In the hyper-competitive e-commerce sector, identifying churn before it happens is the difference between sustainable growth and terminal decline. Aivra’s predictive models achieved an 88% accuracy rate for a Tier-1 retailer.
Customer retention is a critical challenge for e-commerce platforms. High acquisition costs mean that losing a customer can significantly impact the bottom line. A leading e-commerce platform approached AIVRA with a high churn rate and needed a data-driven solution to identify at-risk customers and implement proactive retention strategies.
The Challenge: Reactive Retention
The client was primarily relying on reactive strategies—sending discount codes to customers who hadn't made a purchase in 90 days. By that point, however, many customers had already shifted their loyalty to competitors. The goal was to build an early-warning system that identified churn intent weeks before the "quiet quit."
Our Methodology:
- Data Integration: We consolidated fragmented data from clickstream logs, support tickets, review sentiments, and transaction history into a unified profile.
- Feature Engineering: Our team identified key indicators such as "Reduced session duration," "Increased price-comparison behavior," and "Unresolved support friction."
- ML Model Deployment: We utilized an ensemble of Random Forest and XGBoost models, refined through continuous training cycles.
The Strategic Solution: Real-Time Intervention
The prediction was only half the battle. AIVRA architected an automated response layer that triggered specific workflows for customers flagged as "High Risk":
- Dynamic Pricing: Personalized "come-back" offers were generated in real-time, specifically tailored to categories the user had previously browsed.
- Priority Support: At-risk customers were automatically routed to senior support agents to ensure immediate resolution of any outstanding issues.
- Agentic Engagement: Automated, natural-language surveys were deployed via WhatsApp to capture qualitative sentiment data.
Measurable Outcomes
After six months of full-scale deployment, the results validated our strategic approach:
- Churn Reduction: A measurable 22% decrease in overall churn rate.
- LTV Improvement: A 14% increase in the average Customer Lifetime Value (LTV) for the targeted cohort.
- Marketing Efficiency: A 30% reduction in "wasted" promotional spend by targeting only those with a high probability of churn.
Conclusion: Data-Led Sovereignty
This engagement demonstrates the power of predictive intelligence when integrated into core operational cycles. By moving from hindsight to foresight, e-commerce leaders can safeguard their market position and ensure long-term profitability. AIVRA is committed to architecting the systems that make this possible.