I started with proxy signals from the existing data, things like how often 1:1 calls happen in group chat contexts, whether people make back-to-back calls to the same set of contacts within a short window, that kind of thing.
Start by defining what 'demand' means in terms of observable behaviors in existing call data, such as frequency of group voice calls, call duration, and user feedback. Then propose a method to infer unmet need by analyzing patterns that suggest users are trying to achieve group video calling through workarounds or by looking at adjacent metrics like group voice call growth and user retention. Finally, suggest a validation approach, such as an experiment or survey, to confirm the inferred demand.
Pro tip: Focus on actionable metrics that can be measured with existing data and avoid overcomplicating with unavailable data; show that you can derive insights from what's available and propose a testable hypothesis.
Clarify what 'real demand' means: e.g., users frequently initiating group voice calls, long durations, or high engagement. Form hypotheses about what patterns would indicate demand for video, such as users switching to other apps for video after a voice call.
List available call-level data: group voice call frequency, duration, participant count, time of day, and user demographics. Also consider adjacent data like message activity during calls or app usage after calls.
Look for proxies of unmet demand: e.g., high frequency of group voice calls among users who also use video calling in one-on-one chats, or increased group voice call usage after a video call feature is introduced elsewhere. Compare group voice call behavior to one-on-one video call behavior.
Estimate the size of the opportunity: what percentage of users engage in group voice calls? How many would likely adopt video? Use segmentation to identify high-potential user groups (e.g., frequent group callers, younger demographics).
Suggest a follow-up experiment or survey to confirm demand, such as an A/B test with a small rollout of group video calling or a user survey asking about interest. Emphasize that data analysis alone can only suggest demand, not prove it.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Mentioned survey data, competitor usage stats, and support ticket themes.
Start by clarifying the decision context and the primary metric, then propose a balanced set of quantitative and qualitative data that directly addresses gaps in the current analysis. Explain how you would triangulate these sources to validate hypotheses, quantify trade-offs, and recommend a data-driven action.
Pro tip: Emphasize that you would prioritize data based on its potential to change the decision, and always consider the cost of acquiring additional data versus the value it adds.
Restate the decision to be made, the primary success metric, and what data is already available. Identify any assumptions or gaps in the current analysis.
List specific quantitative sources such as user behavior logs, A/B test results, survey metrics, or external benchmarks that could fill gaps and provide statistical power.
Propose qualitative sources like user interviews, usability studies, open-ended survey responses, or social media sentiment to uncover motivations and context behind the numbers.
Describe methods to combine data, such as triangulation, regression analysis, or thematic analysis, and how you would weigh evidence to inform the decision.
Show how the integrated findings would lead to a clear recommendation, including potential trade-offs and next steps for validation or implementation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The single-metric question is where I fumbled a bit.
Start by clarifying the product goal and user segments for group video calls, then define success metrics that measure adoption, engagement, and retention, and guardrail metrics that ensure quality and safety. Finally, select one North Star metric that best captures the core value proposition and explain its alignment with long-term business objectives.
Pro tip: When choosing a single success metric, prioritize one that balances user value and business impact, such as 'weekly active group call participants per host,' and justify it by linking to network effects and monetization potential.
Understand the purpose of group video calls (e.g., social connection, remote work) and identify key user segments (e.g., hosts, participants, businesses) to tailor metrics.
Propose metrics that measure adoption (e.g., % of users who start a group call), engagement (e.g., average call duration, frequency), and retention (e.g., 7-day repeat call rate).
Identify metrics that ensure quality and prevent negative side effects, such as call failure rate, average audio/video quality, user reports, and privacy concerns.
Choose a single metric that best represents the product's core value, such as 'weekly active group call participants per host,' and explain why it captures both user and business value.
Articulate how the chosen metric drives long-term growth, network effects, and monetization, and how it balances with guardrail metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the meatiest part and honestly where I spent the most mental energy.
Start by clarifying the experiment's goal and metrics, then walk through the design choices (randomization unit, spillover mitigation, novelty handling) and finally the analysis plan with statistical and business thresholds. Emphasize trade-offs and how you'd validate assumptions before launch.
Pro tip: Proactively discuss how you'd detect and mitigate network effects, such as using cluster randomization or switchback tests, and how you'd set guardrail metrics to catch unintended harm.
Clarify the hypothesis, primary success metric (e.g., CTR, conversion), and guardrail metrics (e.g., user retention, revenue). Ensure metrics align with business goals.
Select the randomization unit (user, session, or cluster) based on spillover risk. For social features, consider cluster randomization (e.g., by friendship clusters) to minimize interference.
Mitigate spillover via cluster randomization or switchback designs. For novelty, run the experiment longer than typical, analyze time-based trends, and consider holdout groups.
Calculate required sample size using power analysis (80% power, 5% significance) and expected effect size. Ensure duration covers at least one full business cycle to account for seasonality.
Use appropriate statistical tests (e.g., t-test, bootstrap) to assess significance. Define business significance (e.g., minimum detectable effect) and consider practical significance (e.g., cost-benefit).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.