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Business Analytics. Database Marketing & Statistical Modeling Douglas Cohen, Director of Business Analytics @ Beachmint. Online Consumer Market. Why do companies bother with database marketing? Margin Players Online Gaming, e-Commerce, Lead Generation Buy low, sell high

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business analytics

Business Analytics

Database Marketing & Statistical Modeling

Douglas Cohen, Director of Business Analytics @ Beachmint

online consumer market
Online Consumer Market
  • Why do companies bother with database marketing?
  • Margin Players
    • Online Gaming, e-Commerce, Lead Generation
    • Buy low, sell high
      • Cost To Acquire a Customer < Customer Lifetime Value
  • Big Budgets
    • Zyngaspent over $40 million in 2011 Q1
    • Acquisition spend rising in many industries
  • Competitive landscape
    • Companies are competing for the same customers
    • Cost to Acquire a Customer is rising
  • Marketing Analytics
    • Companies working to understand their target audience
      • Which customers have highest Lifetime Value, LTV?
customer database
Customer Database
  • Data store used to record all customer information
    • Attributes
      • Name, Address, Demographics, Marketing Attribution
    • Transactions
      • Internal Sales, Content Delivery
    • Behavior
      • Click stream, Visits, Feature Usage
  • Drives personalized communication
    • Target customers for products / services
      • New home owner, recently married, birthday
    • Customer Lifecycle based promotion
      • Versus traditional business centric promotion
  • Importance of Data Warehousing
    • High attention to data driven discovery
    • Allows companies to understand their target audience
slide6
Your Average CustomerEach individual customer contributes to the understanding of the customer as a whole.
data mining
Data Mining
  • Marketing Campaign Assessment
    • Analysis shows whether campaigns were effective
    • Identify which customer segments responded well
      • Visualization Tools
        • Excel, Tableau, Pentaho, D3
      • Statistical Models
        • Great when number of segments is large
        • R, Mahout, Weka, Orange
statistical modeling
Statistical Modeling
  • Model customer behavior using statistical techniques
  • Campaign Management & LTV Prediction
    • Campaign managers need accurate forecasts of LTV
    • Buy Till You Die Model
  • Customer Retention & Survival Analysis
    • Understand how to improve customer loyalty & reduce churn
    • Proportional Hazards Regression
      • Calculate variation in hazard rates among customer segments
  • General Profit Maximization
    • Product Recommendations
      • Increase probability of purchase versus size of purchase
    • Response Rate Modeling
      • Optimize response from customer communication efforts
    • Price Discrimination
      • Dynamically assign pricing based on customer income levels
buy till you die model most firms lump customers into segments predict ltv per segment
Buy Till You Die ModelMost firms lump customers into segments & predict LTV per segment
buy till you die model
Buy Till You Die Model
  • Increase accuracy by looking at customer level data
  • Transaction Process (“Buy”)
    • While active, the number of transactions made by a customer follows a Poisson Process with a transaction rate
    • Transactions rates are distributed gamma across the population
  • Dropout Process (“Die”)
    • Each customer has an unobserved lifetime length, which is distributed exponential with a dropout rate
    • Dropout rates are distributed gamma across a population
  • Approximates complexity in customer behavior
    • Simpler to implement than a psychographic model
  • Astonishingly good fit & predictive performance
buy till you die model1
Buy Till You Die Model
  • Poisson, Exponential & Gamma Distributions
  • Fit the appropriate curve to each customer segment
  • Coefficients have direct interpretation
    • Transaction, Dropout Rates are lambda
    • Gamma distribution describes heterogeneity
  • Store coefficients in data warehouse & feed into reports
buy till you die model2
Buy Till You Die Model
  • Implementation
    • Customers subscribing 2011, predict behavior in 2012
      • Fit in calibration period was great.
      • Fit in holdout period was … horrible.
      • Why?
        • BeachMint made significant changes in discounting 2012.
        • Behavior did not transpose correctly for 2011 customers.
    • Solution: Segmentation
      • Customers starting with no discount should be less prone to change
      • Segment Customers by starting discount amount
      • Split into 2 similar sized groups
        • Start Discount = 0 %
        • Start Discount = 50 %
buy till you die model3
Buy Till You Die Model
  • Goodness of fit within calibration.
    • More repeat transactions from 0% Start Discount
buy till you die model4
Buy Till You Die Model
  • Goodness of fit within hold-out period.
    • Customers binned based on calibration period transactions.
buy till you die model5
Buy Till You Die Model
  • Actual vs. expected incremental purchasing behavior.
    • Monthly periodicity from subscription model.
buy till you die model6
Buy Till You Die Model
  • Actual vs. expected cumulative purchasing behavior.
    • Irregularities in the holiday period not captured.
buy till you die model7
Buy Till You Die Model
  • Transaction Rate Heterogeneity
    • Distribution of Customers’ Propensities to Purchase
buy till you die model8
Buy Till You Die Model
  • Dropout Rate Heterogeneity
    • Distribution of Customers’ Propensities to Drop Out
buy till you die model9
Buy Till You Die Model
  • Discounted Expected Residual Transactions
    • Given Behavior during Calibration Period
buy till you die model10
Buy Till You Die Model
  • Discounted Expected Residual Transactions
    • Higher Frequency & Recency has more impact for Discounters.
proportional hazards model explain what factors contribute to survival over time
Proportional Hazards ModelExplain what factors contribute to survival over time.
  • Explain hazards of various conditions / customer variables
  • Commonly used in medical industry to compare risks of treatment groups
  • Hazard Ratios
    • Simple, easy to interpret
    • Relative risk ratios
    • Example 2X increase
  • Weibull versus Gamma distribution
    • Better curve fitting