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# A Simple Tutorial on Conjoint and Cluster Analysis

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A simple tutorial to show conjoint analysis and cluster analysis. please send your feedback, this version is still rough and I would like to iteratively improve it so it is useful for most.

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### A Simple Tutorial on Conjoint and Cluster Analysis

1. 1. Marketing Research Rags Srinivasan Customer Segmentation and Market Share Estimation With Conjoint Analysis
2. 2. Marketing is about segmentation and targeting Nothing more strategic than segmentation Cannot treat the Value proposition is Target them differently – SKUs, whole market as different for each one segment messaging Rags Srinivasan IterativePath.com
3. 3. What defines a segment? Internally homogenous, externally heterogeneous
4. 4. Is your segmentation valid? Meaningful, relevant and Not too small, Not too intuitively identified by large constituent variables Rags Srinivasan IterativePath.com
5. 5. Conjoint analysis helps you with the clustering Premise: The whole is the sum of its parts. We can infer the relative importance of parts from the customer preference of the whole. Rags Srinivasan IterativePath.com
6. 6. For Example Assign a value between 1 and 100 to these options. 100 means most likeable and 1 means least likeable Price: \$2499 Price: \$799 Price: \$1999 Screen: 50” Screen: 42” Screen: 42” Display: LCD Display: Plasma Display: LCD Rags Srinivasan IterativePath.com
7. 7. Conjoint analysis helps identify clusters Brand conscious Price Sensitive Screen size Display type Rags Srinivasan IterativePath.com
8. 8. … and relative importance of attributes What is the utility value a customer assigns to each attribute? Rags Srinivasan IterativePath.com
9. 9. But you cannot ask customers about every combination Use commercial software to generate a manageable set of profiles Conjoin Manageabl t Attributes Levels e set of Survey Softwar profiles e Rags Srinivasan IterativePath.com
10. 10. Let Us Walk Through An Example: My Work On Airline Unbundled Pricing Questions: How much do airline customers value services like free- baggage, free drinks etc? Are airlines better off increasing ticket price instead of unbundling pricing? SFO JFK
11. 11. With Following Options … 3 Airlines 2 Price Extras for Baggage, Pillows and Soft- levels: \$275, drinks \$250
12. 12. Created 8 Profiles For Measuring Customer Utility Brand: 3 levels A manageable set of 8 Price: 2 levels profiles that stand-in for Software Baggage Fees: 2 levels all variable Pillow Fees: 2 levels combinations Drink Fee: 2 levels
13. 13. Survey customers to find their utility value for each profile Rate your likelihood of choosing the option on a scale of 1 – 10 ( 8 profiles)
14. 14. Model: Utility = f(Brand, Price,Fees) Write customer utility (their likelihood of picking the airline) as a linear function of these variables U = Constant + b1 * JetBlue + b2* Delta + b3* Price\$275 + b4* BaggageFee\$20 + b5 * PillowFee\$4 +b6 * DrinkFee\$2 JetBlue and Delta are mutually exclusive – 1 or 0 AA is implicitly defined when both JetBlue and Delta are 0 Price\$275 = 1 means price is \$275 , if it is 0 the price is \$250 So on and so forth b1, b2, … are the regression coefficients that are the relative utilities of attributes that we seek to find
15. 15. Use SPSS to indentify clusters This margin is too narrow to contain it. Stay tuned I will add a Camtasia demo of using SPSS to do Cluster analysis and Regression. Rags Srinivasan IterativePath.com
16. 16. Run multiple regression for each cluster to find the coffecients U = 8.36 + 0.88 * JetBlue – 0.06 * Delta – 1.9 * Price\$275 – If we did not 2.41 * BaggageFee\$20 – 0.83 * PillowFee\$4 – 0.79 * cluster DrinkFee\$2 U = 7.9 + 1.28 * JetBlue – 0.16 * Delta – 2.34 * Price\$275 – Cluster 1 3.14 * BaggageFee\$20 – 0.92* PillowFee\$4 – 0.87 * DrinkFee\$2 U = 8.6 + 0.4 * JetBlue + 0.17 * Delta – 1.24 * Price\$275 – Cluster 2 1.68 * BaggageFee\$20 – 0.63* PillowFee\$4 – 0.58 * DrinkFee\$2 Rags Srinivasan IterativePath.com
17. 17. You can see the difference between two clusters JetBlue, \$250, Baggage Fee \$20, Pillow Fee \$4, Drink Fee \$2 Cluster 1 Cluster 2 JetBlue 9.18 9 \$250 0 0 Baggage Fee \$20 -3.14 -1.68 Pillow Fee \$4 -0.92 -0.63 Drink Fee \$2 -0.87 -0.58 Total Utility 4.25 6.11
18. 18. Compute market share from the utility values of the brands Market Utility Share of Product i of Product i  Ui MS i     U1 U 2 U 3 Feb 11, 2009
19. 19. The net of this is When you want to segment customers and target them with multiple SKUs you need to do cluster analysis Conjoint analysis gets you there and more