NCDM Datamining Case Study 2010

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Given at the Annual NCDM Conference. It is a real case study on data mining and segmentation analysis for an NBA team.

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NCDM Datamining Case Study 2010

  1. 1. NBA Team Scores with Data Mining: A Case Study in Modeling and Profiling<br />Presented by:<br />James R. Stafford<br />
  2. 2. Game Plan<br />What is modeling & profiling?<br />Who uses modeling and profiling?<br />Common approaches<br />7 steps to success<br />Case study - NBA team upsell study<br />
  3. 3. Modeling & Profiling<br />Who will respond?<br />Identify cross-sell opportunities<br />Who is likely to lapse/churn?<br />What do my best customers look like and how can I get more?<br />Who should receive what message?<br />Increase revenues, profit, and maximize ROI on marketing $ <br />
  4. 4. What is Predictive Modeling?<br />Predicting outcomes and future events based on historical data relating to:<br />- past response- transactions/purchase history- geo-demographic- lifestyle, and other attributes<br />
  5. 5. What is Customer Profiling?<br />Profiling is a data discovery procedure that uses standard queries and statistical analysisto segment customers and prospects based on important traits like R,F,M, transaction/purchase behavior, and demographics.<br />
  6. 6. Who uses PredictiveModeling?<br />The Industry<br />The Problem<br /><ul><li>Banks & Financial Services
  7. 7. Publications
  8. 8. Retail
  9. 9. Catalogers
  10. 10. Telco’s
  11. 11. High-Tech
  12. 12. Hospitality & Gaming</li></ul>Response<br />Cross-Sell<br />Lapse/Churn<br />Reactivate<br />Lifetime Value<br />Most Profitable<br />
  13. 13. Which approach should be used?<br />If the business problem has a...<br />limited number of answers<br />wide range of answers<br />Linear regression<br />CHAID<br />Neural nets<br />RFM<br />CHAID<br />Linear regression<br />Logistic regression<br />Neural nets<br />
  14. 14. 7 steps to successful modeling and implementation<br />Identify the business problem<br />Data audit -- what’s available and relevant?<br />Create training and validation files<br />Use best modeling approach and appraise results<br />Does the model make sense?<br />Validate the model<br />Test campaign<br />
  15. 15. Case StudyinModeling& Profiling<br />
  16. 16. The Business Problem<br />National Basketball Association Team<br />Declining attendance<br />Expanding to new stadium with more seats<br />Marketing Objectives<br />Up-sell: Mini-plan to Season ticket holders<br />Prospecting: identify Season ticket plan prospects<br />
  17. 17. Applicability to you...<br />Retention and up-sell -- NBA franchise has products/services and desires repeat buyers<br />Desire to differentiate customers with different purchasing behavior<br />Desire to acquire new & profitable customers<br />Create marketing efficiency & cut promotion costs<br />
  18. 18. Data audit - customer data<br />Street address<br /># of Seats<br />7 game mini-plans & 14 game combos<br />7A = “World’s Best” -- Dream Team players<br />7B = “Weekend Fest” -- Fri., Sat., Sun. games<br />7C = “Wild West” -- Western conference teams & Chicago Bulls<br />21 game mini-plans<br />Full season ticket holders<br />
  19. 19. Data preprocessing & overlay<br />Correct and standardize addresses<br />Geo-code addresses to census neighborhoods<br />Append updated area-level demographics<br />Append PRIZM lifestyle cluster types<br />
  20. 20. Create training and validation files<br />Training file - 1884 records (75% of file)<br />Validation file - 651 records (25% of file)<br />Must always use random sampling!<br />
  21. 21. Use best modeling approach<br />CHAID<br />Linear regression<br />Logistic regression<br />Neural net<br />
  22. 22. Use best modeling approach<br />
  23. 23. Let’s just mail to the 50% most likely to respond, and we’ll get 70% of the likely responders<br />_______<br />Highly targeted and saves money<br />Appraise results – Gains chart for our best model<br />
  24. 24. Appraise results - Gains chart for our bestlogistic regression model<br />
