webinar - a beginners guide to choice-based conjoint analysis

A Beginner’s Guide toChoice-based Conjoint

AnalysisPaul Richard McCullough & Ray Poynter

Agenda

1. Introduction2. What is Choice-based Conjoint

Analysis?3. Key steps in Conducting Choice-based

studies4. Presenting the results5. Critical elements for success6. Q & A 2

Ray PoynterMD The Future

Place

Paul Richard McCullough President, MACRO Consulting

3

Simple Example – Flying

Return flight journey – 2 to 3 hours

3 Simple AttributesAirline: Shabby, OK, ClassyCost: $75, $150, $225Route: Direct, 1 change, 2

changes 4

Task 1

Classypt OK Shabby$225 $75 $150Two

changes Direct One change☐ ☐ ☐

5

Task 1

Classy OK Shabby$225 $75 $150Two

changes Direct One change☐ ☐

PickedShabby 0OK 1Classy 0$75 1$150 0$225 0Direct 1One change 0Two changes 0

Task 2

Classy OK Shabby$75 $150 $225

Direct One changeTwo

changes ☐ ☐


Task 3


One Change DirectTwo

Changes ☐ ☐


Task 3


One Change DirectTwo

Changes ☐ ☐

Picked % of 3Shabby 0 0%OK 1 33%Classy 2 67%$75 2 67%$150 1 33%$225 0 0%Direct 2 67%One change 1 33%Two changes 0 0%

Utilities After 11 Tasks

Picked OfferedTransfor

mImportan

ceShabby 2 18%OK 4 36%Classy 5 45%$75 6 55%$150 4 36%$225 1 9%Direct 7 64%One change 3 27%Two changes 1 9%



mImportan

ceShabby 2 18% 0OK 4 36% 33Classy 5 45% 50$75 6 55% 83$150 4 36% 50$225 1 9% 0Direct 7 64% 100One change 3 27% 33Two changes 1 9% 0



m

RelativeImportan

ceShabby 2 18% 17

21%OK 4 36% 50Classy 5 45% 67$75 6 55% 83

36%$150 4 36% 50$225 1 9% 0Direct 7 64% 100

43%One change 3 27% 33Two changes 1 9% 0

Basic Concepts And Vocabulary

Attributes are variables that define the productLevels are variable values Attribute Levels

Brand CokePepsi

7upPrice 50¢

$1.00 $1.50

Twist Off Cap IncludedNot Included

13

Alternatives and Tasks

Combinations of attribute levels define alternatives:

Alternative #1: Pepsi @ $1.00 with a twist off cap

A choice task is a set of alternatives14

Choice-based Conjoint Task

15

Utility Weights

Utility weights are a measure of how important an attribute level is to the choice decision

Utility weights are analogous to beta weights, aka, regression coefficients, in a regression model

16

Partworths and VectorsPartworth attributes are categorical, e.g., brand,

color, package designEach level within a partworth attribute has a

utility weightVector attributes are interval or ratio, e.g., price,

CPU speed, horsepowerVector attributes receive one utility weight for

the entire attribute (all levels)17

Partworths and Vectors

18

Attribute Relative Importance

Attribute Relative Importance is the difference between high and low attribute level utility weights, ie, gap or delta, percentaged against all other attribute deltas

It’s used almost universallyAnd it’s often misleading

19

Attribute Relative Importance

Attribute Levels Util Delta ImportanceBrand Coke 6 5 50%

Pepsi 27up 1

Price 50¢ 7 4 40%$1 5

$1.5 3Twist Off Cap Included 0.5 1 10%

Not Included -0.5

20

What Happens When I Change Price?

Attribute Levels Util Delta ImportanceBrand Coke 6 5 63%

Pepsi 27up 1

Price 75¢ 6 2 25%$1 5

$1.25 4Twist Off Cap Included 0.5 1 13%

Not Included -0.5

21

When To Use Conjoint?❏The value can be approximated from the sum of the parts White goods, computers, financial products, pack & size combinations for CPG, X Soft drinks, clothing, TV shows, political programs, life partners

❏Attributes do not interact Credit card features, mobile phone packages, car rental options, ski holiday

elementsX Fragrances, speeches, meal ingredients, newspaper front page designs, advertising

messages

❏Attributes can be described in discrete and unambiguous terms

Apple iOS or Android, 2 or 4 door, $100 or $200 or $300, glass or plastic bottleX Friendly, Clean, Quick, Sexy, Sporty, Ambitious, Relaxed

22

Choice-based Conjoint – Key Steps

❏Context- What is the business issue we want to answer?❏Attributes and levels- How do we define the alternatives in

terms of attributes and levels?

❏Model specification- Which form of CBC is best?

❏Analysis- Which analytic approach is best?

