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- 1. Introduction to Uplift Modelling An online gaming application
- 2. A few words about me • 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
- 3. Plan • Introduction / Clients situation • Uplift use case examples • Uplift modelling • Uplift evaluation & results
- 4. Client situation • French Online Gaming Company (RPG) • A lot of users are leaving • let’s do a churn prediction model ! • Target : no come back in 14 or 28 days. (14 missing days -> 80 % of chance not to come back 28 missing days -> 90 % of chance not to come back) • Features : • Connection features : • Time played in 1,7,15,30,… days • Time since last connection • Connection frequency • Days of week / hours of days played • Equivalent for payments and subscriptions • Age, sex, country • Number of account, is a bot … • No in game features (no data)
- 5. Client situation • Model Results : • AUC 0.88 • Very stable model • Marketing actions : • 7 diﬀerent actions based on customer segmentation (oﬀers, promotion, … ) • A/B test -> -5 % churn for persons contacted by email • Going further : • Feature engineering : guilds, close network, in game actions, … • Study long term churn …
- 6. Client situation • But wait ! • Strong hypothesis : target the person that are the most likely to churn
- 7. Client situation • But wait ! • Strong hypothesis : target the person that are the most likely to churn • What is the gain / person for an action ? • cost of action • value of the customer • independent variables • “treated” population and “control” population • • Value with action : • Value without action : • Gain (if independent of treatment ) : c vi i X T C Y = ⇢ 1 if customer churn 0 otherwise ET (Vi) = vi(1 PT (Y = 1|X)) c EC (Vi) = vi(1 PC (Y = 1|X)) vi E(Gi) = vi(PC (Y = 1|X) PT (Y = 1|X)) c
- 8. Client situation • But wait ! • Strong hypothesis : target the person that are the most likely to churn • What is the gain / person for an action ? • Objective : maximize this gain • Targeting highly probable churner -> minimize But not the diﬀerence ! • Intuitive examples : • : action is expected to make the situation worst. Spam ? • : user does not care, is already lost Upli& = Model E(Gi) = vi(PC (Y = 1|X) PT (Y = 1|X)) c PT (Y = 1|X) PC (Y = 1) ⇡ PT (Y = 1) P PC (Y = 1) < PT (Y = 1)
- 9. Uplift • Model eﬀect of the action • 4 groups of customers / patients • 1 Responded because of the action (the people we want) • 2 Responded, but would have responded anyway (unnecessary costs) • 3 Did not respond and the action had no impact (unnecessary costs) • 4 Did not respond because the action had a negative impact (negative impact) • Incomplete knowledge
- 10. Uplift Examples • Healthcare : • A typical medical trial: • treatment group: gets the treatment • control group: gets placebo (or another treatment) • do a statistical test to show that the treatment is better than placebo • With uplift modeling we can find out for whom the treatment works best • Personalized medicine • Ex : What is the gain in survival probability ? -> classification/uplift problem
- 11. Uplift Examples • Churn : • E-gaming • Other Ex : Coyote • Retail : • Compare coupons campaigns
- 12. Uplift Examples • Mailing : Hillstrom challenge • 2 campaigns : • one men email • one woman email • Question : who are the people to target / that have the best response rate
- 13. Uplift Examples • Common pattern • Experiment or A/B testing -> Test and control • Warning : Control can be biased easily : • Targeted most probable churners and control is the rest • Call only the people that come to a shop • Limited experiment trial -> no bandit algorithm : (once a medicine experiment is done, you don’t continue the “exploration”) -> relatively large and discrete in time feedbacks.
- 14. Uplift modelling • Three main methods : • Two models approach • Class variable modification • Modification of existing machine learning models
- 15. Uplift modelling : Two model approach • Build a model on treatment to get • Build a model on control to get • Set : PT (Y |X) PC (Y |X) P = PT (Y |X) PC (Y |X)
- 16. Uplift modelling : Two model approach • Advantages : • Standard ML models can be used • In theory, two good estimators -> a good uplift model • Works well in practice • Generalize to regression and multi-treatment easily • Drawbacks • Diﬀerence of estimators is probably not the best estimator of the diﬀerence • The two classifier can ignore the weaker uplift signal (since it’s not their target) • Algorithm focusing on estimating the diﬀerence should perform better
- 17. Uplift modelling : Class variable modification • Introduced in Jaskowski, Jaroszewicz 2012 • Allows any classifier to be updated to uplift modeling • Let denote the group membership (Treatment or Control) • Let’s define the new target variable : • This corresponds to flipping the target in the control dataset. G 2 {T, C} Z = 8 < : 1 if G = T and Y = 1 1 if G = C and Y = 0 0 otherwise
- 18. Uplift modelling : Class variable modification • Why does it work ? • By design (A/B test warning !), should be independent from • Possibly with a reweighting of the datasets we should have : thus P(Z = 1|X) = PT (Y = 1|X)P(G = T|X) + PC (Y = 0|X)P(G = C|X) P(Z = 1|X) = PT (Y = 1|X)P(G = T) + PC (Y = 0|X)P(G = C) G X P(G = T) = P(G = C) = 1/2 2P(Z = 1|X) = PT (Y = 1|X) + PC (Y = 0|X)
- 19. Uplift modelling : Class variable modification • Why does it work ? Thus And sorting by is the same as sorting by 2P(Z = 1|X) = PT (Y = 1|X) + PC (Y = 0|X) = PT (Y = 1|X) + 1 PC (Y = 1|X) P = 2P(Z = 1|X) 1 P(Z = 1|X) P
- 20. Uplift modelling : Class variable modification • Summary : • Flip class for control dataset • Concatenate test and control dataset • Build a classifier • Target users with highest probability • Advantages : • Any classifier can be used • Directly predict uplift (and not each class separately) • Single model on a larger dataset (instead of two small ones) • Drawbacks : • Complex decision surface -> model can perform poorly • Interpretation : what is AUC in this case ?
