Conjoint Analysis. Intro to Conjoint Analysis. Professor Raghu Iyengar

Transcription

1 Conjoint Analysis Intro to Conjoint Analysis Professor Raghu Iyengar

2 New Products Conjoint Analysis: Inferring attribute importance

3 A Big Success Story

4 A Big Success Story

5 A Big Success Story Conjoint in Action!

6 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.

7 Collect Data From Travelers Key features Building size Landscaping / Pool Food Room Size Room Quality Service standards Leisure Security

8 A Snapshot of Findings Building Shape Highest point in each graph - Most liked level L-shaped Coutyard Pool Design Landscaping None Rectangular Freeform Indoor/Outdoor 0 Minimal Moderate Elaborate

9 Courtyard Marriott

10 Outline The basics of conjoint analysis

11 Outline The basics of conjoint analysis Managerial uses of conjoint analysis

12 Outline The basics of conjoint analysis Managerial uses of conjoint analysis Examples

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

14 Why Conjoint 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

15 Conjoint Analysis Attributes, Part-worths and Utilities Professor Raghu Iyengar

16 Laptop Conjoint Questionnaire 16 profiles

17 Attributes Conjoint analysis represents products or services as bundles of attributes

18 Attributes Conjoint analysis represents products or services as bundles of attributes An attribute may be any clearly defined feature or characteristic

19 Attributes Conjoint analysis represents products or services as bundles of attributes An attribute may be any clearly defined feature or characteristic Examples Price Brand Hard Drive

20 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

21 Getting the Attributes Right is Very Important! What do you think will happen if you are missing a crucially important attribute?

22 Getting the Attributes Right is Very Important! What do you think will happen if you are missing a crucially important attribute? Be very skeptical about results

23 Getting the Attributes Right is Very Important! What do you think will happen if you are missing a crucially important attribute? Be very skeptical about results Best practice:

24 Getting the Attributes Right is Very Important! What do you think will happen if you are missing a crucially important attribute? Be very skeptical about results Best practice: Pilot studies to determine attributes to include

25 Getting the Attributes Right is Very Important! What do you think will happen if you are missing a crucially important attribute? Be very skeptical about results Best practice: Pilot studies to determine attributes to include Open ended surveys, ratings, ranking

26 Getting the Attributes Right is Very Important! What do you think will happen if you are missing a crucially important attribute? Be very skeptical about results Best practice: Pilot studies to determine attributes to include Open ended surveys, ratings, ranking Use empirical range in product category to determine range of attributes

27 Getting the Attributes Right is Very Important! What do you think will happen if you are missing a crucially important attribute? Be very skeptical about results Best practice: Pilot studies to determine attributes to include Open ended surveys, ratings, ranking Use empirical range in product category to determine range of attributes More levels depending on how sensitive managerial decision making is going to be

28 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.

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

30 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. 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

31 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

32 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.

33 Conjoint Analysis Forms of Conjoint Professor Raghu Iyengar

34 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.

35 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.

36 Laptop Conjoint Questionnaire 16 profiles

37 Conjoint Analysis Conjoint Analysis One Person Professor Raghu Iyengar

38 Laptops Ratings Five different criteria Brand RAM Hard Drive Attributes 5 attributes Speed Price

39 Laptops Brand Acer Lenovo Attribute Levels 3 levels Dell Speed 2.5GHz Attribute Levels 2 levels 3.1GHz

40 Data From One Person Laptop Preference How attractive is each laptop

41 Regression Output for One Person s Data SUMMARY OUTPUT Regression Statistics Multiple R 0.99 R Square 0.98 Adjusted R Square 0.96 Standard Error 5.24 Observations 16 ANOVA df SS MS F ignificance F Regression E-05 Residual Total Coefficients tandard Erro t Stat P-value Lower 95% Upper 95% Intercept Lenovo Dell Memory 6GB Memory 8GB Hard Drive 1 TB Speed - 3.1GHz Price -$ Price -$

