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BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models

arXiv 2203.16155 ↗First posted 30 Mar 2022cs.LGcited by 0

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Abstract (arXiv)

We consider the problem of developing a clickstream modeling framework for real-time customer event prediction problems in SaaS products like QBO. We develop a low-latency, cost-effective, and robust ensemble architecture (BBE-LSWCM), which combines both aggregated user behavior data from a longer historical window (e.g., over the last few weeks) as well as user activities over a short window in recent-past (e.g., in the current session). As compared to other baseline approaches, we demonstrate the superior performance of the proposed method for two important real-time event prediction problems: subscription cancellation and intended task detection for QBO subscribers. Finally, we present details of the live deployment and results from online experiments in QBO.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 57 references that are in the map. First posted on arXiv 30 Mar 2022.

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References · 15

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  11. 2019I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Applicationcited by 10
  12. 2019Customer churn prediction in telecom using machine learning and social network analysis in big data platformcited by 7
3 more
  1. 2016A Latent-class Model for Estimating Product-choice Probabilities from Clickstream Datacited by 3
  2. 2017Games and Big Data: A Scalable Multi-Dimensional Churn Prediction Modelcited by 3
  3. 2015Predicting online user behaviour using deep learning algorithmscited by 2

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