# Profiling Players with Engagement Predictions

## TL;DR

This paper investigates using engagement predictions and deep learning to profile high-spending video game players by analyzing survival curves, game progress, and lifetime value, offering a promising method for user segmentation.

## Contribution

It introduces a novel approach combining survival analysis and LSTM-based lifetime value prediction for player profiling in video games.

## Key findings

- Engagement metrics can effectively classify high-value players.
- LSTM models accurately predict player lifetime value.
- Survival curves reveal distinct player behavior patterns.

## Abstract

The possibility of using player engagement predictions to profile high spending video game users is explored. In particular, individual-player survival curves in terms of days after first login, game level reached and accumulated playtime are used to classify players into different groups. Lifetime value predictions for each player---generated using a deep learning method based on long short-term memory---are also included in the analysis, and the relations between all these variables are thoroughly investigated. Our results suggest this constitutes a promising approach to user profiling.