as well. Reliability refers to the degree that independent but comparable

measures of the same construct agree (Churchill, 1979). It is often estimated with a test-retest

design, i.e. repeating a task in the experiment and check if respondents give the same answer.

One should not pick the first task though due to learning effects (cf. Section 3.5.4). Another way

to estimate reliability is by measuring internal consistency with hold-out tasks. Here, the

researcher includes the same task across respondents and then tests whether the statistical

estimates obtained from the non-hold-out tasks can predict the choice(s) made in the hold-out

task. In case no hold-out tasks are available, one could divide the sample and use some

respondents as hold-out respondents, as applied by e.g. Orme (2010b). Validity refers to how

close observed estimates are to the actual estimates one tries to measure (Churchill, 1979). In this

case, how close the estimated WTP is to the actual WTP. According to Wittink & Cattin (1989),

the closest conjoint studies ever come to validation is by comparing their share predictions with

the actual market shares. Below follows a number of comparative descriptions on topics which

have an influence on either accuracy, reliability, or validity.

Sample randomness: A pure random sample should allow unbiased inferences regarding the

population, however they are usually hard to collect. For example, some people may dislike

participating in any kind of market research study and hence there is a certain non-response bias

in every sample. Conducting a study online likely introduces selection bias, however since most

products in these studies are of digital nature, there should not be a noteworthy bias regarding the

inferences. But is a pure random sample even desirable? Orme (2010a) mentions that a firm is

mostly interested in the WTP of its current or potential customers, not the whole market. As can

30

Like mean squared error (MSE), mean absolute error (MAE), percent absolute error (PAE) or simply 1 – R2.

56

be seen in Section 4.1, this is exactly what has been done for this study, as respondents were pre-

selected according to certain criteria like having used the newspaper website before.

Unfortunately, there is no information available as to how exactly the data has been collected.

The two most likely options would be that either a panel has been used or that respondents were

recruited via the website of the newspaper company. In the first case, a broader distributed

sample is possible than in the second, even with pre-selection. Considering the pre-selection

criteria, it is anyways highly probable that a panel has been used. And more importantly, from the

sources I do have I can be sure that the data collection method has been the same for both

methods, hence neither has a (dis)advantage over the other in that regard. But this doesn’t mean

the issue is unimportant, because as Ding and Huber (2009) speculated, panel respondents are

less likely to distort their answers due to boredom, confusion or some hypothetical biases than the

general population, because they are more experienced and will be removed when they complete

survey too fast or only choose random answers. Lastly, it needs to be considered that having two

different samples for CBCA and MBCA introduces a certain amount of sampling error, which

refers to the fact that two samples of the same population usually have slightly differing statistical

moment estimates.

Sample size: The bigger the sample is, the closer its mean will be to the population mean, on

average. Sawtooth Software recommends at least 300 – 500 respondents for CBC experiments

while Orme (2010a) recommends to following rule of thumb:

, where n stands

for the number of respondents, TPR for the number of tasks per respondents, APT the number of

alternatives per task (not including the none-option) and c for the number of analysis cells. In a

main effects only model, c equals the largest number of levels of any single attribute; in an

interaction model it would equal the largest product of any two attributes (Orme, 2010a).

Plugging in n=1388, TPR=12, APT=4, and c=10 the equation yields 6662.4 which is

considerably more than 500 (a bare minimum) and comfortably exceeding the suggested security

margin of 1000 (Orme, 2010a, 2013b). However, what this formula is actually trying to measure

is the average number of responses per analysis cell. And since Werder (2013) reports that the

none-option has been chosen in 70.131% of all choice tasks, these should not be counted. Hence,

I augment the formula to

, which still yields 1990.0 and therefore still

comfortably exceeds the recommended 1000. Regarding MBC experiments, Orme (2013a)

57

provides the following formula as rule of thumb:

, where p stands for the number

of price levels. Plugging in n=1462, TPR=6 and p=7 in above formula results in 1253.1, which is

above the recommended 1000. From the counting analysis, it can be seen that the none-option

was chosen 67.86% of the time. Augmenting the formula to

yields

402.8 and even if using p=6 the result is still only 469.9, well below the recommended 1000 and

even below the bare minimum mentioned for the CBC studies of 500. This might explain why

many reported standard errors in the aggregate logit analysis are well above the recommended

0.10 (Orme, 2013a). One caveat regarding these formulas is made by Orme (2013b), where he

cautions that especially the one for CBC was developed when aggregate logit was used to analyze

the data and hence doesn’t directly apply to modern day situations using HB.

Number of tasks per respondent: Unlike sample size, this number should not be increased

indefinitely as respondents get tired or even reluctant (Orme, 2010a). Johnson and Orme (1996)

suggest that for CBC experiments, up to 20 tasks per respondent should be ok. For MBC

experiments, Orme (2010c) reports that respondents are still motivated after completing up to 16

tasks in a row. With 6 MBC TPR and 12 CBC TPR, both studies are comfortably below the

maximum amount. However, Orme (2013b) emphasizes that such guidelines should be used with

caution, as it really depends on survey complexity, respondents’ motivation and possibly other

factors. While I do not have any information on incentives (or other motivation boosters) offered

to respondents, I am at least sure that they were the same for both methods. Regarding

complexity, the MBC survey was quite simple (no bundles, just one easy understandable

dependent choice), whereas the CBC survey seemed a bit more complex with four APT and up to

seven perceived attributes plus price. With HB estimation, which has been used in both studies

and delivered the utilities entered into the market simulations, the minimum TPR also become an

issue. Unfortunately, there is no literature on it. Asking Sawtooth Software about this issue, Orme

(2013b) answered that a lot of experience is needed, and roughly guesses that in standard CBC

studies (discrete choice, about six attributes with two to five levels each, three to four APT,

estimating main effects only) 10 to 12 TPR is all that’s needed for stable HB estimation. If price

is considered just one attribute, then this CBC study should be within these limits. But as I

understand it, the alternative specific prices are all coded as an attribute, which means that they

are estimated as different parameters, and hence more TPRs might have been needed. With

respect to MBC, no answer was provided. However, it should be taken into consideration that

58

MBC features multiple choice answers which provides more information per task. Also, less

parameters needed to be estimated as opposed to the CBC case. Therefore, I believe the MBC

study got an advantage over the CBC study in this category.

Number of Attributes: CVA and CBC studies should include a maximum of six attributes,

possibly fewer, depending on the length of the attribute text (Sawtooth Software, 2007, 2013).

With a total of four attributes, this would be fulfilled for the underlying study. But one should pay

attention to the fact, that a respondent might perceive the attribute levels as attributes, hence an

alternative might show between 2 to 8 ‘perceived attributes’. Then again, these perceived

attributes are fairly easy to compare for respondents (e.g. smartphone vs. smartphone & tablet).

Regarding MBC, Kamakura and Kwak (2012) found in pre-tests to their study, that they should

not burden respondents with more than 8 tasks, including 16 attributes each. Since the MBC

study featured 6 TPR with 9 attributes each, I believe it to be within the standard limits.

Reliability and validity: While I can’t influence the absence of any kind of measure, I can at

least point them out as scope for future research. If a future study plans to compare the predicted

pricing structure and markets shares with the implemented pricing structure and achieved market

shares, it should at least calculate some measures of reliability because “reliability is a necessary

but not sufficient condition for validity” (Churchill, 1979, p. 65). Orme (2013a) suggests that in

MBC, comparing counts data to the market simulations data might be even more valuable than

hold-out tasks, provided a good randomized design has been used. Unfortunately, Ung (2012)

only provided the one-way probabilities of choice counts data, but in the market simulations only

the price level dependent data. Therefore, the only reliability check I could perform is to see if the

counts data is between the minimum and maximum price level range. This is the case for the

none-option and six other attributes. The tablet app, day pass and game pass however

experienced higher counts data than their maximum preference share. Without access to the raw

data I can’t investigate this issue further, however I suppose these options were not chosen very

often and are therefore more susceptible to random noise, even though the relative magnitude of

their standard errors are not much higher than those of other attributes. Again, it should be kept in

mind that counts is only then a valid indicator when a good randomized design has been used

(Orme, 2013a)! With regard to the CBC study, no counts validation could be performed due to

the alternative specific design.

59

After looking at the quality indicators, there are other issues which can impact the results

retrieved from the two studies. For example, the questionnaire designs, the price structure, bundle

discounts and heroic assumptions. These will be looked at now.

Regarding the questionnaire design itself, there are also some important reservations. Johnson

(2008) recognizes that compensatory models can fit many kinds of choice behavior, even non-

compensatory ones like lexicographic or conjunctive. But he points out that problems occur if an

analyst uses the minimal overlap design. Minimal overlap is appealing and leads to efficient

questionnaires if answers are made according to the logit rule, but if respondents use a non-

compensatory strategy, minimal overlap exposes the analyst to the risk of overstating the

predictive abilities of the conjoint data (Johnson, 2008). This is because minimal overlap shows

as few attribute levels together in a task as possible, but if those are the salient attributes in the

non-compensatory decision making process, this does not provide much information on the other

attributes (Johnson, 2008). Orme (2003), assuming compensatory decision making, mentioned

that minimal overlap could be efficient together with an alternative specific design, because

minimal overlap will apply to the attributes, but the price levels can still have more overlap.

Therefore, the earlier recommendation was to use minimal overlap with an alternative-specific

design and the new insights from Johnson (2008) might not have diffused to all conjoint users yet.

Also, Chrzan and Orme (2000) find that balanced overlap is less efficient (i.e. needs higher

sample size) in estimating main effects than the minimal overlap models (for more details, cf.

Section 3.4.1). And with this studies sample size, reliably estimating interaction effects is out of

question. Sadly, as mentioned in the in the introduction of this chapter, I was unable to obtain the

information on how the CBC and MBC questionnaires were designed. It could have been a fixed

design or a randomized design, and in the case of CBCA maybe with minimal or maybe with

balanced overlap. Randomized designs do seem superior over fixed designs, as they check more

potential combinations. Especially in MBCA, allowing prices to be randomized likely enhances

the efficiency of the estimated parameters, considering only six tasks are asked per respondent

(although in a fixed design, different blocks could be used). With regard to CBCA, it would be

imperative to avoid minimal overlap since there is evidence that respondents use non-

compensatory decision rules (cf. Section 3.5.2 and 3.5.3) and hence minimal overlap would lead

to biased estimates of the non-salient attributes, while the sample size is big enough to

accommodate for the slightly less efficient balanced overlap design.

60

Additionally, it needs to be mentioned that different pricing structures were applied for the

CBC and MBC study, as can be seen in Appendix E. Particularly, the price ranges in the MBC

study tend to be lower than in the CBC study, which might also lead to lower optimal price

estimates due to lower price anchors if assuming respondents to exhibit a prospect theory value

function (cf. Kahneman & Tversky, 1979; Thaler, 1985). Furthermore, different pricing

structures might bias the optimal price estimation to the extent that price is seen as a quality

indicator. In fact, as found in Section 4.3.2, the CBC method exhibits optimal prices at higher

price levels than the MBC method.

There are also technical reasons which are important with regard to pricing structure, particularly

the alternative specific design employed in the CBC study. Orme (2003) warns that with a

conditional price design, main effects no longer correspond to preferences for attribute levels

because the utility of the ‘better’ attribute levels (e.g. larger package size) also include a negative

utility intercept from higher average prices. However, it could be corrected by estimating the

price-attribute interactions or by using an alternative-specific design, which will induce the

software to calculate these effects automatically (Orme, 2003). According to Werder (2013) and

judging from the study design, an alternative-specific design has been used. Therefore, no utility

bias is included due to the conditional prices, as the software automatically estimated the

necessary price-attribute interactions and incorporated them into the results (Orme, 2003).

However, when using interaction effects, the rule-of-thumb formula provided to calculate

minimum sample size would then need a c=80 input. This would yield only 832.8 and even a

puny 248.7 when controlling for ‘none’ answers, below the recommended 1000 and even

strongly below the bare minimum of 500. Therefore, the accuracy of the estimates might have

been compromised.

When it comes to bundle discounts, they have been applied in the CBC but not the MBC study.

However, many researchers found the bundle discount impact on sales to be crucial, see e.g.

Liechty et al. (2001), Ben-Akiva & Gershenfeld (1998) and Koukova et al. (2008). In that respect,

it should not surprise that MBCA returns lower preference shares for bundling scenarios.

According to Lieb (2013a), heroic assumptions are assumptions which cannot be tested. Ung

(2012) used the Sawtooth Software Menu-Based Choice software and Werder (2013) used the

Sawtooth Software Choice-Based Conjoint software. Judging from the MBC instruction manual,

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it seems that the SCE approach has been incorporated. Therefore, the same heroic assumptions

inherent to MNL and HB apply to both the CBC and MBC methods (cf. Section 3.5.1).

Table 19 summarized the findings in this section. The assessments ‘negative’, ‘neutral’ and

‘positive’ have to be understood as relative to an industry standard, where ‘neutral’ means in

accordance with the standards, ‘negative’ does not satisfy all criteria of a standard and ‘positve’

means that some or even all standard criteria are surpassed.

Table 19: Summary of (Quality) Indicators and Their Impact

Indicator CBCA MBCA

Accuracy

- Sample Randomness neutral neutral

- Sample Size positive negative

- # TPR neutral positive

- # Attributes neutral neutral

Reliability - slightly negative, if

randomized design used

Validity - -

Questionnaire Design danger of bias if minimal

overlap was used -

Price Structure (Theory) higher anchor point and higher

quality signal

lower anchor point and lower

quality signal

Price Structure (Technical) negative neutral

Bundle Discounts present not present

Heroic Assumptions neutral neutral


5.2 Differences in the Study Analyses

Apart from the study design, which has been chosen by the marketing research company which

conducted the underlying study, there are also differences in the way Werder (2013) and Ung

(2012) analyzed the data. They will be looked at in this section.

With regard to model building, the two methods are different by nature. In comparison, the CBC

software is quite simple to use. Werder specified a main effects only model with standard settings,

only the HB settings have been adapted as described below. As described above, the main effects

model is the only feasible one, considering the sample size and the high amount of none-option

usage. The MBC software on the other hand requires sophisticated model building. The user has

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to choose how to code the data, it is possible to code the data as EA model or as SCE model (cf.

Section 3.5.1). Ung used the SCE approach and included only significant cross effects. This

‘pruning’ approach is encouraged by Orme (2013a) because else the model could run the risk of

overfitting and random noise reversals. Also Moore (2010) found in his study that the pruned

SCE model had a higher predictive accuracy than the SCE model including all cross-effects.

Orme (2013a) recommends including all cross-effects significant at a 20% level, i.e. a p-value of

0.20 or less, which is exactly what Ung did. However, Cordella et al. (2012a) warn that SCE

model building requires a lot of experience and expertise, and a mechanistic approach like

described above might exclude some logical and therefore important cross-effects. Indeed when

looking at Ung’s SCE sub-models, some decisions might be questioned. Specifically, he did not

include the smartphone app in the tablet app sub-model (p=0.20), not include the day pass in the

season pass sub-model (p=0.20) and not include print in the print sub-model (p=0.21). All of

these would, in my opinion, have logical and statistical grounds to be included, especially when

considering that the p≤0.20 level is quite an arbitrary threshold after all. Furthermore, I believe

smartphone app (p=0.96) and tablet app (p=0.52) would have had logical but not statistical

grounds to be included in the newspaper app model and similarly one game pass (p=0.69) should

have been included in the season pass model. While using the SCE instead of EA approach was

the right decision considering the size of the overall model, I believe some of the SCE sub-

models could have been specified differently.

