Feature Success Measurement Questions
Focuses on measuring the impact of a single feature or product change. Key skills include defining a primary success metric, selecting secondary and guardrail metrics to detect negative side effects, planning measurement windows that account for ramp up and stabilization, segmenting users to detect differential impacts, designing experiments or observational analyses, and creating dashboards and reports for monitoring. Also covers rollout strategies, conversion and funnel metrics related to the feature, and criteria for declaring success or rollback.
MediumTechnical
0 practiced
You ran an experiment measuring average spend per user, but the distribution is highly right-skewed with many zeros and heavy outliers. Describe at least four statistical approaches to analyze treatment effect robustly, discuss pros and cons for each (interpretability, bias, power), and state which approach you'd pick as the primary analysis and why.
HardTechnical
0 practiced
Case study: A 'fast-reply' feature was rolled to 20% of users for two weeks. Primary metric: replies per user in 7 days. Observed result after two weeks: replies per user +8% (p=0.02) but Daily Active Users (DAU) decreased 1%. Describe a full analysis plan: verify data quality and instrumentation, run robustness checks (pre-trends, falsification tests), examine heterogeneity by segment, quantify net business impact (engagement lift vs DAU decline and any revenue implications), make a recommendation to rollout or rollback with risk assessment, and propose a monitoring plan for full rollout.
EasyTechnical
0 practiced
Given a table events(event_id, user_id, event_name, event_time, is_bot boolean), write an ANSI SQL query that returns daily unique visitors, daily unique purchasers (event_name = 'checkout'), and daily conversion_rate (purchases/visitors) for the last 7 days. Exclude is_bot=true and show columns: date, visitors, purchases, conversion_rate_percent. Explain assumptions about timezones and how you deduplicate users across sessions.
HardTechnical
0 practiced
Describe a Bayesian approach for A/B testing conversion rates. Explain how you'd choose priors (uninformative vs informed), compute posterior distributions (Beta-Binomial), evaluate the posterior probability that treatment is better than control, choose a decision threshold for rollout, and how sequential updating affects decisions without requiring alpha correction.
EasyTechnical
0 practiced
Explain the difference between a randomized A/B test and an observational (post-hoc) analysis when measuring feature impact. For each approach, list three advantages and three limitations, and provide an example scenario where observational analysis is the only practical option.
Unlock Full Question Bank
Get access to hundreds of Feature Success Measurement interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.