I started with CTR and immediately sensed that wasn't going to be enough.
Start by clarifying what the Memories feature is and its goals, then define success metrics across engagement, retention, and user experience. Structure your answer around a metrics framework, experimentation methodology, and iteration process, emphasizing how you'd validate and refine the framework over time.
Pro tip: Anchor your framework in the company's overarching goals (e.g., meaningful connections) and propose a north star metric with guardrails to show strategic thinking. Also, mention how you'd handle trade-offs between short-term engagement and long-term user well-being.
Understand the purpose of Memories (e.g., resurfacing past moments to drive engagement and connection) and identify key user segments (e.g., new vs. existing users, active vs. passive).
Select a north star metric (e.g., weekly active users engaging with Memories) and supporting metrics (e.g., time spent, shares, retention). Include guardrail metrics (e.g., user reports, negative sentiment) to monitor unintended consequences.
Outline A/B tests to measure causal impact, ensuring proper randomization, sample size, and duration. Consider holdout groups and long-term holdouts to assess sustained effects.
Use statistical methods to evaluate metric changes, segment analysis to uncover heterogeneous effects, and qualitative feedback to interpret results. Iterate on the feature based on findings.
Establish ongoing monitoring dashboards and periodic reviews to ensure metrics remain aligned with evolving product goals and user needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said yes without thinking and then had to walk it back.
Start by clarifying what 'Memories' refers to and the context of the 20% CTR, then explain that CTR alone is not inherently good or bad—it must be benchmarked against relevant comparisons. Outline a structured benchmarking approach using internal historical data, external industry standards, and product-specific factors, while considering the metric's role in the broader product ecosystem.
Pro tip: Emphasize that benchmarking should be tied to business objectives—a 20% CTR might be excellent for a low-intent surface but poor for a high-intent one. Also, mention that you'd validate the metric definition and data quality before drawing conclusions.
Define what 'click-through rate' means for Memories (e.g., clicks per impression) and understand the surface, user intent, and placement. Confirm the time period and any segmentation (e.g., by user demographics, device, geography).
Determine appropriate comparison points: historical CTR for Memories, CTR of similar surfaces (e.g., News Feed, Stories), industry benchmarks for similar products, and A/B test results if available.
Check if the 20% CTR is stable over time and across segments, and whether differences from benchmarks are statistically significant. Consider confidence intervals and sample size.
Link CTR to downstream metrics like engagement, retention, or revenue. A high CTR might be good for engagement but could indicate clickbait if it doesn't lead to meaningful actions.
Suggest actions based on the benchmarking: if CTR is low, propose experiments to improve it; if high, investigate why and whether it can be replicated. Always consider potential trade-offs with other metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that total time-spent is a common but flawed proxy for impact, then systematically break down why it fails for Memories specifically. Propose a more reliable framework that ties usage to meaningful outcomes, such as memory creation, sharing, or retention, and discuss how to validate it with data.
Pro tip: Show maturity by recognizing that time-spent can be gamed and may not align with user value; instead, advocate for a multi-metric approach that includes counter-metrics to guard against unintended consequences.
Clarify what 'impact' means for Memories: is it user engagement, retention, monetization, or well-being? Align with product goals to set the right measurement target.
Assess whether time-spent correlates with the defined impact. Consider if more time spent is always good (e.g., passive scrolling vs. active memory creation).
Discuss issues like vanity metrics, lack of causality, and potential negative user experiences (e.g., regret, FOMO) that time-spent might mask.
Suggest metrics that better capture impact, such as memory creation rate, share rate, return frequency, or user-reported satisfaction.
Outline how to test the reliability of time-spent vs. alternatives using A/B tests, longitudinal studies, or causal inference methods.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one was more concrete and I felt better about it.
Frame the problem as a content moderation and ranking challenge: first diagnose why the post resurfaced (e.g., recirculation algorithms, lack of freshness signals), then propose a multi-layered solution combining detection, demotion, and prevention. Emphasize measurable outcomes and iterative testing to ensure the post doesn't reappear in 2021.
Pro tip: Acknowledge the trade-off between aggressively suppressing content and avoiding false positives that could harm user experience or free expression; propose a human-in-the-loop review for borderline cases and monitor for unintended consequences.
Identify the root causes: why did the 2019 post reappear in 2020? Analyze recirculation triggers (e.g., shares, comments, algorithmic recommendations) and gaps in existing moderation systems.
Establish clear metrics: zero reappearances of the specific post, reduction in similar resurfacing events, and minimal impact on overall engagement or false positive rates.
Propose solutions across detection (e.g., hash matching, content classifiers), demotion (e.g., downranking in feeds), and prevention (e.g., freshness penalties, user reporting improvements).
Roll out changes via A/B tests or staged deployment, measuring against success metrics. Use holdout groups to quantify impact and detect regressions.
Set up ongoing monitoring for resurfacing patterns, adapt to adversarial behavior, and refine models to ensure long-term effectiveness into 2021 and beyond.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.