As an aside, Moore (2010) notes that a shortcoming of CBCA lies in areas where multiple

products can be chosen per task, because this could potentially impact the size of the market. He

feels that such market size changes could be important to properly understand metrics like profit

and proposes that volumetric CBC models or, of course, MBC models are possible solutions

(Moore, 2010). However, since the global none-option has been included in the CBC

questionnaire, Werder could not have coded a volumetric CBC model. Furthermore, I believe that

the combinatorial attribute coding which has been implemented can at least partially capture such

market size changes, at least to a higher degree than the binary coding approach.

For utility estimation, they both did similar things. Both first looked at counts and aggregate

logit and finally used HB estimation. Werder decided to use higher than standard settings to

ensure convergence, specifically 25’000 burn-in iterations and 100’000 iterations for calibration,

with a skip factor of 2. Ung decided to use the standard settings, which were 20’000 burn-in

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iterations, 200 draws per respondent and a skip factor of 100. Both then used the HB utilities to

enter into the respective market simulation tools.

It is in the market simulations where the most pronounced differences occur. To be exact, two

kinds of differences occurred, one of technical nature, the other one of model building nature.

The differences concerning the model building nature are extensively discussed in Appendix B,

especially in Section B.2. Summed up, the following approaches have been used: In the pure

bundling cases, both used the same approach, i.e. only the products actually available in the

bundle entered the market simulations. This means, there was no ‘competition’ from other

products, e.g. in Bundle 8 (B & SP), the season pass did not have to compete for preference share

with the day pass or game pass. Werder used the appropriate combinatorial attributes, while Ung

used the MBC software’s combinatorial choice outcomes, which means that the software first

predicted the most likely combinatorial selections made by individual respondents (Orme, 2013a)

and used these as basis for bundle shares. In the stand-alone cases, Werder again entered only the

products actually available in the respective bundling scenario into the market simulator and

estimated them together. Robert Ung on the other hand estimated each product by itself (personal

communication, July 2, 2013), i.e. he conducted a pure single product estimation and then simply

added them up for the ten stand-alone scenarios.31

Werder again used the appropriate

combinatorial attributes. So for the stand-alone case, the CBC approach is clearly superior,

because it allows similar products to ‘cannibalize’ each other’s shares. Additionally, Werder also

estimated six products as pure single products like Ung, but she did not use these estimates in the

ten stand-alone scenarios. Neither of them conducted any kind of sensitivity analysis with respect

to varying price levels, they both used the same price level across all products. Lastly, the mixed

bundling cases featured the biggest variety between the two. Werder again chose the appropriate

combinatorial bundle attribute and combinatorial stand-alone attribute and entered them together

into the market simulator. She did not conduct any kind of sensitivity analysis with regard to

price, e.g. if revenue could be improved by keeping the bundle price constant and only varying

the stand-alone prices. Ung provided no details on his approach, however I was able to

successfully replicate it with the available data. Unlike previously, he entered all products into

31

Judging from the data outputs available in Ung’s thesis and the MBC instruction manual (Orme, 2013a), it actually

seems as if menu choices have been simulated not completely by themselves, but in form of their specified SCE

models. While this could explain lower MBC stand-alone preference shares (compared to CBC), it is contrary to

what Robert Ung told me via email. Without access to the software or raw data, I therefore take his word for it.

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the market simulation, which explains why the MBC mixed bundling revenues are so much

higher compared to the pure bundling and stand-alone revenues. As opposed to Werder, he

additionally conducted a sensitivity analysis (keeping the bundle price fixed and varying the

prices of all other products). This greatly boosts the chance of capturing the revenue maximizing

mixed bundling case.32

There are also technical settings regarding the market simulator which could generate different

results. Werder used the ‘First Choice’ setting in the CBC market simulator. Other options would

have been ‘Share of Preference’, ‘Share of Preference with Correction for Similarity’, ‘Purchase

Likelihood’ and ‘Randomized First Choice’ (RFC). According to SSI Web Help (n.d.), First

Choice assumes that a respondent chooses the product with the highest overall utility and

therefore requires individual level estimation. While this captures the behavior of respondents

with a very high or low probability to choose the product well, those near the buying threshold

are also divided into the two extremes, which might not be as realistic (Sawtooth Software, 2012).

Furthermore, when facing two very similar but not identical products, First Choice will only

choose the utility maximizing one, i.e. it will not reflect any random buyer behavior (Sawtooth

Software, 2012). Also, First Choice usually needs higher sample sizes because information about

the relative preference of other products is lost in the simulation (Orme, 2010a). The Share of

Preference models use the logit rule to estimate shares, which results in more realistic preference

share predictions (SSI Web Help, n.d.). Share of Preference utilizes more information on

respondent’s product preferences than the First Choice model (SSI Web help, n.d.). However,

Share of Preference models are subject to IIA. Although the impact of IIA can be reduced

through HB estimation, it can’t be completely eliminated because IIA still holds at the individual

level (Sawtooth Software, 2012). Share of Preference with Correction for Similarity used some

(possibly arbitrary) corrections to alleviate the IIA problem, but is inferior to the more recently

developed RFC model (Sawtooth Software, 2003). The Purchase Likelihood model estimates the

stated purchase likelihood for products, but needs especially calibrated data (SSI Web Help, n.d.).

It is recommended for products which are completely new to the market (Sawtooth Software,

2003). Therefore, it correctly hasn’t been considered. Finally, the Randomized First Choice

Model is based on First Choice, but includes a random term to alleviate the tendency for extreme

32

To make sure the overall revenue maximizing case is captured, an additional sensitivity analysis among the ‘other

product’ prices would be required – a daunting endeavor, considering Ung already estimated 667 + 477 = 448

mixed bundling revenues.

65

outcomes (‘share inflation’) and is not subject to IIA (Sawtooth Software, 2003, 2012). It is an

improvement over the Share of Preference with Correction for Similarity model, but still suffers

from random noise and can’t distinguish between different prices in a conditional design when

they have the same level (Orme, 2003; Sawtooth Software, 2012). Though it is not mentioned if

that applies to an alternative-specific design as well, this should at the very least be tested before

applying it. Therefore only two options remain. Whereas Share of Preference suffers from IIA, it

is more realistic when it comes to capturing buyer behavior when their product preferences are

near the buying threshold. First Choice on the other hand doesn’t suffer from IIA, but is too

extreme when it comes to similar products. Also, Sawtooth Software (2003) states that First

Choice gives too much preference share to desired products and too little preference share to

undesired products. So which one should have been used? Share of Preference seems

theoretically superior in most situations when using HB estimation, for example, Orme and Baker

(2000) and Cordella et al. (2012a) found Share of Preference to outperform First Choice in their

respective study. The MBC instruction manual (Orme, 2013a) describes two ways of market

simulation. The first one is called simulating respondent choices and is based on the SCE models

which have been specified during the model building process for utility estimation. It applies the

logit rule and is therefore comparable to the Share of Preference method. Point estimates or

draws can be used, where Ung decided to use draws (personal communication, July 2, 2013).

These are the results Ung used for the stand-alone preference shares. The second method is called

simulating combinatorial choice outcomes which delivers the combinations most likely chosen

by respondents. It calculates the choice probability of each menu item for each respondent with

the specified SCE models and using the logit rule, but then discretizes these probabilities into

‘chosen’ or ‘not chosen’, similar to First Choice (Orme, 2013a). If using draws instead of point

estimates, like Ung did, then this procedure is repeated for every draw (200 times in this case).

The software then counts which combinations of the projected First Choices occurs the most

often among the 200 draws and only reports that one as the individuals choice combination

(Orme, 2013a). Ung decided to use the weighted draws setting (personal communication, July 2,

2013), which gives a higher weight to combinations that are actually observed (Orme, 2013a).

Orme (2013a) calls attention to the fact that weighted draws are not theoretically grounded, but

he also provides evidence that this method outperforms unweighted draws in predictive ability.

So how do the CBCA and MBCA market simulations settings compare? Orme and Baker (2000)

argue that HB draws and the RFC method are theoretically similar, as they both reflect

66

uncertainty about point estimates. While they found that the RFC method performs better,

because HB draws are subject to two sources of bias, the HB draws still outperformed Share of

Preference which in turn outperformed First Choice (Orme & Baker, 2000). Hence, the CBC

market simulations using First Choice are not perfectly comparable to the MBC market

simulations which use Share of Preference and HB draws.

Table 20 summarized the findings in this section. In contrast to Table 19, the assessments

‘negative’, ‘neutral’ and ‘positive’ refer not only to a hypothetical industry standard, but also

include an assessment of how comparable the measures are to the respective other method (e.g.

MBCA mixed bundling is not poorly estimated but the estimation method chosen makes it

incomparable to the other revenue estimates).

Table 20: Summary of the Analysis Indicators

Indicator CBCA MBCA

Model Building neutral slightly negative

Utility Estimation neutral neutral

Market Simulations (Model)

- Pure Bundling positive positive

- Stand-Alone positive neutral

- Mixed Bundling neutral negative

Market Simulations (Technical) negative slightly positive


5.3 Theoretical Differences

Most theoretical differences between CBCA and MBCA have already been discussed in Section

3.5 and summarized in Section 3.5.6. In this section, I would like to quickly check if they are

relevant to the two studies that are compared here.

First, the MBC tasks are not necessarily more realistic than the CBC tasks, because no bundling

strategy has been implemented so far, and all bundling strategies seem to be a viable option.

Second, when it comes to simplifying choice strategies, it is likely that CBC respondents used

them to a much higher extent than MBC respondents, but I could not check the data set for any

clues, nor do I have any meta-data like answering times per task to substantiate my claim. Third,

when it comes to identifying ‘natural bundles’, MBCA by nature does a better job. The most

frequently chosen combinations output from the counting analysis is a simple but effective form

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of identifying natural bundles and there is no such counterpart in CBCA. The Sawtooth Software

MBC module also offers a measure for attribute complementarity and substitutability via their

counts module, namely by dividing the actual two-way joint likelihood by the expected joint

likelihood. However, in this study almost all two-way combinations are identified as

complements by this approach which does not seem very realistic. Hence a more robust method,

like a correlation matrix as used by Liechty et al. (2001), would be necessary. In judging

substitutability and complementarity from the obtained revenues, both CBCA and MBCA do a

good job. Fourth, both methods do a good job at measuring attribute price sensitivity, although

some demand curves look a bit erratic when using the CBC data. At first, I strongly suspected

MBCA to deliver the more realistic price sensitivity measure, but had to revise that upon

discovering CBCA to be more price sensitive. But when it comes to share predictions, the MBCA

shares look more realistic. Fifth, on which method was more enjoyable to respondents, I

unfortunately don’t have any meta-data available to investigate that issue. Sixth, in this study,

MBCA could most likely not show its superiority in handling many attributes due to the fact that

most attributes are of binary nature (although coded in a combinatorial way in the CBC study).

Seventh, intangible features have not been an issue in this study, therefore this hasn’t hindered

the effectiveness of MBCA. Eighth, although I can’t proof it with any data, theory suggests that

the MBC approach is more susceptible to hypothetical biases (especially the self-explication

related biases) because trade-off is directly with price, not with other attributes as in CBCA.

Ninth, negatively valued attributes haven’t been an issue by themselves, however some CBC data

suggests, that some attributes could obtain a negative value when bundled (cf. Section 4.3.6).

There is also one theoretical difference not discussed in Section 3.5, which addresses the

situational context of consumer WTP. By this I don’t refer to a customer’s emotional state while

shopping, but rather I believe the two methods might measure different WTP because they

present price information in a different manner. My belief is not only supported by the mental

accounting theory of Thaler (1985) but also by empirical evidence, such as Johnson, Herrmann,

and Bauer (1999) who find support for mental accounting principles in a bundling context.

Thaler’s mental accounting principles are based on the value function of Kahneman and

Tversky’s (1979) prospect theory. That value function is basically an S-shaped curve which

depends on gains and losses rather than final assets and is steeper for losses (Kahneman &

Tversky, 1979). Based on this value function, Thaler distinguishes four cases: Pure gains, pure

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losses, net gains and net losses. A buying situation is a loss and hence it depends on how a

customer mentally codes such losses. In the CBCA case, there is just one price so respondents

most likely efficiently code this event as one huge loss. The MBCA case is less clear, especially

since the simple menu approach is used (which means there will be no perceived savings from

buying a bundle). It might be possible, that relatively low cost add-ons to a more expensive base

product might be integrated, even though the car study in Johnson et al. suggests otherwise. In

our case however, there is no single integrated end product, the prices of the bundle components

are closer together and there are no discounts for choosing more than one product (which

presumably enforces the perception of buying single products instead of an integrated bundle).

Hence, respondents will likely segregate their losses. Because the loss function is convex, this

leads a higher perceived total loss than in the integrated (CBCA) case. This means that according

to the mental accounting theory, the same bundle with the same benefits will elicit different

willingnesses-to-pay under the two conjoint methods. This could in fact be observed in Chapter 4

and in Appendix D, although mental accounting is likely not the only reason responsible for the

differences.

Out of the ten discussed points, Table 21 summarizes the ones in which I suspect or found to be

theoretical differences among the two studies. These findings are a centerpiece of my thesis and

provide added value to the conjoint literature. Since I am not able to provide conclusive proof for

some of them, those would be fruitful areas for future research. Especially the effects of

hypothetical bias, decision heuristics, and mental accounting are intertangled in the results and

need a carefully designed experiment to clearly separate them.

Table 21: Summary of the Theoretical Differences Applicable in This Study

Simplifying choice strategies are expected to influence CBCA results more than MBCA results.

MBCA is better able to identify natural bundles.

CBCA is more price sensitive than MBCA on a within method basis, but less price sensitive on a across

method basis.

Theory suggests that MBCA is more susceptible to hypothetical biases than CBCA.

CBCA identified negative attribute values in certain bundling scenarios.

Mental accounting theory suggests that respondents display a higher willingness-to-pay under CBCA due

to integrating losses.


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5.4 Conclusion

The result of this master’s thesis which immediately catches one’s eye and exhibits the highest

need for explanation concerns the salient differences between the predicted optimal bundling

strategies and the maximum predicted revenue levels shown in Table 6 and Table 7. The CBCA

method suggests pure bundling while MBCA suggests mixed bundling, furthermore CBCA

predicts maximum pure bundling revenues up to 40% higher than maximum MBCA mixed

bundling revenues. The differences regarding optimal bundling strategy can almost entirely be

explained with the diverging market simulation methods which have been applied. But with the

bundling strategies aligned, there is still the issue of the very different maximum revenue

forecasts between these methods. They can be explained with the mental accounting theory

(CBCA respondents integrate their loss), varying influence of hypothetical biases, and the

different price structure, which was higher in the CBC study and therefore likely gave CBC

respondents a higher anchor point and probably served as a quality signal, and the bundle

discounts which were implemented in the CBC study. Furthermore, MBC respondents have more

price information, as they know the prices for all attributes on the menu and make a trade-off

between each attribute and its price. CBC respondents on the other hand only see a total price and

make a trade-off between the attributes of the different alternatives. Since price is just one of

many attributes, it seems highly probable that it received less consideration relative to the MBCA

study prices.

6 MANAGERIAL IMPLICATIONS

While an exhaustive overview on the MBC theory and adding to the conjoint literature by

comparing a CBC to an MBC study were two important contributions of this thesis, I also believe

that the managerial implications which can be derived from the results are very fascinating.

