Evaluate campaign success and decide new trading pair

## Context You are a Data Scientist at a crypto exchange. You work with Growth Marketing and Product to evaluate marketing spend and to make listing/launch decisions. ## Part A — Measure whether a marketing campaign succeeded A paid marketing **campaign** ran for 4 weeks across multiple channels (e.g., Search, Social, Affiliates). You have user-level event data (impressions/clicks optional), sign-ups, KYC completion, first deposit, first trade, and revenue. **Task:** Propose how you would evaluate whether the campaign was successful. Include: - **Primary success metric** (pick one and justify): e.g., incremental net revenue, incremental funded accounts, incremental traders. - **Diagnostic metrics** (examples): CTR, CVR by funnel stage (visit→signup→KYC→deposit→trade), CPA/CAC, activation rate, D1/D7 retention, ARPPU. - **Guardrails** (examples): fraud rate, chargebacks, customer support tickets, churn, compliance/KYC failure rate. - How you would compare performance across **channels** fairly. - How you would estimate **incrementality** (vs. naive attribution), and key threats to validity (selection bias, seasonality, overlapping campaigns, delayed conversions). State any assumptions you need (e.g., timezone, attribution window, whether user-level holdout is possible). ## Part B — Decide whether to launch a new trading pair Product proposes listing a **new trading pair** (e.g., `ABC-USD`). **Task:** Describe the decision framework you would use to recommend “launch” vs “do not launch” (or “launch with constraints”). Cover: - Demand and expected usage (user interest signals, projected volume). - Unit economics: expected fees, market-maker costs/fees, spreads, incentives, cannibalization of existing pairs. - Liquidity and market quality: spread, depth, slippage, volatility, wash trading risk. - Risk/compliance: regulatory constraints, manipulation risk, concentration risk, custody/technical risk. - Launch evaluation plan: what metrics define success post-launch, how you’d monitor, and whether any experiment/rollout strategy is feasible given network effects.