Robinhood · Product & Business Case

Diagnose sustained drop in executed trades

TrueInterview

October 7, 2026 · 2 min read

Context: You are the analyst for a brokerage product that spans onboarding through trading. During the past five weeks, executed_trades per active user dropped 22% and has remained depressed. No major outages have been reported. How would you isolate the root cause and confirm it?

Construct a KPI tree from acquisition → KYC approval → first funding → first order attempt → execution quality → short-term retention. For each node, define a precise metric, its expected range, and guardrail thresholds (for example, day-7 retention for new users, order rejection rate, deposit success rate, P95 quote latency). Explain how you would determine which node or nodes account for most of the variation in executed_trades.
Lay out a drilldown plan and justify the exact order of cuts: acquisition channel, signup cohort, account tenure, funding method, platform (iOS/Android/Web), asset class (equity/option/crypto), order type (market/limit), market-hours vs after-hours, geo, and order reject_code family. For each cut, specify the diagnostic you would run (such as waterfall attribution or interaction effects) and the decision you would make for each possible result.
Separate product causes from external causes. List at least five sanity checks with their data sources: market volatility/indices, holiday calendar, competitor promos/fee changes, symbol halts, exchange connectivity, release calendar, instrumentation drift. Describe exactly how each check could support or rule out a hypothesis.
Suppose an onboarding flow change was released on 2025-07-10 across all platforms. Design a confirmation plan that does not expose 100% of traffic: pick an experiment unit (user, geo, or time-based switchback), outline rollout/rollback, define primary and guardrail metrics, and detail power analysis and monitoring windows to avoid novelty bias.
If instrumentation is insufficient, specify the minimum additional events/dimensions you would add (for example, KYC step_id, funding error_code, order entry latency, quote staleness, partial fill counts) and how you would backfill or triangulate with logs to avoid a blind spot while the fix is underway.

Overview: This question tests product analytics and data science skills, including KPI decomposition, causal troubleshooting, experiment design, and instrumentation for diagnosing a sustained decline in executed trades per active user.

Robinhood · Product & Business Case

Diagnose sustained drop in executed trades

TrueInterview

October 7, 2026 · 2 min read

Construct a KPI tree from acquisition → KYC approval → first funding → first order attempt → execution quality → short-term retention. For each node, define a precise metric, its expected range, and guardrail thresholds (for example, day-7 retention for new users, order rejection rate, deposit success rate, P95 quote latency). Explain how you would determine which node or nodes account for most of the variation in executed_trades.
Lay out a drilldown plan and justify the exact order of cuts: acquisition channel, signup cohort, account tenure, funding method, platform (iOS/Android/Web), asset class (equity/option/crypto), order type (market/limit), market-hours vs after-hours, geo, and order reject_code family. For each cut, specify the diagnostic you would run (such as waterfall attribution or interaction effects) and the decision you would make for each possible result.
Separate product causes from external causes. List at least five sanity checks with their data sources: market volatility/indices, holiday calendar, competitor promos/fee changes, symbol halts, exchange connectivity, release calendar, instrumentation drift. Describe exactly how each check could support or rule out a hypothesis.
Suppose an onboarding flow change was released on 2025-07-10 across all platforms. Design a confirmation plan that does not expose 100% of traffic: pick an experiment unit (user, geo, or time-based switchback), outline rollout/rollback, define primary and guardrail metrics, and detail power analysis and monitoring windows to avoid novelty bias.
If instrumentation is insufficient, specify the minimum additional events/dimensions you would add (for example, KYC step_id, funding error_code, order entry latency, quote staleness, partial fill counts) and how you would backfill or triangulate with logs to avoid a blind spot while the fix is underway.