Section 5.4 just explained the most salient differences found between the two methods, but how

does it affect a manger’s decision? Which method to trust to what extent? A manager might be

more enticed by the rather simple but pragmatic comparison approach chosen in Section 4.3.2,

which can at least make clear the decision’s impact. But even then the question remains, how

accurate are the revenue forecasts of both methods?

First of all, one should be aware of the fact that both CBCA and MBCA in this study only

measured stated preferences, not revealed preferences. As discussed in Section 3.2, stated

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preferences have hypothetical biases. Though it is argued that conjoint methods, especially CVA,

ACA and CBCA, can alleviate some of these biases. Ding and Huber (2009) also investigated the

question of hypothetical bias and identified the most common reason for distortion to be boredom,

confusion, simplification strategies and projecting a desired image and pointed out CBC can

especially alleviate the last problem. But how do these biases influence the stated WTP?

Arguments could be made for both, upwards and downwards adjustments to real WTP. The

empirical studies conducted by Ding et al. (2005) found, that respondents are less price sensitive

in the hypothetical CBC exercise than in the incentive-aligned CBC exercise. While price

sensitivity by itself doesn’t actually say anything about the actual level of the WTP, one usually

thinks that a price insensitive consumer is willing to pay more than a price sensitive consumer.33

This reasoning is supported by statements of other authors. For example, Lieb (2013b) stated that

perceived value analysis (in which he includes CVA, CBCA and MBCA among other methods)

tends to create prices which are upward biased compared to observed market prices due to

hypothetical biases. Orme (2010c) reported that respondents tend to be less price sensitive when

answering hypothetical tasks as compared to real world choices. A possible explanation could be

that CBC respondents usually tend to avoid the none-option, supposedly due to helping behavior

(Sawtooth Software, 2009a). Miller et al. (2011) also empirically identified customers in the

hypothetical exercises to be less price sensitive than under the incentive-aligned settings and they

showed that average WTP is significantly (p < 0.05) higher under the hypothetical methods than

under the incentive-aligned methods. I believe this establishes the case that hypothetical methods

in general deliver price estimates which are too high, something a manager should be aware of

before making a final price decision.

Second, I think it is important to stress that preference shares are not the same as market shares,

even in the absence of any hypothetical biases. Specific to this case, this is because no competing

products were included. Even though respondents could screen themselves out via the none-

option, the absence of any competitor in the questionnaire made the situation less realistic as

respondents were not exposed to competing brands. This is also important because in the real

world, competition drives down prices. So even if either the CBC or MBC study didn’t have any

hypothetical bias (or, more likely, the biases cancelled themselves out), it would only have

measured the WTP in a monopoly situation. But once a competitor offers lower prices and/or

33

Although this master’s thesis found a counter example when using the demand function slope as a measure, cf.

Section 4.3.5.

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better content, customers are likely to switch. But even if competing products would have been

included, there are some generally applicable reasons why preference shares aren’t equal to

market shares. For example, Orme and Johnson (2006) pointed out that the Sawtooth Software

market simulators make a series of simplifying assumptions like distribution channels are equally

available to respondents, that respondents have perfect information, that competing products

enjoy the same marketing efforts and sales force effectiveness, that all attributes influencing the

product choice have actually been included in the study and more. Again, a manager must be

aware of these issues before making a final pricing decision.

After this has been made clear, the third important issue boils down to which method should be

trusted more. One indicator would be the hypothetical bias. Without access to the raw data or any

meta data, I can only repeat the theory on this. Theory says that CBCA is more vulnerable to

simplifying choice strategies, while MBCA is more vulnerable to self-explication related

hypothetical biases. With regard to what each method is actually designed to measure, CBCA is

better suited to investigate pure bundling offerings while the MBCA simple menu approach is

better suited to investigate stand-alone offerings. This indeed matters due to the realism of the

choice tasks and due to the predictions from the mental accounting theory. While this should be

kept in mind as a general reminder, Section 5.2 outlined which of the studies in question here

delivered the theoretically more reliable bundling strategy results. Another important indicator

could be the reliability of each method, but unfortunately no such measures were provided and

without access to the raw data, I couldn’t calculate any by myself. If one method would show a

significantly lower reliability measure (like test-retest or hold-out predictions) then its results

must be questioned in general. Sadly, I am unable to give a more satisfying answer, all I can do is

to point it out for future research.

However, after all this being said, there are some managerial implications I can draw. First of all,

both the CBC and MBC studies likely forecasted WTP estimates which are too high compared to

the real world situation. Second, it could be seen in Section 4.3.6 that some products might

become negatively valued within bundles if bundle pricing is opaque, and Section 4.3.4 showed

that content utility clearly trumps form utility, except when it comes to the print format.

Therefore, I would recommend to keep complexity low by only offering a limited amount of

bundles which are ‘logical’ with respect to complementarity and digitalism. Specifically, I don’t

think it makes sense to offer a ‘browser and smartphone app’ bundle and a ‘browser, smartphone

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app and tablet app’ bundle. It would make more sense to offer an ‘all website products’, an ‘all

website products plus all digital newspaper products’, and an ‘all website products plus all digital

newspaper products plus print’ bundle. Third, there are other bundling and pricing strategies

which could be implemented, like customized bundling. This could be implemented in different

forms, e.g. on an article basis, on a X out of Y newspapers per month basis or for the sports

league, a X out of Y single games or match days basis. Fourth, Liechty et al. (2001) recognized

that for very price sensitive respondents, who are not satisfied with the bundle discount(s), a free

trial period could be offered. But since offering its content for free this is exactly what the media

company is trying to avoid, a heavily discounted trial offer might be the better solution in this

case.

7 CONCLUDING REMARKS

This master’s thesis added value to the conjoint literature in two respects: First, I compiled a

comprehensive overview on MBCA theory which can’t be found anywhere else. I used all

available sources related to conjoint theory, but also identified papers related to choice modeling

in general and summarized them in Appendix A. Especially with regard to model building, these

might provide valuable insights to the interested researcher. Second, I compared CBCA to

MBCA in a way not applied before. I was only able to find four studies comparing CBCA to

MBCA. They all used the same respondents, but gave these respondents just one MBC task. This,

of course, greatly limits the scope of analysis with regard to MBCA. There are MBCA studies

giving several MBC tasks to respondents, but don’t compare the results to CBCA. This study did

not use the same respondents for both methodologies, instead they were assigned to either CBC

or MBC tasks within the same market research study in order to investigate the same marketing

issue, using the same attributes and attribute levels (although differently coded and with

sometimes differing price levels). So the salient feature of this master’s thesis is, that both

methodologies had a big sample size and several tasks per respondent. No such study has been

published before.

Despite differences arising from study designs and analyses, I was able to investigate four

hypotheses. Summarized, I think it would be likely to conclude that mixed bundling is a superior

bundling strategy, while pure bundling is not necessarily the worst. With regard to price

sensitivity, I made the surprising finding that CBCA exhibits more price sensitivity within

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methods, while MBCA is more price sensitive across methods. The most likely explanation is,

that the price sensitive MBC respondents choose to buy fewer products in total than the CBC

respondents. And when it comes to negatively valued attributes, I found the available information

to be insufficient to draw a conclusion. I also conducted a pragmatic cross-reference analysis to

provide a rough worst-case scenario regarding pricing decisions and had a look at the

cannibalization potential, which was especially high among highly substitutable products, but not

limited to those. I also had a look at content and form utility and found content utility to be

dominating, which resulted in my recommendation to make logic bundles with respect to

complementarity and digitalism, as print excelled when it came to form utility. I then discussed

these findings in Chapter 5 and drew some managerial implications in Chapter 6.

As with any thesis, there are some limitations. As mentioned before, my scope of comparison

was limited as the data has been pre-collected and both studies have been pre-analyzed already,

and I did not have any access to the raw data. This precluded me from investigating certain

peculiar or inconclusive findings, from calculating any measure of reliability or from looking at

meta-data like task order effects or how much respondents enjoyed the survey. Also, I didn’t even

have all necessary information regarding the study designs and I would have wished to discuss

some points regarding the MBC analysis in more detail with Robert Ung, especially the market

simulations. With respect to the theory part, I was unable to get access on an ART Forum

presentation which addressed MBCA and was held in 2007. Unfortunately, the ART Forum

doesn’t publish any proceedings and I contacted the ART Forum along with several other

possible possessors of the slides without success. Also, I would like to point forward to the

Sawtooth Software Conference 2013, which will be held from October 14 to 18. Judging from the

session overview, the conference will feature at least one very exciting presentation regarding

MBCA.

These limitations already point out some scopes for future research: Measures for reliability

could be calculated and validity could be checked once the new price structure is decided on and

implemented by the media company. Also, some of the market simulations in the CBCA and

MBCA studies could be re-done, making sure the same or at least the most comparable methods

are used. By reducing the study analysis differences, it would also become clearer how big the

effects of the theoretical differences are. Some of the proposed theoretical differences could also

be more closely investigated with the raw data. The more daunting task of trying to find the truly

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revenue maximizing cases could also be tackled, although one would almost certainly need a

specialized software for that, as there are 511 unique possible bundling scenarios and each of

them would need a complete sensitivity analysis and sub-bundle enumeration.34

More general

ideas for future research would be to investigate if form utility indeed loses importance as more

content utility enhancing products are added to a bundle (cf. Section 4.3.4). Also, I noticed that in

all MBC studies I’ve seen so far, respondents were unable to choose more than one of each

product. But especially in e.g. the fast food industry, it is very common to receive orders such as

three cheeseburgers and a coke, or to order two big burgers instead of a menu. In fact, some fast

food restaurants offer coupons which offer a deal on exactly such situations. This behavior,

however, has so far not been mimicked by MBC questionnaires. Niraj, Padmanabhan, &

Seetharaman (2008) have successfully incorporated quantity decisions into a cross-category

choice model, but not in the context of conjoint analysis.

34

This number only accounts for the possible amount of bundling scenarios, including unique products, not the total

amount of possible sub-bundle scenarios. E.g. in the bundling scenario including B, SA, TA & P, the products could

be offered as stand-alone or pure bundle but also as B, SA & TA bundle with only P as stand-alone or as a B, SA &

TA bundle and a B, SA, TA & P bundle.

75

8 REFERENCES

Agarwal, M. K., & Chatterjee, S. (2003). Complexity, uniqueness, and similarity in between-

bundle choice. Journal of Product & Brand Management, 12(6), 358–376. doi:10.1108/

10610420310498795

Arthur, B. (1996). Increasing returns and the new world of business. Harvard Business Review,

74(4), 100–109.

Bakken, D., & Bayer, L. (2001, September). Increasing the value of Choice-Based Conjoint with

“Build Your Own” configuration questions. In: Proceedings of the Sawtooth Software

Conference. Paper presented at the Sawtooth Software Conference 2001, Victoria, BC (pp. 99–

110). Retrieved from http://www.sawtoothsoftware.com/download/techpap/2001Proceedings.pdf

Bakken, D., & Bond, M. K. (2004, October). Estimating preferences for product bundles vs. a la

carte choices. In: Proceedings of the Sawtooth Software Conference. Paper presented at the

Sawtooth Software Conference 2004, San Diego, CA (pp. 125–133). Retrieved from http://www

.sawtoothsoftware.com/download/techpap/2004Proceedings.pdf

Bakken, D. G., & Bremer, J. (2003, June). Estimating utilities from Design-Your-Own Product

data [Presentation slides]. Presented at the 14th annual Advanced Research Techniques (ART)

Forum in Monterey, CA.

Bakos, Y., & Brynjolfsson, E. (2000). Bundling and competition on the internet. Marketing

Science, 19(1), 63–82.

Balderjahn, I., Hedergott, D., & Peyer, M. (2009). Choice-Based Conjointanalyse. In D. Baier &

M. Brusch (Eds.), Conjointanalyse: Methoden - Anwendungen – Praxisbeispiele (pp. 129–146).

Heidelberg: Springer.

Baumgartner, B., & Steiner, W. J. (2009). Hierarchisch bayesianische Methoden bei der

Conjointanalyse. In D. Baier & M. Brusch (Eds.), Conjointanalyse: Methoden - Anwendungen –

Praxisbeispiele (pp. 147–159). Heidelberg: Springer.

Becker, G. M., DeGroot, M. H., & Marschak, J. (1964). Measuring utility by a single-response

sequential method. Behavioral Science, 9(3), 226–232.

76

Bell, D. R., & Lattin, J. M. (1998). Shopping behavior and consumer preference for store format:

Why large basket shoppers prefer EDLP. Marketing Science, 17(1) 66–88.

Ben-Akiva, M., & Gershenfeld, S. (1998). Multi-featured products and services: Analysing

pricing and bundling strategies. Journal of Forecasting, 17, 175–196.

Ben-Akiva, M., McFadden, D., Abe, M., Böckenholt, U., Bolduc, D., Gopinath, D., Morikawa, T.,

Ramaswamy, V., Rao, V., Revelt, D., & Steinberg, D. (1997). Modeling methods for discrete

choice analysis. Marketing Letters, 8(3), 273–286.

Billings, R. S., & Marcus, S. A. (1983). Measures of compensatory and noncompensatory models

of decision behavior: Process tracing versus policy capturing. Organizational Behavior and

Human Performance, 31, 331–352.

Bradlow, E. T., & Rao, V. R. (2000). A Hierarchical Bayes model for assortment choice. Journal

of Marketing Research, 37(2), 259–268.

Carson, R. T., Louviere, J. J., Anderson, D. A., Arabie, P., Bunch, D. S., Hensher, D. A., Johnson,

R. M., Kuhfeld, W. F., Steinberg, D., Swait, J., Timmermans, H., Wiley, J. B. (1994).

Experimental analysis of choice. Marketing Letters, 5(4), 351–368.

Chrzan, K., & Orme, B. (2000, March). An overview and comparison of design strategies for

Choice-Based Conjoint Analysis. In: Proceedings of the Sawtooth Software Conference. Paper

presented at the Sawtooth Software Conference 2000, Hilton Head Island, SC (pp. 161–177).

Retrieved from http://www.sawtoothsoftware.com/download/techpap/2000Proceedings.pdf

Chuang, C. I., & Sirbu, M. A. (1999). Optimal bundling strategy for digital information goods:

Network delivery of articles and subscriptions. Information Economics and Policy, 11(2), 147–

176. http://dx.doi.org/10.1016/S0167-6245(99)00008-6

Chung, J., & Rao, V. R. (2003). A general choice model for bundles with multiple-category

products: Application to market segmentation and optimal pricing for Bundles. Journal of

Marketing Research, 40(2), 115–130.

Churchill, G. A., Jr., (1979). A paradigm for developing better measures of marketing constructs.

Journal of Marketing Research, 16(1), 64–73.

77

Cohen, S. H. (1997). Perfect Union: CBCA marries the best of conjoint and discrete choice

models. Marketing Research, 9(1), 12–17.

Cohen, S. H., & Liechty, J. C. (2007). Have it your way. Marketing Research, 19(3), 28–34.

Cordella, P., Borghi, C., van der Wagt, K., & Loosschilder, G. (2012a, March). Menu-based

choice modeling (MBC): An empirical comparison of two analysis approaches. In: Proceedings

of the Sawtooth Software Conference. Paper presented at the Sawtooth Software Conference 2012,

Orlando, FL (pp. 247–256). Retrieved from http://www.sawtoothsoftware.com/download/

techpap/2012Proceedings.pdf

Cordella, P., Borghi, C., van der Wagt, K., & Loosschilder, G. (2012b, March). Menu-based

choice modeling (MBC): An empirical comparison of two analysis approaches [Presentation

Slides]. Paper presented at the Sawtooth Software Conference 2012, Orlando, FL.

