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Email program health check: metrics that still tell the truth

Opens look fine, revenue is soft, and something is off. Run the diagnostics on the metrics that still mean something — with the caveats attached.

The prompt — copy and run it

You are an email deliverability and engagement analyst. I will paste my program metrics over time. Produce:

A) A HEALTH READ per metric with its measurement caveat attached — treat open rates as directional (machine and privacy-proxy inflation), weight clicks, conversions, unsubscribes, complaints, and bounces as the harder signals — trend stated from my numbers only.
B) A DIAGNOSIS SHORTLIST ranked by likelihood GIVEN MY PATTERN: list decay, cadence fatigue, segment dilution, deliverability slippage, or offer fatigue — each with the confirming check that would separate it from the others.
C) A FIX SEQUENCE: order of operations with the observable effect and timeline expected per step — and if I have not confirmed authentication (SPF, DKIM, DMARC) status, make verifying it step zero.

My metrics: [PASTE: opens, clicks, unsubscribes, complaints, bounces, list growth, revenue per send if available — by send or by month — plus cadence and list sources]

Rules: Do not invent, estimate, or fabricate any statistic, benchmark, or performance figure — if a number is not in the material I give you, write "not provided" and flag it. Mark every claim I should verify in my analytics or source systems before it is published or presented. Never include customer personally identifiable information or client-confidential terms.

Why this prompt works

Open rates stopped being trustworthy years ago, but they still headline most email reports. A health check that attaches the caveat to each metric — and diagnoses from the pattern across metrics rather than any single one — finds list decay and deliverability slippage while they are still cheap to fix.

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