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Conjoint AnalysisIntro to Conjoint Analysis

Professor Raghu Iyengar

New Products

Conjoint Analysis:Inferring attribute importance

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A Big Success Story

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http://marriott.com/SATCA


Conjoint in Action!

A Big Success Story

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Joint Collaboration • Wharton and Marriott

• Two Marketing Professors: Paul Green and Jerry Wind

• Goal: Develop a new hotel chain for travelers who were not happy with current offerings.

• Marriott was running out of sites to put their typical hotels

• What type of hotel facilities and services should be offered.

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• Key features

• Building size

• Landscaping / Pool

• Food

• Room Size

• Room Quality

• Service standards

• Leisure

• Security

Collect Data From Travelers

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Pool Design

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None Rectangular Freeform Indoor/Outdoor

Building Shape

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0.2

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0.6

0.8

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1.2

1.4

L-shaped Coutyard

Landscaping

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Minimal Moderate Elaborate

Highest point in each graph- Most liked level

A Snapshot of Findings

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Courtyard Marriott

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Outline

• The basics of conjoint analysis

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Outline


• Managerial uses of conjoint analysis

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Outline


• Managerial uses of conjoint analysis

• Examples

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• Typical Goals• Predict the performance of new products• Help re-design or better position existing products• Better understand customer needs for product features

Why Conjoint

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• Typical Goals• Predict the performance of new products• Help re-design or better position existing products• Better understand customer needs for product features

• Conjoint Analysis can be used for (among other things)• New product development• Price elasticity of demand / Willingness to pay• Market segmentation

Why Conjoint

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Conjoint AnalysisAttributes, Part-worths and Utilities


Laptop Conjoint Questionnaire – 16 profiles

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Attributes• Conjoint analysis represents products or services as

bundles of attributes

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• An attribute may be any clearly defined feature or characteristic

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• An attribute may be any clearly defined feature or characteristic

• Examples

• Price

• Brand

• Hard Drive

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Attribute selection• Attributes in conjoint should be

• unambiguous• useful for determining choice or preference• actionable

• The total number of attributes should be kept low • 6 is the average

• Use qualitative research to decide on attributes/ levels• Conjoint is the end of the road, not the beginning

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• What do you think will happen if you are missing a crucially important attribute?

Getting the Attributes Right is Very Important!

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• Be very skeptical about results


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• Best practice:


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• Best practice:

• Pilot studies to determine attributes to include


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• Best practice:


• Open ended surveys, ratings, ranking


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• Best practice:



• Use empirical range in product category to determine range of attributes


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• Best practice:



• Use empirical range in product category to determine range of attributes

• More levels depending on how sensitive managerial decision making is going to be


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Part-worths• The utility for a specific level of a particular attribute is called a

part-worth

• e.g. how much is “more” memory worth to me.

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part-worth


• It designates how much that part of the product is worth to the consumer

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part-worth


• It designates how much that part of the product is worth to the consumer

• Part-worths are the building blocks of the entire conjoint analysis method

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Utilities• Regression is used to translate “preference” (which may takes

different forms) data into partworths.

• The basic idea is to relate the collected experimental data to the presence or absence of an attribute• Ex: If you always choose the low price product, price must

be “important to you”

• Multiple regression is routinely used for this step

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Forms of conjoint – Response Format• Ratings-Based Multiple Regression

• This is similar to the multiple regression we covered in a separate lecture

• Choice-Based Multinomial Regression

• This gives the same type of results but the type of regression used is different.

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Conjoint AnalysisForms of Conjoint


Forms of conjoint – Response Format

• Ratings-Based Multiple Regression

• This is similar to the multiple regression we covered in a separate lecture

• Choice-Based Multinomial Regression

• This gives the same type of results but the type of regression used is different.

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Laptop Conjoint Questionnaire – 16 profiles

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Conjoint AnalysisConjoint Analysis – One Person


• Ratings

• Five different criteria

• Brand

• RAM

• Hard Drive

• Speed

• Price

Attributes5 attributes

Laptops

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• Brand

• Acer

• Lenovo

• Dell

• Speed

• 2.5GHz

• 3.1GHz

Attribute Levels3 levels

Attribute Levels2 levels

Laptops

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How attractive is each laptop

Laptop Preference

1 202 443 854 625 86 997 718 599 1510 2611 5912 4913 5214 4315 4916 92

Data From One Person

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Regression Output for One Person’s DataSUMMARY OUTPUT

Regression StatisticsMultiple R 0.99R Square 0.98Adjusted R Square 0.96Standard Error 5.24Observations 16

