Class project ds_604_revise

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Class project ds_604_revise

  1. 1. Conjoint Analysis Ryan Louie, I-Hua Lin, Letty Kwok
  2. 2. Conjoint Analysis <ul><li>Used when marketing a new product/service, pricing purposes, forecasting of the product/service </li></ul><ul><li>Also known as Discrete Choice, Choice Modeling, Hierarchical Choice, Tradeoff Matrices </li></ul><ul><li>The goal of Conjoint Analysis is to assign specific values to the range of options buyers have when considering making a purchasing decision </li></ul>
  3. 3. Model Advantages <ul><li>Able to use physical objects </li></ul><ul><li>Measures preferences at the individual level </li></ul><ul><li>Estimates psychological tradeoffs that consumers make when evaluating several attributes together </li></ul>
  4. 4. Model Disadvantages <ul><li>Only a limited set of features can be used because the number of combinations increase quickly as more features are added </li></ul><ul><li>Information gathering stage can be complex </li></ul><ul><li>Respondents are unable to articulate attitudes toward new categories </li></ul><ul><li>Time lag </li></ul>
  5. 5. Defining a Product or Service <ul><li>Define each attribute’s </li></ul><ul><li>(i) importance </li></ul><ul><li>(ii) most desired level for each consumer </li></ul>
  6. 6. Study Design <ul><li>1. Determine Relevant Attributes and their levels </li></ul><ul><li>2. Determine Product Presentation: Content and Form </li></ul><ul><li>3. Determine Respondent - Researcher Interaction format </li></ul><ul><li>4. Decide on Response Type , rating, ranking, or choice </li></ul><ul><li>5. Determine Criterion for judging, liking, purchasing or willing to pay </li></ul><ul><li>6. Decide on Data Analysis Technique </li></ul>
  7. 7. from “conjoint and discrete designs for pricing research” by Vinay Kanetkar
  8. 8. Analysis the Outputs <ul><li>1. Aggregate analysis </li></ul><ul><li>2. Segmentation analysis </li></ul><ul><li>3. Scenario simulations </li></ul>
  9. 9. Aggregate Analysis <ul><li>To give the analyst an overall feeling for: </li></ul><ul><li>(i) which attributes are generally important </li></ul><ul><li>(ii) what is the most desired level of each attribute </li></ul>
  10. 10. Example <ul><li>Monthly Avg. </li></ul><ul><li>Access Value Phone Avg. Value Contract Avg. </li></ul><ul><li>Charge Price Term Value </li></ul><ul><li>$12.95 0.000 Free 0.000 1 year 0.000 </li></ul><ul><li>$24.95 -0.921 $29.95 -1.174 2 years -1.181 </li></ul><ul><li>$44.95 -3.406 $69.95 -3.138 3 years -1.864 </li></ul><ul><li>$79.95 -5.677 $119.95 -3.142 </li></ul><ul><li>from “conjoint and discrete designs for pricing research” by Vinay Kanetkar </li></ul>
  11. 11. Range <ul><li>Monthly access charge = 0-(-5.677) = 5.677 </li></ul><ul><li>Phone price = 0-(-3.142) = 3.142 </li></ul><ul><li>Contract term =0-(-1.864) = 1.864 </li></ul>
  12. 12. <ul><li>Monthly access charges </li></ul><ul><li>=5.677/(5.677 + 3.142 + 1.864) = 53.18% </li></ul><ul><li>Phone price </li></ul><ul><li>=3.142/(5.677 + 3.142 + 1.864) = 29.4% </li></ul><ul><li>Contract term </li></ul><ul><li>=1.864/(5.677 + 3.142 + 1.864) = 17.4% </li></ul>
  13. 13. Segmentation Analysis <ul><li>Strategy to analysis on customers segment </li></ul><ul><ul><li>Cluster analysis can be used to produce “benefit segments” </li></ul></ul><ul><ul><li>Look at predefined group of customers based on some prior knowledge about them </li></ul></ul>
  14. 14. Example:
  15. 15. Example:
  16. 16. Scenario Simulations <ul><li>Predicting market shares or unit sales in various scenarios </li></ul>
  17. 18. <ul><li>Example: </li></ul>from “conjoint and discrete designs for pricing research” by Vinay Kanetkar
  18. 20. Accuracy <ul><li>There are three methods to verify accuracy </li></ul><ul><li>Common-sense test, or face validity </li></ul><ul><li>Holdout prediction </li></ul><ul><li>Actual vs. predicted market share </li></ul>
  19. 21. <ul><li>Thank You </li></ul><ul><li>Questions???? </li></ul>

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