42 Regression Output for One Person s Data SUMMARY OUTPUT Regression Statistics Multiple R 0.99 R Square 0.98 Adjusted R Square 0.96 Standard Error 5.24 Observations 16 ANOVA df SS MS F ignificance F Regression E-05 Residual Total Coefficients tandard Erro t Stat P-value Lower 95% Upper 95% Intercept Lenovo Dell Memory 6GB Memory 8GB Hard Drive 1 TB Speed - 3.1GHz Price -$ Price -$

43 Regression Output for One Person s Data SUMMARY OUTPUT Regression Statistics Multiple R 0.99 R Square 0.98 Adjusted R Square 0.96 Standard Error 5.24 Observations 16 ANOVA df SS MS F ignificance F Regression E-05 Residual Total Coefficients tandard Erro t Stat P-value Lower 95% Upper 95% Intercept Lenovo Dell Memory 6GB Memory 8GB Hard Drive 1 TB Speed - 3.1GHz Price -$ Price -$

44 Conjoint Equation for One Person Rating = *Lenovo * Dell *RAM_6 GB *RAM_8 GB *HDrive_ 1TB *Speed_3.1GHz *Price_ *Price_1000 Conjoint Analysis helps determine how much consumers weight different attributes

45 Conjoint Analysis Across Attribute Comparison Professor Raghu Iyengar

46 Relative Attribute Importance for One Person Partworth Range Percentage Brand Acer 0.00 Lenovo % Dell Memory 4GB GB % 8GB Hard Drive 500GB % 1TB Speed 2.5GHz % 3.1GHz Price $ $ % $1, Sum of range %

47 Conjoint Analysis Part-Worth Plots and Willingness to Pay Professor Raghu Iyengar

48 Part-worth Plots

49 Part-worth Plots

50 Willingness to Pay for One Person $600 $800 : points

51 Willingness to Pay for One Person $600 $800 : points 1 point = $12

52 Willingness to Pay for One Person $600 $800 : points 1 point = $12 4 GB 6GB = 30 points

53 Willingness to Pay for One Person $600 $800 : points 1 point = $12 4 GB 6GB = 30 points $ value = 30*12 = $360

54 Importance of Attributes Memory is most important attribute

55 Three New Laptops Laptop A Brand Lenovo Ram 6GB Hard drive 500GB Speed 3.1GHz Price - $800 Laptop B Brand Acer Ram 8 GB Hard drive 1TB Speed 3.1GHz Price - $1000 Laptop C Brand Dell Ram 8GB Hard drive 1TB Speed 3.1GHz Price - $1000 Which one will be chosen?

56 Three New Laptops Choice Prediction Partworth Profile 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 Brand Acer Lenovo Dell Memory 4GB GB GB Hard Drive 500GB TB Speed 2.4GHz GHz Price $ $ $1, Total Part-Worth

57 Three New Laptops Choice Prediction Partworth Profile 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 Brand Acer Lenovo Dell Memory 4GB GB GB Hard Drive 500GB TB Choice - B Speed 2.4GHz GHz Price $ $ $1, Total Part-Worth

58 Three New Laptops Choice Prediction Partworth Profile 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 Brand Acer Lenovo Dell Memory 4GB GB GB Hard Drive 500GB TB Choice - B Speed 2.4GHz GHz Price $ $ $1, Total Part-Worth Add up partworths for overall utility of a product

59 Summary Your preferences are based on trade-offs between attributes

60 Summary Your preferences are based on trade-offs between attributes You are not considering one attribute at a time to evaluate your options. Instead you are considering all attributes jointly. Hence, conjoint analysis

61 Summary Your preferences are based on trade-offs between attributes 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.

62 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.

