# Early Churn Prediction from Large Scale User-Product Interaction Time Series

**Authors:** Shamik Bhattacharjee, Utkarsh Thukral, Nilesh Patil  
**arXiv:** [2309.14390](https://arxiv.org/abs/2309.14390)  
**Date:** 2023-09-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.14390v1/

## Key points

- User churn is a significant issue for businesses in Business-to-Customer scenarios
- Churn can impact promotional discounts and retention campaigns, especially in fast-moving sectors like fantasy sports
- Transaction history and user-product interaction are useful indicators for predicting churn but require extensive feature engineering and domain knowledge
- The study focuses on developing a model that forecasts customer churn likelihood using historical data
- The approach treats churn prediction as multivariate time series classification and leverages user behavior data
- Transformer-based models outperform traditional methods in noisy Business-to-Customer settings, reducing the need for extensive feature engineering
- Distributed training approach using PyTorch and Petastorm was implemented for efficient training on large-scale data from Amazon S3
- Models were trained for 100 epochs with techniques like gradient accumulation and concurrent computations to ensure model consistency and accelerate training process
- The study demonstrates the effectiveness of utilizing user-product interaction time series data for early churn prediction in dynamic industries like fantasy sports

## Summary

User churn is a significant issue for businesses in various Business-to-Customer scenarios. It has economic implications and can impact promotional discounts and retention campaigns, especially in fast-moving sectors like fantasy sports where spending habits can be influenced by international sports events. While transaction history and user-product interaction are useful indicators for predicting churn, they often require extensive feature engineering and deep domain knowledge. In this study, we focus on developing a model that forecasts the likelihood of customer churn using historical data. By treating churn prediction as multivariate time series classification and leveraging user behavior data, we aim to help businesses understand attrition patterns and formulate effective retention strategies. Our approach utilizes Transformer-based models that outperform traditional methods in noisy Business-to-Customer settings, reducing the need for extensive feature engineering. To efficiently train our models on large-scale data, we implemented a distributed training approach using PyTorch and Petastorm for high-speed data loading from Amazon S3. The training process involved running the models for 100 epochs to capture intricate patterns effectively. By employing techniques like gradient accumulation and concurrent computations, we ensured model consistency and accelerated the training process. Overall, our study demonstrates the effectiveness of utilizing user-product interaction time series data for early churn prediction in complex business contexts like fantasy sports. Our experiments show that our approach improves upon traditional churn prediction methods by leveraging neural networks and Transformers to accurately predict customer churn with limited features. This refined summary highlights the importance of understanding customer behavior through data analysis to drive effective retention strategies in dynamic industries like fantasy sports.