DoorDash·Data Scientist·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

DoorDash data scientist interview with a product analytics case focused on mobile web users who never convert to the native app. The question was meaty and required thinking through funnel measurement, experiment design, and incentive structures all at once.

Questions Asked (1)

A large portion of users place orders through mobile web but never install the app. How would you evaluate the current funnel from web order to app install, and what experiments would you design to improve that conversion rate?

A/B Testing & ExperimentationProduct Analytics & MetricsProduct Sense & Ideation

Author's notes

I started with cohort definitions which felt right, but I got a bit tangled trying to scope the baseline metrics while simultaneously pitching experiment ideas.

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AI HintsAI Generated

Suggested Approach

Start by mapping the end-to-end user journey from mobile web order to app install, identifying key drop-off points and defining success metrics. Then propose a mix of qualitative and quantitative analyses to diagnose friction, followed by prioritized A/B tests targeting the biggest opportunities. Emphasize a test-and-learn approach with clear measurement plans.

Pro tip: Focus on the 'why' behind user behavior—segment by user intent (e.g., one-time vs. repeat) and leverage qualitative insights to form hypotheses before jumping to experiments. This shows you understand that not all users should install the app, and you avoid optimizing for a vanity metric.

1. Map the funnel and define metrics

Outline the steps from mobile web order completion to app install, including post-order prompts, email/SMS touchpoints, and app store visits. Define primary metric (web-to-app install conversion rate) and secondary metrics (order frequency, retention, LTV).

2. Analyze current funnel and identify drop-offs

Use quantitative data to measure conversion at each step, segment by user cohorts (new vs. returning, order value, device). Conduct qualitative research (surveys, user interviews) to understand motivations and barriers to installing the app.

3. Form hypotheses and prioritize experiments

Based on insights, generate hypotheses for improving conversion (e.g., incentivize install with discount, simplify install flow, show value prop). Prioritize using ICE (Impact, Confidence, Ease) or similar framework.

4. Design and run A/B tests

For top hypotheses, design experiments with proper control/treatment groups, sample size calculation, and success metrics. Ensure tests are isolated to measure causal impact, and consider sequential testing if needed.

5. Measure, iterate, and scale

Analyze test results for statistical significance and practical significance. If successful, roll out to broader audience; if not, learn and iterate. Continuously monitor long-term effects on retention and LTV.

Key Points to Mention

Define clear success metrics beyond install rate, such as order frequency and retention, to avoid optimizing for a vanity metric.
Segment users by behavior (e.g., order frequency, recency) to tailor interventions and avoid one-size-fits-all solutions.
Consider the trade-offs of incentivizing installs (e.g., discounts) vs. organic conversion, and potential cannibalization.
Leverage qualitative methods (surveys, user testing) to uncover friction points not visible in quantitative data.
Ensure experiments are properly powered and account for multiple testing corrections if running many tests.
Think about the entire user journey, including post-install experience, to ensure the app delivers on the promise that drove the install.

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