Analyze User Transfer Distribution in Initial Launch Period

Q: Analyze User Transfer Distribution in Initial Launch Period

This question evaluates proficiency with count-data distributions, summary statistics (mean, median, mode, percentiles), and the impact of adoption and retention dynamics on product metrics, testing statistical modeling and analytical reasoning in the Statistics & Math domain for a Data Scientist role.

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Question

P2P Payments: Transfers per User in the First 30 Days

Scenario

A new peer‑to‑peer (P2P) payments feature has just launched. You are asked to analyze how many transfers users make early on.

Assume a 30‑day calendar window after launch. Clarify your denominator: either (a) all eligible users of the app, or (b) only users who made at least one P2P transfer ("activated" P2P users). State your assumption or answer for both.

Question

What distribution do you expect for the number of transfers per user in the first 30 days post‑launch? Briefly justify (e.g., right‑skew, long tail, log‑normal/Pareto behavior).
Estimate where the mean, median, mode, and 95th percentile would lie in that period.
After two months, how and why would this distribution change?

Hints

Discuss right‑skew, zero inflation, long tail, and possible log‑normal/Pareto or negative‑binomial behavior.
Consider adoption, onboarding frictions, network effects, and churn/retention dynamics.

Analyze User Transfer Distribution in Initial Launch Period

P2P Payments: Transfers per User in the First 30 Days

Scenario

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