Churn prediction data modeling
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This document discusses how to model customer churn through machine learning. It defines churn as customers leaving or stopping usage. There are two types of churn - for subscription models where leaving can be clearly defined, and non-subscription models where leaving must be approximated. The document recommends predicting churn through classification models to identify potential churners, using customer behavioral and profile features over time. It also discusses evaluating models on validation data and using models to predict future churn and inform retention offers.
Churn prediction data modeling
- 1. How to data model Churn Real life examples
- 2. Quick quizz • How many of you are familiar with Churn issue? • with Machine Learning? Logistic Regression, Random Forest, Gradient Boosting trees? (Not the subject here) • With SQL? (we may see some code later) • What database tech do you use? What about EMC Greenplum or Vertica?
- 3. Who I am • Senior Data Scientist at Dataiku (worked on churn prediction, fraud detection, bot detection, recommender systems, graph analytics, smart cities, … ) • Occasional Kaggle competitor • Mostly code with python and SQL • Twitter @prrgutierrez
- 4. Churn definition • Wikipedia: “Churn rate (sometimes called attrition rate), in its broadest sense, is a measure of the number of individuals or items moving out of a collective group over a specific period of time” = Customer leaving
- 5. Two types of Churn • Subscription models: • Telco • E-gamming (Wow) • Ex : Coyote -> 1 year subscription -> you know when someone leave • Non subscription models: • E-Business (Amazon, Price Minister, Vente Privée) • E-gamming (Candy Crush, free MMORPG) -> you approximate someone leaving Candy Crush: days / weeks MMORPG: 2 months (holidays) Price Minister: months
- 6. Two types of Churn • Blurred Separation: • Ex: T-mobile: 1 month subscription -> paying each call • Ex: Wow: 1 month to 6 month subscription • Banking? • Focus : no subscription: • Can be seen as a generalization where you have to approximate the target • Bonus : Seller churn • Market places • Clients that participate product life • Forums (Reddit) • E-gamming (Korean competitions, guilds etc.)
- 7. Dealing with churn • Motivations : • Saturated market -> cost get new client >>> cost keep client • Ex : http://www.bain.com/publications/articles/breaking-the-back-of-customer-churn.aspx • Wireline company : 2% to 2.5 % churn rate per month. • If 5 M customers -> 1.32 M churn per year • When reducing from 2.5% to 2% lowest estimation : 240 M $ in 18 month
- 8. Dealing with churn • Predict churn : • One model for performance <- our focus, short term, more ML • One model for understanding <- long term, more Analytics • Act on it (short term) : • Special offer (telco call, free in game money, discount coupon … ) • Does it work? Feedback loop needed! • Model probabilities of leaving because of offer. A/B tests. Multi arms Bandit? • Significant LTV for activation? • Act on it (long term) : • Is there a problem in my purchasing funnel? • Is the game too hard at some point?
- 9. Dealing with churn • Candy Crush Rumor : • Change the distribution of probabilities of candies / bombs • Change the difficulty of the game • Loosing a lot makes the game easier
- 10. Modelling Churn • Machine learning model (classification) -> target: • Known in subscription • Unknown in general • Step 1 : Maintain customer status • Do you care only about your best? • Anyway churn action won’t be the same • Has a client churned? -> target = churner = don’t buy / visit since time X -> best = buy / visit more than y since time Y • Can be refined (“new customer”, several class of best or inactive, reactivated…) • Storage : maintain only the difference!
- 11. Modelling Churn • Machine learning model -> features: • Explicative factors to use as input for the model • Step 2 : Maintain customer features • Social (woman, age, etc.) • Behavioral! • Utilization / buying rate • Trend in utilization / buying rate • Ad hoc features : • WoW / Social game churn: take into account friend network churn • Telco: call to call centers • Beware of time dependence!
- 12. Data Model
- 14. Ex : Train and predict scheme Time T : present ,me T – 4 month Data is used for target crea,on : ac,vity during the last 4 months Data is used for feature genera,on. Use model to predict future churn Train model using features and target
- 15. Ex : Train Evaluation and Predict Scheme Time T : present ,me T – 4 month Data is used for target crea,on : ac,vity during the last 4 months Data is used for feature genera,on Valida&on set Use model to predict future churn Training Evaluate on the target of the valida,on set T – 8 month Data is used for features genera,on. Data is used for target crea,on : ac,vity during the last 4 months
- 16. Thank you for your attention !