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.

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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  • 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.
  • 6. Who uses PredictiveModeling?
    The Industry
    The Problem
    Response
    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