Dec 17, 2024

by Sabarna December 17th, 2024 Too Long; Didn't Read Uplift Modeling is a way to model the impact of marketing campaigns on customer conversion. It aims to identify the customers that are the most incremental for an ad campaign. Uplift models the effect treatment has on conversion. When faced with the problem of trying to influence prospective customers into buying your product an obvious choice is to run precisely targeted Marketing campaigns. Now what do the words precise targeting mean. In the Universe of prospective customers there are only so many types. Sure Things : These customers will buy whether or not they receive an ad impression. They are your loyal, brand aware customers that don’t need any convincing to buy from you Lost Causes : These customers are the exact opposite of Sure Things. They will not buy no matter how many ad impressions you target them with. They are probably aware of your brand but are committed to a different vendor or just don’t have a need for the product you sell. Persuadables : These are the customers you want to work with. While they might be initially uncommitted to your brand they will change their mind when exposed to the right kind of ad-messaging. Much of Uplift Modeling is about finding these customers in your population. Sleeping Dogs : Now these are the bunch of customers you do not want to impress with an ad. Theoretically they are the people that, while otherwise a paying customer, would become non-committal when exposed to an ad. Think of suddenly becoming conscious of your automatic payment to Netflix, when shown an ad about a new release and deciding to cancel it. The following is a nice visualization of the different types and their response to treatment(ad exposure) Problem Formulation Imagine you are a Data Scientist working for an e-commerce company, tasked with the objective of identifying the customers that are the most incremental for an ad campaign, to ensure that the campaign budget is spent in the most optimal fashion. The keyword here is incremental. Drawing from our discussions so far, we could define incrementality by looking at the difference between the effect treatment has on customer conversion versus control. That is : Uplift = P(Conversion|Treatment) - P(Conversion|Control) i.e., what is the incremental probability that a customer will convert when shown an ad versus not. Let’s further understand what this metric means for the different types of entities in our Universe of customers. Sure Things : uplift=u.predictUplift(df_test) References [1] Athey, S., & Imbens, G. W. (2015). Machine learning methods for estimating heterogeneous causal effects. stat, 1050(5). [2]Nicholas J Radcliffe and Patrick D Surry. Real-world uplift modelling with significance based uplift trees. White Paper TR-2011–1, Stochastic Solutions, 2011. [3]Gutierrez, P., & Gérardy, J. Y. (2017, July). Causal Inference and Uplift Modelling: A Review of the Literature. In International Conference on Predictive Applications and APIs (pp. 1–13). [4]Piotr Rzepakowski and Szymon Jaroszewicz. Decision trees for uplift modeling with single and multiple treatments. Knowledge and Information Systems, 32(2):303–327, 2012 [5]Leo Guelman, Montserrat Guill´en, and Ana M P´erez-Mar´ın. Uplift random forests. Cybernetics and Systems, 46(3–4):230–248, 2015. Acknowledgements A big thanks to Robert Yi and Will Frost for their package (Pylift) and our discussions that helped me understand Uplift modeling better. Also thanks to Kaashyap Thiyagaraj helping me test CHUC and provide crucial feedback. L O A D I N G . . . comments & more!