Presented at the Annual NCDM Conference. Real-life case study on data mining and segmentation profiling for an NBA team.

1. NBA Team Scores with Data Mining: A Case Study in Modeling and Profiling Presented by: James R. Stafford

2. Game Plan What is modeling & profiling? Who uses modeling and profiling? Common approaches 7 steps to success Case study - NBA team upsell study

3. Modeling & Profiling Who will respond? Identify cross-sell opportunities Who is likely to lapse/churn? What do my best customers look like and how can I get more? Who should receive what message? Increase revenues, profit, and maximize ROI on marketing $

4. What is Predictive Modeling? Predicting outcomes and future events based on historical data relating to: - past response- transactions/purchase history- geo-demographic- lifestyle, and other attributes

5. What is Customer Profiling? 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.

7. Publications

8. Retail

9. Catalogers

10. Telco’s

11. High-Tech

12. Hospitality & GamingResponse Cross-Sell Lapse/Churn Reactivate Lifetime Value Most Profitable

13. Which approach should be used? If the business problem has a... limited number of answers wide range of answers Linear regression CHAID Neural nets RFM CHAID Linear regression Logistic regression Neural nets

14. 7 steps to successful modeling and implementation Identify the business problem Data audit -- what’s available and relevant? Create training and validation files Use best modeling approach and appraise results Does the model make sense? Validate the model Test campaign

15. Case StudyinModeling& Profiling

16. The Business Problem National Basketball Association Team Declining attendance Expanding to new stadium with more seats Marketing Objectives Up-sell: Mini-plan to Season ticket holders Prospecting: identify Season ticket plan prospects

17. Applicability to you... Retention and up-sell -- NBA franchise has products/services and desires repeat buyers Desire to differentiate customers with different purchasing behavior Desire to acquire new & profitable customers Create marketing efficiency & cut promotion costs

18. Data audit - customer data Street address # of Seats 7 game mini-plans & 14 game combos 7A = “World’s Best” -- Dream Team players 7B = “Weekend Fest” -- Fri., Sat., Sun. games 7C = “Wild West” -- Western conference teams & Chicago Bulls 21 game mini-plans Full season ticket holders

19. Data preprocessing & overlay Correct and standardize addresses Geo-code addresses to census neighborhoods Append updated area-level demographics Append PRIZM lifestyle cluster types

20. Create training and validation files Training file - 1884 records (75% of file) Validation file - 651 records (25% of file) Must always use random sampling!

21. Use best modeling approach CHAID Linear regression Logistic regression Neural net

22. Use best modeling approach

23. Let’s just mail to the 50% most likely to respond, and we’ll get 70% of the likely responders _______ Highly targeted and saves money Appraise results – Gains chart for our best model

24. Appraise results - Gains chart for our bestlogistic regression model

25. Appraise results - Gains chart for our bestlinear regression model

26. Does the model validate? Training Data Validation Data

27. Does the model make sense? Most Important Variables Cluster code Home value Age-male Home value>=100K # of HHs # of seats

28. Does the model make sense -- what do my customers look like?

29. Does the model make sense -- what do my customers look like?

30. Does the model make sense -- what do my customers look like?

31. Does the model make sense -- what do my customers look like?

32. Does the model make sense -- what do my customers look like?

33. Does the model make sense -- what do my customers look like?

34. PRIZM from Claritas, Inc. Classification of 226,000 US neighborhoods 62 types repeat across country Second City, Urban, Suburban, Town & Rural Based on “birds of a feather flock together” or “you are where you live” Extend beyond demographics to include: Travel, automobiles, financial, media Purchasing, readership, hobbies, activities

35. PRIZM Cluster Groups T1: Landed Gentry C1: 2nd City Society S1: Elite Suburbs U1: Urban Uptown R1: Country Families T2: Exurban Blues S2: The Affluentials SocioeconomicStatus C2: 2nd City Centers U2: Urban Upscale S3: Inner Suburbs R2: Heart- landers R3: Rustic Living T3: Working Towns U3: Urban Cores C3: 2nd City Blues Urbanization

36. EliteSuburbs UrbanCores PRIZM cluster composition for segments Modeled C1 C2 S1 S2 S3 U1 U3 Segment 1 1.6 2.4 31.2 4.0 12.8 12.0 34.4 2 2.4 16.356.5 1.6 0.8 13.7 4.0 10 5.5 28.4 11.0 5.5 18.1 1.6 4.7 19 2.8 2.8 10.1 32.1 4.6 2.8 0.0 20 5.5 0.9 18.4 22.9 2.8 0.0 0.0 TOTAL 6.0 9.5 24.0 14.9 11.0 6.1 4.9 Top demi-decile, i.e., those most likely to become season ticket holders

37. Education

38. Household income

39. Occupation

40. Household size

41. Summary profile of “the best” segments U3 - Urban Cores S1 - Elite Suburbs Wealthy whites, Asians and Arabic High spending levels Highest income High education High investment Multi-racial Multi-lingual Dense/urban Home & apartment renters High % of singles High % of single parents High unemployment Lowest income group

42. Mostlikelytoo...

43. Mostlikelytoo...

44. Mostlikelytoo...

45. How Can You Use This Information ? For each major customer segment, you can... Develop different messages Use different media/marketing approaches to reach them Buy prospect lists based on best segment profiles Develop retention and prospecting plans with customized offers (e.g., free CD’s based on their particular tastes in music) ===>> improved customer up-sell and retention and better prospecting!

46. Potential marketing plans

47. Potential marketing plans

48. Potential marketing plans

49. Summary - why model & profile? To identify those customers most likely to behave in certain ways (respond, cancel, etc.) To see what those customers are like (high income, infrequent purchasers, etc.) To identify what motivates our customers (price, frequency of contact, etc.) To create mass personalizations

50. Expected results Increased ROI on marketing dollars - e.g., only mail to those most likely to respond Increased customer loyalty Decreased attrition rates Higher actual lifetime value Maximize each customer relationship

51. NBA Team Scores with Data Mining: A Case Study in Modeling and Profiling Presented by: Jim Stafford