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Conjoint Analysis


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Conjoint Analysis-Learning with Pradeep Chintagunta

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Conjoint Analysis

  1. 1. Conjoint Analysis
  2. 2. What is Conjoint Analysis? <ul><li>CA is a multivariate technique used specifically to understand how respondents develop preferences for products or services. It is based on the simple premise that consumers evaluate the value or utility of a product / service / concept / idea (real or hypothetical) by combining the utility provided by each attribute characterizing the product / service / concept / idea </li></ul><ul><li>CA is a decompositional method. Respondents provide overall evaluations of products that are presented to them as combos of attributes. These evaluations are then used to infer the utilities of the individual attributes comprising the products. In many situations, this is preferable to asking respondents how important certain attributes are, or to rate how well a product performs on each of a number of attributes </li></ul>
  3. 3. Managerial uses of Conjoint Analysis <ul><li>After determining the contribution of each attribute to the consumer’s overall evaluation, one could </li></ul><ul><li>1. Define the object with the optimal combo of features </li></ul><ul><li>2. Predict market shares of different objects with different sets of features </li></ul><ul><li>3. Isolate groups of customers who place differing importances on different features </li></ul><ul><li>4. Identify marketing opportunities by exploring the market potential for feature combos not currently available </li></ul><ul><li>5. Show the relative contributions of each attribute and each level to the overall evaluation of the object </li></ul>
  4. 4. Commercial Applications <ul><li>Technique is widely used by consumer and industrial product companies, service companies, marketing research, advertising and consulting firms </li></ul><ul><li>Over 400 commercial applications per year even in the mid 80s </li></ul><ul><li>Types of applications include </li></ul><ul><ul><li>Consumer durables: automobiles, refrigerators, car stereos, condos, food processors, HDTV </li></ul></ul><ul><ul><li>Industrial products: copy machines, forklift trucks, computer software, aircraft </li></ul></ul><ul><ul><li>Consumer nondurables: bar soaps, hair shampoos, disposable diapers </li></ul></ul><ul><ul><li>Services: car rentals, credit cards, hotels, performance art series, rural health care systems, BART </li></ul></ul><ul><ul><li>Other: MBA job choice </li></ul></ul>
  5. 5. A Survey <ul><li>Familiarity & usage of value assessment methods </li></ul><ul><li>58 industrial firms in the top 125 of the Fortune 500 list </li></ul><ul><li>16 market research firms from the top 40 </li></ul>
  6. 6. Survey Results
  7. 7. P&G and Disposable Diapers <ul><li>P&G makes extensive use of CA to guide product modification </li></ul><ul><li>Question: What value do consumers associate with two improved features in disposable diapers: </li></ul><ul><ul><li>Improved absorbency </li></ul></ul><ul><ul><li>Elastic waistband </li></ul></ul><ul><li>Context: P&G had a patent on the elastic waistband, but a competitor imitated the modification. If the imitation was illegal, what damage should P&G claim? </li></ul><ul><li>Potential answers: </li></ul><ul><li>1. Use market data to estimate the effect of the elastic waistband on market share. Problem: Elastic waistband + Increased absorbency were introduced simultaneously </li></ul><ul><li>2. Use CA to separately estimate the effects </li></ul>
  8. 8. Steps in CA <ul><li>Identification of respondents </li></ul><ul><li>Identification and definition of attributes in customer language </li></ul><ul><li>Specification of attribute variation and levels </li></ul><ul><li>Creation of objects (experimental design) </li></ul><ul><li>Creation of instrument, including socioeconomic, demographic and usage questions </li></ul><ul><li>Sampling plan </li></ul><ul><li>Data collection </li></ul><ul><li>Data analysis: Typically, regression analysis separately by respondent </li></ul><ul><li>Market simulation: exploration of “what-if” questions </li></ul>