ANOVAdf SS MS F ignificance F

Regression 8 10508.17 1313.521 47.76926 2.01E-05Residual 7 192.4804 27.4972Total 15 10700.65

Coefficients tandard Erro t Stat P-value Lower 95% Upper 95%Intercept 20.84 4.49 4.64 0.00 10.21 31.47Lenovo 3.98 3.21 1.24 0.26 -3.62 11.57Dell -13.03 3.71 -3.51 0.01 -21.80 -4.26Memory 6GB 30.93 3.41 9.08 0.00 22.87 38.98Memory 8GB 39.17 3.78 10.35 0.00 30.22 48.11Hard Drive 1 TB 12.64 3.03 4.18 0.00 5.49 19.80Speed - 3.1GHz 26.57 2.73 9.74 0.00 20.11 33.02Price -$800 -16.03 3.41 -4.71 0.00 -24.08 -7.97Price -$1000 -17.50 3.78 -4.62 0.00 -26.45 -8.55

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Conjoint Equation for One Person

Conjoint Analysis – helps determine how much consumers weight different attributes

Rating = 20.84 + 3.98*Lenovo -13.03* Dell + 30.93*RAM_6 GB + 39.17*RAM_8 GB + 12.64*HDrive_ 1TB

- 16.03*Price_800 - 17.5*Price_1000

+ 26.57*Speed_3.1GHz

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Conjoint AnalysisAcross Attribute Comparison


Relative Attribute Importance for One PersonPartworth Range Percentage

BrandAcer 0.00Lenovo 3.98 17.01 15.07%Dell -13.03

Memory4GB 0.006GB 30.93 39.17 34.70%8GB 39.17

Hard Drive500GB 0.00 12.64 11.20%1TB 12.64

Speed2.5GHz 0.00 26.57 23.54%3.1GHz 26.57

Price$600 0.00$800 -16.03 17.50 15.50%$1,000 -17.50

Sum of range 112.89 100.00%

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Conjoint AnalysisPart-Worth Plots and Willingness to Pay


Part-worth Plots

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Willingness to Pay for One Person• $600 $800 : 16.03 points

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• 1 point = $12

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• 1 point = $12

• 4 GB 6GB = 30 points

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• 1 point = $12

• 4 GB 6GB = 30 points

• $ value = 30*12 = $360

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Importance of Attributes

Memory is most important attribute

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Three New Laptops

Laptop A

Brand – Lenovo

Ram – 6GB

Hard drive – 500GB

Speed – 3.1GHz

Price - $800

Laptop BBrand – Acer

Ram – 8 GB

Hard drive – 1TB

Speed – 3.1GHz

Price - $1000

Which one will be chosen?

• Laptop C− Brand – Dell− Ram – 8GB− Hard drive – 1TB− Speed – 3.1GHz− Price - $1000

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Three New Laptops – Choice Prediction

PartworthProfile of Laptop A

Profile of Laptop B

Profile of LaptopC

Part-Worth of Laptop A

Part-Worth of Laptop B

Part-Worth of Laptop C

BrandAcer 0 1 0Lenovo 3.98 1 3.98Dell -13.03 1 -13.03

Memory4GB 0.006GB 30.93 1 30.938GB 39.17 1 1 39.17 39.17

Hard Drive500GB 0.00 1 0.001TB 12.64 1 1 12.64 12.64

Speed2.4GHz 0.003.1GHz 26.57 1 1 1 26.57 26.57 26.57

Price$600 0.00$800 -16.03 1 -16.03$1,000 -17.50 1 1 -17.50 -17.50

45.44 60.88 47.85Total Part-Worth

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Choice - B


Profile of Laptop B

Profile of LaptopC





Memory4GB 0.006GB 30.93 1 30.938GB 39.17 1 1 39.17 39.17

Hard Drive500GB 0.00 1 0.001TB 12.64 1 1 12.64 12.64

Speed2.4GHz 0.003.1GHz 26.57 1 1 1 26.57 26.57 26.57

Price$600 0.00$800 -16.03 1 -16.03$1,000 -17.50 1 1 -17.50 -17.50


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Choice - B


Profile of Laptop B

Profile of LaptopC





Memory4GB 0.006GB 30.93 1 30.938GB 39.17 1 1 39.17 39.17

Hard Drive500GB 0.00 1 0.001TB 12.64 1 1 12.64 12.64

Speed2.4GHz 0.003.1GHz 26.57 1 1 1 26.57 26.57 26.57

Price$600 0.00$800 -16.03 1 -16.03$1,000 -17.50 1 1 -17.50 -17.50


Add up partworths for overall utility of a product

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• Your preferences are based on trade-offs between attributes

Summary

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• You are not considering one attribute at a time to evaluate your options. Instead you are considering all attributes jointly. Hence,…conjoint analysis

Summary

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• You are not considering one attribute at a time to evaluate your options. Instead you are considering all attributes jointly. Hence,…conjoint analysis

• Overall preference for each option = the sum of the utility that you derive from each attribute (level) or how much that attribute (level) is worth to you.