23

Context

CBC is about choiceBut which choice?Purchase is most common choice measuredThere are others:• Subscribe to a magazine• Ask someone for a date• Rent a movie for yourself and your significant other• Order dinner• Consume electricity in your yacht• Plan a vacation for the familyBe clear to set the context for the choice at the beginning of the exercise

24

Case Study

Manufacturer of Safety GlassesHighly regulated environmentPurchasers are experienced and knowledgeable about

categoryChoice in this context is purchase of safety glasses for

company

25

Attributes and Levels

Brand (5 brands)Style (8 styles graphically displayed)Price (5 price levels)Origin:

Made in the USA Made in Asia

Included/Not included:1. Adjustable nose bridge2. Fully adjustable3. Ratcheting temples4. Multiple Sizes5. Soft temple Materials6. Low-Weight7. Flexible Fit (self adjust)8. Coating - Anti-scratch9. Coating - Anti-Fog10. Coating - Extreme Anti-Fog11. Coating - Anti Reflective12. Coating - Easy to Clean13. Coating - No coating

26

Choice-based Conjoint

a.k.a. Discrete Choice Models (DCM) Respondent chooses one or none from a set of

choices He/she sees several choice sets Based on a logit model, not linear regression

model Historically done at aggregate level

27

Discrete Choice ModelsPro’s

More realistic data collection (but not always) More accurate price and market share estimates

Con’s Limited number of attributes (unless more advanced form is

used) Less information per respondent Requires more knowledge to execute 28

Safety Glasses CBC Specifications17 attributes, 46 levels12 choice tasks4 alternatives per taskTraditional none option500 choice task set versionsSample size = 500

29

30

Choice Task

Other Design ConsiderationsLarge Number of Attributes: Full profile trade-off uses all attributes in each tested product

configuration Partial profile trade-off uses a subset of all attributes in each tested

product configuration Adaptive Methods generally attempt to eliminate or diminish

undesirable attributes prior to the conjoint tasks Hybrid Methods combine multiple exercises

31

Heterogeneity

What the heck is that?Half your sample loves CokeHalf your sample loves PepsiAn aggregate model will have a zero coefficient for brandDisaggregate models capture individual level differences,

ie, heterogeneity32

Estimating Disaggregate Choice Utilities

Ratings-based conjoint provides enough information to estimate individual level models for each respondent

Choice-based conjoint models typically do not

Two ways to estimate disaggregate CBC models: Latent Class Logistic Regression- Searches for homogeneous

subgroups Hierarchical Bayes Estimation- Estimates individual level

coefficients and draws from the total sample when needed33

Safety Glasses CBC Specifications

17 attributes, 46 levels12 choice tasks4 alternatives per taskTraditional none option500 choice task set versionsSample size = 500Full ProfileNull prohibitions (5-7)Hierarchical Bayes utility estimationLatent class choice utility estimation (for segmentation)

34

The Results

❏ Utilities❏ What-if

Models❏ Forecasting

35

Utilities


m

RelativeImportan

ceShabby 2 18% 0

21%OK 4 36% 33Classy 5 45% 50$75 6 55% 83

36%$150 4 36% 50$225 1 9% 0Direct 7 64% 100

43%One change 3 27% 33Two changes 1 9% 0 36

Utilities

A B CShabby 2 18% -0.82462OK 4 36% 0.17414Classy 5 45% 0.67506$75 6 55% 1.17188$150 4 36% 0.17078$225 1 9% -1.33086Direct 7 64% 1.67235One change 3 27% -0.32794Two changes 1 9% -1.33064

37

Utilities

R1 R2 R3 R4 R5 AVG TRANSShabby -0.73 -1.17 -1.11 -0.86 -0.88 -0.95 0OK -0.33 -0.18 -0.25 -0.45 -0.50 -0.34 24Classy 0.18 0.63 0.45 0.22 0.49 0.39 53$75 1.74 0.98 1.31 1.52 1.09 1.33 100$150 0.39 0.56 0.09 0.60 0.75 0.48 67$225 -1.41 -1.28 -0.95 -1.41 -1.16 -1.24 0Direct 1.03 1.25 1.47 1.09 1.19 1.21 93One change 0.16 0.45 0.29 0.31 0.45 0.33 59Two changes -1.04 -1.23 -1.29 -1.02 -1.42 -1.20 0

38

What-if Models

R1 R2 R3 R4 R5Shabby -0.73 -1.17 -1.11 -0.86 -0.88OK -0.33 -0.18 -0.25 -0.45 -0.50Classy 0.18 0.63 0.45 0.22 0.49$75 1.74 0.98 1.31 1.52 1.09$150 0.39 0.56 0.09 0.60 0.75$225 -1.41 -1.28 -0.95 -1.41 -1.16Direct 1.03 1.25 1.47 1.09 1.19One change 0.16 0.45 0.29 0.31 0.45Two changes -1.04 -1.23 -1.29 -1.02 -1.42

A - Shabby/$75/1chng 1.17B - OK/$150/Drct 1.10Buy A

Simple First Choice Model

39

What-if Models


A - Shabby/$75/1chng 1.17 0.26B - OK/$150/Drct 1.10 1.63Buy A B


40

What-if Models


A - Shabby/$75/1chng 1.17 0.26 0.49 0.97 0.65B - OK/$150/Drct 1.10 1.63 1.31 1.25 1.44Buy A B B B B


