Introduction to Choice-Based Conjoint (CBC) Copyright Sawtooth Software, Inc.

Introduction to Choice-Based Conjoint (CBC)

Copyright Sawtooth Software, Inc.

Conjoint Methods: Card-Sort Method (CVA)

Using a 100-pt scale where 0 means definitely

would NOT and 100 means definitely WOULD…

How likely are you to purchase…

Coke

6-pack

$1.89

Your Answer:___________

Conjoint Methods: Pairwise Method (ACA or CVA)

Which would you prefer?

Coke Pepsi

6-pack 8-pack

$1.89 $2.29

Strongly Prefer Strongly Prefer

Left Right

1 2 3 4 5 6 7 8 9

Choice-Based Conjoint Question

Comparing the Methods (cont.):

Traditional Card Sort:

– Respondent task is not as realistic as CBC

– Ranking or ratings typically provide enough information to compute utilities (preferences) for each individual

– Usually only compute Main Effects (no interactions)


Pairwise Presentation:

– Respondent task is often not as realistic as CBC

– Ratings typically provide enough information to compute utilities (preferences) for each individual

– Usually only compute Main Effects (no interactions)


Choice-Based Conjoint Pros:

– Making choices in CBC questions is similar to what buyers do in the marketplace

– CBC can include a “None” option, so respondents who have no interest in purchasing can opt out of the question

– Because we can analyze results by pooling respondent data, CBC permits measurement of Main Effects AND Interactions. More overall parameters can be estimated.


Choice-Based Conjoint Pros (cont.):

– Because we can pool respondent data, each respondent can answer as few as just 1 question

– Respondents can answer at least up to 20 choice questions with high reliability

– Randomized designs permit showing respondents all combinations of levels and are quite efficient

– Particularly well suited to pricing studies


Choice-Based Conjoint Cons:

– Choices are inefficient: they indicate only which product is preferred, but not by how much

– Aggregate models assume respondent homogeneity, which may be inaccurate representation for a market (but Latent Class analysis and new developments in Bayesian estimation techniques help resolve this issue)

– Usually requires larger sample sizes than with CVA or ACA


Choice-Based Conjoint Cons (cont.):

– Tasks are more complex, so respondents can process fewer attributes (early academics recommended six or fewer, but in practice it seems respondents can evaluate a few more than that if the text is concise and the tasks are laid out well)

– Complex tasks may encourage response simplification strategies


Analyzing the Data:

– ACA: Ordinary Least Squares regression (OLS) or Hierarchical Bayes (HB)

– CVA: OLS (ratings), Monotone regression (rankings) or Hierarchical Bayes (HB)

– CBC: Counting analysis, Multinomial Logit, Latent Class, or Hierarchical Bayes (HB)

– Adaptive CBC (ACBC): Hierarchical Bayes (HB), Monotone regression

Main Effects Versus Interactions

Main Effects:

- Isolating the effect (impact) of each attribute, holding everything else constant

Assume two attributes:

– BRAND: Coke, Pepsi, Store Brand

– PRICE: $1.50, $2.00, $2.50

Main Effects Versus Interactions (cont.):

Hypothetical Main Effects Utilities:

Interpretation: Across all Interpretation: Across all brands (holding brand brands (holding brand constant), $1.50 is worth 80 constant), $1.50 is worth 80 points, etc.points, etc.

Levels Utilities

Coke 50

Pepsi 30

Store Brand 10

$1.50 80

$2.00 40

$2.50 10


We can add the main effect utilities together and infer the preference for each brand at each price. But this assumes the same price function for each brand.

$1.50 $2.00 $2.50

Coke 50 + 80 = 130 50 + 40 = 90 50 + 10 = 60

Pepsi 30 + 80 = 110 30 + 40 = 70 30 + 10 = 40

Store Brand 10 + 80 = 90 10 + 40 = 50 10 + 10 = 20


This may not be an accurate representation of how price changes affect preference for each brand. Perhaps price changes have a different impact depending on the brand. That would imply an interaction.

Main Effect Price x Brand Curves

020406080

100120140

$1.50 $2.00 $2.50

Price

Uti

lity

Coke

Pepsi

Store Brand


CBC counts the percent of times each brand/price combination is chosen. Each cell in the grid above is directly and independently measured (two-way interaction).