Summary

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What can you do with the results?

• Measure “part-worth” utilities

• Measure relative importance of attributes

• Predict preferences for new options even when they have never been rated.

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• Measure “part-worth” utilities

• Measure relative importance of attributes

• Predict preferences for new options even when they have never been rated.

• Account for customer heterogeneity

• Predict market shares accommodating heterogeneity

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Conjoint AnalysisCustomer Heterogeneity



• Account for customer heterogeneity

• Predict market shares accommodating heterogeneity

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Market – 20 individuals

• 20 individuals answered the survey

• The data was put into regression

• Partworths for each customer was collated

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Market – 20 individuals

• 20 individuals answered the survey

• The data was put into regression

• Partworths for each customer was collated

• Differences across customers highlight how they may value different attributes

• Opportunity for segmentation on attribute importance

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How attractive is each laptop

Laptop Preference - Customer 1

Preference Customer 2 … Preference

Customer 20

1 20 35 252 44 60 553 85 70 614 62 35 305 8 25 406 99 80 557 71 45 758 59 65 609 15 25 4010 26 25 6011 59 42 3512 49 78 6213 52 35 3014 43 68 5515 49 58 3516 92 35 60

Data

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Customer Intercept Lenovo Dell Memory 6GB

Memory 8GB

Hard Drive 1

TB

Speed - 3.1GHz

Price -$800

Price -$1000

1 27.14 16.40 1.14 10.23 15.35 10.63 25.35 -15.25 -29.132 20.24 29.82 -3.60 15.10 19.15 14.55 31.14 -9.72 -12.283 15.19 5.42 -1.24 18.85 20.59 19.19 15.05 -10.82 -25.264 28.22 -1.00 25.00 12.41 28.12 6.84 30.30 -11.11 -16.495 25.07 15.20 4.57 14.63 33.99 10.92 24.91 -4.12 -19.136 27.29 7.83 2.50 20.69 32.03 6.23 25.42 -11.15 -12.487 12.17 14.63 -1.19 24.99 21.58 5.96 33.73 -10.14 -18.068 18.93 7.00 -1.32 21.44 32.55 7.43 21.40 -10.14 -19.319 11.57 18.89 -6.40 15.37 30.25 4.56 28.24 -11.97 -13.56

10 14.10 9.96 0.21 15.32 21.32 14.37 29.34 -10.95 -16.1211 10.96 25.00 2.21 5.39 28.33 14.75 35.00 -8.23 -17.1712 20.71 14.48 -9.85 15.54 19.95 19.27 22.95 -13.24 -14.3513 24.06 11.51 -3.51 11.60 24.80 5.42 31.47 -7.83 -14.3714 9.90 6.64 -2.54 22.38 23.94 25.56 22.30 -11.60 -10.6015 34.72 15.62 1.25 16.30 37.17 7.47 31.95 -11.64 -17.2316 20.84 3.98 -13.03 30.93 39.17 12.64 26.57 -16.03 -17.5017 11.32 17.75 -1.37 17.28 26.09 15.19 23.36 -10.52 -12.8118 21.35 -0.88 -2.57 9.20 15.34 20.85 15.33 -8.05 -13.8519 26.51 7.36 -4.56 16.04 19.30 7.15 23.74 -11.50 -13.3420 23.63 14.47 -3.51 28.69 25.27 15.07 19.91 -6.58 -15.09

Partworths – 20 Individuals

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Conjoint AnalysisRelative Importance


Average: 16 27 13 27 17

Customer Brand Memory Hard Drive Speed Price1 17 16 11 26 302 30 17 13 28 113 8 24 22 17 294 24 26 6 28 155 15 33 10 24 186 9 38 7 30 157 16 25 6 34 188 9 37 8 24 229 25 30 4 28 1310 11 23 16 32 1811 21 24 12 29 1412 24 20 19 23 1413 16 27 6 35 1614 10 26 28 24 1315 14 34 7 29 1616 15 35 11 24 1617 20 27 16 24 1318 4 23 31 23 2019 16 26 9 31 1820 19 30 16 21 16

