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Activity Number: 192
Type: Contributed
Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Marketing
Abstract - #309359
Title: Simulation and Modeling of Churn and Customer Lifetime Value in Mobile Applications
Author(s): Alex Zolot*+ and Yakov Keselman
Companies: Medio Systems and Medio Systems
Keywords: churn and LTV ; empirical Bayes ; Maximum A-Posteriori Probability ; HMM ; Kaplan-Meier estimate ; simulation
Abstract:

Developers of mobile applications need to know the effect of various app features on their customers' churn and lifetime value (LTV). The only available data often are activity log files for short period of observation, and "truth info" about churn and LTV is absent, so even being able to reliably label customers as "churned" or "active" is a challenge.

We describe variations of the MAP (Maximum A-Posteriori Probability), HMM (Hidden Markov Model), and the empirical Bayes approach that we have developed to address the problem.

For non-parametric analysis of data, we generalized Kaplan-Meier technic used in survival analysis to the case of "fully censored" data, i.e., when the dates for both, "birth" and "death", and even facts of "death" are unknown.

We have validated our approaches both on simulated data (where the truth is known) and on real data.


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