Dahan, E. & Hauser, J. R. (2002). The virtual customer. The Journal of Product Innovation

Management, 19, 332–353.

Diamond, P. A., & Hausman, J. A. (1994). Contingent valuation: Is some number better than no

number? The Journal of Economic Perspectives, 18(4), 45–64.

Ding, M., Grewal, R., & Liechty, J. (2005). Incentive-aligned conjoint analysis. Journal of

Marketing Research, 42(1), 67–82.

Ding, M., & Huber, J. (2009, March). When is hypothetical bias a problem in choice tasks, and

what can we do about it? In: Proceedings of the Sawtooth Software Conference. Paper presented

at the Sawtooth Software Conference 2009, Delray Beach, FL (pp. 263–272). Retrieved from

http://www.sawtoothsoftware.com/download/techpap/2009Proceedings.pdf

Duvvuri, S. D., Ansari, A., & Gupta, S. (2007). Consumers’ price sensitivities across

complementary categories. Management Science, 53(12), 1933–1945. doi:10.1287/mnsc.1070

.0744

Farquhar, P. H., & Rao, V. R. (1976). A balance model for evaluating subsets of multiattributed

items. Management Science, 22(5), 528–539.

Frank, R. H. (2006). Microeconomics and behavior (6th int. ed.). New York: McGraw-Hill/Irwin.

78

Gibson, L. D. (2001). What's wrong with conjoint analysis? Marketing Research, 13(4), 16-19.

Gilbride, T. J., & Allenby, G. M. (2004). A choice model with conjunctive, disjunctive, and

compensatory screening rules. Marketing Science, 23(3), 391–406.

Green, P. E., Krieger, A. M., & Wind, Y. (2001). Thirty years of conjoint analysis: Reflections

and prospects. Interfaces, 31(3), 56–73.

Green, P. E., & Rao, V. R. (1971). Conjoint Measurement for Quantifying Judgmental Data.

Journal of Marketing Research, 8(3), 355–363.

Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook.

Journal of Consumer Research, 5(2), 103–123.

Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: New developments with

implications for research and practice. Journal of Marketing, 54(4), 3–19.

Guiltinan, J. P. (1987). The price bundling of services: A normative framework. Journal of

Marketing, 51(2), 74–85.

Gustafsson, A., Herrmann, A., & Huber, F. (Eds.). (2000). Conjoint measurement: Methods and

applications. Heidelberg, Germany: Springer. doi:10.1007/978-3-662-06395-8

Hagens, J., & Johnson, B. (2011). Adaptive Build Your Own (ABYO): An alternative approach to

feature level valuation. Retrieved from http://www.iprcorp.com/assets/Marketor/ABYO-An-

Alternative-Approach-to-Feature-Level-Valuation.pdf

Hansen, K., Singh, V., & Chintagunta, P. (2006). Understanding store-brand purchase behavior

across categories. Marketing Science, 25(1), 75–90. doi:10.1287/mksc.1050.0151

Hauser, J. R., & Rao, V. R. (2004). Conjoint Analysis, Related Modeling, and Applications. In Y.

Wind & P. E. Green (Eds.), Marketing Research and Modeling: Progress and Prospects (pp.

141–168). New York: Springer. doi:10.1007/978-0-387-28692-1

Hensher, D. A., Rose, J. M., & Greene, W. H. (2005). Applied choice analysis: A primer. New

York: Cambridge University Press.

79

Hitt, L. M., & Chen, P.-Y. (2005). Bundling with customer self-selection: A simple approach to

bundling low-marginal-cost goods. Management Science, 51(10), 1481–1493. doi:10.1287/mnsc

.1050.0403

Huber, J. (1998). Achieving individual-level predictions from CBC data: Comparing ICE and

Hierarchical Bayes (Research Paper Series). Retrieved from http://www.sawtoothsoftware.com

/download/techpap/indlvcbc.pdf

Huisman, D. (2011, July). MBC, as easy as ABC? An introduction to Menu Based Conjoint

[Presentation slides]. Presented at the Advanced Quant Techniques, Online webinar.

Investopedia. (n.d.). Price Sensitivity [Website]. Retrieved from http://www.investopedia.com/

terms/p/price-sensitivity.asp

Iyengar, R., Ansari, A., & Gupta, S. (2003). Leveraging information across categories.

Quantitative Marketing and Economics, 1(4), 425–465.

Jedidi, K., Jagpal, S., & Manchanda, P. (2003). Measuring heterogeneous reservation prices for

product bundles. Marketing Science, 22(1), 107–130.

Johnson, E. J., Meyer, R. J., & Ghose, S. (1989). When choice models fail: Compensatory

models in negatively correlated environments. Journal of Marketing Research, 26(3), 255–270.

Johnson, M. D., Herrmann, A., & Bauer, H. H. (1999). The effects of price bundling on consumer

evaluations of product offerings. International Journal of Research in Marketing, 16, 129–142.

Johnson, R. M. (1974). Trade-off analysis of consumer values. Journal of Marketing Research,

11(2), 121–127.

Johnson, R. (2008). A perspective on adaptive CBC (what can we expect from respondents?)

(Research Paper Series). Retrieved from http://www.sawtoothsoftware.com/download/techpap/

acbcperspective.pdf

Johnson, R. M., & Orme, B. K. (1996). How many questions should you ask in Choice-Based

Conjoint studies? (Research Paper Series). Retrieved from http://www.sawtoothsoftware.com

/download/techpap/howmanyq.pdf

80

Johnson, R., & Orme, B. (2003). Getting the most from CBC (Research Paper Series). Retrieved

from http://www.sawtoothsoftware.com/download/techpap/cbcmost.pdf

Johnson, R. M., & Orme, B. K. (2007). A new approach to adaptive CBC (Research Paper

Series). Retrieved from http://www.sawtoothsoftware.com/download/techpap/acbc10.pdf

Johnson, R., Orme, B., & Pinnell, J. (2006, March). Simulating market preference with “Build

You Own” data. In: Proceedings of the Sawtooth Software Conference. Paper presented at the

Sawtooth Software Conference 2006, Delray Beach, FL (pp. 239–253). Retrieved from http://

www.sawtoothsoftware.com/download/techpap/2006Proceedings.pdf

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk.

Econometrica, 47(2), 263–292.

Kamakura, W. A., & Kwak, K. (2012). Menu-choice modeling. Retrieved from SSRN: http://dx

.doi.org/10.2139/ssrn.2162019

Kopalle, P. K., Krishna, A., & Assunção, J. L. (1999). The role of market expansion on

equilibrium bundling strategies. Managerial and Decision Economics, 20(7), 365–377.

Koukova, N. T., Kannan, P. K., & Kirmani, A. (2012). Multiformat digital products: How design

attributes interact with usage situations to determine choice. Journal of Marketing Research,

49(1), 100–114.

Koukova, N. T., Kannan, P. K., & Ratchford, B. T. (2008). Product form bundling: Implications

for marketing digital products. Journal of Retailing, 84(2), 181–194.

Li, M., Feng, H., Chen, F., & Kou, J. (2013). Numerical investigation on mixed bundling and

pricing of information products. International Journal of Production Economics, 144(2), 560–

571. doi:10.1016/j.ijpe.2013.04.015

Lieb, G. (2013a). Perceived value analysis. Retrieved from http://www.lieb.com/Documents

/PVALUE4.pdf

Lieb, G. (2013b). Pricing research methods. Retrieved from http://www.lieb.com/Documents

/PRICING5.pdf

81

Liechty, J., Ramaswamy, V., & Cohen, S. H. (2001). Choice menus for mass customization: An

experimental approach for analyzing customer demand with an application to a web-based

information service. Journal of Marketing Research, 38(2), 183–196.

Louviere, J., & Bunch, D. S. (1998). Discussion of `Multi-featured products and services:

Analysing pricing and bundling strategies' by Moshe Ben-Akiva and Shari Gershenfeld. Journal

of Forecasting, 17, 197–201.

Louviere, J. J., & Woodworth, G. (1983). Design and analysis of simulated consumer choice or

allocation experiments: An approach based on aggregate data. Journal of Marketing Research,

20(4), 350–367.

Luce, R. D., & Tukey, J. W. (1964). Simultaneous conjoint measurement: A new type of

fundamental measurement. Journal of Mathematical Psychology, 1(1), 1–27.

Manchanda, P., Ansari, A., & Gupta, S. (1999). The “shopping basket”: A model for

multicategory purchase incidence decisions. Marketing Science, 18(2), 95–114.

McAfee, R. P., McMillan, J., & Whinston, M. D. (1989). Multiproduct monopoly, commodity

bundling, and correlation of values. Quarterly Journal of Economics, 104(2), 371–383.

McAlister, L. (1979). Choosing multiple items from a product class. Journal of Consumer

Research, 6(3), 213–224.

McEwan, B. (2013, July 25). Can CBC recognize parts of bundles if they are coded as one

attribute? [Online forum comment]. Retrieved from http://www.sawtoothsoftware.com/forum

/4011/can-cbc-recognize-parts-bundles-they-are-coded-one-attribute

McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka

(Ed.), Frontiers on Econometrics (pp. 105–142). New York: Academic Press.

Mehta, N. (2007). Investigating consumers’ purchase incidence and brand choice decisions

across multiple product categories: A theoretical and empirical analysis. Marketing Science,

26(2), 196–217. doi:10.1287/mksc.1060.0214

82

Miller, K. M., Hofstetter, R., Krohmer, H., & Zhang, Z. J. (2011). How should consumers’

willingness to pay be measured? An empirical comparison of state-of-the-art approaches. Journal

of Marketing Research, 48(1), 172–184.

Mohr, J., Sengupta, S., & Slater, S. (2010). Marketing of high-technology products and

innovations (3rd ed.). Upper Saddle River, NJ: Pearson.

Moore, C. (2010, October). Analysing pick n’ mix menus via choice models to optimize the

client portfolio – theory & case study. In: Proceedings of the Sawtooth Software Conference.

Paper presented at the Sawtooth Software Conference 2010, Newport Beach, CA (pp. 239–253).


Moore, W. L., Gray-Lee, J., & Louviere, J. J. (1998). A cross-validity comparison of conjoint

analysis and choice models at different levels of aggregation. Marketing Letters, 9(2), 195–207.

Morikawa, T. (1989). Incorporating stated preference data in travel demand analysis (Doctoral

dissertation, Massachusetts Institute of Technology). Retrieved from http://hdl.handle.net/1721.1

/14326

Myung, E., & Mattila, A. S. (2010). Influence of price on consumer meal choice in a bundling

context. Journal of Foodservice Business Research, 13(2), 114–126.

Niraj, R., Padmanabhan, V., & Seetharaman, P. B. (2008). A cross-category model for

households’ incidence and quantity decision. Marketing Science, 27(2), 225–235. doi:10.1287/

mksc.1070.0299

Orme, B. (2003). Special features of CBC software for packaged goods and beverage research

(Research Paper Series). Retrieved from http://www.sawtoothsoftware.com/download/techpap/

speccbc.pdf

Orme, B. (Ed.). (2005). CBC/HB v4: Software for Hierarchical Bayes estimation for CBC data.

Retrieved from http://www.marketing.uni-frankfurt.de/fileadmin/Publikationen/CBCHB_Manual

.pdf

Orme, B. K. (2009). Which conjoint method should I use? (Research Paper Series). Retrieved

from http://www.sawtoothsoftware.com/download/techpap/whichmth.pdf

83

Orme, B. (2010a). Getting started with conjoint analysis: Strategies for product design and

pricing research (2nd ed.). Madison, WI: Research Publishers LLC.

Orme, B. K. (2010b). Menu-based choice modeling using traditional tools (Research Paper

Series). Retrieved from http://www.sawtoothsoftware.com/download/techpap/mbcconf2010.pdf

Orme, B. K. (2010c). Task order effects in menu-based choice (Research Paper Series). Retrieved

from http://www.sawtoothsoftware.com/download/techpap/mbcorder2010.pdf

Orme, B. (2013a). Menu-Based Choice (MBC) for multi-check choice experiments. Retrieved

from http://www.sawtoothsoftware.com/download/mbcbooklet.pdf

Orme, B. (2013b, July 31). Minimum amount of tasks per respondent for HB estimation [Online

forum comment]. Retrieved from http://www.sawtoothsoftware.com/forum/4027/minimum-

amount-of-tasks-per-respondent-for-hb-estimation

Orme, B. K., & Baker, G. C. (2000). Comparing Hierarchical Bayes draws and Randomized

First Choice for conjoint simulations (Research Paper Series). Retrieved from http://www

.sawtoothsoftware.com/download/techpap/rfcdrw.pdf

Orme, B., & Johnson, R. (2006). External effect adjustments in conjoint analysis (Research Paper

Series). Retrieved from http://www.sawtoothsoftware.com/download/techpap/externaleffects.pdf

Rice, J., & Bakken, D. G. (2006, March). Estimating attribute level utilities from “Design Your

Own Product” data – chapter 3. In: Proceedings of the Sawtooth Software Conference. Paper

presented at the Sawtooth Software Conference 2006, Delray Beach, FL (pp. 239–253).


Riedesel, P. (2003, June). A critical test of self-explicated utility measurement vs.

decompositional conjoint. Presented at the 14th annual Advanced Research Techniques Forum,

Monterey.

Russell, G. J., & Petersen, A. (2000). Analysis of cross category dependence in market basket

selection. Journal of Retailing, 76(3), 367–392.

84

Sawtooth Software. (2003). Advanced Simulation Module (ASM) for product optimization v1.5

technical paper (Technical Paper Series). Retrieved from http://www.sawtoothsoftware.com/

download/techpap/asmtech.pdf

Sawtooth Software. (2007). The ACA/Web v6.0 technical paper (Technical Paper Series).

Retrieved from http://www.sawtoothsoftware.com/download/techpap/acatech.pdf

Sawtooth Software. (2009a). ACBC technical paper (Technical Paper Series). Retrieved from

http://www.sawtoothsoftware.com/download/techpap/acbctech.pdf

Sawtooth Software. (2009b). The CBC/HB system for Hierarchical Bayes estimation version 5.0

technical paper (Technical Paper Series). Retrieved from http://www.sawtoothsoftware.com/

download/techpap/hbtech.pdf

Sawtooth Software. (2012). Conducting Market Simulations [Powerpoint slides]. Retrieved from

http://www.sawtoothsoftware.com/download/techpap/curriculum3_5.ppt

Sawtooth Software. (2013). The CBC system for Choice-Based Conjoint Analysis version 8

(Technical Paper Series). Retrieved from http://www.sawtoothsoftware.com/download/techpap/

cbctech.pdf

Schmalensee, R. (1984). Gaussian demand and commodity bundling. Journal of Business 57(1),

211–230.

Schweidel, D. A., Bradlow, E. T., & Fader, P. S. (2010). Portfolio dynamics for customers of a

multi-service provider. Management Science, 57(3), 471–486, 2011. Retrieved from SSRN:

http://ssrn.com/abstract=1424235

Seetharaman, P. B., Chib, S., Ainslie, A., Boatwright, P., Chan, T., Gupta, S., Mehta, N., Rao, V.,

& Strijnev, A. (2005). Models of multi-category choice behavior. Marketing Letters 16(3/4),

239–254.

