Conjoint Analysis [Autosaved]

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Published on July 19, 2009

Author: nidhiatray

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Conjoint Analysis : Conjoint Analysis Dr. Milne Basic Problem : Basic Problem Metric/non-metric input (preferences) converted to interval scaled output (utility) I like lobster more than catfish, which I like more than octopus. What does it mean to say that my liking for lobster over catfish is greater than my liking for salmon over tuna? An interval level scale for preferences is needed. Parting of ways : Parting of ways Psychometrics: rigorous and idealistic Marketing research: approximate and pragmatic Conjoint is becoming very much removed from theoretical roots Numerical measurement of behavior Additive Compound stimuli Factorial designs Testing is rapidly ignored Moving from non-metric to metric Conjoint measurement vs. conjoint analysis Managerial Uses of Conjoint Analysis : Managerial Uses of Conjoint Analysis Find the product with the optimum set of features Determine the relative importance of each feature in consumer choices Estimate market share among products Identify market segments Evaluate the impact of price changes or other marketing mix decisions. A Simple Example : A Simple Example Scenario: a man buying a basic cartridge camera (faced with eight choices) Major brand $80 Major brand $50 Major brand $30 Major brand $20 Store brand $80 Store brand $50 Store brand $30 Store brand $20 Slide 6: Respondent’s Ranking of Eight Camera Brands Price($) Major Brand Store Brand Average Rank 20 8 6 7.0 30 7 4 5.5 50 5 2 3.5 80 3 1 2.0 Average rank 5.75 3.25 Note, 8 is most preferred and 1 is least preferred Slide 7: Respondent’s Utility Values of Eight Camera Brands Price($) Major Brand Store Brand Average Rank Utility 20 8 6 7.0 1.00 30 7 4 5.5 .70 50 5 2 3.5 .30 80 3 1 2.0 .00 Average rank 5.75 3.25 Utility .75 .25 Slide 8: Rank Order of Respondent’s Total Utilities. Price($) Major Brand Store Brand Marginal Utility 20 8 (1.75) * 6 (1.25) 1.00 30 7 (1.45) 4 (.95) .70 50 5 (1.05) 2 (.55) .30 80 3 (.75) 1 (.25) .00 Marginal Utility .75 .25 * 1.75 = .75 (major brand utility) + 1.00 ($20 utility) Slide 9: Utility Values for Three Respondents Adam Bob Carl Brand x .60 .80 .33 y .80 .35 .33 z .40 .10 .33 Price ($) 20 1.00 .70 1.00 30 .80 .60 .80 50 .00 .20 .50 80 .00 .00 .00 Coupon Value($) 2 .20 .20 .20 5 .75 .20 .30 10 .95 .60 .80 Slide 10: Utility Values for Respondent Adam Brand (x) .60 (y) .80 (z) .40 Price ($30) .80 ($20) 1.00 ($50) .00 Coupon Value ($2) .20 ($2) .20 ($2) .20 Total Utility 1.60 2.00 .60 Slide 11: Respondent’s Estimated Preferences for Three Camera Brands Brand Price ($) Coupon Value ($) %Preferring X 40 5 50 Y 20 2 35 Z 80 10 15 Slide 12: Effects of Change in Marketing Mix on Respondents’ Preferences % Preferring Each % Preferring Each Brand for Original Brand After X’s Brand Situation Change in Price Change (%) X 50 55 +5 Y 35 35 0 Z 15 10 -5 Conjoint Analysis : Conjoint Analysis Decompositional model—An individual’s overall preference or evaluation for a product (expressed as a combination of attributes) is decomposed by relating the know attributes to the evaluation. Best suited for understanding consumers’ reactions to and evaluations of predetermined attribute combinations that represent potential products or services. An applied method used in marketing research Slide 14: Which of the two flights described below would you chose? A B-707 flown by New Zealand Air that will depart within two hours of the time you would like to leave and that is often late in arriving in Sydney. The plane will make two intermediate stops, and it is anticipated that it will be 50% full. Flight attendants are “warm and friendly” and you would have a choice of multiple movies for entertainment. A B-747 flown by Quantas that will depart within four hours of the time you would like to leave and that is almost never late in arriving in Sydney. The flight is nonstop, and it is anticipated that the plane will by 90% full. Flight attendants are “cold and curt” and only magazines are provided for entertainment. Slide 