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

Guest Lecture Value Measurement and Communication in B2B Setting 2 Review Dr. Yacheng Sun, UC Boulder

CompetitorOur Magazine Circulation1,400,0001,550,000 Cost of ad $29,000 $67,400 3 How do you justify your price? Dr. Yacheng Sun, UC Boulder

CompetitorOur MagazineAdvantage Circulation1,400,0001,550,000 11% Readers per copy 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 % Sales per customer $180 $200 Gross margin 30% Value of ad $40,062 $124, % Cost of ad $29,000 $67,400 Return on ad $11,062 $57,201 4 Dr. Yacheng Sun, UC Boulder

Illustrating Value: Pricing of Market Research Market research helps to provide information and reduce uncertainty in decision making 5 Dr. Yacheng Sun, UC Boulder

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. Dr. Yacheng Sun, UC Boulder6

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 Dr. Yacheng Sun, UC Boulder 7

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%? Dr. Yacheng Sun, UC Boulder8

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? Dr. Yacheng Sun, UC Boulder9

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 Dr. Yacheng Sun, UC Boulder10

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? Dr. Yacheng Sun, UC Boulder11

Case 2.1Case 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 Dr. Yacheng Sun, UC Boulder12

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? Dr. Yacheng Sun, UC Boulder13

0 Prob. of Success Value of MR Dr. Yacheng Sun, UC Boulder14

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 Dr. Yacheng Sun, UC Boulder15

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 Dr. Yacheng Sun, UC Boulder16

 Identify the scenario(s) in which marketing research will change the course of action. Dr. Yacheng Sun, UC Boulder17 Step 2

 Determine the gain conditional on the relevant scenario(s). Scenario # 2 Scenario # 3 Dr. Yacheng Sun, UC Boulder18 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 Dr. Yacheng Sun, UC Boulder19 Step 4

EVA - based on product differentiation Reference Value or Reference Price Positive Differentiation Value Negative Differentiation Value Total Economic Value +$ - $ Final $ 20 Dr. Yacheng Sun, UC Boulder

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 21 Dr. Yacheng Sun, UC Boulder

Importance of 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? 22 Dr. Yacheng Sun, UC Boulder

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. 23 Dr. Yacheng Sun, UC Boulder

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 Dr. Yacheng Sun, UC Boulder24

Uncontrolled Studies of Actual Purchases 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 Dr. Yacheng Sun, UC Boulder25

“+” 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. Dr. Yacheng Sun, UC Boulder26 Using Past Data

Sample Surveyed at T 1 Same Sample also Surveyed at T 2 T1T1 T2T2 Cross- Sectional Design Longitudinal Design Time Cross-Sectional vs. Longitudinal Designs Figure 3.6 Cross Sectional vs. Longitudinal DesignsFigure 3.6 Cross Sectional vs. Longitudinal Designs 27

Cross-Sectional Data May Not Show Change Brand Purchased Time Period Period 1Period 2Survey Brand A Brand B Brand C Total

Longitudinal Data May Show Substantial Change Brand Purchased in Period 1 Brand Purchased in Period 2 Brand ABrand BBrand C Total Brand A Brand B Brand C Total

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 Dr. Yacheng Sun, UC Boulder30

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) Dr. Yacheng Sun, UC Boulder 31

Cell 3: Uncontrolled Studies of Preferences and Intentions Variable MeasuredUncontrolled 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 Dr. Yacheng Sun, UC Boulder32

“-” 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. Dr. Yacheng Sun, UC Boulder33

Experimentally Controlled Studies of Actual Purchases 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 Dr. Yacheng Sun, UC Boulder34

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) Dr. Yacheng Sun, UC Boulder35

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 at high risk of competition contamination Inappropriate for products that are durable/expensive. Dr. Yacheng Sun, UC Boulder 36

Experimentally Controlled Studies of Preferences and Intentions 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 Dr. Yacheng Sun, UC Boulder37

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. Controlled Conditions Dr. Yacheng Sun, UC Boulder38

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. Dr. Yacheng Sun, UC Boulder39

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 Boulder40

Online (Virtual) Conjoint Analysis 41

Next Lecture More on Conjoint Analysis 44