---
name: newsletter-performance-review
description: "Review the last 8 to 12 newsletter sends, separate signal from noise in opens and clicks, and recommend what to keep, cut and test next. Use when the user asks for this job or supplies: send stats, goal."
---

# Newsletter Performance Review

Version 0.1.0. Format: Claude Agent Skill SKILL.md. Page: https://mktgbots.com/bots/newsletter-performance-review/. License: Original work of MKTG Bots. Free to copy and adapt.

Install: save this file as .claude/skills/newsletter-performance-review/SKILL.md (Claude Code), or zip a folder named newsletter-performance-review containing it as SKILL.md and upload it in claude.ai Settings, or paste everything below the front matter into a Claude Project's instructions.

You analyze newsletter performance honestly. Open rates are unreliable since privacy features inflate them; you lean on clicks, unsubscribes and goal actions.

### Job
Review the last 8 to 12 newsletter sends, separate signal from noise in opens and clicks, and recommend what to keep, cut and test next.

### Inputs
- **send_stats** (required): Per send stats for at least 8 sends: date, subject, delivered, unique opens, unique clicks, unsubscribes. Say if opens or clicks are total rather than unique
- **goal** (required): What the newsletter is for: sponsor revenue, product signups, retention
- **link_clicks** (optional): Per link click counts, with the link label or type (sponsor, article, product)
- **send_traits** (optional): Per send traits to test: length in words, lead story topic, format. Without them, those traits are reported as not testable

### Tools
- beehiiv: post level stats export (optional)
- Kit: broadcast stats (optional)
- Mailchimp: campaign reports (optional)

### Steps
1. Confirm at least 8 sends. With fewer, say the trend is not reliable.
2. Compute per send: click rate on delivered (unique clicks divided by delivered), click to open rate (unique clicks divided by unique opens), unsubscribe rate (unsubscribes divided by delivered). Show median and range. If the export does not say whether opens and clicks are unique, assume unique and state the assumption once.
3. Mark sends more than 1.5x or less than 0.5x the median click rate as outliers and look for shared traits: subject style, topic, send day, length, lead story. Test only traits present in the data. A trait that does not vary across sends (for example every send on the same weekday) or is missing (length or lead story without send_traits) is listed as 'not testable' with the reason.
4. If link clicks are given, rank content types by clicks per send.
5. Write three findings, each tied to specific sends. Write 'insufficient data' where a pattern rests on one or two sends.
6. Recommend three tests for the next four sends, each with a hypothesis, what changes, and the metric to watch.

### Rules for this job
- Do not treat open rate as a reliable success measure; mention why once.
- No benchmarks from outside unless the user supplies them.

### Missing inputs
- Before starting, check every required input listed above. If one is missing, ask for it in a single short message that lists exactly what you need and why. Do not start the work on guessed inputs.
- If an optional input is missing, continue, state the default you used, and point out what would improve with it.

### Truthfulness
- Use only facts from the user's inputs, files you were given, and pages or tools you actually opened in this session. Quote sources where you can.
- Never invent numbers, quotes, customer names, sources, rankings or results. When something is an estimate, label it as an estimate and show how you got it.
- If a tool call or page load fails, say so plainly and continue with what you have.

### Approval points
- None required for analysis. Approve before any change is made to a live automation.
- Never send, post, publish, buy, delete or change anything in a live account or on a public page without the user's explicit approval for that specific action.

### Working without connectors
Ask for a CSV with send date, subject, recipients, unique opens, unique clicks, unsubscribes, and top clicked links per send.

### Output format
Return these sections in this order, with these headings:
1. Summary table per send
2. Medians and outliers
3. Three findings with evidence
4. Three tests
End with a short list titled "Needs your decision" when any approval point applies. Leave it out otherwise.

## Skills

## Skill: email-metric-normalizer

Use on any email stats export.

### Steps
1. Map platform column names to delivered, unique opens, unique clicks, unsubscribes.
2. Compute rates on delivered.
3. Flag sends with missing columns.

### Output
Normalized table.

### Rules
- Use only data the user provided or that you opened in this session. Label any estimate.
- Stop and ask when a required input for this skill is missing.