1. Preferences and Decision-Making Decision Making and Risk, Spring 2006: Session 7

2. Decision Making Framework Option A Option B Outcome A1 Outcome A2 Outcome B1 Outcome B2 Payoff Portfolio A1 Payoff Portfolio A2 Payoff Portfolio B1 Payoff Portfolio B2 p(A1) p(A2) p(B1) p(B2) Consequences/Payoff Portfolio Simple consequences $ metric Complex consequences Revenues Costs Learning Turnover Morale Comp. Response Future Options Tech Market Facilities Integrating payoffs to determine overall utility Outcomes Known Outcomes Unknown Outcomes Outcome probabilities Distribution Alternatives Known Options Unknown Options Deferred Decision Decision Problem Discovering the right decision problem. Decision Problem

3. Central Logic in Decision Making  Two key questions in regard to any decision: What are the consequences of the options?  In other words, what will happen with each alternative? What is our preference for those consequences?  In other words, do we know what we want among the various consequences that can occur?

4. The Health Screening Preferences  The goal of this questionnaire is to understand people’s preferences for generic screening and diagnostic tests.  Tests vary on:  Accuracy  Frequency  Invasiveness  Time commitment from you, the patient  Pain and discomfort  Exposure to radiation  Please fill out the questionnaire provided.

6. Conjoint Analysis

7. Decision Options as Attribute Bundles  Each option has multiple attributes Processor Speed, RAM, Screen Size, Price  Decision is a function of what is more important.  Problem? What is not important?

8. Assessing Preferences  Stated Preference What is important to you?  Independent importance scores.  Relative importance scores.  Revealed Preference Forced tradeoffs. More realistic.

9. Example When making the decision to buy a laptop computer, how important on a scale from 5 (very important) to 1 (not so important) is: Price__ Processor Speed__ Screen Size__ RAM__ Drives__ Now, please rate each attribute offering on a scale from 5 (acceptable) to 1(not acceptable) Price:$1800__ $1200__ $1000__ Screen Size:17” __ 15” __ 14.1” __

10. Example Instead… Consider the following 3 models. Please rank these from 3 (Most Preferred) to 1 (Least Preferred): 1. $1800, 17”, 3GHz, 256MB, DVD/CD_____ 2. $1200, 15”, 2.8GHz, 512MB, DVD-RW_____ 3. $1500, 15”, 3 GHz, 512 MB, DVD-RW_____ ….

11. Managerial Questions Focus on 2.8 GHz or 3.0 GHz? What drives customer preferences? What if we increased screen size but reduced screen resolution? How do customers trade-off attributes? What would be the market-share? What if we offered a DVD-RW for $120 more? What if we removed “free shipping” and offered to upgrade the RAM?

12. Conjoint Analysis  Conjoint Analysis  Conjoint Analysis is a versatile marketing technique that can provide valuable information, enables us to answer all the questions that were listed earlier.  Conjoint Analysis  Conjoint Analysis is popular because it is a less expensive and more flexible method than concept testing. Superior diagnosticity Parallels real-world decisions

13. Uses of Conjoint  Concept Optimization.  Quantifying impact of change in product design.  Volume forecasting: for categories that can be described fully by components.  Measuring Brand Equity.  Quantifying price sensitivity.  Estimating interactions in “menu” choices with a survey.  Quantifying lifetime value of a customer.

14. A brief overview Input: Rankings/ratings of attribute bundles Output: relative importance of attributes. “what-if” simulations of hypothetical attribute bundles. estimates of market share, volume, and attribute sensitivity. Process part-worths, utilities

15. Assumptions in Conjoint  Product is a bundle of attributes  Attributes are “describable”  Customers are able to rate/rank  Rating/ranking is an indicator of underlying utility

16. How Conjoint Works  Assume CPU and screen size are two attributes of consequence in a notebook computer.  Assume three CPUs: 2.8 GHz 3.0 GHz 3.4 GHz  Assume two screen sizes: 14.1” 15”

17. Rank Ordering Combinations Screen Size CPU14.1”15” 2.8 GHz64 3 GHz32 3.4 GHz51

18. Generating Utilities Screen Size CPU14.1”15”Average 2.8 GHz021 3.0 GHz343.5 3.4 GHz153 Average1.333.66

19. Determining Relevant Attributes  Physical Attributes  Performance Benefit  Psychological positioning

20. Stimulus Representation  Full-profile all relevant attributes are presented jointly for each product  more realistic from product presentation point of view  less realistic and more complex from consumer decision point of view  Partial profile subset of attributes subset varies over the exercise until stable utilities are estimated

21. Response Type  Paired comparison Choose one profile over the other  3.4 GHz CPU with 14.1” screen vs.  3.0 GHz CPU with 15” screen  Complexity increases with number of attributes  Ranking Rank the set of attribute bundles in order of preference.  Can be very complicated if number of attribute bundles increase.

22. Response Criterion  Preference  useful for market share predictions  Purchase likelihood  useful for market size estimation

23. Analyzing Output  Aggregate analysis  Homogeniety of sample  Importance of each level of attribute  Importance of each attribute based on range of importance scores for the various levels Caveat, misspecification of attribute level can artificially inflate attribute importance.  Segmentation analysis  Scenario simulations  First or maximum choice rule  Share of preference rule

24. Overview of the Conjoint Process  Develop a list of attributes to describe the product.  Identify an experimental design to select product profiles.  Develop selected product profiles into stimuli and collect respondents’ evaluations (ratings, rankings, choices).  Decompose these evaluations into part worths or utilities for each attribute level.  Report marginal utility curves or aggregate attribute importance data.  Run simulations (using utilities) to estimate share for benchmark product or other products of interest.  Segmentation analysis based on the utilities.

25. Data Analysis: Simulations  Simulations attempt to predict choices based on utilities.  Specify a competitive scenario of brands available and describe them in terms of attributes.  For every respondent, calculate the total utility of competing brands.  Select a choice rule to apply these utilities (usually the maximum choice rule).  Count the choices to estimate how many respondents would select each brand.

26. Data Analysis: Simulation Rules  All conjoint simulation rules accept the rating scale you use as a direct measure of utility.  A number of choice rules are available and the maximum utility choice rule has the best track record.  Maximum utility choice rule: consumer chooses with certainty the option offering the highest total utility.  Probabilistic choice rules: respondents have a non-zero probability of choice for all brands available, related to the magnitude of utility each offers.  Simplest probabilistic choice rule is the attraction type rule: Prob Profile X = Utility Profile X Σ Utilities All Profiles in the Scenario

30. Conjoint Caveats  Products as attribute bundles  Researcher preselects important attributes  Ratings are meaningful  Attributes are actionable