Design experiment and analyze volume drop scenario

Answer the following two prompts as a Data Scientist working on Risk / Product Analytics.

Prompt 1 — Experiment design: increase new-user first trade rate

Coinbase wants to increase the new-user first trade rate.

Design an experiment (A/B test) to evaluate a product change intended to increase activation.

Your answer should cover:

1. Hypothesis and example treatment(s).
2. Metric design
   • Primary metric (e.g., “first trade within 7 days of signup”).
   • Diagnostic metrics (funnel steps, time-to-first-trade, etc.).
   • Guardrails (risk/fraud, customer experience, revenue, platform reliability).
3. Experiment setup
   • Eligibility criteria (which users are included/excluded).
   • Randomization unit (user/device/account) and why.
   • Runtime considerations (novelty/learning effects, ramping, SRM checks).
4. Threats to validity
   • Selection bias, interference/spillovers, delayed conversion, missing data.
5. If results are not significant
   • How you would debug, refine metrics, and decide next steps.

Prompt 2 — Business case: volatility up, retail trading volume down

Recently BTC volatility increased significantly, but retail user trading volume decreased.

Describe how you would investigate this end-to-end:

• How you would validate the data and define the metric(s).
• How you would decompose the drop (e.g., users × frequency × size) and segment the problem.
• What product, market-structure, and risk/compliance factors could explain the pattern.
• What analyses or follow-up experiments you would run, and what decisions you could support.