Reducing Churn with Data‑Led Customer Experience
An end‑to‑end analysis of refunds, late deliveries and subscription behaviors across 20 hubs in Ahmedabad, turning insights into actionable playbooks for retention and efficiency.
Overview
Big Basket's bb daily service operates a high‑frequency subscription model where customer experience hinges on consistent delivery quality and reliable product handling. We partnered to examine the full journey—from order to doorstep—to quantify where friction occurs and how it affects churn.
Using hub‑level data across Ahmedabad, we analyzed complaints, refunds, and delivery windows, then connected those events to subscription changes. The result is a practical playbook that prioritizes operational fixes with the largest impact on retention.
Objectives
- • Identify top drivers of churn across product, subscription, and service experience.
- • Quantify refunds by reason and their financial impact.
- • Analyze delivery timeliness windows and complaint patterns.
- • Recommend actions to improve retention and operational efficiency.
Methodology
Data sources: orders, refunds, complaints, delivery windows
Techniques: cohorting, time-series, contribution analysis
Deliverable: interactive retention & complaints dashboard
Key Findings
- Late deliveries concentrated between 5:00–6:30 AM accounted for a significant share of complaints.
- Refunds (~₹2.1M) were driven largely by less‑packed quantity and damaged products.
- 65% of churn linked to subscription changes and reduced need.
Recommendations
Shift routes & staffing to late window; live hub monitoring
Strengthen packaging QA; SKU‑level refund alerts
Soft‑retention during subscription change events