15: Compositional versus Decompositional Techniques Compositional Y = w1 X1 + w2 W2 Collect x1 and x2 and relate it to Y. Estimate weights to create a predictive model Decompositional Y = w1 X1 + w2 W2 Collect Y and relate it to X1 and X2 which are already fixed, and determine weights. Note: computationally similar, but design and conceptually very distinct. Unique Features of Conjoint : Unique Features of Conjoint Specifiying the Conjoint Variate The only data provided by the subject is the dependent variable. The independent variable is prespecified. Separate Models for Each Individual A unique model is specified for each individual. Predictive accuracy is made for each individual. Not limited to linear relationships. Objectives : Objectives To determine the contributions of predictor variables and their respective values to the determination of consumer preferences. To establish a valid model of consumer judgments useful in predicting the consumer acceptance of any combination of attributes, even those not originally evaluated by consumers. Questions to resolve : Questions to resolve Defining the total worth of the object Need to select attributes that accurately reflect judgment process. Need to include both potential positive and negative factors Specifying the determinant factors Attributes must also be selected so that they differentiate between the objects. These are the key to decision making. Slide 19: Research Problem Define Stimuli (factors and levels) Basic model form Data collection Full profile Trade off Pairwise Data Collection (Create stimuli) Factorial design Fractional factorial Select preference measure Form of Survey Administration Assumptions Select estimation technique Evaluate results Interpret results Validate Apply results Conjoint Analysis Decision Process This technique requires a lot of upfront work to think through the design, data collection, and analysis options. Determining Factors and Selecting the Levels for each Factor : Determining Factors and Selecting the Levels for each Factor Actionable measures Communicable measures Number of attributes Balanced number of attributes Rate of attribute levels Attribute multicollinearity Specifying Model form : Specifying Model form Additive – add up the values to each attribute (partworth) to obtain the overall worth of the model. This is the most common approach. Composition with interaction is possible –the sum may be more or less than the whole—but not as common and the prediction is not as good. Slide 22: Level Level Level Preference Preference Preference Linear Quadratic or idea Part-worth Selecting the Part-worth relationship Slide 23: Trade-off Approach $1.19 $1.39 $1.49 $1.69 Factor 1: Price Generic KX-19 Clean-all Tidy-UP Factor 2: Brand Name Pros: Easy, simple, few cognitive decisions Cons: Sacrifice in only see a few attributes at a time, large number of judgments, easy to get confused and pattern response, can’t use pictoral or non written stimuli, only non metric responses, can’t use fractional factorial designs. Slide 24: Full Profile Approach Brand Name : KX – 19 Price : $ 1.19 Form: Powder Color brightener: Yes Shows all attributes at once Pros: Better, more realistic, flexible scaling, fewer judgments. Cons: As the number of factors increases so does the possibility of information overload--can be overwhelming if have > 6 attributes. The order in which the factors are listed on the stimulus card may have an impact on the evaluation. Slide 25: Paired Comparison Brand Name: KX-19 Price: $1.19 Form: Powder Brand Name: Generic Price: $1.49 Form: Liquid VERSUS A combination of approaches. Does not show all the attributes. It is similar to trade-off in that pairs are evaluated. But, like profile, the judgments are made about combinations of attributes. Approach used in adaptive conjoint analysis. Creating Stimuli : Creating Stimuli Factorial design – 4 variables with 4 levels each would result in 256 stimuli. (4x4x4x4). Fractional factorial design selects a sample of stimuli (16 in this case). Can only be used for estimating the main effects. The stimuli are chosen for orthogonality. Designs are