Basket completion rate dropped suddenly in quick commerce replenishment. Diagnose the root cause

Problem Statement Description

Zepto has observed a sudden drop in basket completion rate for its quick-commerce replenishment experience, especially among busy households restocking everyday essentials. This flow may include users adding frequently purchased grocery, household, baby, personal care, or fresh items to cart and attempting to complete checkout within a short delivery promise window.

Your task is to diagnose the root cause of the decline as a product manager. Treat this as an incident-style RCA where the metric has moved unexpectedly and the business needs to understand whether the issue is caused by user behavior, product changes, inventory availability, pricing, payments, delivery constraints, app performance, or operational execution across dark stores and fulfillment.

You should focus on structuring the investigation, identifying the right cuts of data, validating instrumentation, and narrowing from broad hypotheses to evidence-backed root causes. The goal is not to redesign the entire replenishment experience, but to show how you would isolate what changed, where it changed, who was affected, and what should be done next.

The experience should consider:

- Clear definition of “basket completion rate,” including numerator, denominator, time window, and where the funnel starts and ends.

- Segmentation by city, dark store, delivery zone, user type, platform, app version, category, basket size, payment method, and new versus repeat users.

- Checks for tracking or instrumentation issues before assuming a real customer behavior change.

- Funnel breakpoints such as search, add-to-cart, stock availability, substitutions, cart review, coupon application, delivery slot promise, payment, and order confirmation.

- Operational factors including dark-store stockouts, picker accuracy, item availability refresh delays, delivery capacity, and minimum order or fee changes.

- Customer-facing friction such as unavailable replenishment staples, price changes, coupon failures, payment errors, app latency, accessibility issues, or confusing checkout states.

- Evidence needed to distinguish correlation from causation, including recent launches, experiments, backend changes, marketing campaigns, supply disruptions, and competitor or seasonal effects.

- Immediate mitigation, communication, monitoring, and prevention steps once the likely root cause is identified.

The goal is to demonstrate a practical RCA approach that protects customer trust and business continuity while balancing speed, data quality, operational load, and confidence in the diagnosis.