41

Interactive Model ExampleDiscrete Choice Demonstration Model

42

What-if Models

❏Add, delete, modify products to test

❏Demographic sub-groups, e.g. male/female, users/non-users

❏Iterative test to identify optimum strategies

❏Add factors such as cost, revenue and profit

43

Forecasting

❏Converting models to the real world

❏Converting preference to predictions Use benchmarks or models

❏Adding in external effects Distribution, advertising, marketing,

inertia, etc44

Conjoint Challenges

❏Number of Levels Effect

❏IIA

❏Interactions

45

Number of Levels EffectStudies show that increase the number of levels & attribute becomes more importantFor example if we have 2 attributesColour: Red and BluePrice $1 & $2Might find that Colour accounted for 40% and Price for 60%

But if the options wereColour: Red and BluePrice $1, $1.50 & $2Might find Colour accounting for 30% and Price for 70%

46

IIA Problems

IIA - Independence from Irrelevant Alternatives Problems

Red Bus, Blue Bus problem

Respondent gives 50% to taxi & 50% to Blue BusIn this model the color has no transport value

Add Red Bus to the modelSame profile as Blue BusShares: Taxi 33%, Red Bus 33%, Blue Bus 33%

47

Interactions

In Conjoint theory attributes should be independent, i.e. they all have a single value, ignoring the other attributes they a combined with

However some attributes interact: You might like Iced Coke, and Hot Tea, and Iced Tea, but not Hot Coke – an

interaction

Two solutions: 1. Combine the attributes Hot tea, Iced Tea, Cold Coke2. Use more complex designs and analysis 48

Main Forms of Conjoint ModelsThere are four main classes of trade-off models:

Choice-based Conjoint-• Discrete Choice• Menu-based Conjoint- Respondents “build” their own productRatings-based Conjoint- Respondents rate one product profile at a timeSelf-Explicated Scaling-• Each attribute level is rated separately• Not really conjoint but is used in the same way, similar resultsHybrid models-Two different techniques are bridged together, usually CBC or ratings-based conjoint and self-explicated• Adaptive conjoint, eg, ACA or ACBC, are hybrid models• Adaptive models usually attempt to eliminate or diminish attributes prior to

the conjoint exercise

49

Choice-based Conjoint Task

50

Menu-based Conjoint

51

Ratings-based Conjoint

52

Self-Explicated Ratings

1

Adds no value

whatsoever 2 3 4 5 6 7 8 9

10 Adds a great

deal of value

200 mhz CPU speed nmlkj nmlkji nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj USB Port nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkji nmlkj nmlkj nmlkj Printer Port nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkji nmlkj Ethernet Port nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkji nmlkj nmlkj nmlkj 30 day satisfaction guarantee nmlkj nmlkj nmlkj nmlkj nmlkji nmlkj nmlkj nmlkj nmlkj nmlkj 300 mhz CPU speed nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkji 24/7 live phone tech support nmlkj nmlkj nmlkji nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Wireless keyboard nmlkj nmlkji nmlkj nmlkj nmlkj nmlkj nmlkj

nmlkj nmlkj nmlkj

2. Below are potential Network Computer features. Please rate each feature on how much value they add to a Network Computer assuming a rating of 10 means the feature adds a great deal of value and a rating of 1 means the feature adds no value whatsoever to the Network Computer system.

53

Presenting The Results1. The conjoint study is not the project, the

business problem being tackled is the project

2. The conjoint data / model is not the answer, a business action or solution will be the answerClients don’t want to hear how hard you worked, or how clever

you are.They want answers to their problems, not a lesson on your interests

54

Workshops Are Often Best

55

General Advice For Quant Presenting

❏ Minimize the use of numbers

❏ Minimize the number of digits, 1, 2, or occasionally 3 significant digits

❏ Avoid or minimize decimal places and negative numbers

❏ Make differences as large as possible – regroup if necessary

❏ Order and structure the information (e.g. rank by importance or value)

❏ Show comparisons

56

Bad and Not So Bad Examples

21.25%

40.67%

38.08%

Attribute Importance

Standard Price Changes

Price41%

Changes38%

Standard21%

Attribute Importance

57

Bad Example – Questionnaire Order

OK Shabby Classy $225 $150 $75 Direct One change

Two changes

-0.34

-0.95

0.39

-1.24

0.48

1.33 1.21

0.33

-1.20

Utilities

Better

$225 $150 $75 Two changes

One change

Direct Shabby OK Classy

0

67

100

0

60

94

0

24

52

Utilities

Critical Elements for Success

1. Understanding the business problem2. Including all of the relevant attributes and levels – e.g. the 0

level3. Create a survey that respondents can complete

Not too long, not confusing, suitable for the devices being used4. Creating a fixed context for each interview (e.g. breakfast menu)5. Attributes that do not interact & which are unambiguous6. Build a model early, 10 to 20 interviews, to check you are on

track7. Try to use a workshop as the debrief, rather than a report or

presentation8. Focus on business problem, not the conjoint methodology/model

60

Ray PoynterMD The Future Place

Paul Richard McCullough President, MACRO Consulting

@questionpro

[email protected]

www.questionpro.com

Questions?

webinar - a beginners guide to choice-based conjoint analysis

Marketing