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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
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Dr. Yacheng Sun, UC Boulder 1
Lecture 4
Value-based Pricing
Guest Lecture
Value Measurement and Communication in B2B Setting
2
Review
Dr. Yacheng Sun, UC Boulder
Competitor Our MagazineCirculation 1,400,000 1,550,000 Cost of ad $29,000 $67,400
3
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
5Dr. 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 Boulder 6
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 Boulder 8
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 Boulder 9
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 Boulder 10
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 Boulder 11
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
Dr. Yacheng Sun, UC Boulder 12
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 Boulder 13
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
Dr. Yacheng Sun, UC Boulder 14
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 Boulder 15
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 Boulder 16
Identify the scenario(s) in which marketing research will change the course of action.
Dr. Yacheng Sun, UC Boulder 17
Step 2
Determine the gain conditional on the relevant scenario(s).
Scenario # 2
Scenario # 3
Dr. Yacheng Sun, UC Boulder 18
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 = $11.67 million
Dr. Yacheng Sun, UC Boulder 19
Step 4
EVA - based on product differentiation
Reference Valueor
Reference Price
PositiveDifferentiation
Value
NegativeDifferentiation
Value
Total Economic Value
+$- $Final $
20Dr. Yacheng Sun, UC Boulder
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
21Dr. Yacheng Sun, UC Boulder
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?
22Dr. 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.
23Dr. 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 Boulder 24
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 Boulder 25
“+” 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 Boulder 26
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
27
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
28
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
29
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 Boulder 30
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 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) 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.
Dr. Yacheng Sun, UC Boulder 33
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 Boulder 34
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 Boulder 35
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 Studiesof 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 Boulder 37
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 Boulder 38
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 Boulder 39
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
41
Next Lecture
More on Conjoint Analysis
44