I started with the obvious stuff: open rates, dismiss rates, mute/disable events.
Start by defining what 'high-quality' means for a notification in terms of user value and business goals, then outline immediate engagement signals and longer-term retention/well-being metrics. Emphasize segmentation by user, notification type, and context to avoid misleading aggregate conclusions.
Pro tip: Acknowledge the tension between short-term engagement and long-term user well-being; showing awareness of Meta's responsible notification design principles (e.g., avoiding clickbait) demonstrates maturity.
Clarify that a high-quality notification should be relevant, timely, and actionable, driving positive user outcomes without harming long-term engagement or well-being.
List short-term metrics such as open rate, click-through rate, conversion rate, time-to-action, and dismissal/hide rate to gauge initial user response.
Consider metrics like retention, DAU/MAU, notification opt-out rate, user satisfaction (surveys), and downstream engagement to assess sustained impact.
Break down metrics by user demographics, notification type, frequency, time of day, and user tenure to uncover heterogeneous effects and avoid Simpson's paradox.
Combine immediate and long-term signals into a composite quality score, and validate with A/B tests or causal inference to ensure the notification truly causes positive outcomes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I spent most of my time and I think it went reasonably well.
Start by clarifying the goal of notification quality—likely to maximize user engagement without harming user experience. Propose a primary success metric that directly measures the value users get from notifications, then define diagnostic metrics to understand drivers and guardrails to prevent negative side effects. Ensure each metric is precisely defined with clear formulas and data sources.
Pro tip: Anchor your primary metric to a long-term user value, such as meaningful engagement or retention, rather than short-term clicks. This shows product sense and avoids optimizing for vanity metrics that could harm the user experience.
Confirm that the goal is to improve notification quality, balancing user engagement and user experience. Ask if there are specific constraints or focus areas (e.g., reducing opt-outs).
Propose a metric like 'Notification-Attributed Daily Active Users (DAU)' or 'Notification-Driven Meaningful Sessions per User'. Define it precisely: e.g., number of users who engage with a notification and then complete a core action within a session, divided by total notified users.
List metrics that explain the primary metric, such as click-through rate (CTR), conversion rate post-click, and time-to-action. Define each with clear formulas and data sources.
Identify metrics that ensure no harm, such as notification opt-out rate, user-reported spam rate, and app uninstall rate. Define thresholds or acceptable ranges.
Recap the metrics, emphasizing how they work together. Suggest how to monitor them and iterate on notification strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Randomized at user level, treatment gets geo notifications, control gets none.
Start by clarifying the feature's goal and defining a primary success metric (e.g., notification click-through or event attendance). Then outline a randomized controlled experiment, specifying the randomization unit (likely user-level), treatment vs. control conditions, and duration based on power analysis and novelty effects. Finally, discuss potential pitfalls like network effects and guardrail metrics.
Pro tip: Mention that you'd randomize at the user level but also consider cluster randomization if there are social spillovers, and always run an A/A test beforehand to validate the randomization.
State a clear hypothesis (e.g., geographic notifications increase event attendance) and choose primary (e.g., click-through rate) and guardrail metrics (e.g., notification opt-outs).
Treatment group receives the new geographic notification; control group receives either no notification or the existing notification (if any). Ensure both groups are otherwise identical.
Randomize at the user level to avoid contamination, but consider cluster randomization (e.g., by city) if social or geographic spillovers are likely.
Calculate required sample size using power analysis (80% power, 5% significance). Run for at least one full week to capture weekly patterns, and extend if novelty effects are suspected.
Compare metrics between groups using appropriate statistical tests, check for novelty effects, and decide whether to launch, iterate, or abandon based on results and guardrails.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered novelty effects, seasonality, and the location-sharing selection bias.
Start by clarifying the experiment's design and metrics, then systematically categorize threats into internal, external, construct, and statistical validity. For each threat, propose concrete mitigation strategies, emphasizing practical trade-offs and how you would validate assumptions.
Pro tip: Demonstrate awareness that geo experiments often violate independence due to spillover effects, and mention techniques like synthetic control or switchback designs as alternatives. Also, highlight the importance of pre-registering analysis plans to avoid p-hacking.
Ask questions to understand the geo notification experiment: what is the intervention, how are geos assigned, what metrics are used, and what is the duration? This ensures you address the right validity concerns.
Systematically go through internal (e.g., confounding, selection bias), external (e.g., generalizability), construct (e.g., metric validity), and statistical (e.g., power, multiple testing) validity threats relevant to geo experiments.
For each threat, suggest specific solutions such as randomization checks, matching, difference-in-differences, spillover adjustments, or robust statistical methods.
Acknowledge limitations of mitigations and how you would validate assumptions (e.g., placebo tests, sensitivity analyses). Emphasize iterative learning and adaptability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the mixed results and framing the trade-off between short-term engagement and user experience. Then, systematically break down the metrics, hypothesize potential causes, and recommend next steps such as deeper analysis or experiment iteration. Emphasize the importance of long-term user value and overall ecosystem health.
Pro tip: Highlight that opt-outs and mutes are strong negative signals that can erode long-term engagement and trust. Suggest analyzing whether the increase in short-term engagement is driven by a small segment of users, which might mask broader dissatisfaction.
Define what 'short-term engagement', 'opt-outs', and 'mutes' mean in this context. Consider the timeframe, user segments, and any recent changes that might have triggered these shifts.
Assess whether the increase in engagement is worth the rise in negative signals. Quantify the impact: e.g., how many users are opting out relative to the engagement lift, and whether the engagement is concentrated in a small group.
Generate possible explanations: e.g., the change might be too aggressive, irrelevant, or spammy for some users. Consider if it's a novelty effect or if it's harming user experience.
Propose actions like segmenting users to identify who is opting out, running follow-up experiments with variations, or implementing safeguards to reduce negative signals while preserving engagement.
Emphasize the need to monitor long-term metrics such as retention, user satisfaction, and overall ecosystem health. Suggest holding the change or iterating based on further data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.