Start by framing the App Store as a two-sided marketplace and define a north-star metric that balances merchant value and developer success, such as 'incremental merchant GMV driven by apps.' Then break down success metrics for each stakeholder, add guardrails, outline the data model, and finish with a concrete A/B test design for ranking/recommendation changes.
Pro tip: Emphasize that the north-star should be a 'healthy' metric—one that captures long-term ecosystem value, not just short-term installs—and explicitly discuss how you'd measure incrementality (e.g., holdout groups) to avoid rewarding apps that cannibalize existing sales.
Choose a north-star metric that aligns all parties, such as 'incremental merchant GMV attributable to apps,' and map how it reflects value for merchants, developers, and Shopify.
For merchants: app adoption, retention, and impact on key outcomes (e.g., conversion, AOV). For developers: installs, revenue, retention, and time-to-first-sale. For Shopify: app ecosystem revenue, merchant retention, and platform health.
Include metrics that ensure changes don't harm the ecosystem, such as app quality ratings, support ticket volume, page load time, and merchant churn.
Describe key entities (merchants, apps, installs, transactions, reviews) and how you'd join them to compute metrics, including event-level data for funnel analysis and experimentation.
Propose an A/B test with randomization at the merchant level, define primary and guardrail metrics, and plan for long-term holdout to measure incrementality and novelty effects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.