I went with conversion rate (trial users who subscribe divided by total users assigned to treatment), churn rate within 30 days (cancels over paid conversions), and revenue per user.
Start by clarifying the goal: measure how the free trial affects subscription behavior. Then define a primary metric that directly captures the trial's impact on conversion, and support it with secondary metrics that explain the 'why' behind the primary metric. Ensure each metric is precisely defined with clear numerator, denominator, and time window.
Pro tip: Emphasize that the primary metric should be tied to the business objective and be sensitive enough to detect the trial's effect; avoid vanity metrics. Also, mention that you would pre-register the metric and analysis plan to avoid p-hacking.
Confirm that the goal is to measure the trial's impact on subscription behavior, not just trial sign-ups. This ensures alignment with business stakeholders.
Select one primary metric that directly measures the trial's effect on subscription, such as trial-to-paid conversion rate. Define it precisely: numerator (number of users who convert to paid within a defined window after trial start) and denominator (number of users who start the trial).
Include secondary metrics that provide context and explain changes in the primary metric, such as time to convert, retention rate, and average revenue per user. Define each with clear formulas and time windows.
For each metric, specify the time window (e.g., 30 days post-trial), the unit of analysis (user-level), and how to handle edge cases (e.g., cancellations, refunds).
Ensure metrics are measurable with available data and consider guardrail metrics to monitor unintended consequences. Be prepared to refine definitions based on data quality or business feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Two-sample proportion z-test, straightforward enough.
First, clearly define the conversion metric and compute the conversion rates for both groups, then calculate the absolute and relative lift. Next, perform a two-proportion z-test (or chi-square test) to determine statistical significance, and interpret the p-value alongside confidence intervals to assess practical impact.
Pro tip: Always check assumptions (e.g., independence, sample size) and consider practical significance—a statistically significant lift may not be meaningful if the effect size is tiny. Also, mention that you'd verify the experiment was run properly (randomization, no SRM) before trusting results.
Clarify what 'conversion to paid subscription' means (e.g., user-level conversion within a defined window). State the null hypothesis (no difference) and alternative hypothesis (difference exists).
Compute conversion rate for control (C) and treatment (T): p_c = conversions_c / users_c, p_t = conversions_t / users_t. Then absolute lift = p_t - p_c, relative lift = (p_t - p_c) / p_c.
Use a two-proportion z-test (or chi-square test) to compare rates. Calculate the test statistic and p-value, assuming a significance level (e.g., α = 0.05).
If p-value < α, reject the null and conclude a statistically significant difference. Also compute a confidence interval for the lift to understand the range of plausible effects.
Consider whether the observed lift is large enough to matter for the business. Discuss potential next steps, such as segment analysis or extending the experiment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clearly defining the treatment, control, and the 30-day cancellation metric, then estimate the treatment effect using appropriate statistical methods (e.g., difference in proportions or regression adjustment). Assess practical meaningfulness by comparing the effect size to a pre-defined minimum detectable effect or business threshold, and consider confidence intervals and cost-benefit implications.
Pro tip: Always tie the statistical significance to business impact by quantifying the effect in terms of expected revenue or customer lifetime value, and discuss whether the effect size justifies the cost of implementing the treatment.
Clarify what constitutes a cancellation within 30 days, the treatment and control groups, and the randomization unit. Ensure the metric is well-defined and measurable.
Compute the difference in cancellation rates between treatment and control, and use statistical tests (e.g., t-test, chi-square) or regression to estimate the effect size and confidence interval.
Check the p-value and confidence interval to determine if the observed effect is unlikely due to chance. Consider multiple testing corrections if needed.
Compare the effect size to a pre-determined minimum detectable effect or business threshold. Consider the cost of the treatment and the potential revenue impact to judge if the effect is meaningful.
Summarize findings, highlight both statistical and practical significance, and provide a clear recommendation on whether to implement the treatment based on business goals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the trial's goal and success metrics, then evaluate the experiment's statistical validity and practical significance. Consider secondary metrics, guardrails, and long-term effects before making a recommendation, and acknowledge any limitations or need for further analysis.
Pro tip: Show that you think beyond the primary metric by discussing potential novelty effects, cannibalization, and long-term retention, which demonstrates product and business acumen.
Confirm the goal of the free trial (e.g., increase conversion, engagement) and the primary success metric (e.g., conversion rate, retention). Identify guardrail metrics to ensure no harm.
Check if the experiment was properly randomized, had sufficient power, and ran for an appropriate duration. Look for sample ratio mismatch, novelty effects, or external validity issues.
Evaluate the primary metric's statistical significance and effect size. Examine secondary metrics and guardrails for trade-offs. Consider segment-level analysis to see if effects vary across user groups.
Estimate the potential ROI, scalability, and long-term effects (e.g., retention, churn). Think about whether the trial aligns with strategic goals and if there are risks like cannibalization.
Synthesize findings: if results are positive, significant, and align with business goals, recommend rollout. If not, suggest further testing or modifications. Acknowledge uncertainty and propose next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.