Design and Defend a Free-Trial Conversion Analysis

You've been asked to take on a case-study exercise: a product team wants to understand how well its free trial is converting users into paying customers, and wants you to turn raw product data into a small set of prioritized recommendations, delivered as a short slide deck followed by a live Q&A where you defend your choices.

No dataset or schema is provided as part of this prompt. That's intentional: walk through how you would approach the assignment end to end — what you'd check before trusting the data, how you'd define the population and the metric, what kinds of findings you'd consider credible enough to act on, and how you'd structure the final presentation — without inventing a specific conversion number, segment effect, or causal claim that you haven't actually derived.

Cover the following as you talk through your approach:

• How you'd define the trial funnel and the cohort you're measuring: what counts as a trial start, what counts as a conversion, and how you'd handle users whose trial hasn't had enough time to convert yet by the time you run the analysis.
• What you'd check in the data before drawing any conclusions from it — the kinds of issues that could masquerade as a real behavioral pattern but are actually an artifact of logging, event ordering, or who ends up in the dataset in the first place.
• How you'd decide which patterns in the data are strong enough to put in front of stakeholders, versus which ones need a bigger sample, a cleaner comparison group, or a follow-up experiment before anyone should act on them.
• How you'd structure the final deck so that each recommendation is traceable to a specific piece of evidence and an honest statement of how confident you are in it, rather than a wall of charts.

What a Strong Answer Covers

• A precise definition of the trial funnel and cohort, explicitly addressing users who started too recently to have had a fair chance to convert
• A short list of concrete data-quality checks that could distinguish a genuine behavioral signal from a logging gap, a selection effect, or a timezone/double-counting bug
• A standard for what counts as a "real" insight — appropriate denominators, segment sizes large enough to trust, and uncertainty stated alongside any estimate
• Recommendations ranked by strength of evidence and ease of acting on them, each tied back to a specific finding and a plan for validating it if it ships
• A deck structure built around a handful of decisions, not a tour of every chart produced along the way

Follow-up Questions

• If a third of the trials in your dataset started within the last few days, how does that distort a simple "trials started vs. trials converted" ratio, and what would you do about it?
• A stakeholder looks at your chart and says "users who used the product more during the trial converted at a much higher rate — so let's push everyone to use it more." How do you respond?