This felt deceptively easy and I kind of fumbled it.
Start by clarifying the business objective of the notification (e.g., reduce churn, increase engagement) and the specific subscription product. Then define 'power user' and 'high-intent user' behaviorally using available data, and propose a metric that captures both engagement and intent signals, validated against a target outcome like conversion or retention.
Pro tip: Avoid defining power users solely by activity volume; instead, focus on intent signals that predict subscription behavior, such as repeated visits to premium features or high session frequency with specific actions. Also, consider segmenting by user lifecycle stage, as intent varies for new vs. existing users.
Ask clarifying questions to understand the notification's goal (e.g., drive subscriptions, reduce churn) and the subscription product (e.g., Nitro). This ensures your definition aligns with business needs.
List behaviors that indicate high engagement and intent, such as frequent use of premium features, high message frequency, or repeated visits to the subscription page. Use data to validate these signals.
Propose a composite metric (e.g., engagement score) or specific thresholds (e.g., >X messages/week, >Y premium feature interactions) to classify users. Consider using percentiles or clustering to set data-driven cutoffs.
Test the definition by checking if identified users have higher conversion or retention rates. Use A/B testing or historical analysis to refine the criteria.
Continuously monitor and adjust the definition based on performance. Segment by user lifecycle (new vs. existing) or other dimensions to tailor notifications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clearly defining the hypothesis and the target population of high-intent users, then outline a randomized controlled experiment with a well-chosen primary metric and guardrail metrics. Walk through the design choices—randomization unit, sample size, duration, and analysis plan—while addressing potential pitfalls like interference and novelty effects.
Pro tip: Propose an intent-to-treat analysis and discuss how you'd handle users who opt out of notifications, since ignoring non-compliance can bias results. Also, mention that you'd pre-register the analysis plan to avoid p-hacking.
State a clear, testable hypothesis (e.g., sending a subscription notification increases subscription rate among high-intent users) and precisely define 'high-intent' using behavioral criteria.
Decide on a randomized controlled trial (A/B test) with random assignment at the user level, ensuring high-intent users are split into treatment (notification) and control (no notification) groups.
Define a primary success metric (e.g., subscription conversion rate) and guardrail metrics (e.g., unsubscribe rate, user engagement). Calculate required sample size and experiment duration based on baseline rates and minimum detectable effect.
Launch the experiment, monitor for technical issues and sample ratio mismatch, and ensure no peeking that could inflate false positives.
Perform statistical analysis (e.g., t-test or regression) to compare groups, check for heterogeneous treatment effects, and decide whether to roll out the notification based on statistical and practical significance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with heterogeneous treatment effects across user segments and whether the notification degrades experience for users who don't convert.
Frame your answer around a logical experimentation roadmap that builds on the initial success, exploring both depth (optimizing the winning variant) and breadth (expanding to new user segments or surfaces). Demonstrate product thinking by connecting follow-up experiments to Discord's core business goals like retention, monetization, and engagement. Show that you understand experimentation as an iterative process, not a one-off event.
Pro tip: Avoid simply listing random follow-up ideas — instead, prioritize investigations using an impact-effort framework and explicitly tie each next step back to the metric that defined 'success' in the original experiment, showing you think in terms of causal chains rather than isolated tests.
Clarify which metrics improved (e.g., click-through rate, subscription conversions, retention) and whether there were any negative side effects like notification fatigue or opt-out increases. This anchors all follow-up investigations to the right north star.
Determine whether the effect holds across different user segments (new vs. power users, mobile vs. desktop, server types) and geographies. Segmentation analysis can reveal heterogeneous treatment effects that unlock more targeted strategies.
Explore follow-up experiments that iterate on the notification's content, timing, frequency, and personalization to squeeze additional lift from the proven concept. For example, test whether personalized subscription recommendations outperform generic ones.
Investigate whether similar notification strategies can be applied to other surfaces or moments in the user journey, such as in-app banners, email nudges, or server discovery prompts. This tests the scalability of the underlying mechanism.
Run a holdout or long-term follow-up study to ensure the short-term gains don't erode over time due to habituation or notification fatigue. Also examine downstream effects on broader engagement metrics like DAU and server activity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Randomization level was the first thing I got asked to justify.
Start by clarifying the goal of the autocomplete feature—likely to improve friend-adding efficiency and success. Then outline a structured A/B test: define hypotheses, choose metrics (e.g., friend request sent, time to complete), design variants, determine sample size, run the test, and analyze results with statistical rigor. Emphasize guardrail metrics and segment analysis to ensure robustness.
Pro tip: Mention that you would run an A/A test first to validate the experimentation setup and check for any instrumentation issues, especially on mobile where client-side metrics can be noisy. Also, consider using a holdout group to measure long-term effects.
Clearly state the hypothesis (e.g., autocomplete reduces time to send friend request) and select primary, secondary, and guardrail metrics. Primary could be friend request send rate; guardrails include app performance and user retention.
Decide on control (no autocomplete or current experience) and treatment (autocomplete with specific algorithm). Ensure randomization unit (e.g., user ID) and consider potential network effects in social apps.
Calculate required sample size based on minimum detectable effect, power, and significance level. Account for Discord's user base and traffic to estimate test duration, ensuring it covers weekly seasonality.
Launch the test, monitor for technical issues, and check that metrics are tracking correctly. Avoid peeking at results prematurely to prevent false positives.
Perform statistical analysis (e.g., t-test or Bayesian methods), check for novelty effects, and segment by user cohorts. Make a ship/no-ship decision based on primary metric and guardrails.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Primary metric I went with was successful friend request rate among users who previously got null results.
Start by clarifying the goal of the autocomplete feature on the 'Add Friend' page, then define success metrics across engagement, efficiency, and quality dimensions. Structure your answer by mapping metrics to the user journey and business objectives, and mention how you would validate them through A/B testing.
Pro tip: Focus on metrics that directly tie to the feature's purpose—reducing friction in finding friends—and consider guardrail metrics to ensure you're not optimizing for the wrong behavior. Also, think about how you would measure success differently for new vs. existing users.
Understand that autocomplete aims to help users quickly find and add friends by suggesting relevant usernames as they type. Confirm whether the goal is to increase friend requests sent, accepted, or reduce time to add a friend.
Identify key metrics such as click-through rate on suggestions, time to send a friend request, number of friend requests sent, and successful friend additions. Also consider suggestion quality metrics like acceptance rate of suggested friends.
Include metrics to ensure the feature isn't causing harm, such as increased error rates, user frustration (e.g., rapid backspacing), or privacy concerns. Monitor for unintended consequences like spammy suggestions.
Propose an experiment comparing autocomplete vs. no autocomplete, with randomization at the user level. Define primary and secondary metrics, sample size, and duration to detect meaningful changes.
Analyze results by user segments (e.g., new vs. existing users, mobile vs. desktop) to uncover differential impacts. Use insights to refine the autocomplete algorithm and UI.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.