Retention for developers declined in Prime. What is your analysis plan

Problem Statement Description

Product context: Amazon is a commerce, logistics, media, devices, and cloud company; its products include Marketplace, Prime, Prime Video, Alexa devices, ads, fulfillment, and AWS.

Amazon has observed a decline in retention among developers associated with Prime. In this context, developers may include external or partner developers building integrations, services, tools, or customer experiences that depend on Prime-related APIs, benefits, fulfillment, media, devices, or commerce capabilities. You are asked to outline how you would investigate the decline, not to jump directly to fixes.

Frame the problem as a root-cause analysis for a product ecosystem where developer retention depends on successful onboarding, API reliability, documentation quality, business value, partner incentives, support responsiveness, and continued usage of Prime-related capabilities. The analysis should distinguish whether this is a true behavioral decline, a measurement issue, a cohort mix shift, or the result of a recent product, policy, pricing, platform, or market change.

Your plan should reflect Amazon’s operating context: high-scale systems, multiple developer segments, complex dependencies across commerce, logistics, devices, media, and cloud infrastructure, and a strong emphasis on customer obsession, operational excellence, and long-term ecosystem health.

The experience should consider:

- How “developer retention” is defined, including the denominator, retention window, activation criteria, and what counts as meaningful ongoing usage.

- Whether the decline is broad-based or concentrated by developer segment, use case, geography, tenure, acquisition channel, integration type, API surface, partner tier, or business size.

- Instrumentation and data-quality checks, including tracking changes, identity stitching, event loss, delayed reporting, bot/test traffic, and changes to cohort definitions.

- Timeline analysis around product launches, API changes, documentation updates, SDK releases, policy changes, pricing changes, support SLAs, outages, or partner-program changes.

- Behavioral funnel signals such as onboarding completion, API key creation, first successful call, error rates, latency, sandbox-to-production conversion, feature adoption, and support-ticket patterns.

- External and competitive factors, including shifts in developer attention toward other commerce, cloud, media, or platform ecosystems.

- How you would prioritize hypotheses, gather evidence, size impact, and determine whether mitigation is urgent or should wait for stronger validation.

- Prevention mechanisms such as monitoring, alerting, cohort dashboards, experiment readouts, post-launch reviews, and developer feedback loops.

The goal is to present a structured investigation plan that would help Amazon identify the most likely drivers of the retention decline, separate signal from noise, estimate business and developer impact, and guide the organization toward evidence-based next steps without prematurely prescribing a solution.