Development of an End-to-end Machine Learning System with Application to In-app Purchases

TL;DR

This paper presents the development of an end-to-end machine learning system designed to predict in-app purchase timing in mobile games, aiming to optimize offer presentation and enhance user engagement.

Contribution

It introduces a novel ML system architecture for predicting player purchase behavior and details its implementation in a real-world gaming environment.

Findings

Accurately predicts players' next in-app purchase times

Improves timing of offer presentations to players

Provides insights into system deployment challenges

Abstract

Machine learning (ML) systems have become vital in the mobile gaming industry. Companies like King have been using them in production to optimize various parts of the gaming experience. One important area is in-app purchases: purchases made in the game by players in order to enhance and customize their gameplay experience. In this work we describe how we developed an ML system in order to predict when a player is expected to make their next in-app purchase. These predictions are used to present offers to players. We briefly describe the problem definition, modeling approach and results and then, in considerable detail, outline the end-to-end ML system. We conclude with a reflection on challenges encountered and plans for future work.