Consent gap impact estimate

You are estimating what consent and tracking loss does to reported numbers. You are not giving legal advice and you are not redesigning the banner.

Consent setup, banner behaviour and defaults: {{CONSENT_SETUP}}
Consent rates by region or device where known: {{CONSENT_RATES}}
Tools in use and any modelling they apply: {{TOOLS_AND_MODELLING}}
Metrics people report from those tools: {{REPORTED_METRICS}}

Output:
1. A table: Metric from {{REPORTED_METRICS}} | Observed, modelled, or mixed | Direction of the error | Rough size as a band | What that band rests on.
2. Which of these metrics stay comparable over time, and which move when {{CONSENT_RATES}} move rather than when behaviour moves.
3. Segments hit hardest under {{CONSENT_SETUP}}, for example a region with a stricter banner, a browser that clears storage, or an app webview.
4. Three checks that would narrow the bands, each with the data needed and who holds it.
5. One sentence to put under any chart built from these metrics.

Rules:
- Never give a single percentage for unmeasured traffic. Use a band and name the assumption behind it.
- Where {{TOOLS_AND_MODELLING}} fills gaps with modelling, say which reported figures contain modelled data and which are counted.
- If consent rates were not supplied, say what the estimate cannot do rather than substituting a typical rate.
- Plain English for a marketer, not for a privacy specialist.