I went with creator-level randomization pretty quickly, reasoning that if a viewer sees a tipped post and then a non-tipped post from a different creator, you've already got contamination.
Start by defining the treatment and control groups, then choose a randomization unit that minimizes interference, such as clustering by user or post. Discuss how to handle supply-demand dynamics by measuring both creator and consumer side metrics, and propose methods like cluster randomization or switchback tests to control spillovers.
Pro tip: Consider using a cluster-randomized design where clusters are formed based on social graph connectivity or content similarity to reduce spillover, and always pre-register your analysis plan to avoid p-hacking.
Clearly state the hypothesis, treatment (tipping feature), and primary metrics (e.g., tipping rate, creator revenue, user engagement). Include guardrail metrics to monitor negative effects.
Select a randomization unit (user, post, or cluster) that balances interference control and statistical power. For cross-user spillovers, cluster by social communities; for cross-post spillovers, cluster by creator or content topic.
Model how tipping affects creator supply (e.g., more posts) and consumer demand (e.g., more tips). Use metrics like creator output and viewer engagement, and consider time-based randomization (switchback) to capture dynamic effects.
Determine sample size and duration, ensuring clusters are balanced. Use appropriate statistical methods (e.g., cluster-robust standard errors, mixed models) to account for correlation within clusters.
Run A/A tests to check for bias, monitor for spillover via network analysis, and be prepared to adjust clustering or switch to alternative designs if interference persists.
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Start by clarifying the experiment's goal and the contamination risks specific to US and Asia time zones, then propose a robust eligibility rule based on user attributes like region and time zone, and exposure rules that ensure consistent treatment assignment across time zones. Emphasize the importance of randomization at the user level and monitoring for contamination.
Pro tip: Highlight that time zones can affect user behavior and experiment metrics, so consider using a fixed time window (e.g., UTC) for analysis and ensure that treatment assignment is independent of time zone to avoid bias.
Understand the experiment's objectives and identify potential contamination sources across US and Asia time zones, such as users traveling or interacting across regions.
Specify which users qualify for the experiment, using stable attributes like region, time zone, or language, and exclude users likely to cause contamination (e.g., frequent travelers).
Determine when and how users are exposed to the treatment, ensuring consistent assignment across time zones and avoiding time-based biases by using a universal trigger like first app open after a fixed UTC time.
Randomize at the user level to prevent contamination, and set up monitoring to detect and address any cross-time zone contamination or imbalance.
Run a pre-experiment check to validate the rules, and be prepared to adjust based on early data or unexpected user behavior across time zones.
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Payer conversion per DAU as the north star, ARPPU second, creator revenue share third.
Start by clarifying the experiment's goal and the tipping feature's mechanics, then structure metrics into a hierarchy: primary success metric, secondary metrics, and guardrail metrics. For each guardrail category (retention, session length, abuse, ad revenue cannibalization), define specific metrics and thresholds, and explain how you would monitor them to ensure no negative impact.
Pro tip: Emphasize that guardrail metrics should be pre-registered with clear non-inferiority bounds, and consider using a composite metric or sequential testing to balance multiple guardrails without inflating false positives.
Ask clarifying questions about the tipping feature (e.g., who can tip, where it appears) and confirm the primary objective, such as increasing creator revenue or engagement.
Choose a primary metric that directly measures success (e.g., tipping revenue per user or tip conversion rate) and secondary metrics that provide additional insight (e.g., number of tips, average tip amount).
List the four guardrail areas: retention, session length, abuse, and ad revenue cannibalization. For each, select specific metrics (e.g., D1/D7 retention, average session duration, abuse report rate, ad revenue per user).
Define acceptable thresholds (e.g., no more than 1% relative decrease) for each guardrail and describe how you would monitor them during the experiment, including any stopping rules.
Present the metric hierarchy in a clear table or list, and discuss potential trade-offs (e.g., increased revenue vs. decreased retention) and how to prioritize if conflicts arise.
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Said at minimum one full weekly cycle to capture weekend behavior differences, then ramped from maybe 1% to 5% to 20% with SRM checks at each gate.
Start by framing the experiment around a clear hypothesis and primary metric, then walk through power analysis to determine sample size and duration, emphasizing TikTok's scale and variance. Discuss ramp planning to mitigate risk, SRM detection to ensure data integrity, and pre-registered stop rules to avoid p-hacking. Conclude with how you'd monitor and adapt during the experiment.
