BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models for Real-Time Customer Event Prediction in SaaS Products like QBO

Temel Kavramlar

Proposing BBE-LSWCM as a robust ensemble architecture for real-time clickstream modeling, demonstrating superior performance in predicting subscription cancellation and intended task detection for QBO subscribers.

Özet

The content introduces the BBE-LSWCM framework developed by Arnab Chakraborty, Vikas Raturi, and Shrutendra Harsola at Intuit. It focuses on real-time customer event prediction problems in SaaS products like QBO. The abstract highlights the combination of aggregated user behavior data from historical windows with recent user activities to improve predictions. The article discusses the methodology, challenges faced in SaaS products, and the proposed approach's advantages. It also includes an ACM reference format for further reading.

1. Introduction

   • Objective: Develop a clickstream modeling framework for real-time customer event prediction.
   • Examples: Subscription cancellation prediction, intended task detection, task abandonment prediction.

2. Motivation

   • Focus on eCommerce domain vs. customer events like churn.
   • Importance of incorporating current session vs. historical user behavior.

3. Proposed Approach

   • Introduction to BBE-LSWCM: Block-Bootstrapped Ensemble combining long and short window data.

4. Data Extraction

   • "On average in a day the number of clicks per active user can vary between 50 to 5000."
   • "The entire clickstream data considered contains over 1 billion clicks with around 350 unique pages and 2500 unique events."

5. Results

   • Comparison with baselines in churn identification: BBE-LSWCM outperforms batch models, in-session models, survival models, and joint models in terms of lift scores at different deciles, AUROC score, and average time from warning to churn.

6. Baseline Approaches

   • Baselines include batch models (LR, GBDT), in-session models (GBDT), survival models (batch-RSF), joint models (xDeepFM).

7. Ablation Study

   • Conducted ablation study comparing LWM, SWM, LSWCM models for both churn identification and intended task detection.

8. Experimentation Results

   • All experiments conducted on AWS Sagemaker instance with results showing significant improvement using BBE-LSWCM compared to baselines.

İstatistikler

"On average in a day the number of clicks per active user can vary between 50 to 5000."
"The entire clickstream data considered contains over 1 billion clicks with around 350 unique pages and 2500 unique events."

Alıntılar

(No striking quotes provided)

Önemli Bilgiler Şuradan Elde Edildi

BBE-LSWCM

by Arnab Chakra... : arxiv.org 03-25-2024

https://arxiv.org/pdf/2203.16155.pdf

Daha Derin Sorular

How does the incorporation of both long window historical data and short window recent activity enhance predictive accuracy

BBE-LSWCM enhances predictive accuracy by incorporating both long window historical data and short window recent activity through a hybrid ML framework. By leveraging aggregated user behavioral data over a longer historical window (e.g., last few weeks) and user activities over a short window from the recent past (e.g., current session or last hour), the model can capture a comprehensive view of user behavior.
The long window historical data provides context and trends in user behavior over an extended period, capturing patterns that may indicate potential churn or intended tasks. On the other hand, the short window recent activity captures real-time interactions and immediate signals of user intent or behavior changes. By combining these two sources of information, BBE-LSWCM can effectively balance the importance of in-session versus longer history behaviors based on underlying data dynamics.
This approach allows for more robust predictions as it considers both macro-level trends and micro-level interactions, leading to improved accuracy in identifying high-risk users for subscription cancellation or predicting intended tasks accurately.

What are the implications of the study's findings on real-time customer event prediction beyond SaaS products

The findings of this study on real-time customer event prediction have implications beyond SaaS products in various industries. The ability to model clickstream data for predicting customer events such as churn detection or task abandonment has broad applications across different sectors:

Retail: In e-commerce platforms, understanding customer behavior in real-time can help predict purchase decisions, cart abandonment rates, and personalize product recommendations.

Finance: Banks and financial institutions can use similar models to predict account closures, identify fraudulent activities early on, and offer personalized financial services based on customer needs.

Telecommunications: Telecom companies can leverage clickstream modeling to anticipate subscriber churn rates, optimize service offerings based on usage patterns, and enhance customer retention strategies.

Healthcare: Healthcare providers could utilize real-time event prediction models to foresee patient disengagement with treatment plans or appointments scheduling optimization based on patient interaction history.

By applying advanced clickstream modeling techniques like BBE-LSWCM across industries beyond SaaS products, organizations can improve customer experience management strategies through proactive interventions tailored to individual preferences and behaviors.

How might advancements in clickstream modeling impact other industries or applications

Advancements in clickstream modeling have significant implications for various industries by enabling more accurate predictions of user behavior and enhancing decision-making processes:

Personalized Marketing: Improved clickstream models allow businesses to deliver targeted marketing campaigns based on individual browsing habits and preferences resulting in higher conversion rates.

Customer Retention: Enhanced predictive analytics using clickstream data helps identify at-risk customers early-on allowing companies to implement retention strategies proactively.

Operational Efficiency: Streamlining operations by analyzing clickstreams leads to better resource allocation efficiency while optimizing processes according to actual consumer needs.

4Fraud Detection: Advanced clickstream analysis aids in detecting anomalies indicative of fraudulent activities providing enhanced security measures against cyber threats
Overall advancements in clickstream modeling not only benefit businesses by improving customer satisfaction but also contribute towards operational excellence across diverse industry verticals through informed decision-making processes driven by actionable insights derived from granular user interaction data streams."