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Conjoint Analysis

Conjoint Analysis. Session: March 22-26; 2010. 1. Objectives/Purpose. An extremely powerful and useful analysis tool Used to determine the relative importance of various attributes to respondents, based on their making trade-off judgments Useful in

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Conjoint Analysis

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  1. Conjoint Analysis Session: March 22-26; 2010

  2. 1. Objectives/Purpose • An extremely powerful and useful analysis tool • Used to determine the relative importance of various attributes to respondents, based on their making trade-off judgments Useful in • Helping to select features on a new product/service • Predicting sales • Understanding decision processes/consumer judgments Aaker, Kumar, Day

  3. 1. Objectives (ctd) • E.g. • UvT: What drives students’ choice of (and willingness to pay for) a room? • How can Albert Heijn compose its assortment of cereals to improve customer appeal? • Nike: What are the optimal features for a new type of sneakers? Aaker, Kumar, Day

  4. 2. Steps • Design • Assumptions • Model estimation and fit • Interpreting results • Validation Aaker, Kumar, Day

  5. 2.1. Design • Method: • Select attributes (number, type) • Choose model form (additive? dependent variable?) • Individual or aggregate estimation? • Traditional, Choice-based or Adaptive conjoint? Aaker, Kumar, Day

  6. 2.1. Design • Stimuli: Factor (= Attribute) selection • Criteria: • Differentiate • Able to communicate • Actionable • Price  Could enter as separate attribute, mind correlations or infeasible stimuli • Levels: • Strive for Balance • Range: Feasible, Relevant, Stretch Aaker, Kumar, Day

  7. 2.1. Design • Stimuli: Utility specification • Part worth, Ideal Point or Linear model? • Main effects or interactions? Aaker, Kumar, Day

  8. Alternative Models Aaker, Kumar, Day

  9. 2.1. Design • Data collection: • Presentation: • Trade-off • Full profile (Fractional factorial)? • Preference Measure: • Ranking • Rating • Choice (no-) • Task per respondent (Regular, Adaptive, Hybrid?) Aaker, Kumar, Day

  10. Example: Sneakers 3 attributes, 3 levels each: • Sole: Rubber, Polyurethane, Plastic • Upper: Leather, Canvas, Nylon • Price: 30$, 60$, 90$ Fractional Factorial: 9 out of 27 profiles (3 sole x 3 upper x 3 price) evaluated 3 2 1 1 2 3 1 2 3 Aaker, Kumar, Day

  11. Example: Profiles for Sneakers (attribute level) Aaker, Kumar, Day

  12. 2.2. Assumptions • Few statistical assumptions • Theory-driven design, estimation and interpretation • Overfitting? • GIGO (Garbage in Garbage out)? Aaker, Kumar, Day

  13. 2.3. Model Estimation and Fit • E.g. Additive Model, part-worths: • where U(X)=utility of alternative X, m=# attributes, ki=#attribute levels of attribute i, xij=1 for level j of i, 0 elsewhere, ij=part worth for level j of i Bv (Usneakers2)= 11 + 22 + 32

  14. 2.3. Model Estimation and Fit (ctd) • Purpose: Find levels of ij that reflect consumers’ stimuli evaluations as closely as possible • Method: • Ranking: MONANOVA, Linmap • Rating: Dummy-variable regression • Choice: MNL or Probit model • Fit: • Correlate actual/predicted ranks • Hit rate • R2 Aaker, Kumar, Day

  15. Example: Profiles for Sneakers (attribute level) Aaker, Kumar, Day

  16. 2.3. Model Estimation and Fit (ctd) • Example Sneakers: Preference ratings and Variable IndicatorCoding (last level = Base) : Preference Rating Sneaker Rubber Poly Leather Canvas 60$ 30$ 1 2 3 4 5 6 7 8 9 Sole Upper Price Aaker, Kumar, Day

  17. 2.3. Model Estimation and Fit (ctd) Aaker, Kumar, Day

  18. 2.4. Interpreting results • Assess part-worths for attribute levels • Evaluate attribute importance • Use choice simulator Aaker, Kumar, Day

  19. Assess part-worths for attribute levels • Example: Indicator Coding, Attribute=Sole • b11= coëfficiënt Sole1=1 b12= coëfficiënt Sole2=-.333 b13=0 Average: (1-.333+0)/3=.222 • Calculate part worths such that sum = 0? -> 11= b11-Average=1-.222=. 778 12= b12-Average=-.333-.222-.556 13= b13-Average=-.222 Aaker, Kumar, Day