  25. 25. Appraise results - Gains chart for our bestlinear regression model<br />
  26. 26. Does the model validate?<br />Training Data<br />Validation Data<br />
  27. 27. Does the model make sense?<br />Most Important Variables<br />Cluster code<br />Home value<br />Age-male<br />Home value>=100K<br /># of HHs<br /># of seats<br />
  28. 28. Does the model make sense -- what do my customers look like?<br />
  29. 29. Does the model make sense -- what do my customers look like?<br />
  30. 30. Does the model make sense -- what do my customers look like?<br />
  31. 31. Does the model make sense -- what do my customers look like?<br />
  32. 32. Does the model make sense -- what do my customers look like?<br />
  33. 33. Does the model make sense -- what do my customers look like?<br />
  34. 34. PRIZM from Claritas, Inc.<br />Classification of 226,000 US neighborhoods<br />62 types repeat across country<br />Second City, Urban, Suburban, Town & Rural<br />Based on “birds of a feather flock together” or “you are where you live”<br />Extend beyond demographics to include:<br />Travel, automobiles, financial, media<br />Purchasing, readership, hobbies, activities<br />
  35. 35. PRIZM Cluster Groups<br />T1: Landed Gentry<br />C1: 2nd<br />City<br />Society<br />S1: Elite<br />Suburbs<br />U1: Urban<br />Uptown<br />R1:<br />Country<br />Families<br />T2:<br />Exurban<br />Blues<br />S2: The<br />Affluentials<br />SocioeconomicStatus<br />C2: 2nd<br />City<br />Centers<br />U2: Urban<br />Upscale<br />S3: Inner<br />Suburbs<br />R2:<br />Heart-<br />landers<br />R3:<br />Rustic<br />Living<br />T3:<br />Working<br />Towns<br />U3: Urban<br />Cores<br />C3: 2nd City Blues<br />Urbanization<br />
  36. 36. EliteSuburbs<br />UrbanCores<br />PRIZM cluster composition for segments<br />Modeled C1 C2 S1 S2 S3 U1 U3<br />Segment<br /> 1 1.6 2.4 31.2 4.0 12.8 12.0 34.4<br />2 2.4 16.356.5 1.6 0.8 13.7 4.0<br />10 5.5 28.4 11.0 5.5 18.1 1.6 4.7<br />19 2.8 2.8 10.1 32.1 4.6 2.8 0.0<br />20 5.5 0.9 18.4 22.9 2.8 0.0 0.0<br />TOTAL 6.0 9.5 24.0 14.9 11.0 6.1 4.9<br />Top demi-decile, i.e., those most likely to become season ticket holders<br />
  37. 37. Education<br />
  38. 38. Household income<br />
  39. 39. Occupation<br />
  40. 40. Household size<br />
  41. 41. Summary profile of “the best” segments<br />U3 - Urban Cores<br />S1 - Elite Suburbs<br />Wealthy whites, Asians and Arabic<br />High spending levels<br />Highest income<br />High education<br />High investment<br />Multi-racial<br />Multi-lingual<br />Dense/urban<br />Home & apartment renters<br />High % of singles<br />High % of single parents<br />High unemployment<br />Lowest income group<br />
  42. 42. Mostlikelytoo...<br />
  43. 43. Mostlikelytoo...<br />
  44. 44. Mostlikelytoo...<br />
  45. 45. How Can You Use This Information ?<br />For each major customer segment, you can...<br />Develop different messages<br />Use different media/marketing approaches to reach them<br />Buy prospect lists based on best segment profiles<br />Develop retention and prospecting plans with customized offers (e.g., free CD’s based on their particular tastes in music)<br />===>> improved customer up-sell and retention and better prospecting!<br />
  46. 46. Potential marketing plans<br />
  47. 47. Potential marketing plans<br />
  48. 48. Potential marketing plans<br />
  49. 49. Summary - why model & profile?<br />To identify those customers most likely to behave in certain ways (respond, cancel, etc.)<br />To see what those customers are like (high income, infrequent purchasers, etc.)<br />To identify what motivates our customers (price, frequency of contact, etc.) <br />To create mass personalizations<br />
  50. 50. Expected results<br />Increased ROI on marketing dollars - e.g., only mail to those most likely to respond<br />Increased customer loyalty<br />Decreased attrition rates <br />Higher actual lifetime value<br />Maximize each customer relationship<br />
  51. 51. NBA Team Scores with Data Mining: A Case Study in Modeling and Profiling<br />Presented by:<br />Jim Stafford<br />

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