Dr. Yacheng Sun, UC Boulder1 Lecture 4 Value-based Pricing.

Dr. Yacheng Sun, UC Boulder 1

Lecture 4

Value-based Pricing

Guest Lecture

Value Measurement and Communication in B2B Setting

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Review

Dr. Yacheng Sun, UC Boulder

Competitor Our MagazineCirculation 1,400,000 1,550,000 Cost of ad $29,000 $67,400

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How do you justify your price?

Dr. Yacheng Sun, UC Boulder

Competitor Our Magazine AdvantageCirculation 1,400,000 1,550,000 11%

Readers per copy 1.8 2.1Readership 2,520,000 3,255,000 29% % See ad 9.20% 14.50%

% Motivated/ad seen 1.6% 2.2%% Sold/motivated 20% 20%# Readers sold 742 2077 180%

Sales per customer $180 $200

Gross margin 30% 30%Value of ad $40,062 $124,601 221%Cost of ad $29,000 $67,400

Return on ad $11,062 $57,201

4Dr. Yacheng Sun, UC Boulder

Illustrating Value: Pricing of Market Research

Market research helps to provide information and reduce uncertainty in decision making


Value of Information

How much can you charge for the information?

Sell as much as the information is worth, but no more

Value of information is based on improved decision!

Value of imperfect information will be less than

value of perfect information .


Previous Example

Context A company has to decide whether to switch to a new

product or keep selling the current product.

Payoffs: Current product: $5 million New product: $ 1 million (failure), $ 6 million

(success)

Consider two general cases: (1) there is no uncertainty in prospect of the new product. (2) there is uncertainty in the prospect of the new product


Case 1: No uncertainty in revenue

What should the company do if the probability of success is 0%?

What should the company do if the probability of success is 100%?


Case 2.1: Uncertainty in revenue

Suppose that manager’s belief about success: 50%

Now assume that a marketing research project can be done to accurately predict the success or failure of the new product. The cost of doing research is $200,000

Can you sell the research? Why?


Case 2.1 Decision without MR (step 1) Stay with current product

Odds that MR will change the decision (step 2) 50%

Gain conditional on the change (step 3)

$1 million

Value of research(step 4) $0.5 million

You can sell the research for a profit


Case 2.2: Uncertainty in revenue

Suppose that manager’s belief about success: 90%

Now assume that a marketing research project can be done to accurately predict the success or failure of the new product. The cost of doing research is $450,000

Can you sell the research? Why?


Case 2.1 Case 2.2 Decision without MR (step 1)

Stay with current product

Odds that MR will change the decision (step 2)

50%

Gain conditional on the change (step 3)

$1 million

Value of research(step 4) $0.5 million

You cannot sell the research for a profit


Point of Reflection

What will be the most important information that we should ask our client (the manager) in order to compute the price of the research?


0 Prob. of Success

Value of MR

1.0

0.8

0.6

0.4

0.2

0.2

0.4

0.6

0.8


One More Example

The estimated R&D cost is estimated to be $20 million and the marketing cost is $5 million.

Suppose with 1/3 of chance, the product will be a great success, bringing $90 million in revenue; with chance of 1/3 it will be a failure, bringing $15 million revenue, and with 1/3 it will be a disaster and will generate zero revenue.

What is the value of the marketing research here? The answer is $11.67 million


Identify the status quo course of action when no marketing research (info) is available. Expected Cost $20million + $5million = $25million Expected Revenue 1/3 x $90million + 1/3 x $15 million + 1/3 x $0

million = $35 million

Step 1


Identify the scenario(s) in which marketing research will change the course of action.


Step 2

Determine the gain conditional on the relevant scenario(s).

Scenario # 2

Scenario # 3


Step 3

Multiply the conditional gain and the probability for the occurrence of the scenario.

Notice that in this case, there are 2 scenarios (#2 and #3) in which the research has the potential to change status quo course of action and be valuable.

Thus, we need to calculate the expected value of the marketing research, accounting for both scenarios.

Expected Revenue 1/3 x $10million + 1/3 x $25 million = $11.67 million


Step 4

EVA - based on product differentiation

Reference Valueor

Reference Price

PositiveDifferentiation

Value

NegativeDifferentiation

Value

Total Economic Value

+$- $Final $


Importance of differentiation value

Selling hot dogs at the street corner of NYC

Your cost

Competitor cost

Case A Case B

Your cost

Competitor cost

WTP

WTP


Importance of differentiation value

Netflix Cleanfilms.com

Inventory

Approx. 100,000 Approx. 1,000

# of distribution center

40+ 1

Price charged For 2 at a time

$17.99 $19.99

Secret of survival?


A not-so-fairy-tale ending

• In 2006, Judge Richard P. Matsch of the United States District Court for the District of Colorado ruled that it was a copyright violation to distribute re-edited movies without the consent from the movie studios.

• Cleanfilms.com notified its subscribers the loss of the battle while ensuring them that they commit to rent only the “clean” films.

• Cleanfilms.com went out of business soon after.

• The Directors Guild of America and the Motion Picture Association of America sued most of these industry players for copyright infringement and claims regarding derivative works.


