I went straight to click-through and conversion rates and then kind of stalled.
Start by clarifying the business goal of the recommendation engine (e.g., increase conversion, AOV, or discovery) and the merchant context. Then structure your answer around a metric framework that covers model performance, user engagement, and business impact, with specific data points for each. Emphasize how you would balance short-term and long-term metrics.
Pro tip: Tie your metrics to Shopify's merchant success: show how recommendation quality drives GMV for merchants, not just platform engagement. Also, mention the importance of guardrail metrics to avoid cannibalization or poor recommendations.
Clarify what the recommendation engine is optimizing for: e.g., increasing conversion rate, average order value, cross-sell/upsell, or product discovery. Align with Shopify's merchant-first mission.
Choose offline metrics like precision@k, recall@k, NDCG, or MAP to evaluate the relevance of recommendations. These help assess the model before deployment.
Track user interactions such as click-through rate (CTR), add-to-cart rate, and time spent on recommended products. These indicate real-time user response.
Use metrics like conversion rate, average order value (AOV), revenue per session, and incremental sales attributable to recommendations. Consider lift over control group.
Include metrics like return rate, customer satisfaction (CSAT), diversity of recommendations, and repeat purchase rate to ensure recommendations don't harm user experience or merchant trust.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.