Singh, V. P., Hansen, K. T., & Gupta, S. (2005). Modeling preferences for common attributes in

multicategory brand choice. Journal of Marketing Research, 42(2), 195–209.

Song, I., & Chintagunta, P. K. (2006). Measuring cross-category price effects with aggregate

store data. Management Science, 52(10), 1594–1609. doi:10.1287/mnsc.1060.0563

85

Song, I., & Chintagunta, P. K. (2007). A discrete–continuous model for multicategory purchase

behavior of households. Journal of Marketing Research, 44(4), 595–612.

SSI Web Help. (n.d.). Estimating utilities with logit [Website]. Retrieved from http://www

.sawtoothsoftware.com/support/issues/ssiweb/online_help/index.html?estimating_utilities_with

_logi.htm

SSI Web Help. (n.d.). Market simulator models [Website]. Retrieved from https://www

.sawtoothsoftware.com/support/issues/ssiweb/online_help/index.html?hid_smrt_gettingstarted

.htm

Stahl, F., Schäfer, M.–F., & Maass, W. (2004). Strategies for selling paid content on newspaper

and magazine web sites: An empirical analysis of bundling and splitting of news and magazine

articles. The International Journal on Media Management, 6(1&2), 59–66.

Statistics Canada. (2009). Statistics Canada Quality Guidelines (5th ed.). Ottawa, Canada:

Minister of Industry. Retrieved from http://www.statcan.gc.ca/pub/12-539-x/12-539-x2009001-

eng.pdf

Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214.

Ung, R. (2012). Pricing and bundling of different digital channels for a newspaper (Unpublished

master’s thesis). University of Zurich, Switzerland.

Venkatesh, R., & Chatterjee, R. (2006). Bundling, unbundling and pricing of multiform products:

The case of magazine content. Journal of Interactive Marketing, 20(2), 21–40. doi:10.1002/dir

.20059

Werder, K. (2013). Bundling of digital information goods through a Choice-Based Conjoint

approach (Unpublished master’s thesis). University of Zurich, Switzerland.

Wind, J., & Mahajan, V. (1997). Issues and opportunities in new product development: An

introduction to the special issue. Journal of Marketing Research, 34(1), 1–12.

Wittink, D. R. (2000). Predictive validation of conjoint analysis. In: Proceedings of the Sawtooth

Software Conference. Paper presented at the Sawtooth Software Conference 2000, Hilton Head

86

Island, SC (pp. 221–237). Retrieved from http://www.sawtoothsoftware.com/download/techpap/

2000Proceedings.pdf

Wittink, D. R., & Cattin, P. (1989). Commercial use of conjoint analysis: An update. Journal of

Marketing, 53(3), 91–96.

Yadav, M. S., & Monroe, K. B. (1993). How buyers perceive savings in a bundle price: An

examination of a bundle's transaction value. Journal of Marketing Research, 30(3), 350–358.

87

APPENDIX A

Table 22: Exhaustive Overview on MBCA Literature

Author(s) Topic Study Design Methodology Limitations Conclusion &

Contributions

Ben-Akiva

&

Gershenfeld

(1998),

Journal

Paper

modeled MBCA,

using an EA MNL

model

EMAa (13 SO

b, 0-6 BO

b, NO

b, binary

choices), 3 price levels, varying.

Respondents could choose max. 1

bundle per task, 6 TPRc, n=1000

with 5$ participation incentive

phone-mail-phone survey;

applying an EA approach within

a nested MNL model, using

‘sampling of alternatives’ to

reduce estimation time, aggregate

level estimation

assumes customers are

homogenous in their

sensitivities for the

various features and

(total) price, needed to

use ‘sampling of

alternatives’

demonstrated the

usefulness of MBCA and

importance of bundle

discounts

Liechty,

Ramaswamy

& Cohen

(2001),

Journal

Paper

modeled MBCA,

using an MVP

model; compared

the MVP to the

MNP approach

EMA (5 SO, 1 ‘hidden’ BO, NO,

binary choices), 3 price levels.

Candidates were pre-screened, 10

TPR (1HOc), n=360 SME marketing

managers with 50$ participation

compensation

paper administered survey;

preserving the individual menu

choices by applying an MVP

model, using individual level

estimation.

computationally

intensive, needs

customized software

showed that MVP has

superior predictive

performance over MNP

and therefore MNL,

importance of discount

structure

Bakken &

Bayer

(2001),

Conference

Paper

compared MBCA

to CBCA, using the

same attribute

levels and the same

individuals

CBC tasks prompted first, 9 attributes

with 3 levels, TPR & APTc n/a, 4

price points weakly conditional on

one attribute level; SMAa (9 SO, 3

levels each), 1 TPR, prices correlated

to attribute level and constant

between respondents, n=967

HPOL online panel survey;

CBC/HB main effects only

estimation, comparing price

sensitivityd and product

preference shares between the

two methods

no none-option in

MBCA, conditionality

in CBCA not perfectly

implemented

found respondents to be

more price sensitive in

the MBC tasks,

especially regarding less

important features;

concluded that MBCA

might be useful to find

optimal product designs

CBC prompted first, 12 attributes,

varying levels, partial profile design,

25 TPR (5 HO), 3 APT, random

prices; SMA (12 SO, varying levels),

1 TPR, prices correlated to attribute

level and constant between

respondents, n=1170


CBC/HB main effects only

estimation, comparing price

sensitivity and the attribute level

shares of ‘brand’ and ‘form

factor’ between the two methods

no none-option in

MBCA

88

Dahan &

Hauser

(2002),

Journal

Paper

compared MBCA

to conjoint analysis,

using the same

attribute levels

ACA: 8 TPR, paired comparison, 9-

point rating scale, 6 binary attributes

plus price; SMA (6 SO), 1 TPR,

fixed attribute prices, n=n/a, different

individuals

online survey; aggregate level

estimation, logistic regression of

attribute choice shares between

the two methods

only including MBC

respondents who chose

all 6 attributes, fixed

prices in MBC task

showed the feasibility of

online market research;

ACA: 0.91 correlation,

significant at 0.01 level;

CVA: prediction rates

61.3% and 73.1%,

improvable with logit

model; MBC

respondents select

configurations which are

near their CVA predicted

ideal

CVA: 1TPR, APT n/a, 3 attributes


fixed attribute prices, n=245, same

individuals

online survey; individual level

estimation on attribute

importance and price sensitivity,

then use this information to

predict if respondents would

select the attribute at the price

shown in MBC task

fixed prices in MBC

task CVA: 1 TPR, 12 APT, 6 attributes


fixed attribute prices, n=130, same

individuals

Bakken &

Bremer

(2003),

Conference

Slides

estimated utilities

from MBC data and

compared it to CBC

data

1 self-explication task (appeal ratings

for each attribute); followed by CBC,

15 attributes, partial profile design;

followed by SMA (SO, total 64

levels), 1 TPR, same attribute prices

for all respondents, n=500


posited attribute level choice

probability as a function of its

intrinsic utility, its relative

pricee, the relative price of other

chosen attribute levels and an

error term; used Bayesian

modeling for MBC data and HB

for CBC data

needs customized

software; needs a self-

explication exercise;

assumes that 1$ always

yields the same utility,

no matter which

attribute it is spent on

‘proof of concept’ that it

is possible to estimate

utilities and preference

shares with very limited

MBC data

Bakken &

Bond

(2004),

Conference

Paper

used CBC/HB to

capture a mixed

bundling decision

process

EMA (1 BO, 3 SO), BO includes all

3 SO, TPR n/a, ‘think-aloud’ pretest,

n=227 qualified decision makers


modeling each of the four menu

options as a separate logit model

with Sawtooth’s CBC/HB, then

combine them in the market

simulator

no none-option,

respondents can’t

choose BO and SO, a

SO consisted of several

attributes

showed how CBC

software can be used to

estimate menu choice

problems; only feasible if

only a few menu options

present, else getting very

complex

Rice &

Bakken

(2006),

Conference

Paper

compared MBCA

to CBCA, using the

same attribute

levels and the same

individuals

1 self-explication task, followed by

CBC, 15 attributes, partial profile

design, 25 (5 HO) TPR, APT n/a,

feature-specific prices, followed by

SMA (SO, total 64 levels), 1 TPR,

prices correlated to attribute level and


treating each menu feature

decision as a separate choice

task, therefore converting a

menu choice into a series of k

choice models, using ‘relative

need to hypothesize a

conditional decision

process for the MBC

data; ISC model only

works well if choices

are uncorrelated

MBCA exhibited higher

price elasticity in 8 of 10

features; the 3 CBC

models predicted nearly

identical preference

shares, but MBC model

89

constant between respondents, n~500 prices’ for MBC, finally using

Sawtooth’s CBC/HB to estimate

1 MBC and 3 CBC models

deviates quite a bit

Johnson,

Orme &

Pinnell

(2006),

Conference

Paper

compared MBCA

to CBCA using the

same attribute

levels and the same

individuals; tested

three MBCA

estimation models

CBC prompted first, 4 TPR (2 HO), 3

APT, 8 attributes plus total price,

varying levels, prices shown for each

attribute; SMA (9 SO, 2-4 levels), 1

TPR, prices correlated to attribute

level and 3 varying price sets

between respondents, n=605; idea:

same interview time for both methods

GMI online panel survey;

comparing 3 MBCA estimation

approaches: EA MNL model,

ISC MNL model and ‘counts’

model, expecting EA MNL

model to outperform the others;

CBC estimated via aggregate

logit

no none-option in CBC

and MBC tasks, small

samples, alternatives

were weighted in EA

MNL model which

might have destroyed

orthogonality

found the three MBC

estimation methods to

deliver almost identical

results; MBC and CBC

both predicted HO not

well, both returned poor

price sensitivity

estimates; unveiled a

fundamental trade-off

difference between CBC

and MBC similar to

Bakken & Bayer (2001)

Cohen &

Liechty

(2007),

Journal

Paper

discussed why and

when MBCA

should be used

instead of CBCA

n/a

reviewing and comparing the ISC

model, the EA model and the

MVP model on a theoretical

basis

n/a

pointed out advantages

of MBCA and

summarizing the most

important approaches

Moore

(2010),

Conference

Paper

optimized pricing in

a competitive

environment, using

CBCA and MBCA

CBCA, 12 TPR (2 HO), 2 APT, no

NO, competitor menu had fixed

prices in all tasks; EMA (18 SO,

2BO, NO, constrained binary choice),

12 TPR, 5 varying price levels,

n=1490 pre-screened respondents

GfK NOP online panel survey; 2-

stage approach: first CBCA to

compare client to 1 of 4 possible

competitors, then MBCA to

optimize client’s menu; CBC and

MBC data analyzed with

CBC/HB, applying SCE model

for MBCA; using OptQuest

Software to find optimal profits

by combining CBCA with

MBCA results and using

calibration based on real world

data supplied by client

possibility of strange

cross-effect impacts due

to random error; less

accurate in predicting

the actual combination

of menu items selected

by individual

respondents (inferior to

MVP)

found that some cross

price elasticity effects are

due to budget

constraints; very robust

hold-out task results; the

proposed prices were

implemented in a test

store and profit went up

+15% vs control stores in

6 months; model can be

improved by excluding

insignificant cross-

effects; price elasticity

constant across MBC

tasks

90

Orme

(2010b),

Research

Paper

analyzed MBC data

with CBC software

by re-coding data;

compared four

methods

first self-explication task, base price

in later tasks conditional on this task;

then SMA (8 SO, binary choice), 8

TPR, 4 varying price levels; then

EMA (choose 1 of 3 CPb with 4 SO

each), 8 TPR, varying prices: 4 at

SO, 3 at CP; n=1629 pre-screened

respondents, 806 for model, 829

hold-out

Western Wat online panel

survey; analyzed SMA data with

volumetric CBC model, quite

well behaved; analyzing EMA

data with 2-stage model, EA

MNL model and SCE model

no none-option in EMA

task, theoretically

inferior to MVP

change on base price had

a very large impact on

choice likelihood; all

models exhibited high

aggregate predictive

validity; aggregate logit

doing as well as HB,

concluded that IIA might

be less of a problem in

MBC models

Orme

(2010c),

Research

Paper

explored the effects

of the order MBC

tasks are presented

in

Study 1 data (cf. Orme, 2013a):

EMA (15 SO, 3 BO, 1 NO), 8 TPR,

varying price levels, n=681;

Study 2 data (cf. Orme, 2010b): SMA

(8 SO, binary choice), 8 TPR, 4

varying price levels, n=806

Western Wat online panel

survey; analyzing the data of two

other studies with the ‘counts’

method

true price elasticity of

respondents unknown,

hence no valid

conclusion possible

which tasks to trust;

Moore 2010 reports

stable price elasticity in

his MBC study

price elasticity increased

dramatically over the

first few tasks in both

studies; none-option

usage increased from 5%

to 15% (study 1);

respondents find MBC

tasks not monotonous

(study 2)

Hagens &

Jonson,

2011,

Research

Paper

introduced their

MBC method

which is called

ABYO

n/a

ABYO delivers individual level

WTP, using several BYO tasks in

a row and a simple rule to adapt

price points; no need for

Bayesian inference

simplistic rule to

determine WTP which

furthermore depends on

arbitrarily chosen

maximum prices

enriched the range of

MBC models with an

approach that doesn’t

need statistical

estimations

Cordella,

Borghi, van

der Wagt &

Loosschilder

(2012a),

Conference

Paper

compared two

MBC methods

SMA (12 SO, binary choice, 1 CP, 3

levels), 9 TPR (2 HO), 3

orthogonally varying price levels,

n=1408

survey details n/a; building a

CSS model and comparing it to

Sawtooth’s SCE model; using

HB estimations for both

no significant cross-

effects found in the data,

hence the CSS model

couldn’t exploit its pot-

ential advantage; CSS

coded as main effects

only model, capturing

substitutability via indi-

vidual HB estimations

and their substitution

between alternatives

both models predicted

hold-out tasks extremely

well, but found SCE

model to be slightly

more accurate; both

models predicted

combinatorial choices

roughly equally good

91

Kamakura &

Kwak

(2012),

Unpublished

Paper

analyzed menu

choice data with an

auto-logistic model

SMA (16 SO, binary choice, 1 CP, 1

level, NO), discount offered for

choosing 4 or more SO, 8 TPR, tasks

not randomized (4 different blocks), 6

varying price levels for SO, 3 for CP,

n=216

the auto-logistic model captures

interactions between items within

tasks, using an MNL CSS

approach with random sampling;

6 tasks to fit model and the 7th

task to cross-validate; 8th task is

used as hold-out to check

predictive performance

estimation only at

segment level; using

random sampling

instead of importance

sampling, yielding some

counterintuitive results;

no empirical comparison

to other models

described advantages of

AL model over MVP;

formulated a usable AL

model; showed that

menu items do interact

even if accounting for

heterogeneity and

correlation in preferences

for individual items;

assumed interactions

among attributes are

homogenous across

consumers

Orme

(2013a),

MBC

Software

Instruction

Manual

explained how

Sawtooth’s MBC

software can be

used, elaborating on

MBC theory and

Sawtootht’s

experience using

examples

Study 1 (2006 data): EMA (3 BO, 9

rSOb, 6 SO, NO, all binary choice),

10 TPR (2 HO), 4 varying price

levels, n=681

Western Wat/Opinionology

online panel survey; Study 1

estimated by SCE model and

comparing weighted draws to no

weights in market simulator;

Study 2 estimated with ‘full’

SCE model (21 binary models)

and ‘reduced’ SCE model (4

combinatorial models)

theoretically inferior to

MVP approach

showed that weighted

draws can improve

combinatorial choice

prediction and that

combinatorial model

coding in SCE can

outperform the ‘full’

approach

Study 2 (2011 data): EMA (3 BO, 18

SO, all binary choice), 12 TPR,

n=1698; split sample: n=850 for

model calibration, 8 randomized

tasks, 4 HO; n= 848 hold-out

respondents answering 1 of 2 fixed

questionnaires

a EMA stands for ‘extended menu approach’ and SMA stands for ‘simple menu approach’, cf. Section 3.5 for more details

b SO stands for ‘single option(s)’, BO stands for ‘bundle option(s)’ NO stands for ‘none-option’, CP stands for ‘core profiles’ and rSO stands for ‘restricted single

option(s)’. k stands for the total number of options, excluding the none-option. c TPR stands for ‘tasks per respondent’ and the stated number includes HO. HO stands for ‘hold-out tasks’ and APT stands for ‘alternatives per task’.

d Measured as incremental attribute level preference share changes.

e Relative price was defined as attribute price divided by total price for the configured product.

