1) Companies use database marketing to understand their target customers and maximize profits by acquiring high-value customers at low costs. This involves tracking customer data, analyzing marketing campaigns, and building statistical models. 2) Statistical models like the "Buy Till You Die" model can predict customer lifetime value (LTV) by modeling individual customer transactions and dropout rates over time. These models are fitted to customer data and segments to improve predictions. 3) Data visualization and statistical modeling help companies test marketing campaigns, identify high-value customer segments, and optimize strategies to improve customer retention and response rates.

1. Business Analytics
Database Marketing & Statistical Modeling
Mohcine Madkour@ Houston Data Vis Meetup

2. 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
• Zynga spent 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?

3. Internet Advertising Revenues

4. Margin Players

5. 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

6. Your Average Customer
Eachindividualcustomercontributestothe understandingof thecustomerasawhole.

7. Database Marketing Cycle
Databaseisthecenterofthemarketingcycle.

8. 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

9. Visualization Example

10. Statistical Model Example
Decisiontreeusedtofindsegmentswithhighresponserates.

11. 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

12. Buy Till You Die Model
Mostfirmslumpcustomersintosegments&predictLTVper segment

13. 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

14. 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

15. 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 %

16. Buy Till You Die Model
• Goodness of fit within calibration.
• More repeat transactions from 0% Start Discount

17. Buy Till You Die Model
• Goodness of fit within hold-out period.
• Customers binned based on calibration period transactions.

18. Buy Till You Die Model
• Actual vs. expected incremental purchasing behavior.
• Monthly periodicity from subscription model.

19. Buy Till You Die Model
• Actual vs. expected cumulative purchasing behavior.
• Irregularities in the holiday period not captured.

20. Buy Till You Die Model
• Transaction Rate Heterogeneity
• Distribution of Customers’ Propensities to Purchase

21. Buy Till You Die Model
• Dropout Rate Heterogeneity
• Distribution of Customers’ Propensities to Drop Out

22. Buy Till You Die Model
• Discounted Expected Residual Transactions
• Given Behavior during Calibration Period

23. Buy Till You Die Model
• Discounted Expected Residual Transactions
• Higher Frequency & Recency has more impact for Discounters.

24. Proportional Hazards Model
Explainwhatfactorscontributetosurvivalovertime.
• 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

25. Questions?