I went straight to GMV and click-through rate as primary, then fumbled a bit on secondary.
Start by clarifying the goal of the Instagram Shopping Tab: to drive product discovery and purchases. Then define a north star metric (e.g., shopping revenue) and break it down into primary metrics (e.g., engagement, conversion) and secondary metrics (e.g., retention, satisfaction) that align with the user journey and business objectives.
Pro tip: Tie metrics to the user funnel and business impact, and mention guardrail metrics to ensure you're not optimizing one area at the expense of another (e.g., user experience).
Confirm the primary objective of the Shopping Tab: to increase product discovery and purchases within Instagram. Align with the interviewer on the business model (e.g., commission-based, ad revenue).
Propose a single metric that best captures the tab's success, such as 'Shopping Tab Revenue' or 'Purchases from Shopping Tab'.
Select 2-3 metrics that directly measure progress toward the north star, such as click-through rate to product pages, add-to-cart rate, and conversion rate.
Choose supporting metrics that provide context, such as engagement (time spent, scroll depth), retention (return visits), and user satisfaction (NPS, surveys).
Include metrics to monitor unintended consequences, such as app performance, user churn, or negative feedback, ensuring holistic health.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sizing questions always stress me out a little.
Start by clarifying the goal of the Shopping Tab (e.g., increase engagement, revenue, or merchant value) and the target population. Then, use a combination of top-down (market sizing, user behavior) and bottom-up (funnel, A/B test power analysis) approaches to estimate potential impact, and validate with a small-scale experiment or holdout.
Pro tip: Frame your estimate as a range with assumptions, and emphasize that the goal is to inform launch decisions and set success metrics, not to predict exactly. Also, mention that you would align with cross-functional partners (PM, Eng, Finance) to ensure assumptions are realistic.
Define what 'impact' means (e.g., incremental revenue, DAU, session time) and the target population (e.g., all users, specific geos). Confirm the launch context and any constraints.
Decide between top-down (market sizing, analogous products) and bottom-up (funnel, user-level modeling). Consider using both to triangulate.
Identify available data (historical metrics, user surveys, market research) and state key assumptions (e.g., adoption rate, frequency, conversion).
Construct a simple model (e.g., impact = reach * frequency * value per action) and compute a range (low, mid, high) based on sensitivity analysis.
Propose a small-scale pilot or A/B test to validate assumptions, and outline how you would refine the estimate based on early results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the goal of the dashboard: to track the health of the Shopping Tab, diagnose issues, and inform product decisions. Then structure your answer around a hierarchy of metrics (engagement, monetization, user experience) and propose specific visualizations for each, emphasizing how they would be used in practice.
Pro tip: Tie your dashboard design to the decision-making process: for each visualization, explain what action it would trigger (e.g., if CTR drops, investigate ad relevance). This shows you think like a product data scientist, not just a reporter.
Ask who will use the dashboard (PM, engineers, execs) and what decisions it should support. This ensures the dashboard is actionable and not just a data dump.
Organize metrics into engagement (e.g., DAU, sessions, time spent), monetization (e.g., ad revenue, CTR, RPM), and user experience (e.g., load time, error rates). This provides a comprehensive view.
For each metric, choose the right chart: time series for trends, bar charts for comparisons, funnel for conversion, heatmaps for engagement depth. Ensure they are intuitive and highlight anomalies.
Allow slicing by user demographics, device, geography, and traffic source. This helps identify root causes of changes and tailors insights to specific segments.
Include thresholds and automated alerts for key metrics to detect issues early. Also, design the dashboard to support A/B test readouts by showing metric lifts and confidence intervals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.