Preferences and Decision-Making

Decision Making and Risk, Spring 2006: Session 7

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 consequencesRevenuesCostsLearningTurnoverMoraleComp. ResponseFuture Options

TechMarketFacilities

Integrating payoffs to determine overall utility

OutcomesKnown Outcomes

Unknown OutcomesOutcome probabilities

Distribution

Alternatives

Known OptionsUnknown OptionsDeferred Decision

Decision Problem

Discovering the right decision problem.

DecisionProblem

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?

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.

Stated Versus Revealed

30.0

9.9

14.0

16.4

11.1

17.1

38

4.7

28

4.7

19

4.7

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

Accuracy Frequency Invasiveness Time Commitment Pain and Discomfort Exposure to Radiation

Attribute

Nor

med

Rel

ativ

e Pr

efer

ence

Stated

Revealed

Conjoint Analysis

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?

Assessing Preferences

Stated Preference What is important to you?

Independent importance scores. Relative importance scores.

Revealed Preference Forced tradeoffs. More realistic.

ExampleWhen 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” __

ExampleInstead…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 _____

….

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?

Conjoint Analysis

Conjoint AnalysisConjoint Analysis is a versatile marketing technique that can provide valuable information, enables us to answer all the questions that were listed earlier.

Conjoint AnalysisConjoint Analysis is popular because it is a less expensive and more flexible method than concept testing.

Superior diagnosticity Parallels real-world decisions

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.

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

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

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”

Rank Ordering Combinations

Screen Size

CPU 14.1” 15”

2.8 GHz 6 4

3 GHz 3 2

3.4 GHz 5 1

Generating Utilities

Screen Size

CPU 14.1” 15” Average

2.8 GHz 0 2 1

3.0 GHz 3 4 3.5

3.4 GHz 1 5 3

Average 1.33 3.66

Determining Relevant Attributes

Physical Attributes

Performance Benefit

Psychological positioning

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

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.

Response Criterion

Preference useful for market share predictions

Purchase likelihood useful for market size estimation

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

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.

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.

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:

ProbProfile X = UtilityProfile X

ΣUtilitiesAll Profiles in the Scenario

Conjoint Caveats

Products as attribute bundles

Researcher preselects important attributes

Ratings are meaningful

Attributes are actionable