Lyft·Product Manager·Onsite - Product Sense / Strategy·Senior

Senior

Apr 2026

Summary

PM interview at Lyft, product sense round focused on marketplace dynamics. Just one question but it had a lot of layers to it.

Questions Asked (1)

What metrics would you track for a food delivery app, and which ones would tell you if there are too many or too few restaurants relative to order volume?

Product Analytics & MetricsProduct StrategyRoot Cause Analysis

Author's notes

I went straight to supply-demand ratios and talked about things like average delivery time and order fulfillment rate.

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AI HintsAI Generated

Suggested Approach

Start by structuring your answer around a metric framework like the HEART or AARRR model, then tailor it to food delivery by focusing on marketplace dynamics. For the supply-demand balance, identify metrics that directly measure restaurant availability, order fulfillment, and customer experience to infer if there are too many or too few restaurants.

Pro tip: Emphasize that the right balance is market-specific and time-dependent; propose segmenting metrics by geography and time of day to avoid oversimplifying. Also, mention that you'd validate metrics with A/B tests or natural experiments to establish causality.

1. Define key metric categories

Outline the main areas to track: customer acquisition and retention, order and delivery efficiency, restaurant supply health, and overall marketplace liquidity. This shows a structured approach.

2. Select specific metrics per category

For each category, list 2-3 concrete metrics. For example, customer: DAU/MAU, retention rate; order: order volume, average order value, delivery time; restaurant: active restaurants, restaurant churn, average prep time.

3. Identify supply-demand balance metrics

Focus on metrics that signal imbalance: order fulfillment rate, restaurant utilization rate, average wait time for orders, and customer search-to-order ratio. These indicate if supply meets demand.

4. Interpret signals for too many or too few restaurants

Explain how each metric behaves in each scenario. For example, too few restaurants: high fulfillment failure, long wait times, low restaurant utilization; too many: low order volume per restaurant, high restaurant churn, low utilization.

5. Recommend actions and validation

Suggest how to act on these metrics, such as onboarding more restaurants or optimizing delivery zones, and how to validate with experiments like geolocation-based A/B tests.

Key Points to Mention

Marketplace liquidity: the balance between supply (restaurants) and demand (orders) is critical for a healthy food delivery ecosystem.
Order fulfillment rate: the percentage of orders successfully delivered; a low rate suggests too few restaurants or capacity issues.
Restaurant utilization rate: orders per restaurant per time period; low utilization indicates oversupply, high utilization with long wait times indicates undersupply.
Customer wait time and order cancellation rate: proxies for supply shortage; high values suggest too few restaurants.
Restaurant churn rate: high churn may indicate too many restaurants competing for limited orders.
Geographic and temporal segmentation: metrics should be analyzed by city, neighborhood, and time of day to capture local imbalances.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.