1. Review Pricing a marketing research Value of Information EVPI = |EMVwPI – EMVwoI|

2. 2 0 -5 Success 0.5 Failure 0.5 25 0 Approve Disapprove Approve Disapprove EMV(the best alternative with free perfect info) = 12.5 With perfect information Approve Disapprove Success 0.5 Failure 0.5 25 0 -5 EMV(the best alternative without new info) = 10 Without information Suppose with 50% of chance, the product will be a great success, bringing $40 million in revenue; and 50% of chance it will be a failure, bringing $10 million. EVPI = EMVwPI - EMVwoI = 12.5-10 = 2.5million  The most you are willingness to pay for any information

3. Example from Previous Class 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

4. 1. Identify the status quo course of action when no marketing research (info) is available. Expected Cost Expected Revenue Status quo course of action

5.  2. Identify the scenario(s) in which marketing research will change the course of action. Scenario # 2 When the revenue is $15 million Scenario # 3 When the revenue is $0 million

6.  3. Determine the gain conditional on the relevant scenario(s). Scenario # 2 The conditional gain is Scenario # 3 The conditional gain is

7.  4. 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

8. EVA - based on product differentiation Reference Value or Reference Price Positive Differentiation Value Negative Differentiation Value Total Economic Value +$ - $ Final $ 8

9. Importance of Differentiation Value Selling hot dogs at the street corner of NYC Your cost Competitor cost Case ACase B Your cost Competitor cost WTP 9

10. Importance of Having Differentiation Value NetflixCleanfilms.com InventoryApprox. 100,000Approx. 1,000 # of distribution center 40+1 Price charged For 2 at a time $17.99$19.99 Secret of survival? 10

11. 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. 11

12. Value Communication 12

13. Value Communication CompetitorOur MagazineAdvantage Circulation1,400,0001,550,000 11% Readers per copy 1.8 2.1 Readership 2,520,000 3,255,000 29% % See ad 9.20% 14.50% % Motivated/ad seen 1.6% 2.2% % Sold/motivated 20% # Readers sold 742 2077 180% Sales per customer $180 $200 Gross margin 30% Value of ad $40,062 $124,601 221% Cost of ad $29,000 $67,400 Return on ad $11,062 $57,201 13

14. Value Measurement (Chapter 13)

17. Measuring Price Sensitivity: Uncontrolled Conditions “+” 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

18. Measuring Price Sensitivity: Uncontrolled Conditions Analysis of Historical Data Historical data is used to develop models that explain sales of goods and services. Models are usually regression models (econometrics). Models are limited by the range of data provided and the quality of the data. The assumption is that the past is a good indicator of the future. Example: Measuring New Home Sales

19. Measuring Price Sensitivity: Uncontrolled Conditions Aggregate Sales Data Data is typically collected on weekly or monthly cycles and aggregated across retail outlets. Data can generally be broken out by retail channel. “+” Cheap Abundant “-” Interpretation issue Lack of statistical power Appropriate for Inappropriate for

20. 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

21. 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 Inappropriate for

23. “-” 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. Measuring Price Sensitivity: Uncontrolled Conditions

25. Measuring Price Sensitivity: 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 Inappropriate for products of

26. Measuring Price Sensitivity: Controlled Conditions 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 Inappropriate for products that are

28. 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 Measuring Price Sensitivity: Controlled Conditions

29. 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.

30. 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.

32. Next Lecture Cost and cost-plus pricing, BEP Read Chapter 1