Source: Own Illustration, using each paper as source for itself.

92

As mentioned in Chapter 7, only one potential source is missing in Table 22, namely “Conklin, M., Paris, B., Boehnlien-Kearby, T.,

Johnson, C., Juhl, K., Zanetti-Polzi, A., Gustafson, K., & Palmer, B. (2007). Menu based choice models [Presentation slides]. Presented at

the 14th annual Advanced Research Techniques (ART) Forum in Santa Fe, NM.” I have further been made aware of the 10th

SKIM/Sawtooth event in Vienna, 2011, which also featured MBC as topic. However, no slides are available as well.

Table 23 focuses on menu choice modeling papers not related to conjoint analysis, mostly from the bundling and shopping basket

literature. Although comprehensive, this overview is not totally complete. More structured and encompassing reviews can be found in

Seetharaman et al. (2005) and Mehta (2007).

Table 23: Comprehensive Literature Overview on Choice Modeling Across Multiple Categories

Author(s) Topic & Method Conclusion & Contributions Limitations

Farquhar & Rao

(1976), Journal

Paper

introduced their balance model to evaluate

subsets of multiattributed items; asked

respondents to compare 3 out of 6 products with 6

attributes each; modeled measures weighted sums

of means and dispersions

able to handle menu choice with interactions

among items in a bundling context

can only deal with homogenous items

because it assumes items are comparable

on all attribute levels, model applied to a

small set only

McAlister

(1979), Journal

Paper

modeled the dependence among generically

similar products, used a more parsimonious

approach than the balance model; for choosing

many items and consuming them all, their main

idea is attribute satiation; for choosing many

items and consuming only one their idea is a

lottery model; comparing their models to several

benchmark models

attribute satiation model outperforms all three

benchmark models (including the balance

model); lottery model does not imply that the

item with the second highest expected utility

is the second choice; independent choice

models as corner solution of dependent choice

models

attribute satiation model assumes

Lancasterian additivityd for attributes

and that attributes exhibit decreasing (at

some point even negative) marginal

utility; designed to compare homogenous

items

Bell & Lattin

(1998), Journal

Paper

investigated consumers store choice behavior

when stores use different pricing formats;

modeled category choice with an ISC model

based on MNL; distinguished between large and

small basket shoppers

found that large basket shoppers prefer EDLPf

and small basket shoppers prefer HILOf store

pricing because large basket shoppers are less

responsive to category prices but more

responsive to the overall store price level

ISC model assumes independence across

categories which will not hold for all

categories; assume customers don’t

know which categories are on sale in the

HILO store

93

Manchanda,

Ansari & Gupta

(1999), Journal

Paper

developing an MVP/HB model to investigate

multicategory purchase incidence decisions, retail

context, explicitly allowing dependency across

multicategory items

first model to account for complementarity

(cross-effects), consumer heterogeneity and

co-incidence (defined as residual)

using only four categories, their sample

might not allow for generalizable

managerial implications

Bradlow & Rao

(2000), Journal

Paper

combining the balance model with the

Hierarchical Bayes method for individual level

estimation, using an MNL formulation

tested full model against four simplified

models, found that individual-specific

estimation greatly enhances fit, that dispersion

parameters enhance fit, and that person-level

demographics also enhance fit; found that a

‘choose nothing’ and ‘choose cheapest’

models outperform the original balance model

model deals with simultaneous choice,

not sequential choice which would

require time-dependent interactions;

model designed to deal with

homogeneous items, not heterogeneous

ones; their model could be extended to

include externalities (interactions among

individuals)

Russell &

Petersen (2000),

Journal Paper

AL model on a MNL basis; specify global model

(all category choices) via a series of choice

models for each category by relating the

conditional distribution for each category to the

joint distribution of the basket choice, this

implicitly defines a general utility function over

all 2N market baskets; model also incorporates

consumer characteristics and marketing mix;

empirical application with four categories and

data of 170 households

within-consumer pattern of demand effects is

unrestricted; no need for a specification of the

joint distribution or to observe to order of the

choices; showing their model’s superiority

over choice models assuming independence

across categories in an empirical application

that joint distribution are uniquely

defined by the complete set of full

conditional distributions requires full

conditional distributions to be mutually

consistent; not accounting for

unobserved consumer heterogeneity

Chung & Rao

(2003), Journal

Paper

developed a general model for multicategory

choice, nested logit model using HB, the balance

model and the traditional conjoint model (applied

to product bundling) are special cases of it

model can deal with heterogeneous items

within a bundle; first attribute-

interdependence model to introduce customer

segmentation to bundle markets

they focus on the pure bundling case

only, although stating an extension to

mixed bundling is possible; attribute

comparability might differ between

respondents; they use a discrete choice

model to collect the data

Iyengar, Ansari

& Gupta (2003),

Journal Paper

developed a MNP/HB cross-category model;

model can be used for different sets of available

information

found that in most cases, there is little to gain

from cross-category models; consumer

heterogeneity and high parameter correlation

are necessary for a cross-category model to

add value over a single-category model; if

they are both low, the single-category model

might can even deliver better results

focus on prediction and parameter

recovery when faced with missing data,

hence their model is not as encompassing

as others using full data sets; empirically

showed their findings by using only 2

categories

94

Singh, Hansen,

& Gupta (2005),

Journal Paper

developed a MNL/HB model incorporating

marketing-mix, product attribute and

demographic variables; it can estimate correlation

in preferences for attributes across categories and

decompose these correlations into observable and

unobservable components; unobserved attribute

preferences can be used to predict preferences for

attributes in new categories

conducted 2 studies (n=250, three closely

related categories and n=1017, two less

related categories) with common attributes

across categories but sometimes differing

levels; found significant correlations in

preferences for product attributes in both

studies; showed how their unobserved factors

estimation provides significant value for

predictive purposes (e.g. new product

introduction)

they don’t include interactions between

the marketing variables in their model;

using a maximum of 3 categories with

same attributes; used a static setting for

their model

Hansen, Singh

& Chintagunta

(2006), Journal

Paper

MNL/HB model, imposing a factor structure to

observe unobservable category-specific effects

and household traits; studied 10 categories with 2

national and 1 store brands each, aggregating over

size and flavor of brands

their two factors can explain a substantial

amount of variation in brand preferences and

price sensitivity, consistently across

categories; showed that purchase history is

much more useful than demographics to

estimate HHc preferences

data from one single store only; only

looking at top 3 brands; aggregated

products across size and flavor; used a

conditional choice model which does not

capture purchase indicence

Song &

Chintagunta

(2006), Journal

Paper

developed an AL model on a multivariate logit

(MVL) basis; using utility theory, but defining

utility and error terms at the bundle level;

accounted for synergistic effects of cross-category

purchases and controlled for temporal effects;

included four categories with four brands each;

captured unobserved consumer heterogeneity with

a dedicated term

found small correlation for cross-category

brand preference but strong large correlations

for within-category brand preference,

heterogeneity in price sensitivities, and

asymmetry in the cross-category effects;

compared their model to a log-log regression

model (instrumented for prices) and found

their model to be superior in all considered

criteria

assumed i.i.d.a Gumbel distributed error

terms across bundles, consumers, stores

and weeks, even though bundles share

brands within categories; using store data

which is inferior to household data;

cannot separate heterogeneity from co-

incidence; needed to estimate a total

market size variable to convert volume

data to share data

Song &

Chintagunta

(2007), Journal

Paper

developed an integrated utility maximizing

framework, considering purchase incidence,

quantity and brand choice; used an AL model on

MNL basis; empirical study with four categories,

six brands each, 148 households, using latent

class estimation (no individual level estimations!)

unified framework for three consumer

decisions, single category models are a special

case of this one; account for unobserved

heterogeneity via finite mixtures model; joint

probability of multicategory purchase is not

the product of the corresponding marginal

probabilities; empirical results show major

source of cross-category elasticity is purchase

incidence

while using HITLb allows them to avoid

assuming a specific (direct) utility

function, it leads to unitary income

elasticities; assuming i.i.d. Type I extre-

me value distribution for error terms,

leading to IIA and same price sensitivity

parameter for all categories; force corner

solution for within category brand choice

(only one brand can be chosen per

category); coincidence only indirectly

95

modeled via outside good shock

Duvvuri, Ansari

& Gupta (2007),

Journal Paper

used an MVP/HB model; modeled utility errors

across categories to be correlated to capture

coincidence and intercept depended on HH

demographics; estimated two models: own-effects

only and cross-effects model which allowed

explanatory variables of other categories to

influence a specific category; used 6 categories

and n=226 HH

able to measure purchase complementarity

with price and promotion variables and

consumption complementarity with HH

inventory variable; found price sensitivity to

be a consumer trait, not a category or product

trait and provide logical empirical evidence

with differing correlation results from own-

effects and cross-effects model; uncovered a

complex mosaic of correlations; showed that

targeted discounts can increase profit

used reduced form framework to model

indirect utility across only 6 categories,

increasing the number of categories

might lead to an abundance of

parameters; could have included more

explanatory variables like quantity

Mehta (2007),

Journal Paper

derived an AL model which can handle multiple

decisions (brand choice and category purchase

incidence) across multiple categories; able to

distinguish between consumption and purchase

complementarity

showed that earlier multicategory models

overemphasized cross-effects of market mix

of brands in other categories and fixed it;

empirical application with 3 categories and

n=150 HH, compare their model to an additive

specification and Manchanda et al.’s (1999)

model and outperforms both

didn’t estimate all pairwise interactions

of brands, only category sub-utilities

(brand errors i.i.d. across categories);

needed to define categories in such a way

that at most one brand is purchased per

category; posited a ‘composite’ category;

didn’t model purchase quantity

Niraj,

Padmanabhan &

Seetharaman

(2008), Journal

Paper

developed an AL model on the basis of a two-

stage bivariate logit (BVL) model; included only

two categories but showed how it could be

extended for more; different from SCE models as

it includes a special term to measure

complementary utility, this term also helps

distinguishing marketing-mix complementarity

from intrinsic complementarity; incorporated

purchase quantity decision and considered cross-

category effects for quantities

investigate scanner data of n=293 HH; showed

that their model outperforms a benchmark

model without cross-effects; found

asymmetric cross-effects but admitted this

might vanish when including more categories;

found 85% of cross-category complementarity

are from marketing mix, only 15% from

intrinsic; found that quantity effects are

responsible for 33-40% of the profit increase

due to cross-category promotion effects

unlike the MVP model, BVL doesn’t

capture correlation in the error terms of

households’ random utilities; their model

ignored brand choice; they only looked

at two categories; two-stage model

connected via inclusive variable (IV),

might yield different results when

implementing IV in the other stage

Schweidel,

Bradlow &

Fader (2010),

Working Papere

developed a dynamic hidden Markov model with

an MNL structure to predict service portfolio

choice, depending on its history; included both

‘service stickiness’ and ‘portfolio stickiness’;

model also able to predict customer life time

values

study with n=3393, t=24 months plus 4 HO

months and 8 services, found that portfolio

inertia is much more important than service

inertia and that a simple ‘buy and keep’

approach predicts almost as well as their full

model and much better than a simple MNL

model, but their model exhibits a lower error;

variation across states limited to

transition probabilities and baseline

service affinities; assumed first-order

Markov process which prohibits the

transition among states to be dependent

on the duration of the latent state

96

showed that an MNL model is equivalent to a

series of binary MVL models a i.i.d. abbreviates ‘identical and independent distributed’

b HITL stands for ‘homothetic indirect translog utility’

c HH stands for ‘household’

d Lancasterian additivity means that a consumer values two average attributed items the same as a package of a maximum and a minimum attributed item

e Published as Schweidel, D. A., Bradlow, E. T., & Fader, P. S. (2011). Portfolio Dynamics for Customers of a Multi-Service Provider. Management Science,

57(3), 471–486. But since this version doesn’t include the online appendix, I decided to cite the working paper which does. f EDLP means ‘every day low prices’ and HILO refers to a ‘High-Low’ price policy, i.e. having higher average prices with temporary discounts in some categories.

Source: Own illustration, using each paper as source for itself.

97

APPENDIX B

This Appendix explains how the cross-reference analysis tables were calculated. They look rather

straight forward, however there are quite a few premises and assumptions behind them.

B.1 Price

First of all, the price levels as unit of analysis. In the case of pure bundling, the choice is straight

forward indeed, the bundle price level for CBC and the added single prices for MBC. But when it

comes to comparing the stand-alone products, things aren’t that clear anymore. I chose to simply

add the single prices of the products, which is the same as an equally weighted composite price

with a weight of one. While this ensures ‘natural’ price levels to compare, it could lead to wrong

pricing decisions in cases where one product dominates the total sales. Hence one could also

calculate a weighted composite price. It seems logical to choose the amount of revenue generated

as weights, however the resulting revenue weighted composite prices are either exactly the same,

as demonstrated in Table 24, or very different from any actual product price implemented in the

survey, as demonstrated in Table 25. This difference not only makes the comparison more

difficult, it can even complicate the final pricing decision. The cells with a light grey background

in Table 25 demonstrate this case: The revenue weighted price of 5.16 is the second price level,

but actually closer to the first actual product price levels than the second. In this case it can still

be argued that the second price level corresponds to the second actual price levels, but if for

example the first CBC revenue weighted price level were not 4.47 but instead 5.12 (or even

above 5.16, not an impossible case with the given price structure), it should become clear that the

nature of revenue weighted prices is no less arbitrary than that of equally weighted prices.

Furthermore, those weighted prices often lack any pricing logic.

If one looks at the mixed bundling case, things get even more complicated, especially for MBC,

where non-bundle products and bundle products are allowed to have different price levels. One

could decide to just focus on the respective bundle prices, since in most cases bundles are the

dominant driver of revenues. It could also be argued, however, that since the MBC bundle price

consists of added single prices, the same should be done for CBC. Lastly, revenue weighted

prices (or even another composite price form) could be used. Again, for the sake of simplicity

and to avoid the trap of over-emphasizing the managerial value of a simple method by using

fancy calculations techniques, I decided to use bundle prices as the unit of comparison.