% Relative Importance

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Market Average

How Do Respondents Differ

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Market Average Price most important


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Brand most important


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Brand most importantSpeed most important


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Recall: Willingness to Pay (for one person) • $600 $800 : 16.03 points

• 1 point = $12

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Recall: Willingness to Pay (for one person) • $600 $800 : 16.03 points

• 1 point = $12

• 4 G 6GB = 30 points

• $ value = 30*12 = $360

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Willingness to Pay Distribution

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Demand Curve for feature pricing

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Conjoint AnalysisSegmentation


• The importance weights for the attributes represent the “benefits” that each respondent is seeking from the product

• Benefit segments are groupings of customers making similar trade offs (e.g., willing to pay for speed)

• Cluster analysis can be used to form groups

• Each segment is composed of maximally similar customers while each segment is as distinct as possible from the others

Obtaining Benefit Segments

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Segmentation – 2 segments

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Segment 1 : 20% of market Segment 2 : 80% of market

Segmentation – 2 segments

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Segmentation – Reach

Segment 1AgeGender IncomeActivities

Demographicsand

Psychographics

Segment 2AgeGender IncomeActivities

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Conjoint AnalysisMoving from One Person to the Entire Market


1. Profile competing offerings. Determine the attribute levels for each competitor's product or service.

2. Profile your offering. Determine the attribute levels foryour proposed product or service.

3. Compute the utility of each product offering.4. Compute individual level shares. We will talk about two ways of doing this.5. Calculate aggregate market shares by summing over all respondents.

Application: Modeling the Market

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Three New Laptops- Choice Predictions

Laptop A

Brand – Lenovo

Ram – 6GB

Hard drive – 500GB

Speed – 3.1GHz

Price - $800

Laptop BBrand – Acer

Ram – 8 GB

Hard drive – 1TB

Speed – 3.1GHz

Price - $1000

Which one will be chosen?

Laptop CBrand – DellRam – 8GBHard drive – 1TBSpeed – 3.1GHzPrice - $1000

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Market Shares – Us Versus Them

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Memory 8GB

Hard Drive 1

TB

Speed - 3.1GHz

Price -$800

Price -$1000 LaptopA Laptop B Laptop C

1 27.14 16.40 1.14 10.23 15.35 10.63 25.35 -15.25 -29.13 63.9 49.3 50.52 20.24 29.82 -3.60 15.10 19.15 14.55 31.14 -9.72 -12.28 86.6 72.8 69.23 15.19 5.42 -1.24 18.85 20.59 19.19 15.05 -10.82 -25.26 43.7 44.8 43.54 28.22 -1.00 25.00 12.41 28.12 6.84 30.30 -11.11 -16.49 58.8 77.0 102.05 25.07 15.20 4.57 14.63 33.99 10.92 24.91 -4.12 -19.13 75.7 75.8 80.36 27.29 7.83 2.50 20.69 32.03 6.23 25.42 -11.15 -12.48 70.1 78.5 81.07 12.17 14.63 -1.19 24.99 21.58 5.96 33.73 -10.14 -18.06 75.4 55.4 54.28 18.93 7.00 -1.32 21.44 32.55 7.43 21.40 -10.14 -19.31 58.6 61.0 59.79 11.57 18.89 -6.40 15.37 30.25 4.56 28.24 -11.97 -13.56 62.1 61.1 54.710 14.10 9.96 0.21 15.32 21.32 14.37 29.34 -10.95 -16.12 57.8 63.0 63.211 10.96 25.00 2.21 5.39 28.33 14.75 35.00 -8.23 -17.17 68.1 71.9 74.112 20.71 14.48 -9.85 15.54 19.95 19.27 22.95 -13.24 -14.35 60.4 68.5 58.713 24.06 11.51 -3.51 11.60 24.80 5.42 31.47 -7.83 -14.37 70.8 71.4 67.914 9.90 6.64 -2.54 22.38 23.94 25.56 22.30 -11.60 -10.60 49.6 71.1 68.615 34.72 15.62 1.25 16.30 37.17 7.47 31.95 -11.64 -17.23 86.9 94.1 95.316 20.84 3.98 -13.03 30.93 39.17 12.64 26.57 -16.03 -17.50 66.3 81.7 68.717 11.32 17.75 -1.37 17.28 26.09 15.19 23.36 -10.52 -12.81 59.2 63.1 61.818 21.35 -0.88 -2.57 9.20 15.34 20.85 15.33 -8.05 -13.85 37.0 59.0 56.419 26.51 7.36 -4.56 16.04 19.30 7.15 23.74 -11.50 -13.34 62.2 63.4 58.820 23.63 14.47 -3.51 28.69 25.27 15.07 19.91 -6.58 -15.09 80.1 68.8 65.3

