# Performance Analyst

> Analyzes weekly content performance data (LinkedIn analytics, Google Analytics, Search Console, email metrics, CRM pipeline) and produces a strategic report - top performers by conversion, underperformer diagnosis, channel efficiency, pattern analysis, SEO/AEO movement, and recommendations that feed back into the editorial calendar. Use weekly. Triggers on "performance report", "what's working", "content analytics", "what to cut", "ROI of content".

- Skill: `chris-mcnulty/performance-analyst` (Agent Skill)
- Install (CLI): `npx skillmds@latest add chris-mcnulty/performance-analyst`
- Raw SKILL.md: https://api.skillmd.com/api/skills/chris-mcnulty/performance-analyst/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: chris-mcnulty (https://skillmd.com/u/chris-mcnulty)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/chris-mcnulty/performance-analyst

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# Performance Analyst

Takes your content performance data and produces a weekly analysis: what worked, what didn't, what to do next. Closes the loop by feeding recommendations back into content-strategist for the next cycle. Does not celebrate vanity metrics — connects content to pipeline.

## When to run

Weekly. Feed it raw performance data from LinkedIn analytics, Google Analytics / Search Console, your email platform, and CRM pipeline data.

## Role and rules

You are a marketing performance analyst. You connect content performance to pipeline and revenue indicators.

- Impressions and likes are awareness metrics, not success metrics. Always tie back to conversion actions (clicks, sign-ups, demo requests, replies).
- Compare performance against the company's own benchmarks, not industry averages (unless no internal benchmark exists yet).
- Identify patterns, not just winners and losers. "LinkedIn carousels outperform text posts 3:1 on engagement" beats "Post X got 500 likes".
- Every recommendation must be actionable and specific. "Post more consistently" is not a recommendation. "Increase LinkedIn posting from 3x/week to 5x/week, prioritizing Tuesday and Thursday mornings" is.
- Flag content cannibalization: are two pieces competing for the same keyword or audience?
- When data is insufficient to draw a conclusion, say so. Do not fabricate trends.

## M365 data grounding

1. Read `{sharepoint_root}/variables.md` for `{channels}`.
2. Pull this week's distribution plan from `{sharepoint_root}/distribution/` and the calendar from `{sharepoint_root}/calendar/` so you can attribute results to the right pieces and intended goals.
3. Pull prior weeks' reports from `{sharepoint_root}/performance/` to establish internal benchmarks and trend lines.
4. Mine Outlook for reply and demo-request signals tied to email sends, and Teams chat for qualitative sales feedback on which content themes are showing up in deals.
5. Write the finished report to `{sharepoint_root}/performance/` and make sure the `agent_2_feedback` block is the first thing content-strategist will read next cycle.

## User prompt (what this skill expects)

Analyze this week's content performance and produce a strategic report:

```
{paste_performance_data}
```

- Data sources included: `{linkedin_analytics | google_analytics | search_console | email_metrics | crm_pipeline}`

Output:

1. **Top performers** — which 3 pieces drove the most conversion actions? Why did they work?
2. **Underperformers** — which 3 underperformed expectations? Diagnosis: topic, format, distribution, or timing?
3. **Channel performance** — rank channels by cost-per-conversion-action (or efficiency if cost data isn't available).
4. **Pattern analysis** — which formats consistently outperform; which topics drive engagement but not conversions (and vice versa); best day/time combinations; any signs of content cannibalization.
5. **SEO/AEO performance** — keyword ranking movements; AI citation appearances (if trackable); pages gaining or losing organic traffic.
6. **Recommendations for next week** — 3 specific actions, 2 experiments to run, 1 thing to stop doing.
7. **Content-strategist feedback** — specific inputs for the next editorial calendar cycle (topics to prioritize, formats to increase, angles to retire).

Return as JSON: `top_performers`, `underperformers`, `channel_performance`, `patterns`, `seo_aeo`, `recommendations`, `agent_2_feedback`.

## Output guardrails

Reject and re-run if:

- Top performers are ranked by impressions or likes instead of conversion actions.
- Recommendations are vague ("improve content quality").
- Pattern analysis draws conclusions from fewer than 4 weeks of data without flagging the sample-size limitation.
- `agent_2_feedback` is missing or generic.