Complaints increased for instant grocery substitution and availability after a release. How would you investigate under scale, incentive, and regulatory constraints?

Problem Statement Description

Blinkit has just released changes in the instant grocery ordering experience related to item availability and substitutions. Soon after launch, customer complaints increased, especially among premium quick-commerce users who expect accurate availability, acceptable substitutions, and reliable on-time fulfillment within tight delivery windows.

You are asked to investigate this as a root-cause analysis problem. The issue may involve app experience, inventory signals, substitution logic, picker workflows, dark-store operations, customer communication, delivery SLAs, or incentive structures that influence how substitutions and unavailable items are handled at scale.

The investigation must account for real-world constraints: high order volume during demand spikes, local dark-store inventory variance, operational workload on pickers and support teams, privacy and data-quality limitations, accessibility of substitution choices, regulatory requirements around product information, and the cost of any mitigation.

The experience should consider:

- How to frame the anomaly: complaint type, time window, affected flows, severity, and whether the increase is statistically meaningful versus expected seasonality or demand spikes.

- Which segments to compare, including premium users, new versus repeat users, geographies, dark stores, categories, order sizes, delivery slots, app versions, and substitution opt-in behavior.

- How to validate instrumentation and data quality across inventory availability, substitution offers, picker actions, customer choices, cancellations, refunds, support contacts, and delivery outcomes.

- What hypotheses could explain the complaint spike across product release changes, inventory accuracy, substitution ranking, customer communication, picker incentives, operational capacity, and fulfillment SLA pressure.

- What evidence would confirm or reject each hypothesis, including funnel drops, mismatch rates, out-of-stock rates, substitution acceptance, post-order complaints, refund reasons, and support transcripts.

- How to separate product-caused issues from operational or external factors such as local supply shortages, campaign-driven demand, vendor delays, weather, or dark-store staffing constraints.

- What immediate mitigations could reduce customer harm while the root cause is investigated, without creating excessive cost, regulatory risk, or operational overload.

- How to prevent recurrence through monitoring, alerting, release gates, experiment safeguards, and clearer ownership between product, operations, inventory, support, and compliance teams.

Your goal is to describe a structured investigation plan that identifies the most likely causes of the complaint increase, prioritizes evidence over assumptions, protects customer trust, and supports a responsible decision on whether to rollback, patch, operationally mitigate, or continue monitoring the release.