98

Table 24: Calculating Weighted Prices (Bundling Scenario 1, Stand Alone Case CBC)

Price B Price SA w = 1 %TRa B %TR SA w = %TR w = #TR

b

1.59 1.59 3.18 64.58% 35.42% 1.59 3.18

1.99 1.99 3.98 64.43% 35.57% 1.99 3.98

2.99 2.99 5.98 67.94% 32.06% 2.99 5.98

3.99 3.99 7.98 65.71% 34.29% 3.99 7.98

4.99 4.99 9.98 65.42% 34.58% 4.99 9.98

5.99 5.99 11.98 69.01% 30.99% 5.99 11.98

6.99 6.99 13.98 64.61% 35.39% 6.99 13.98 a %TR refers to the revenue share from the respective stand-alone product in terms of total revenue.

b #TR is a

revenue weighted price, multiplied by the number of available stand-alone products.


Table 25: Comparing Weighted Prices (Bundling Scenario 8, Stand Alone Case)

Price B

CBC

Price SP

CBC

Price B

MBC

Price SP

MBC w = 1

CBC

w = 1

MBC w = #TR

a CBC w = #TR MBC

1.59 2.99 0.99 1.99 4.58 2.98 4.47 2.82

1.99 3.99 1.99 2.99 5.98 4.98 5.16 4.64

2.99 4.99 2.99 3.99 7.98 6.98 7.16 6.50

3.99 5.99 3.99 4.99 9.98 8.98 9.26 8.57

4.99 6.99 4.99 5.99 11.98 10.98 11.20 10.54

5.99 7.99 5.99 6.99 13.98 12.98 13.13 12.59

6.99 9.99 5.99 7.99 16.98 13.98 16.51 13.18 a #TR is a revenue weighted price, multiplied by the number of available stand-alone products.


B.2 Revenue

Second, not only which price to use is an object of discussion, the revenue numbers are debatable

as well. This is because Werder (2013) and Ung (2012) analyzed their data using different

methods, as discussed more extensively in Section 5.2. In pure bundling, they both did

methodologically the same. For each price level, they estimated only the pure bundle itself,

without any other products involved. E.g. in the market simulations of Bundle 5, the pure bundle

consisting of ‘smartphone app’ and ‘print’ was estimated, without any possible competition from

products with cannibalization potential like browser, tablet app, ePaper or newspaper app. So

they considered the case of the media company offering only the products in focus and nothing

else, and since they both used the same assumption, a direct comparison is possible.

In the stand-alone case, their methods already differ. Ung estimated every single product by itself,

without any competition. For every stand-alone case, he then simply added the isolated product

components together. This means, that every product always has the same market share and

99

revenues in each bundling scenario it is involved. Werder on the other hand followed a similar

market simulation procedure as in the pure bundling case. Specifically, she estimated the

products of a bundling scenario together, without competition, using the appropriate

combinatorial attribute level and their corresponding single product price levels, not their bundle

price levels. Therefore, for a specific product, market shares and revenues differ among the

bundling scenarios it is involved. This approach appears superior to me because it can account for

some degree of cannibalization or even latent complementary synergies, as has been

demonstrated in Section 4.3.3. So technically, for comparison reasons, it would have been cleaner

to compare the corresponding added pure single cases, however, in that case CBC would have

had an even bigger advantage which is why I chose to compare them in a separate analysis. On

another note, they both didn’t use the penultimate optimal price levels, instead they both decided

to stick to one price level (i.e. Price Level 4) across all products. But for example in Bundle

Scenario 2, the CBC method estimates maximum revenue for browser under Price Level 6 and

for the tablet app under Price Level 7 and the MBC method estimates maximum revenue for

browser under Price Level 5 and for the tablet app under Price Level 4. So instead of adding these

maximum revenues at different price levels, they only added the revenues of browser and tablet

app for equal price levels and then only reported Price Level 6 (CBC) and 5 (MBC) as the

revenue maximizing prices. But since they both used the same method, it at least allows for a

direct comparison. So I chose not to calculate any penultimate revenues in order to avoid the

above mentioned problem of computing unified price levels, which would have been necessary

for comparison as the paramount goal was to find optimal prices.

When it comes to mixed bundling, things get once again even more complicated. The CBC data

has been analyzed as follows: As in the other cases, no scenario unrelated products (‘no possible

cannibals or complements’) entered the market simulation. Thanks to the alternative-specific

design, bundle data and single product data are different from each other because they were

coded as attribute levels. So when conducting market simulations, Werder (2013) could simply

enter the bundle data along with the single product data. She estimated one common solution for

each price level. Precisely, as in the stand-alone case, no penultimate revenues have been

calculated and no sensitivity analysis with regard to price has been conducted, e.g. if offering the

single products at a higher price level than the bundle would yield additional revenue (which, as

discussed in Section 5.2, is a likely reason for why mixed bundling performed worse than pure

100

bundling). The MBC data on the other hand has been estimated in a different way. Unfortunately,

no description is offered, but successful replication of the results with the available data suggests

the following: All products have been included in the market simulator. The products of the

respective bundling scenario are then called ‘the bundle’. The preference shares of these bundles

suggest that they were not coded as in the MBC pure bundling case (must be chosen together),

instead they were allowed to function as stand-alone products, which are estimated together (as

e.g. in the CBC stand-alone case). Then, the bundle was estimated at a fixed price level, while all

other products were subsequently estimated at Price Levels 1 to 7. If a product had less than

seven price levels, its maximum price level was used as an approximation to fill in the ‘blanks’.

Then, the bundle was estimated at the next higher price level and all other products were again

estimated at Price Levels 1 to 7. Therefore, a sensitivity analysis with regard to the price of the

‘outside’ products was conducted, yielding seven revenue estimates per bundle price. While the

fact, that all products were included in the market simulations (and therefore contribute to

revenue!) already prohibits any convincing comparison between the CBCA and MBCA mixed

bundling cases, the sensitivity analysis poses yet another problem. Namely, which revenues to

compare to each other. In the case of CBC, we have one total revenue per price level, but in the

MBC case, there are 7 total revenues per price level, so depending on which bundle this generates

a grid of 42 or 49 revenues. There are at least four different methods to compare the CBCA and

MBCA revenues, as depicted in Table 26. The bundle price level is plotted on the vertical, while

the prices of the non-bundle products are plotted on the horizontal. An X marks which MBC

price has been chosen for comparison with the CBC method. The diagonal of each table (marked

with bold table borders) are the prices which are equivalent to the CBC method. This case is

depicted in Sub-Table A. While this method ensures the highest degree of comparability, its main

drawback is that it doesn’t capture most of the maximum revenue cases (marked with an orange

background). E.g. in case of Bundle Scenario 6, where only six bundle prices exist but the

revenue maximizing case is with non-bundle prices at Price Level 7, then no MBC revenue

maximizing case is captured at all by this method. Another approach would be to fix the revenue

maximizing bundle price level, and let the non-bundle prices vary, as depicted in Sub-Table B.

This approach seems more arbitrary than Case A and suffers from similar drawbacks, i.e. not

capturing most of the revenue maximizing levels. Also, this method will always deliver seven

revenues, which is confusing if the MBC method only delivers six revenues in the pure bundling

and stand-alone cases. Because price levels above the maximum have been coded as maximum

101

price levels as well, this would deliver two Price Level 6 in Bundling Scenarios 1 through 6. A

third approach, fixing the prices of the non-bundle products to their maximum level and let the

bundle price vary is depicted in Sub-Table C. Again, this approach is more arbitrary than Case A,

but is less likely to miss revenue maximizing MBC prices, because single product prices are

usually higher than the bundle prices. However, in an extreme case, this method could have the

least overlap with the CBC method. For example, in a bundling scenario with only six bundle

prices, while the non-bundle product prices are fixed at Price Level 7. Then, no case from the

diagonal is accounted for. Finally, one could simply choose the revenue maximizing price for

every bundle price level, as depicted in Sub-Table D. This ensures, that all maximum revenue

cases are accounted for, however it suffers from the same drawbacks as Case C. Coincidentally,

using the MBC data from Ung’s study, the Cases C and D are congruent in all scenarios, except

Bundling Scenario 2.

Table 26: Comparing MBC and CBC Mixed Bundling Results

A 1 2 3 4 5 6 7 B 1 2 3 4 5 6 7

1 x 1

2 x 2

3 x 3

4 x 4

5 x 5 x x x x x x x

6 x 6

7 x 7

C 1 2 3 4 5 6 7 D 1 2 3 4 5 6 7

1 x 1 x

2 x 2 x

3 x 3 x

4 x 4 x

5 x 5 x

6 x 6 x

7 x 7 x

‘Bundle Price’ plotted on the vertical, ‘Other Prices’ plotted on the horizontal.

An ‘X’ marks the MBC price chosen for comparison to CBC. Orange

background indicates revenue maximizing MBC prices.


102

For the cross-reference analysis, I chose to follow Approach D, because revenue maximization

has been the imperative driver behind both, the CBC and the MBC study, and this approach

ensures the inclusion of all revenue maximizing cases. Furthermore, the CBC and MBC mixed

bundling methods are already hard to compare due to the fact that all products are included in the

MBC market simulation and the revenue calculations! Still, I decided to compare Case A and

Case D in Table 27 below, using Bundle Scenario 2 as an example. The information with regard

to Scenario D can be found in Table 28, in Appendix C. Using that information, it can be seen

that the MBC revenues according to Method A experience a higher variance than those according

to Method D. Also, the overall revenue maximizing case is not included in Method A, however

its maximum revenue comes close with 97.77%. Moreover, it can also be seen that the revenue

maximizing prices between CBC and MBC Method A coincide, unlike in MBC Method D.

Table 27: Comparing Mixed Bundling Cases A and D in Bundle Scenario 2

CBC

Price

CBC

Revenue

Normalized

Revenue

MBC

Price

MBC

Revenue D

MBC

Revenue A

Normalized

Revenue A

2.99 0.7141 33.38% 1.98 2.0194 0.7856 36.73%

3.99 0.8481 39.65% 3.98 2.1209 1.2686 59.31%

4.99 1.0919 51.04% 5.98 2.1391 1.6220 75.83%

5.99 1.2177 56.93% 7.98 2.1249 1.8832 88.04%

6.99 1.3837 64.69% 9.98 2.1248 2.0913 97.77%

7.99 1.4977 70.02% 11.98 2.1013 2.0698 96.76%

9.99 1.6040 74.98% - - - -

In bold font: Revenue maximizing and minimizing cases. CBC Price refers to the bundle price, MBC price to the added single product prices

included in the bundling scenario. ‘Revenue A’ and ‘Revenue D’ are the MBC revenues according to methods A and D as explained in

Appendix B.2. The normalized revenues are indexed to 2.1391, the highest revenue in this scenario. Price unit: €


103

APPENDIX C

Table 28: Prices and Normalized Revenue for Bundle 2 Components


CBC

Price

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

CBC

Price 2

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

CBC

Price

CBC

Price 2

CBC

Revenue

CBC

Revenue 2

MBC

Price

MBC

Revenue

2.99 47.82% 1.98 1.03% 36.26% 3.18 41.03% 1.98 17.04% 33.65% 2.99 3.18 33.38% 44.52% 1.98 94.40%

3.99 57.80% 3.98 1.89% 66.56% 3.98 52.00% 3.98 32.44% 64.08% 3.99 3.98 39.65% 52.87% 3.98 99.15%

4.99 62.95% 5.98 2.84% 100.00% 5.98 71.45% 5.98 41.39% 81.74% 4.99 5.98 51.04% 68.07% 5.98 100.00%

5.99 70.93% 7.98 1.85% 65.13% 7.98 79.02% 7.98 49.58% 97.92% 5.99 7.98 56.93% 75.92% 7.98 99.34%

6.99 81.63% 9.98 2.03% 71.52% 9.98 89.57% 9.98 50.63% 100.00% 6.99 9.98 64.69% 86.27% 9.98 99.33%

7.99 83.56% 11.98 1.56% 54.85% 11.98 100.00% 11.98 48.13% 95.05% 7.99 11.98 70.02% 93.38% 11.98 98.23%

9.99 100.00% - - - 13.98 91.91% - - - 9.99 13.98 74.98% 100.00% - -



Source: Own Calculations.



CBC

Price

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

CBC

Price 2

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

CBC

Price

CBC

Price 2

CBC

Revenue

CBC

Revenue 2

MBC

Price

MBC

Revenue

2.99 50.79% 2.97 0.98% 33.11% 4.77 42.36% 2.97 24.03% 38.47% 2.99 4.77 32.25% 48.83% 2.97 85.80%

3.99 59.50% 5.97 1.96% 66.56% 5.97 53.74% 5.97 41.52% 66.47% 3.99 5.97 36.94% 55.92% 5.97 91.72%

4.99 67.57% 8.97 2.95% 100.00% 8.97 72.29% 8.97 53.39% 85.48% 4.99 8.97 47.83% 72.41% 8.97 95.12%

5.99 75.39% 11.97 2.07% 70.23% 11.97 80.60% 11.97 61.82% 98.99% 5.99 11.97 54.15% 81.98% 11.97 95.34%

6.99 85.07% 14.97 1.81% 61.49% 14.97 92.18% 14.97 62.46% 100.00% 6.99 14.97 61.84% 93.62% 14.97 100.00%

7.99 88.28% 17.97 0.93% 31.63% 17.97 100.00% 17.97 56.87% 91.06% 7.99 17.97 66.05% 100.00% 17.97 97.67%

9.99 100.00% - - - 20.97 91.46% - - - 9.99 20.97 64.91% 98.27% - -




104



CBC

Price

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

CBC

Price 2

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

CBC

Price

CBC

Price 2

CBC

Revenue

CBC

Revenue 2

MBC

Price

MBC

Revenue

2.99 44.64% 1.98 1.08% 22.26% 3.18 41.58% 1.98 11.60% 40.33% 2.99 3.18 28.63% 46.96% 1.98 82.69%

3.99 52.40% 3.98 2.22% 45.72% 3.98 52.30% 3.98 18.48% 64.27% 3.99 3.98 33.02% 54.17% 3.98 87.56%

4.99 59.77% 5.98 3.34% 68.69% 5.98 68.52% 5.98 24.96% 86.79% 4.99 5.98 39.08% 64.10% 5.98 91.88%

5.99 64.83% 7.98 4.41% 90.69% 7.98 79.81% 7.98 28.76% 100.00% 5.99 7.98 44.83% 73.53% 7.98 99.38%

6.99 75.35% 9.98 4.86% 100.00% 9.98 92.71% 9.98 27.63% 96.09% 6.99 9.98 52.58% 86.24% 9.98 100.00%

7.99 79.98% 11.98 0.64% 13.18% 11.98 98.49% 11.98 20.71% 72.02% 7.99 11.98 54.70% 89.72% 11.98 99.36%