  20. Example Sneakers: Outcome Part worth calculations • Sole: 11=.778, 12= -.556, 13= -.222 • Upper: 21=.445, 22= .111, 23= -.556 • Price: 31=1.111, 32= .111, 33=-1.222 Aaker, Kumar, Day

  21. Part Worths Sneakers Aaker, Kumar, Day

  22. Evaluate attribute importance where i=attribute, j= attribute level, m= number of attributes, Ii = range of part worths for attribute, Wi= attribute importance (share) Aaker, Kumar, Day

  23. Attribute importance Example Sneakers: • Sole: .286 • Upper: .214 • Price: .5 100 € 60 € Aaker, Kumar, Day

  24. Calculating Attribute importance Aaker, Kumar, Day

  25. 2.5. Validation • On holdout sample? • Clusters of respondents • Alternative Models? • Significance (overfitting)? Aaker, Kumar, Day

  26. 3. Case: Channel and Price Offers for Safety Products

  27. Problem Statement • A company specialized in safety-related products, intends to improve its channel- and pricing approach for different types of products. • Preferred combination, by consumers, of information channel, selling channel, and price level? Aaker, Kumar, Day

  28. Problem Statement (ctd) • Consumers can obtain information, and/or purchase products, • through the internet (company’s website) • from a safety consultant /advisor (in home) • in B&M stores • Prices can deviate from a ‘recommended price’ Aaker, Kumar, Day

  29. Research Setup • Use conjoint analysis to assess consumer preference for alternative channel/price combinations • Conduct analysis for three types of products: Bicycle Lock Fire Blanket Alarm system Aaker, Kumar, Day

  30. Design: Stimuli • Attributes • Utility: Part worths, additive Aaker, Kumar, Day

  31. Design: Data Collection Traditional Method: • Full Profile approach • 27 possible combinations: fractional, orthogonal design -> 9 profiles/product/respondent • Preference measure: rating • Respondent task: regular, 2 products Aaker, Kumar, Day

  32. Data Collection (ctd) • Info products/recommended prices • (e.g. fire blanket 46.05Euros, Alarm system 315.70Euros, ) • Info channels: • B&M store (where, what, chain) • Internet (site, what) • Advisors: where, education/expertise Aaker, Kumar, Day

  33. Scenario (Stimulus) 1 • Imagine • You use the internet to gather information on the fire blanket • You purchase the fire blanket in the store • The recommended price is 46.05Euros • In the store, you pay this recommended price –10% • How do you rate this scenario? …./100 Aaker, Kumar, Day

  34. Model and Variable Coding • Dataset: see File Caseconj.sav • Cases= respondents*profiles • Dummy variable regression per product and across respondents, • dependent = rating • Independent = 6 dummy variables (TI, TA, II, IA, PR, PL): reference scenario =transaction and info in B&M, higher price. Aaker, Kumar, Day

  35. Estimation Results • See output file Caseconj.spo Aaker, Kumar, Day

  36. Interpretation • Part Worths and Attribute importance • E.g. Fire Blanket: • Information channel no significant impact • Transaction channel (.365): • Internet -7.78, Advisor -.1, Store 7.88 • Price (.635) • Low 15.22, Medium –2.83, High –12.38 Aaker, Kumar, Day

  37. Validation • Estimation Sample: Correlation between true and predicted scores? (Fire Blankets: .435) • Holdout sample: • Re-estimate and compare coefficients? • Correlate true and predicted scores in holdout Aaker, Kumar, Day

  38. Outcome • Attribute importance? • E.g. Bicycle Lock: First price (27.6%), then transaction channel (15.7%), info channel not important (1.5%) • Most appealing offer customer: • E.g. Bicycle Lock: Store, Low price. Utility: 7.88 +15.23 =23.11 • Trade off: e.g. Bicycle Lock • Store, medium price: 7.88-2.83=5.05 • Internet, low price: -7.78+15.22=7.44 • Prefer latter option! Aaker, Kumar, Day

  39. Outcome (ctd) • Customer heterogeneity? • E.g. Male vs female • Individual analysis? • Product differences in attribute significance, importance, part worths! • E.g. Best info channel depends on product: Bicycle Lock: store, Alarm system: advisor Aaker, Kumar, Day

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