metric-context-and-benchmarkslisted

# Metric context and benchmarks The reference skill for answering "is this number good, bad, or noise?" correctly. Almost every wrong answer to that question comes from quoting a cross-industry average when the user's industry-specific number is 3× different, or quoting a benchmark without checking if the sample size even supports the comparison. Read `analytics-diagnostic-method` first. Sample-size discipline applies here more than anywhere: benchmarks are meaningless if your data is noise. ## How to use this skill 1. Load `analytics-profile.md`. The profile's model + industry row picks the right benchmark. 2. Check the sample size. If the user's number is computed on too few observations, the comparison to any benchmark is moot. Say so and stop. 3. Look up the relevant benchmark with its qualifiers (year, source, population, definition). 4. Answer in the form: "Your X is Y. Benchmark for [specific population] is Z [from source, year, n=]. Your number is [above / at / below] benchmark by W percentage points." 5. Flag metric-specific traps (e.g. GA4 bounce rate is not UA bounce rate; last-click CVR is not the full story). ## The honesty rule Never quote a benchmark as if it's a target. Benchmarks describe populations; they don't tell you what *your* number should be. A business can have a below-benchmark CVR and a healthy LTV:CAC if their AOV or retention is elevated. A business can have a 3× benchmark CVR and still be unprofitable. Always connect the benchmark back to