published. Software can be used. Slide 27: Two Fractional Factorial Designs Stimulus F1 F2 F3 F4 f1 f2 f3 f4 1 3 2 3 1 2 3 1 4 2 3 1 2 4 4 1 2 4 3 2 2 1 2 3 3 2 1 4 4 2 2 3 2 2 4 1 5 1 1 1 1 1 1 1 1 6 4 3 4 1 1 4 4 4 7 1 3 2 2 4 2 1 3 8 2 1 4 3 2 4 2 3 9 2 4 2 1 3 2 3 4 10 3 3 1 3 3 4 1 2 11 1 4 3 3 4 3 4 2 12 3 4 4 2 1 3 3 3 13 1 2 4 4 2 1 3 2 14 2 3 3 4 3 1 4 3 15 4 4 1 4 1 2 2 2 16 4 1 3 2 4 4 3 1 orgthogonal - no correlation among levels across attributes and balanced each level in a factors appears the same number of times. Slide 28: Selecting a measure of consumer preference Trade-off uses only ranking data Full profile uses both ranking and rating data Metric methods are easily analyzed and easily administered even by mail and allow conjoint estimation by multiple regression. For ranking data have 11 point scales for 16 or fewer stimuli and 21 point scale for greater than 16 stimuli. Pencil and paper and computer based surveys. Computer Disks. Web pages. Survey Administration Assumptions : Assumptions Very few statistical assumptions However, theory drives design, estimation, and interpretation. Estimation and Assessing Overall Fit : Estimation and Assessing Overall Fit Rank order calculated with MANANOVA or LINMAP Metric can be estimated with regression or special programs. The standardized betas are the part-worths. Preference = b1 F1+b2 F2 + … + bn Fn Reliability can be estimated by correlating the predicted with the actual ratings for each individual. Corr (pref, Y hat.) Interpret Results : Interpret Results Part worths are standardized Beta Weights so they can be compared. Relative importance of each factor should be calculated. Relative importance is the range of the partworths over the sum of the ranges across all factors. B1H-B1L /{(B1H-B1L) + (B2H-B2L)+…+(BNH-BNL)} Slide 32: Example: Packaged Soup Factors Levels Flavor Onion Chicken Veg Calories 80 100 140 Salt Free Yes No Price 1.89 2.49 Dependent Variable is preference (0-10) 3x3x2x2 = 36 possibilities in a full factorial design Model can be estimated using dummy variable regression where the estimated beta weights are utility preferences Slide 33: Establish the Dummy Variables D1 = 1 if onion, 0 = otherwise D2 = 1 if chicken, 0 = otherwise D3 = 1 if 80 calories, 0 = otherwise D4 = 1 if 100 calories, 0 = otherwise D5 = 1 if salt-free, 0 = otherwise D6 = 1 if price $1.89, 0 = otherwise Example: Onion, 80 calorie, Saltfree soup for $1.19 would be coded as ( 1 0 1 0 1 1) Slide 34: Run Regressions for Each Individual Y = B1 D1 + B2 D2 + B3 D3 + B4 D4 + B5 D5 + B6 D6 + ? Card # Pref Dummy Coding 1 8 1 0 0 1 1 0 2 6 0 1 1 0 1 0 3 3 1 1 1 0 0 0 . . . . . . . . . . . . . . . . 36 5 0 1 1 1 1 1 Slide 35: Check the fit for each regression for each individual Calculate Y for each individual Corr ( Y , Pref) for each individual This is a measure of internal consistency to see if there is a strong relationship between the revealed preference and the stated preference. Include individuals with high correlations. Slide 36: Standardized Beta weights are the part worths Attributes Part worth Flavor onion 3.50 Chicken 0 Vegetable 3.58 Calories 80 2.17 100 .67 140 0 Salt Free Yes 1.89 No 0 Price 1.19 .67 1.49 0 Note the partworths can be rescaled relative to each other. For example if onion = -.08, chicken= -3.58 and Veg = 0 adding 3.58 to each changes the coding to make chicken 0. Utility for an Alternative = sum of the utilities Slide 37: Utility Utility Utility Utility 0 Chicken Onion Vegetable 80 100 140 No Yes $1.89 $2.49 Flavor Salt-Free Price Calories Graphing Individual Part worths Slide 38: Importance Weights Attributes Range Percent Flavor 0 – 3.58 43% Calories 0 – 2.17 26% Salt 0 – 1.89 23% Price 0 - .67 8% Total 8.30 100% Aggregate Analysis : Aggregate Analysis Estimate market share for existing attribute combinations in the market Simulate shifts in share with changes of existing product combinations (Brand A with a higher price). Estimate potential share that a new entrant might obtain (with unique set of attributes) Use the part worths to segment the market with Cluster analysis.

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