AI-Powered Predictive Model for Churn Control
Share this Case Study
Aeries built a churn propensity model that enabled the client to identify high-risk customers and implement targeted retention strategies.
Key Results
AI model detected at-risk customers
Identified churn-prone accounts, becoming one of the top 5 drivers for customer retention strategy.
Data model performance
Deployed model delivered near-perfect accuracy, precision, and recall scores.
Targeted retention campaigns for at-risk customers
Enabled client to run focused retention campaigns for high-risk customers.
About Client
Mid-sized organization, Portfolio company owned by a Private Equity firm
Industry: Domain registration & web hosting
Location: U.S.A.
Revenue: $900 M – $1 B (estimated)
Employees: 3,500+
Predicting and Preventing Customer Churn
Challenge
The client needed a predictive AI solution to reduce rising customer churn.
Client lost $46 M to churn and lacked a way to identify high-risk accounts.
Solution
Aeries developed an AI model scoring customers with 10% – 90% churn risk.
Results
Enabled client to design retention strategies by identifying high-risk customers.
Implementation Roadmap
The 13-week project was delivered 100% remotely during COVID-19 in three iterative phases.
Iteration 1 (Weeks 1–4)
- Objective: Build a comprehensive data snapshot for initial analysis.
- Key Activities: Filtered data by brand, product type, revenue, geography.
- Outcome: Established a baseline model with 84% accuracy.
Iteration 2 (Weeks 5–8)
- Objective: Incorporate time-based behavior & retention outreach signals.
- Key Activities: Added engagement history features & support touchpoints.
- Outcome: Precision jumped to 92%, recall to 88%.
Iteration 3 (Weeks 9–13)
- Objective: Add product usage to identify high-intent users.
- Key Activities: Integrated domain traffic and user activity signals.
- Outcome: Achieved 97% accuracy, 98% precision, and 97% recall in the deployed model.