Market Shares – Us Versus Them

Marketing Analytics

A: 0.33 B: 0.34C: 0.33


Memory 8GB

Hard Drive 1

TB

Speed - 3.1GHz

Price -$800

Price -$1000 LaptopA Laptop B Laptop C Share-

LaptopAShare-

LaptopBShare-

Laptop C

1 27.14 16.40 1.14 10.23 15.35 10.63 25.35 -15.25 -29.13 63.9 49.3 50.5 0.39 0.30 0.312 20.24 29.82 -3.60 15.10 19.15 14.55 31.14 -9.72 -12.28 86.6 72.8 69.2 0.38 0.32 0.303 15.19 5.42 -1.24 18.85 20.59 19.19 15.05 -10.82 -25.26 43.7 44.8 43.5 0.33 0.34 0.334 28.22 -1.00 25.00 12.41 28.12 6.84 30.30 -11.11 -16.49 58.8 77.0 102.0 0.25 0.32 0.435 25.07 15.20 4.57 14.63 33.99 10.92 24.91 -4.12 -19.13 75.7 75.8 80.3 0.33 0.33 0.356 27.29 7.83 2.50 20.69 32.03 6.23 25.42 -11.15 -12.48 70.1 78.5 81.0 0.31 0.34 0.357 12.17 14.63 -1.19 24.99 21.58 5.96 33.73 -10.14 -18.06 75.4 55.4 54.2 0.41 0.30 0.298 18.93 7.00 -1.32 21.44 32.55 7.43 21.40 -10.14 -19.31 58.6 61.0 59.7 0.33 0.34 0.339 11.57 18.89 -6.40 15.37 30.25 4.56 28.24 -11.97 -13.56 62.1 61.1 54.7 0.35 0.34 0.3110 14.10 9.96 0.21 15.32 21.32 14.37 29.34 -10.95 -16.12 57.8 63.0 63.2 0.31 0.34 0.3411 10.96 25.00 2.21 5.39 28.33 14.75 35.00 -8.23 -17.17 68.1 71.9 74.1 0.32 0.34 0.3512 20.71 14.48 -9.85 15.54 19.95 19.27 22.95 -13.24 -14.35 60.4 68.5 58.7 0.32 0.37 0.3113 24.06 11.51 -3.51 11.60 24.80 5.42 31.47 -7.83 -14.37 70.8 71.4 67.9 0.34 0.34 0.3214 9.90 6.64 -2.54 22.38 23.94 25.56 22.30 -11.60 -10.60 49.6 71.1 68.6 0.26 0.38 0.3615 34.72 15.62 1.25 16.30 37.17 7.47 31.95 -11.64 -17.23 86.9 94.1 95.3 0.31 0.34 0.3416 20.84 3.98 -13.03 30.93 39.17 12.64 26.57 -16.03 -17.50 66.3 81.7 68.7 0.31 0.38 0.3217 11.32 17.75 -1.37 17.28 26.09 15.19 23.36 -10.52 -12.81 59.2 63.1 61.8 0.32 0.34 0.3418 21.35 -0.88 -2.57 9.20 15.34 20.85 15.33 -8.05 -13.85 37.0 59.0 56.4 0.24 0.39 0.3719 26.51 7.36 -4.56 16.04 19.30 7.15 23.74 -11.50 -13.34 62.2 63.4 58.8 0.34 0.34 0.3220 23.63 14.47 -3.51 28.69 25.27 15.07 19.91 -6.58 -15.09 80.1 68.8 65.3 0.37 0.32 0.30

Market Shares

Marketing Analytics

• Conjoint analysis is the tool for new product design.

• Segmentation on partworths can be highly managerially relevant.

• Validation is very important• Hold out validation• Predict actual market shares

• Incorporate awareness, distribution

Summary

Marketing Analytics

Conjoint AnalysisSummary


• Conjoint analysis is the tool for new product design.

• Segmentation on partworths can be highly managerially relevant.

• Validation is very important• Hold out validation• Predict actual market shares

• Incorporate awareness, distribution

Summary

Marketing Analytics

intro to conjoint analysis - · pdf filecollect data from travelers. ... • help...

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