$1.50 $2.00 $2.50

Coke .58 .50 .41

Pepsi .46 .32 .23

Store Brand .31 .10 .02


The Store Brand is more price sensitive to changes in price compared to Coke and Pepsi. Coke buyers are most loyal in the face of price changes.

CBC Brand x Price Interaction

0%

10%

20%

30%40%

50%

60%

70%

$1.50 $2.00 $2.50

Price

Ch

oic

e P

rob

ab

ilit

y

Coke

Pepsi

Store Brand


There are many other kinds of interactions besides Brand x Price:

Preference for

color depends

upon the car

Interaction: Cars and Colors

0%

10%

20%

30%

40%

50%

60%

70%

Black Red Blue

Color

Ch

oic

e P

rob

abili

ty LincolnContinental

Mazda Miata

Honda Accord

Sawtooth Software’s CBC Systems

• Windows- or Web-based computer-administered interviews or paper surveys

• Capacity: 30 attributes with up to 250 levels each (with Advanced Design Module)

• Experimental design produced automatically

• Prohibitions between attribute levels can be specified

• Fixed designs can be specified

• Choice sets can include a “none” or “constant” option

• Data analyzed automatically by counting or multinomial logit, optional modules for Latent Class and HB

• Market simulator included

The CBC System: Advanced Modules

• Paper and Pencil Module

– Assists in creating and analyzing data for paper and pencil interviews

• Latent Class Segmentation Module

– Detects and models market segments

– Helps relax the assumption of homogeneity, but still does not achieve individual-level data

– Permits specification of linear terms, and respondent weighting

• Hierarchical Bayes Analysis CBC/HB

Advanced Design Module

• Advanced Design Module:

– Support “brand-specific attribute” designs and estimation (some researchers refer to these as “true” discrete choice designs)

– More than one “Constant Alternative” (None) option

– Expanded number of attributes to accommodate brand-specific attribute designs (up to 30 attributes)

– Ability to conduct/analyze partial-profile experiments

Why Latent Class and HB?

• To reduce the Red Bus/Blue Bus (IIA) Problem, one must account for:

– Substitution effects

– Differential cross-elasticities

– Differential self-elasticities

Aggregate Logit

• Assume an aggregate logit solution where:– Utility (Train) = Utility (Red Bus)

On any given day, difficult to predict which way any one respondent will travel to work.

Resulting in the following aggregate shares:

– Train 50%; Red Bus 50%

Aggregate Logit:

• Assume we add another alternative where:– Utility (Train) = Utility (Red Bus) = Utility (Blue Bus)

Again, difficult to predict which way any one respondent will travel to work.

• Train 33.3%; Red Bus 33.3%; Blue Bus 33.3%

• Net Bus ridership increased from 50% to 66.7% by offering a bus of a different color

Two-Group Latent Class Solution:

• Left Half of Room– Strongly Prefer Buses

• Right Half of Room– Strongly Prefer Trains

In aggregate, it still appears that Utility (Bus) = Utility (Train)

Two-Group Latent Class Simulation:

• Now offer both Red and Blue buses

• Net Bus ridership still 50% (no Share Inflation)

Capturing heterogeneity has resulted in differential substitution effects

Differential Cross-Elasticity under Latent Class

• Now raise price of Blue Bus

– Many Blue Bus riders shift to Red Buses

– Train ridership unaffected

Capturing heterogeneity has revealed differential cross-elasticity

Differential Elasticity under Latent Class

• Assume:

– Train riders = Not price sensitive

– Bus riders = Very price sensitive

Differential Elasticity under Latent Class

• If raise Train price

– Few train riders shift to buses

• If raise Red and Blue bus prices

– Many bus riders shift to trains

Capturing heterogeneity has captured differential elasticities

Conclusions

• Capturing heterogeneity under Latent Class or HB

– Reduces Red Bus/Blue Bus problem

– Automatically accounts for differential substitution, elasticities and cross effects with simple main-effects models

• If those effects are due to differences in preferences between people

Adaptive Extension of CBC

• In 2008, Sawtooth Software released an adaptive form of CBC called ACBC. It is quickly gaining acceptance.

• Shares the strengths of CBC, but provides a more engaging respondent experience.

• Can extend CBC’s ability to study more attributes and levels.

Introduction to Choice-Based Conjoint (CBC) Copyright Sawtooth Software, Inc.

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