Pro tip: Mention that you pre-register not just stop rules but also the exact SRM check and ramp schedule to prevent post-hoc rationalization. Highlight that at TikTok's scale, even tiny SRM can indicate issues, so use sequential testing with alpha spending to allow early stopping without inflating false positives.
Clearly state the null and alternative hypotheses, and select a primary metric (e.g., watch time) with a minimum detectable effect (MDE) that is practically significant. Consider guardrail metrics to monitor for unintended harm.
Use historical data to estimate variance and calculate required sample size per variant for desired power (e.g., 80%) and significance level (e.g., 5%). Translate sample size into duration based on daily traffic, accounting for novelty effects and weekly seasonality.
Design a gradual ramp (e.g., 1%, 5%, 10%, 50%) to catch bugs early and limit user impact. Implement automated SRM checks (e.g., chi-squared test) at each ramp stage and daily, with alerts for deviations.
Specify stopping rules (e.g., stop for harm, futility, or success) using sequential testing or alpha spending to control Type I error. Pre-register the analysis method, including how you'll handle multiple comparisons and missing data.
During the experiment, monitor SRM, guardrails, and primary metric. If SRM occurs, pause and investigate. After completion, analyze according to pre-registered plan and document learnings.
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CUPED was the obvious answer using pre-experiment spend and creator popularity as covariates.
Start by acknowledging that clustered randomization (e.g., by user, session, or geography) introduces correlation within clusters, which violates independence assumptions and inflates variance. Then, propose variance reduction techniques like CUPED, stratification, or post-stratification, and explain how to handle inference using cluster-robust standard errors or mixed-effects models. Emphasize the trade-off between bias and variance and the importance of validating assumptions.
Pro tip: Mention that at TikTok, where user behavior is highly correlated within clusters (e.g., friends, regions), you would combine CUPED with cluster-robust inference to get both efficiency and valid uncertainty estimates. Also, note that you would check for interference between clusters and consider switchback designs if needed.
Explain that clustered randomization (e.g., by user or geography) induces intra-cluster correlation, reducing effective sample size and increasing variance. This violates the independence assumption of standard A/B tests.
Use CUPED with pre-experiment covariates to reduce variance, stratify randomization by cluster-level features, or use post-stratification. For clustered data, consider cluster-level covariates in CUPED.
Use cluster-robust standard errors (e.g., CRSE) or mixed-effects models to account for within-cluster correlation. Alternatively, aggregate data to cluster level and run tests on cluster means, though this may lose power.
Check for balance across clusters, assess intra-cluster correlation (ICC), and run sensitivity analyses. Consider bootstrap or permutation tests for small numbers of clusters.
If interference is a concern, propose switchback or cluster-based crossover designs. Discuss trade-offs between bias and variance in choosing cluster size and number.
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Start by acknowledging the trade-off and proposing a structured framework that quantifies both the short-term monetization gains and the long-term growth impact. Emphasize the need to align with TikTok's strategic priorities, such as user growth and engagement, and suggest running a longer-term holdout experiment to measure the net effect. Conclude with a recommendation based on a holistic cost-benefit analysis and potential mitigation strategies.
Pro tip: Demonstrate awareness of TikTok's unique ecosystem by highlighting how growth metrics (e.g., DAU, retention) are leading indicators of future monetization, and propose a blended metric like LTV/CAC to evaluate the trade-off. This shows you understand the business beyond just the immediate numbers.
Estimate the magnitude and confidence intervals of the changes in monetization and growth metrics from the experiment. Calculate the expected net revenue impact over different time horizons (e.g., 3, 6, 12 months) using predictive models.
Evaluate how the trade-off aligns with TikTok's current strategic goals. For example, if user growth is the top priority, a small drop might be unacceptable; if monetization is critical, it might be acceptable.
Analyze potential long-term consequences of the growth drop, such as reduced network effects, lower content creation, or diminished ad inventory. Use cohort analysis and retention curves to project future impact.
Brainstorm ways to mitigate the growth drop (e.g., tweaking the feature, targeting specific user segments) or alternative monetization strategies that don't harm growth. Test these in follow-up experiments.
Synthesize the above into a clear recommendation: ship, iterate, or abandon. Use a decision matrix or expected value calculation, and communicate the assumptions and uncertainties.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.