Business AnalyticsDatabase Marketing & Statistical ModelingDouglas 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

• 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?

Internet Advertising Revenues

Margin Players

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

Your Average CustomerEach individual customer contributes to the understanding of the customer as a whole.

Database Marketing CycleDatabase is the center of the marketing cycle.

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

Visualization Example

Statistical Model ExampleDecision tree used to find segments with high response rates.

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 ModelMost firms lump customers into segments & predict LTV per segment

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 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 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 Model• Goodness of fit within calibration.• More repeat transactions from 0% Start Discount

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

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

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

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

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

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

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

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

Questions?