63 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. Account for customer heterogeneity Predict market shares accommodating heterogeneity

64 Conjoint Analysis Customer Heterogeneity Professor Raghu Iyengar

65 What can you do with the results? Account for customer heterogeneity Predict market shares accommodating heterogeneity

66 Market 20 individuals 20 individuals answered the survey The data was put into regression Partworths for each customer was collated

67 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

68 Data Laptop Preference - Customer 1 Preference Customer 2 Preference Customer How attractive is each laptop

69 Partworths 20 Individuals Customer Intercept Lenovo Dell Memory 6GB Memory 8GB Hard Drive 1 TB Speed - 3.1GHz Price - $800 Price - $

70 Partworths 20 Individuals Customer Intercept Lenovo Dell Memory 6GB Memory 8GB Hard Drive 1 TB Speed - 3.1GHz Price - $800 Price - $

71 Conjoint Analysis Relative Importance Professor Raghu Iyengar

72 % Relative Importance Customer Brand Memory Hard Drive Speed Price Average:

73 How Do Respondents Differ Market Average

74 How Do Respondents Differ Market Average Price most important

75 How Do Respondents Differ Market Average Price most important Brand most important

76 How Do Respondents Differ Market Average Price most important Speed most important Brand most important

77 Recall: Willingness to Pay (for one person) $600 $800 : points 1 point = $12

78 Recall: Willingness to Pay (for one person) $600 $800 : points 1 point = $12 4 G 6GB = 30 points $ value = 30*12 = $360

79 Willingness to Pay Distribution

80 Demand Curve for feature pricing

81 Conjoint Analysis Segmentation Professor Raghu Iyengar

82 Obtaining Benefit Segments 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

83 Segmentation 2 segments

84 Segmentation 2 segments

85 Segmentation 2 segments

86 Segmentation 2 segments Segment 1 : 20% of market Segment 2 : 80% of market

87 Segmentation Reach Segment 1 Age Gender Income Activities Demographics and Psychographics Segment 2 Age Gender Income Activities

88 Conjoint Analysis Moving from One Person to the Entire Market Professor Raghu Iyengar

89 Application: Modeling the Market 1. Profile competing offerings. Determine the attribute levels for each competitor's product or service. 2. Profile your offering. Determine the attribute levels for your 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.

90 Application: Modeling the Market 1. Profile competing offerings. Determine the attribute levels for each competitor's product or service. 2. Profile your offering. Determine the attribute levels for your 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.

91 Application: Modeling the Market 1. Profile competing offerings. Determine the attribute levels for each competitor's product or service. 2. Profile your offering. Determine the attribute levels for your 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.

92 Application: Modeling the Market 1. Profile competing offerings. Determine the attribute levels for each competitor's product or service. 2. Profile your offering. Determine the attribute levels for your 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.

93 Application: Modeling the Market 1. Profile competing offerings. Determine the attribute levels for each competitor's product or service. 2. Profile your offering. Determine the attribute levels for your 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.

94 Three New Laptops- Choice Predictions Laptop A Brand Lenovo Ram 6GB Hard drive 500GB Speed 3.1GHz Price - $800 Laptop B Brand Acer Ram 8 GB Hard drive 1TB Speed 3.1GHz Price - $1000 Laptop C Brand Dell Ram 8GB Hard drive 1TB Speed 3.1GHz Price - $1000 Which one will be chosen?

95 Market Shares Us Versus Them

96 Market Shares Us Versus Them Customer Intercept Lenovo Dell Memory 6GB Memory 8GB Hard Drive 1 TB Speed - 3.1GHz Price - $800 Price - $1000 LaptopA Laptop B Laptop C

97 Market Shares Customer Intercept Lenovo Dell Memory 6GB Memory 8GB Hard Drive 1 TB Speed - 3.1GHz A: 0.33 B: 0.34 C: 0.33 Price - $800 Price - $1000 LaptopA Laptop B Laptop C Share- LaptopA Share- LaptopB Share- Laptop C

98 Summary 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

99 Summary 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

100 Summary 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

101 Conjoint Analysis Summary Professor Raghu Iyengar

102 Summary 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

103 Summary 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

104 Summary 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

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