- 21. Uplift modeling : Other methods • Based on decision trees : • Rzepakowski Jaroszewicz 2012 new decision tree split criterion based on information theory • Soltys Rzepakowski Jaroszewicz 2013 Ensemble methods for uplift modeling (out of today scope)
- 22. Evaluation • We used : • 2 model approach. -> AUC ? Not very informative. • 1 model approach -> does AUC means something ? • How can we evaluate / compare them ? • Cross Validation : • 4 datasets : treatment/control x train/test • Problem : • We don’t have a clear 0/1 target. • We would need to know for each customer • Response to treatment • Response to control -> not possible
- 23. Evaluation • Gain for group of customers : • Gain for the 10% highest scoring customers = % of successes for top 10% treated customers − % of successes for top 10% control customers • Uplift curve ? : • Diﬀerence between two lift curve • Interpretation : net gain in success rate if a given percentage of the population is treated • Pb : no theoretic maximum • Pb 2 : weird behaviour for 2 wizard models.
- 24. Evaluation : Qini • Qini Measure : • Similar to Gini (Area under lift curve). Lift Curve <-> Qini Curve • Parametric curve defined by : • When taking the first observations • is the total number of 1 seen in target observations • is the total number of 1 seen in control observations • is the total number of target observations • is the total number of control observations • Balanced setting : t f(t) = YT (t) YC(t) ⇤ NC(t)/NT (t) YT YC NC NT f(t) = YT (t) YC(t)
- 25. Evaluation : Qini • Personal intuition : • We can’t know everything : • treated that convert, not treated that don’t convert. What would have happen ? • But we don’t want to see : • Treated not converting • Not treated converting (in our top list) • In we want to minimize : • Very similar to lift taking into account only negative examples. t NT (t) YT (t) + YC(t)
- 26. Evaluation : Qini f(t) = YT (t) YC(t)
- 27. Evaluation : Qini • Best model : • Take first all positive in target and last all positive in control. • No theoretic best model : • depends on possibility of negative eﬀect • Displayed for no negative eﬀect • Random model : • Corresponds to global eﬀect of treatment • Hillstrom Dataset : • For women models are comparable and useful • For men, there is no clear individuals to target
- 28. Evaluation : Qini f(t) = YT (t) YC(t)
- 29. Evaluation : Qini • Back to our study : • Class modification performs best • Two models approach performs poorly • A/B test failure : • Control dataset is way to small ! • Class modification model very close to lift • Two model slightly better than random -> need to redo the A/B test.
- 30. Conclusion • Uplift : • Surprisingly little literature / examples • The theory is rather easy to test • Two models • Class modification • The intuition and evaluation are not easy to grasp • On the client side : • I don’t loose hope we’ll do the A/B test again • A good lead to select the best oﬀer for a customer
- 31. A few references • Data : • Churn in gaming : WOWAH dataset (blog post to come) • Uplift for healthcare : Colon Dataset • Uplift in mailing : Hillstrom data challenge • Uplift in General : Simulated data : (blog post to come)
- 32. A few references • Application • Uplift modeling for clinical trial data (Jaskowski, Jaroszewicz) • Uplift Modeling in Direct Marketing (Rzepakowski, Jaroszewicz)
- 33. A few references • Modeling techniques : • Rzepakowski Jaroszewicz 2011 (decision trees) • Soltys Rzepakowski Jaroszewicz 2013 (ensemble for uplift) • Jaskowski Jaroszewicz 2012 (Class modification model)
- 34. A few references • Evaluation • Using Control Groups to Target on Predicted Lift (Radcliﬀe) • Testing a New Metric for Uplift Models (Mesalles Naranjo)
- 35. Thank you for your attention !

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