  9. 9. Preferences for Sports Cars <ul><li>You are provided 18 hypothetical sports cars each described on 5 features: </li></ul><ul><li>Point of origin : US, Japan, Europe </li></ul><ul><li>Convertibility : Sunroof, Removable top (Manual), Removable top (Automatic) </li></ul><ul><li>Styling : Coupe (2-door), Sedan (4-door) </li></ul><ul><li>ABS : No, Yes </li></ul><ul><li>Acceleration : 0 to 60 in 5.5 secs, 0 to 60 in 8.5 secs </li></ul><ul><li>Assume all 18 cars are roughly equivalent on attributes not mentioned above such as gas mileage, safety, price, etc. </li></ul>
  10. 10. Selecting the stimulus set of profiles <ul><li>In the above example, there are 72 possible profile combos or “cars”. Typically, not all combos of attribute-levels are required to estimate the conjoint model, i.e., fractional factorial designs may be adequate </li></ul><ul><li>How many profiles to include in design? </li></ul><ul><ul><li>Degrees of freedom to estimate individual level parameters </li></ul></ul><ul><ul><li>Data collection costs and respondent load </li></ul></ul><ul><li>Criteria for profile selection </li></ul><ul><ul><li>Look out for dominated profiles and unrealistic profiles </li></ul></ul><ul><ul><li>Most software do the appropriate selection </li></ul></ul>
  11. 11. Steps in the analysis <ul><li>Each of the 18 selected profiles is presented to respondent </li></ul><ul><li>Respondent indicates her/his preference for each of the profiles by: </li></ul><ul><ul><li>Rank ordering the profiles, or </li></ul></ul><ul><ul><li>Rating them on a 1-100 scale, or </li></ul></ul><ul><ul><li>Choosing the most preferred alternative </li></ul></ul><ul><li>Depending on the above, an ordinal regression ( LINMAP ), a regular regression or a logit model is fitted to the data </li></ul><ul><li>Dependent variable is the preference measure. Independent variables are dummy variables, i.e., presence / absence of each of the attribute-levels </li></ul><ul><li>Estimated coefficient are called part worths </li></ul>
  12. 13. Interpreting the Coefficients or PART WORTHS 18K 17K 16K Sun Manual Auto No ABS ABS PRICE CONVERTIBLE BRAKING UTILITIES UTILITIES UTILITIES 30 40 10 40 20
  13. 14. Simulating aggregate choices <ul><li>Objective is to forecast likely market shares of attribute combos which represent potential management actions, in a defined competitive scenario </li></ul>Translating Utilities into Choice Predictions First Choice Rule Highest utility profile chosen by each respondent Share of Preference Rule Predict choice probabilities using a model such as Logit Both methods ignore marketing variables such as advertising weight and distribution which are typically not in the conjoint design. Fix: “Adjust” the market shares using this additional information
  14. 15. Using CA for segmentation Two-Stage Approaches A priori Researcher selects specific attributes Post hoc Full set of attributes used Clustering (K-means) Relate clusters to background variables such as demographics using techniques like discriminant analysis One-Stage Approach Concomitant variable Latent Class Conjoint Simultaneous clustering and profiling using background characteristics
  15. 16. CA with large numbers of attributes <ul><li>Full profile models are unrealistic with a large number of attributes </li></ul><ul><li>Two alternatives </li></ul><ul><ul><li>Self-explicated models: Respondent provides </li></ul></ul><ul><ul><ul><li>a) Rating of desirability of each level of each attribute </li></ul></ul></ul><ul><ul><ul><li>b) Relative importance of each attribute </li></ul></ul></ul><ul><ul><ul><li>Part-worths are given by (a) * (b) </li></ul></ul></ul><ul><ul><ul><li>Compositional, not decompositional approach </li></ul></ul></ul><ul><ul><li>Hybrid models: Combine self explicated with part worth conjoint approaches. Self explicated info is used to pare down the number of attributes / profiles. Then a fractional factorial design is used on the remaining. Hence, needs to be customized for each respondent </li></ul></ul><ul><ul><ul><li>Sawtooth software’s ACA </li></ul></ul></ul>
  16. 17. Choice Based Conjoint <ul><li>Motivation : Using conjoint judgment studies to forecast choices is theoretically unappealing because of the ad hoc assumptions required </li></ul><ul><li>In choice based conjoint, the respondent chooses one profile from the set of alternative profiles known as the choice set. The stated choices are used to estimate the parameters of the choice model such as the logit model. </li></ul><ul><li>Advantage : Greater realism of respondent’s task </li></ul><ul><li>Disadvantage : Given limited information on each respondent, individual level estimation is precluded. Hence, individual differences (heterogeneity) needs to be accounted for in other ways </li></ul>