Conversion in local delivery experience declined after a pricing or policy change. Diagnose it

Problem Statement Description

You are investigating a decline in conversion within a local delivery experience after a recent pricing or policy change. The product connects marketplace sellers with local delivery options, and conversion may refer to sellers or buyers completing a key step such as selecting local delivery, confirming an order, purchasing a shipping label, or completing checkout after seeing delivery terms.

The issue is high-stakes because local delivery depends on trust, predictable costs, operational reliability, and clear expectations between sellers, buyers, couriers, and the marketplace. A pricing or policy update may have changed fees, eligibility, delivery radius, service levels, seller obligations, refund rules, or buyer-facing promises, and the impact may vary meaningfully by geography, seller type, order value, delivery distance, or category.

Your task is to frame how you would diagnose the conversion drop, separate true customer behavior from measurement or rollout artifacts, identify the most likely drivers, and recommend how the team should decide on mitigation. Do not assume the change is necessarily the root cause; evaluate timing, exposure, segmentation, and alternative explanations.

The experience should consider:

- The exact conversion funnel being measured, including numerator, denominator, entry point, and completion event.

- Timing of the decline relative to policy launch, pricing exposure, experiments, communications, seasonality, and operational incidents.

- Segments such as sellers vs. buyers, new vs. repeat users, geography, delivery distance, category, order value, seller size, and courier availability.

- Instrumentation checks, including event logging changes, eligibility logic, pricing display accuracy, attribution, and data latency.

- Behavioral hypotheses around price sensitivity, policy comprehension, perceived fairness, trust, reliability, and checkout friction.

- Marketplace dynamics, including seller opt-outs, inventory availability, buyer abandonment, delivery supply constraints, and support contacts.

- Evidence needed to distinguish correlation from causation, including cohorts, exposed vs. unexposed users, pre/post baselines, and guardrail metrics.

- Immediate mitigation options, longer-term prevention mechanisms, and how to monitor recovery without creating new reliability or trust risks.

The goal is to demonstrate a structured root-cause investigation that is rigorous enough for a large-scale marketplace environment, balances user experience with business and operational constraints, and leads to a decision-ready understanding of whether to roll back, adjust, communicate, or further test the pricing or policy change.