Techniques for Measuring Price Sensitivity

Variable Measured Uncontrolled Experimentally

Controlled

Actual Purchases

• Historical Sales Data

• Panel Data• Store Scanner Data

• In-store Experiments

• Laboratory purchase experiments

Preferences and Intentions

• Direct Questioning• Buy-response

Survey• Depth Interview

• Simulate Purchase Experiments

• Trade-off (Conjoint) Analysis


Uncontrolled Studies of Actual Purchases


Controlled

Actual Purchases• Historical Sales Data• Panel Data• Store Scanner Data




• Direct Questioning• Buy-response Survey• Depth Interview




“+” Easy availability of data

“-” Reliability (confounding factors such as such as number of brands, number of competitors, competitors actions, frequency of advertising, and changes in the economic condition)

Appropriate for existing products Inappropriate for pricing new products or when a new pricing strategy is being introduced that has not been implemented by the company in the past.


Using Past Data

Sample Surveyed at T1

Sample Surveyed at T1

Same Sample

also Surveyed at T2

T1 T2

Cross- Sectional Design

Longitudinal Design

Time

Cross-Sectional vs. Longitudinal Designs

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Cross-Sectional Data May Not Show Change

Brand Purchased Time PeriodPeriod 1 Period 2Survey Survey

Brand A 200 200Brand B 300 300Brand C 500 500Total 1000 1000

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Longitudinal Data May ShowSubstantial Change

Brand Purchased in Period 1

Brand Purchased in Period 2

Brand A Brand B Brand C Total

Brand ABrand BBrand CTotal

100 25 75200

50100150300

50175275500

200 300 5001000

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Measuring Price Sensitivity:Uncontrolled Conditions

Panel Data Consumers keep track of purchases (size, amount, price, where purchased, when purchased, etc.). Consumer diaries are then aggregated to provide market information and brand by brand information. “+”

Short time horizon. Individual-level prices Demographics info. Competitor Information

“-” Biased sample of population Buyer identity


Measuring Price Sensitivity: Uncontrolled Conditions

Scanner Data Data is collected on a store-by-store basis (prices and volume of sales data are collected). Can be linked with demographic information.

“+” More representative sample

“-” Lack of competitor information

Appropriate for consumer-packaged goods. Inappropriate for B2B markets (too few transactions)


Cell 3:Uncontrolled Studies of Preferences and Intentions


Controlled

Actual Purchases

•Historical Sales Data•Panel Data•Store Scanner Data


•Laboratory purchase experiments


•Direct Questioning•Buy-response Survey•Depth Interview

•Simulate Purchase Experiments

•Trade-off (Conjoint) AnalysisDr. Yacheng Sun, UC Boulder 32

• “-” Direct questioning regarding willingness-to-play potentially highly misleading.

• “+”• Data cheap and quick to collect • Can be used to measure WTP of durable/expensive products• Useful for obtaining detailed information for making economic value calculations.

• Buy-response surveys present the respondent with a price and ask if he or she would buy at that price. Since this question is structured more like a purchase, with no opportunity to bargain, the responses are more reasonable.


Experimentally Controlled Studies of Actual Purchases


Controlled

Actual Purchases

• Historical Sales Data• Panel Data• Store Scanner Data








Controlled Conditions

In-Store Purchase Experiments Most common method is to use two or more retail outlets that have similar characteristics (experiment and control).

“+” Ability to disentangle price and other promotion

“-” Can be extremely expensive. Competitors’ actions can contaminate results (special sales promotions, advertising)

Appropriate for products sold through more controlled methods (mail-order)



Laboratory Purchase Experiments These experiments attempt to simulate the real store purchase experience. Mall intercepts an example of laboratory experiments.

Very adaptable.

“+” Inexpensive. High validity Control for demographics

“-” Artificial (Heightened consumer awareness)

Appropriate for products that are at high risk of competition contamination Inappropriate for products that are durable/expensive.


Experimentally Controlled Studiesof Preferences and Intentions


Controlled

Actual Purchases

• Historical Sales Data• Panel Data• Store Scanner Data








Difference between laboratory experiment and simulated experiment “+” Conjoint analysis can be conducted very quickly and at a low cost.

“-” Validity

Appropriate for determining what familiar attributes to include (and at what levels to include them at) during the product/service design process. Inappropriate for attributes that are less familiar to the consumers.



Conjoint Analysis

Most methods used to calculate consumer preference are compositional. For example, consumer ratings of attribute importance represent a compositional approach.

Conjoint analysis is a decompositional approach to measuring consumer preferences. Consumers rate a product while evaluating several product attributes simultaneously.


Conjoint Analysis

Consumer preference data is collected for several product configurations.

Product configurations are presented such that various trade-offs can be assessed on a monetary basis.

Data can be reported on an individual or aggregate basis, which is useful for segmenting a market based on price or other product attribute.

Sensitivity analysis can be conducted with the data to assess the impact that changes in attributes have on price sensitivity. Dr. Yacheng Sun, UC Boulder 40

Online (Virtual) Conjoint Analysis

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Next Lecture

More on Conjoint Analysis

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Dr. Yacheng Sun, UC Boulder1 Lecture 4 Value-based Pricing.

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