Investigate Anomalies in Coinbase Wallet Engagement Metrics
Coinbase Wallet: Anomaly Investigation Framing
Context
You observe an unexpected spike or drop in a key Coinbase Wallet metric (e.g., DAU, transactions sent, swaps, on-chain success rate). Your job is to quickly form falsifiable hypotheses and outline the exact data signals that would confirm or refute each one.
Assume you can access: product analytics events, app/version/OS info, feature flag/experiment logs, backend API metrics, acquisition/CRM data, on-chain metrics (fees, tx counts, chain health), and incident dashboards.
Task
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Propose a concise set of falsifiable hypotheses for a wallet metric anomaly.
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For each hypothesis, list the data signals/slices that would confirm or refute it.
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Identify the key data slices you would use during triage.
Constraints & Assumptions
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Preserve the scope, facts, inputs, and requested outputs from the prompt above.
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If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
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Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
Clarifying Questions to Ask
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Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
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State assumptions about instrumentation, randomization, sample size, and data quality.
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Separate descriptive analysis from causal claims.
What a Strong Answer Covers
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A metric framework with primary, guardrail, and diagnostic metrics.
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A credible analysis or experiment design with clear assumptions and bias checks.
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SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
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An actionable recommendation that explains trade-offs and next steps.
Follow-up Questions
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What sanity checks would you run before trusting the result?
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How would you handle novelty effects, seasonality, or selection bias?
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What decision would you make if metrics disagree?