Raphael de Brito Tamaki
Data Science Lead in the Marketing Science @Meta, where I use causal inference techniques to extract insights to help advertisers increase their marketing performance. Prior to joining Meta, I worked at Wildlife Studios - a mobile game studio with over 2B total downloads - where I was the Tech Lead for the Lifetime Value (LTV) prediction team, and implemented and maintained LTV models in production for over 10 games
Sessions
In this talk, we discuss how we can use the python package PySTAN to estimate the Lifetime Value (LTV) of the users that can be acquired from a marketing campaign, and use this estimate to find the optimal bidding strategy when the LTV estimate itself has uncertainty. Throughout the presentation, we highlight the benefits from using Bayesian modeling to estimate LTV, and the potential pitfalls when forecasting LTV. By the end of the presentation, attendees will have a solid understanding of how to use PySTAN to estimate LTV, optimize their marketing campaign bidding strategies, and implement the best Bayesian modelling solution. All of the contents and numbers in this presentation can be found in the shared GIT