9.99 100.00% - - - 13.98 100.00% - - - 9.99 13.98 60.97% 100.00% - -






CBC

Price

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

MBC

Price

MBC

Revenue

MBC

Price

MBC

Revenue

14.99 95.08% 1.99 2.19% 28.51% 1.99 21.94% 1.99 54.08%

16.99 98.66% 4.99 5.35% 69.67% 4.99 43.59% 4.99 67.57%

18.99 98.50% 7.99 7.51% 97.77% 7.99 63.96% 7.99 80.51%

20.99 100.00% 10.99 7.68% 100.00% 10.99 84.86% 10.99 90.60%

22.99 90.08% 13.99 6.58% 85.60% 13.99 100.00% 13.99 100.00%

24.99 98.62% 14.99 4.58% 59.58% 14.99 93.14% 14.99 99.91%

26.99 97.39% - - - - - - -




105



CBC

Price

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

MBC

Price

MBC

Revenue

MBC

Price

MBC

Revenue

17.98 99.45% 3.98 1.43% 42.66% 3.98 29.18% 3.98 44.40%

20.98 99.29% 7.98 2.03% 60.45% 7.98 52.97% 7.98 64.83%

23.98 97.09% 11.98 1.84% 55.03% 11.98 67.72% 11.98 79.63%

26.98 100.00% 15.98 3.02% 90.14% 15.98 89.13% 15.98 91.43%

29.98 95.17% 19.98 2.91% 86.95% 19.98 99.73% 19.98 100.00%

32.98 95.91% 21.98 3.21% 95.65% 21.98 99.59% 21.98 96.87%

36.98 97.70% 22.98 3.35% 100.00% 22.98 100.00% 22.98 98.96%






CBC

Price

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

CBC

Price 2

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

CBC

Price

CBC

Price 2

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

4.99 49.90% 2.98 4.49% 66.02% 4.58 48.69% 2.98 24.42% 43.62% 4.99 4.58 46.54% 2.98 78.58% 82.39%

5.99 59.56% 4.98 5.60% 82.32% 5.98 55.87% 4.98 38.26% 68.35% 5.99 5.98 54.03% 4.98 87.96% 92.23%

6.99 80.42% 6.98 5.39% 79.35% 7.98 73.16% 6.98 43.45% 77.62% 6.99 7.98 75.23% 6.98 93.29% 97.81%

7.99 59.24% 8.98 6.80% 100.00% 9.98 81.62% 8.98 53.11% 94.88% 7.99 9.98 70.89% 8.98 95.38% 100.00%

8.99 69.98% 10.98 6.75% 99.35% 11.98 90.83% 10.98 53.85% 96.21% 8.99 11.98 67.50% 10.98 94.97% 99.57%

9.99 76.91% 12.98 6.04% 88.83% 13.98 99.42% 12.98 55.52% 99.19% 9.99 13.98 82.91% 12.98 92.04% 96.50%

12.99 100.00% 13.98 6.28% 92.43% 16.98 100.00% 13.98 55.98% 100.00% 12.99 16.98 100.00% 13.98 94.44% 99.01%


are no bundle prices. For CBC, ‘Price’ refers to the bundle price and ‘Price 2’ to the added single product prices. Price unit: €.

Note: In the CBC mixed bundling case, a respondent could get the single components together for a lower price than the bundle (Price 2 < Price 1)!


106



CBC

Price

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

CBC

Price 2

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

CBC

Price

CBC

Price 2

CBC

Revenue

CBC

Revenue 2

MBC

Price

MBC

Revenue

4.99 49.63% 4.96 0.19% 28.66% 7.76 48.31% 4.96 32.55% 44.32% 4.99 7.76 42.44% 49.74% 4.96 77.90%

5.99 57.73% 8.96 0.23% 34.51% 9.96 56.79% 8.96 51.24% 69.77% 5.99 9.96 47.51% 55.69% 8.96 87.91%

6.99 77.70% 12.96 0.33% 49.92% 13.96 74.69% 12.96 61.15% 83.27% 6.99 13.96 66.02% 77.37% 12.96 96.93%

7.99 62.00% 16.96 0.43% 65.33% 17.96 83.08% 16.96 73.44% 100.00% 7.99 17.96 66.54% 77.99% 16.96 96.88%

8.99 68.93% 20.96 n/a n/a 21.96 93.16% 20.96 73.33% 99.85% 8.99 21.96 63.59% 74.53% 20.96 100.00%

9.99 78.75% 24.96 n/a n/a 25.96 100.00% 24.96 69.79% 95.03% 9.99 25.96 76.71% 89.90% 24.96 96.13%

12.99 100.00% 25.96 0.67% 100.00% 30.96 97.29% 25.96 72.80% 99.14% 12.99 30.96 85.32% 100.00% 25.96 99.16%






CBC

Price

CBC

Revenue

MBC

Price

MBC

Revenue

MBC

Revenue 2

MBC

Price

MBC

Revenue

MBC

Price

MBC

Revenue

17.98 98.62% 5.96 0.05% 8.52% 5.96 31.28% 4.96 38.96%

20.98 98.57% 11.96 n/a n/a 11.96 55.31% 8.96 62.11%

23.98 90.60% 17.96 n/a n/a 17.96 71.47% 12.96 79.79%

26.98 96.24% 23.96 n/a n/a 23.96 91.71% 16.96 91.74%

29.98 95.44% 29.96 n/a n/a 29.96 100.00% 20.96 100.00%

32.98 93.61% 33.96 n/a n/a 33.96 95.90% 24.96 96.13%

36.98 100.00% 34.96 0.56% 100.00% 34.96 98.22% 25.96 99.16%




107

APPENDIX D

Table 36: Preference Shares of the CBC and MBC Methods for Pure Bundling Cases 3, 5, 6 & 7

Price CBC Share

B3

MBC Share

B3 Price

CBC Share

B5

MBC Share

B5

CBC Share

B6

MBC Share

B6 Price

CBC Share

B7

MBC Share

B7

2.99 29.47% 0.57% 1.99 - 2.74% - 7.35% 3.98 - 1.33%

3.99 25.87% - 4.99 - 2.67% - 7.09% 7.98 - 0.94%

4.99 23.49% - 7.99 - 2.34% - 6.95% 11.98 - 0.57%

5.99 21.83% 0.57% 10.99 - 1.74% - 5.82% 15.98 - 0.70%

6.99 21.11% - 13.99 - 1.17% - 4.90% 17.98 20.97% -

7.99 19.16% - 14.99 16.21% 0.76% 18.88% 4.28% 19.98 - 0.54%

8.99 - 0.57% 16.99 14.84% - 16.50% - 20.98 17.94% -

9.99 17.36% - 18.99 13.26% - 15.27% - 21.98 - 0.54%

11.99 - 0.30% 20.99 12.18% - 14.12% - 22.98 - 0.54%

14.99 - 0.21% 22.99 10.01% - 12.46% - 23.98 15.35% -

17.99 - 0.09% 24.99 10.09% - 12.25% - 26.98 14.05% -

- - - 26.99 9.22% - 11.89% - 29.98 12.03% -

- - - - - - - - 32.98 11.02% -

- - - - - - - - 36.98 10.01% -

Source: Own illustration with source data from Ung (2012) and Werder (2013). Note: MBC price values were changed from .97 to .99 (B3).

108

Table 37: Preference Shares of the CBC and MBC Methods for Pure Bundling Cases 1, 2, 4, 8, 9 & 10

Price

CBC

Share

B1

MBC

Share

B1

CBC

Share

B2

MBC

Share

B2

CBC

Share

B4

MBC

Share

B4

Price

CBC

Share

B8

MBC

Share

B8

Price

CBC

Share

B9

MBC

Share

B9

Price

CBC

Share

B10

MBC

Share

B10

1.99 29.54% 2.90% - 0.92% - 0.92% 2.99 - 3.82% 4.99 23.27% 0.09% 5.98 - 0.03%

2.99 26.23% - 28.31% - 25.14% - 4.99 25.36% 2.85% 5.99 22.55% - 17.98 20.61% -

3.99 23.56% 2.67% 25.65% 0.84% 22.12% 0.94% 5.99 25.22% - 6.99 26.01% - 20.98 17.65% -

4.99 21.25% - 22.33% - 20.17% - 6.99 29.18% 1.96% 7.99 18.16% - 23.98 14.19% -

5.99 20.97% 2.47% 20.97% 0.84% 18.23% 0.94% 7.99 18.80% - 8.99 17.94% 0.06% 26.98 13.40% -

6.99 19.09% - 20.68% - 18.16% - 8.99 19.74% 1.92% 9.99 18.44% - 29.98 11.96% -

7.99 17.72% 1.53% 18.52% 0.41% 16.86% 0.93% 9.99 19.52% - 12.99 18.01% - 32.98 10.66% -

9.99 - 0.87% 17.72% 0.36% 16.86% 0.82% 10.99 - 1.56% 12.99 18.01% 0.06% 34.98 - 0.06%

11.99 - 0.29% - 0.23% - 0.09% 12.99 19.52% 1.18% 16.99 - 0.06% 36.98 10.16% -

- - - - - - - 13.99 - 1.14% 25.99 - 0.06% - - -

Source: Own illustration with source data from Ung (2012) and Werder (2013). Note: MBC price values were changed from .96 to .98 (B10), from .96 to .99 (B9)

and from .98 to .99 (B1, B2, B4, B8).

Table 38: Preference Shares of the CBC and MBC Methods for Pure Single Product Cases

Price MBC

Share B

CBC

Share B

MBC

Share

SA

CBC

Share

SA

MBC

Share

TA

CBC

Share

TA

Price

MBC

Share

SP

CBC

Share

SP

Price

MBC

Share

NA

CBC

Share

NA

MBC

Share

PDF

CBC

Share

PDF

0.99

0.99 20.57% - 10.68% - 2.16% - 1.99 7.38% - 1.99 3.42% - 4.79% -

1.59 - 32.06% - 26.87% - 24.06% 2.99 6.03% 25.22% 2.99 2.98% - 3.83% -

1.99 18.54% 33.00% 7.19% 27.31% 2.99% 24.28% 3.99 4.07% 21.90% 3.99 2.53% - 3.34% -

2.99 15.43% 29.83% 6.30% 23.56% 2.85% 21.25% 4.99 4.54% 19.67% 4.99 2.15% 16.93% 3.04% 18.23%

3.99 13.43% 25.29% 4.92% 21.25% 2.98% 19.24% 5.99 3.61% 18.30% 5.99 1.87% 15.78% 2.91% 17.58%

4.99 11.16% 22.98% 3.83% 19.52% 2.24% 17.87% 6.99 3.45% 17.72% 6.99 1.64% 15.92% 2.86% 19.38%

5.99 9.28% 21.11% 2.46% 17.36% 1.33% 16.50% 7.99 3.01% 16.21% 7.99 - 13.11% 2.86% 13.76%

6.99 - 16.57% - 15.13% - 14.70% 9.99 - 15.63% 8.99 - 11.74% - 12.18%

- - - - - - - - - - 9.99 - 12.39% - 13.69%

- - - - - - - - - - 12.99 - 13.26% - 14.12%

Source: Own illustration with source data from Werder (2013) and Ung (2012).

109

Table 39: CBCA Pure Single Product Revenues

Price Level B SA TA PDF NA SP

1 0.5098 0.4273 0.3826 0.9096 0.8449 0.7540

2 0.6566 0.5434 0.4832 1.0530 0.9451 0.8739

3 0.8918 0.7044 0.6355 1.3547 1.1129 0.9815

4 1.0090 0.8480 0.7675 1.0995 1.0476 1.0962

5 1.1469 0.9742 0.8916 1.0946 1.0558 1.2388

6 1.2645 1.0400 0.9883 1.3675 1.2380 1.2952

7 1.1583 1.0576 1.0273 1.8343 1.7220 1.5618

Source: Own illustration with source data from Werder (2013).

Table 40: MBCA Pure Single Product Revenues

Price Levela B SA TA P PDF NA SP DP GP

1 0.2036 0.1057 0.0214 0.1179 0.0953 0.0681 0.1469 0.0242 0.0203

2 0.3689 0.1431 0.0595 0.3012 0.1145 0.0891 0.1803 0.0499 0.0354

3 0.4614 0.1884 0.0852 0.4635 0.1333 0.1009 0.1624 0.0562 0.0694

4 0.5359 0.1963 0.1189 0.6685 0.1517 0.1073 0.2265 0.0427 0.0422

5 0.5569 0.1911 0.1118 0.8280 0.1743 0.1120 0.2162 0.0519 0.0446

6 0.5559 0.1474 0.0797 0.8019 0.1999 0.1146 0.2412 0.0595 0.0398

7 0.5631 0.1545 0.1102 0.8019 0.1999 0.1146 0.2405 0.0742 0.0514 a Only SP actually has 7 price levels, see Table 43 for more details. Ung (2012) therefore just repeated the maximum price level for all ‘higher’ price

levels, e.g. using 5.99 for Browser Price Level 7. I applied the same logic to NA, PDF, DP and GP.

Source: Own illustration with source data from Ung (2012).

110

APPENDIX E

Table 41: List of CBC Product Attributes and Their Attribute Levels

Attribute 1: Website Attribute 2: Newspaper Attribute 3: Sports League

Attribute Level Permitted

Price Attribute Attribute Level

Permitted

Price Attribute Attribute Level

Permitted

Price Attribute

Browser Price 1 Newspaper App Price 3 Season Pass Price 2

Smartphone App Price 1 ePaper (PDF) Price 3 Match Day Pass

1.99€ 1.99€

Tablet App Price 1 Print Price 6a

Match Day Pass

2.99€ 2.99€

Browser &

Smartphone App Price 2

Newspaper App

& ePaper (PDF) Price 4

Match Day Pass

3.99€ 3.99€

Browser & Tablet

App Price 2

ePaper (PDF) &

Print Price 6

Match Day Pass

4.99€ 4.99€

Smartphone App

& Tablet App Price 2

Newspaper App

& Print Price 6

Single Game Pass

0.99€ 0.99€

Browser,

Smartphone App

& Tablet App

Price 2

Newspaper App,

ePaper (PDF) &

Print

Price 6 Single Game Pass

1.99€ 1.99€

None - None - Single Game Pass

2.99€ 2.99€

Single Game Pass

Inclusive

included in

total price

None -

a Indicated price attribute only applies to bundles composed of levels from the attributes Website and Newspaper,

excluding Sports League.

Source: Own illustration, after Werder (2013) pp. 15–17.

111

Table 42: List of CBC Price Attributes and Their Attribute Levels

Attribute

Level

Attribute 4:

Price 1

Attribute 5:

Price 2

Attribute 6:

Price 3

Attribute 7:

Price 4

Attribute 8:

Price 5

Attribute 9:

Price 6

1 1.59€ 2.99€ 4.99€ 7.99€ 6.99€ 14.99€

2 1.99€ 3.99€ 5.99€ 8.99€ 9.99€ 16.99€

3 2.99€ 4.99€ 6.99€ 9.99€ 11.99€ 18.99€

4 3.99€ 5.99€ 7.99€ 10.99€ 13.99€ 20.99€

5 4.99€ 6.99€ 8.99€ 11.99€ 15.99€ 22.99€

6 5.99€ 7.99€ 9.99€ 12.99€ 17.99€ 24.99€

7 6.99€ 9.99€ 12.99€ 14.99€ 19.99€ 26.99€

Source: Own illustration, after Werder (2013), p. 18. Prices per month.

Table 43: List of MBC Attributes and Their Price Levels

Attribute / Price

Level B SA TA PDF NA P SP DP GP

1 0.99 0.99 0.99 1.99 1.99 1.00 1.99 0.99 0.99

2 1.99 1.99 1.99 2.99 2.99 3.00 2.99 1.99 1.99

3 2.99 2.99 2.99 3.99 3.99 5.00 3.99 2.99 2.99

4 3.99 3.99 3.99 4.99 4.99 7.00 4.99 3.99 -

5 4.99 4.99 4.99 5.99 5.99 9.00 5.99 - -

6 5.99 5.99 5.99 6.99 6.99 - 6.99 - -

7 - - - - - - 7.99 - -

Source: Own illustration, with source data from Ung (2012). Prices in € per month.

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