This is where I spent most of my time and honestly felt pretty good about it.
Start by defining the key metrics: conversion rate and revenue per user. Then hypothesize why each page might outperform the other on different metrics, considering psychological, economic, and behavioral factors. Finally, discuss how to determine the overall winner based on business goals and suggest follow-up tests to optimize further.
Pro tip: Always tie your hypotheses back to the specific context of TikTok's user base—impulse buying, social proof, and mobile-first behavior—to show you understand the platform's unique dynamics.
Clarify what 'outperforming' means: is it total revenue, profit margin, conversion rate, or long-term customer value? This determines which page is better.
Consider factors like perceived quality, price anchoring, or a niche audience willing to pay more. Higher price may signal premium value or exclusivity.
Consider factors like impulse buying, lower barrier to entry, or volume-based revenue. Lower price may attract a broader audience and drive more conversions.
Compare total revenue, profit, and strategic objectives (e.g., market share vs. profitability). Determine which metric aligns with TikTok's monetization goals.
Suggest follow-up tests: price sensitivity, bundling, or segment-specific pricing. Recommend analyzing user segments to tailor pricing strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with conversion rate and ARPU as primaries, LTV and refund rate as secondaries.
Start by clarifying the specific A/B test scenario (e.g., new feature, algorithm change, pricing) and the hypothesis. Then define one primary metric that directly measures the test's success and 2-3 secondary metrics that capture potential trade-offs or unintended consequences. Explain why each metric was chosen, linking to TikTok's business model and user experience.
Pro tip: Always include a counter-metric or guardrail metric to show you're thinking about long-term health, not just short-term gains. For TikTok, metrics like user retention or content diversity can be critical to monitor.
Ask or state the specific change being tested and the expected outcome. This ensures your metric choices are grounded in the test's purpose.
Choose one metric that directly measures the success of the hypothesis. It should be sensitive to the change and aligned with business goals.
Pick 2-3 metrics that capture other important aspects like user engagement, monetization, or potential negative side effects.
For each metric, articulate why it was chosen, how it relates to the test, and what insights it provides.
Include at least one metric to monitor for unintended harm, such as user retention or satisfaction, to ensure long-term health.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on power calculation specifics.
Start by outlining the key statistical components: defining the null and alternative hypotheses, choosing a significance level (e.g., α = 0.05), and selecting an appropriate test (e.g., t-test or z-test for proportions). Then explain how you would compute power and verify sample size using power analysis, considering effect size, variance, and desired power (e.g., 80%). Emphasize practical considerations like novelty effects and multiple testing corrections.
Pro tip: For TikTok, where metrics like watch time and engagement are often skewed and have high variance, mention that you'd consider non-parametric tests or bootstrapping, and that you'd use sequential testing or CUPED to reduce variance and speed up experiments.
Clearly state the null and alternative hypotheses for the primary metric (e.g., average watch time per user). Define the metric precisely and ensure it aligns with the product goal.
Select a significance level (α) and the appropriate statistical test based on metric distribution (e.g., t-test for means, z-test for proportions). Consider corrections for multiple comparisons if multiple metrics are evaluated.
Determine the minimum detectable effect (MDE) that is practically significant. Use power analysis (e.g., with tools like G*Power or Python's statsmodels) to calculate required sample size given α, desired power (1-β), and variance.
Check assumptions of the chosen test (e.g., normality, independence). If violated, consider transformations or non-parametric alternatives. Ensure data quality and that randomization is properly implemented.
Compute the test statistic and p-value, and compare to α. Also report confidence intervals and effect size. If not significant, check if power was adequate; if underpowered, consider extending the experiment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the ROI calculation for the experiment, then explain how to incorporate the promotional mechanic by adjusting both the incremental revenue and the incremental cost. Emphasize the importance of measuring the true incremental impact of the promotion through proper experimental design and accounting for factors like cannibalization and redemption rates.
Pro tip: Consider the long-term value of customers acquired through the promotion, not just the immediate ROI, as promotions can have lasting effects on retention and lifetime value. Also, be mindful of the breakage rate (unredeemed bonuses) which can significantly impact the actual cost.
Clearly state the ROI formula: (Incremental Revenue - Incremental Cost) / Incremental Cost. This sets the foundation for incorporating the promotion.
Determine the incremental revenue generated by the experiment, including any additional spend from the promotion. Consider both the direct revenue from qualifying purchases and any halo effects.
Calculate the incremental cost of the promotion, including the cost of bonuses (e.g., free items, discounts) and any operational costs. Adjust for expected redemption rates and breakage.
Assess whether the promotion cannibalizes existing sales or shifts revenue from other products. Subtract these effects from incremental revenue to avoid overestimating ROI.
Consider the long-term impact on customer lifetime value, retention, and future spending. Adjust ROI to reflect these potential benefits or costs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said I'd test price point granularity within the winning page variant, basically a multivariate test on price tiers.
Start by briefly summarizing the key findings and any open questions from the initial experiment, then propose a follow-up that directly addresses the most critical uncertainty or opportunity. Frame your proposal as a hypothesis-driven test with clear success metrics, and tie it back to TikTok's business goals like user growth, engagement, or monetization.
Pro tip: Show that you think in terms of iteration and learning velocity—propose a follow-up that is scoped to deliver actionable insights quickly, rather than a perfect but slow experiment. Also, mention how you would prioritize this test against other opportunities using a framework like ICE (Impact, Confidence, Ease).
Briefly state the main outcome of the initial experiment, including whether the hypothesis was validated, invalidated, or inconclusive, and highlight any surprising or ambiguous findings.
Based on the results, pinpoint the most important unanswered question or the biggest opportunity that could drive meaningful business impact.
Propose a clear, testable hypothesis that addresses the open question, specifying the change you would make and the expected outcome.
Outline the experiment design: target audience, sample size, duration, success metrics (e.g., CTR, retention, revenue), and how you would measure statistical significance.
Explain how the follow-up aligns with TikTok's product strategy and what decision you would make based on the results (e.g., scale, iterate, or kill).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.