A Simple Tutorial on Conjoint and Cluster Analysis
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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.

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 A Simple Tutorial on Conjoint and Cluster Analysis Presentation Transcript

  • Marketing Research Rags Srinivasan Customer Segmentation and Market Share Estimation With Conjoint Analysis
  • 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
  • What defines a segment? Internally homogenous, externally heterogeneous
  • Is your segmentation valid? Meaningful, relevant and Not too small, Not too intuitively identified by large constituent variables Rags Srinivasan IterativePath.com
  • 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
  • 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
  • Conjoint analysis helps identify clusters Brand conscious Price Sensitive Screen size Display type Rags Srinivasan IterativePath.com
  • … and relative importance of attributes What is the utility value a customer assigns to each attribute? Rags Srinivasan IterativePath.com
  • 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
  • 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
  • With Following Options … 3 Airlines 2 Price Extras for Baggage, Pillows and Soft- levels: $275, drinks $250
  • 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
  • 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)
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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