performance-analyzer

$npx mdskill add aaron-he-zhu/aaron-marketing-skills/performance-analyzer

Analyze influencer campaign performance against targets and benchmarks.

  • Evaluates campaign results vs goals and industry benchmarks.
  • Depends on campaign data, platform APIs, and historical benchmarks.
  • Ranks influencers, platforms, and content by key metrics.
  • Delivers scorecards, rankings, and actionable optimization insights.

SKILL.md

.github/skills/performance-analyzerView on GitHub ↗
---
name: performance-analyzer
slug: performance-analyzer
displayName: "Performance Analyzer · 效果分析"
summary: "活动效果分析:达成 vs 目标、平台与创作者维度拆解、优化建议"
description: 'Use when the user asks to "analyze influencer campaign performance", "compare influencers", or "find what content worked"; produces metric scorecards vs target and benchmark, platform/influencer/content rankings, engagement-quality and sentiment reads, conversion-attribution breakdowns, and ranked learnings. Not for dollar-level return math — use roi-calculator.'
version: "18.0.0"
license: Apache-2.0
compatibility: "Claude Code and compatible agent-skill hosts"
homepage: "https://github.com/aaron-he-zhu/aaron-marketing-skills"
when_to_use: "Use mid-flight or post-campaign when a user wants to evaluate influencer results, compare creators against each other, find top-performing content or formats, judge engagement quality and comment sentiment, connect influencer activity to conversions, or build performance benchmarks for future planning."
argument-hint: "<campaign name> [platform or influencer handles]"
metadata: {"author": "aaron-he-zhu", "version": "18.0.0", "discipline": "influencer", "phase": "report", "geo-relevance": "low", "hermes": {"tags": ["marketing", "influencer", "report"], "category": "influencer"}, "openclaw": {"emoji": "📣", "homepage": "https://github.com/aaron-he-zhu/aaron-marketing-skills"}}
---

# Performance Analyzer

Analyze influencer campaign performance past surface metrics — score results vs target/benchmark, rank platforms/creators/content, read engagement quality and sentiment, attribute conversions, and write ranked learnings.

> **Cross-discipline (paid ads):** this is also the cross-channel **paid-ads** scorecard/anomaly lens — account-wide metric rollups vs target/benchmark that feed [ad-test-designer](../../../ad/orchestrate/ad-test-designer/SKILL.md) (what to test) and [paid-measurement-loop](../../../ad/scale/paid-measurement-loop/SKILL.md) (what to read back). Save paid runs under `memory/ad/performance-analyzer/`.

## Quick Start

```
Analyze performance of [campaign name] influencer campaign
```

Compare creators within one campaign:

```
Compare performance of these influencers from [campaign]: @handle1, @handle2, @handle3
```

## Skill Contract

- **Reads**: campaign name and date range; native platform analytics (reach, views, engagement); influencer-supplied reports or screenshots; website/GA traffic and conversion data; sales and promo-code redemption data; targets and benchmarks if the user has them; per-creator performance baselines from `memory/creators/<handle-slug>.md` ([creator-registry](../../../protocol/creator-registry/SKILL.md) roster records) when present.
- **Writes**: a performance analysis to `memory/influencer/performance-analyzer/YYYY-MM-DD-<campaign>.md` covering core-metric scorecards, platform/influencer/content rankings, engagement-quality and sentiment reads, conversion attribution, and ranked learnings.
- **Promotes**: durable facts (top-performing creators, winning formats, platform ROI splits, roster renew/drop calls) to `memory/hot-cache.md`.
- **Done when**:
  - Core metrics are scored against target and benchmark with a performance verdict.
  - Top and bottom performers are ranked with reasons, and content patterns that worked are named.
  - Conversions are attributed by method (promo code / UTM / direct / estimated) and 3-5 learnings are written.
- **Primary next skill**: [roi-calculator](../roi-calculator/SKILL.md) — turn measured performance into dollar-level return.

### Handoff Summary

> Emit the standard shape from [skill-contract.md §Handoff Summary Format](../../../references/skill-contract.md).

## Data Sources

This family needs no live integrations (Tier 1). The skill runs entirely on inputs you provide — paste platform exports, influencer report screenshots, GA numbers, and promo-code redemption counts, and it builds the full analysis. Ask the user for whatever is missing rather than blocking.

Where a connector could speed the work, the skill marks it with a `~~` placeholder:

- `~~social platform analytics` — native reach/engagement/video metrics per post.
- `~~web analytics` — site traffic, click-through, and on-site conversion data.

**Measured YouTube post-performance (free key)**: when campaign content lives on YouTube, `python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/youtube.py" videos @creator --limit 20` pulls the actual per-video views/likes/comments for the campaign window — **Measured** platform metrics without waiting for the creator's screenshot export. Keep both labels honest: API numbers are Measured, creator-supplied numbers are User-provided, and the two can legitimately disagree (display rounding, timing). Free `YOUTUBE_API_KEY`. See [scripts/connectors/README.md](../../../scripts/connectors/README.md).
- `~~ecommerce / sales platform` — revenue, orders, AOV, promo-code redemptions.
- `~~influencer database` — historical creator benchmarks for comparison.

No placeholder is required to run. See [CONNECTORS.md](../../../CONNECTORS.md) for the verified free/keyless data recipe per category.

## Instructions

Work the steps in order. Each fill-in template lives in [references/analysis-templates.md](references/analysis-templates.md) — copy the matching block and populate it.

1. **Gather performance data** — log campaign/period/influencers/platforms and the available sources (native analytics, influencer reports, web analytics, sales, promo codes). Template: step 1.
2. **Analyze core metrics** — score reach, impressions, engagements, ER, video views, clicks, promo uses, conversions, and revenue against target and benchmark; assign a performance verdict and call out over/underperformers. Template: step 2.
3. **Analyze by platform** — compare platforms on reach/ER/clicks/conversions/CPA, name the best and worst with reasons, and break out platform-specific formats (IG feed/Reels/Stories, TikTok watch time/completion). Template: step 3.
4. **Analyze by influencer** — rank creators on reach/ER/conversions/ROI, deep-dive top performers (why they won, content anatomy, renew call), and explain underperformers. Template: step 4.
5. **Content performance analysis** — rank top content, compare formats and themes, and name the winning hook/messaging/visual patterns. Template: step 5.
6. **Engagement quality analysis** — break engagement by type and intent, run comment sentiment, surface purchase-intent signals, and score quality /10. Template: step 6.
7. **Conversion & attribution analysis** — draw the funnel, score conversion metrics vs benchmark, attribute by method (promo / UTM / direct / estimated), and table promo-code performance. Template: step 7.
8. **Generate insights & recommendations** — write the top-5 learnings, what worked / what didn't, optimization opportunities, roster renew/drop calls, and future-campaign guidance. Template: step 8.

Before naming any creator/format/platform a real winner, clear the significance bar in [measurement-protocol.md](../../../references/measurement-protocol.md) — otherwise mark it Keep-testing. When a structured score is needed, apply per-dimension STAR analysis (Suitability/Trust/Appeal/Return dimension reads) from [star-benchmark.md](../../../references/star-benchmark.md), and hand the measured inputs to [roi-calculator](../roi-calculator/SKILL.md) for the measured Return (R) evidence — this skill contributes the inputs but does not compute the SQS (the creator-content-auditor gate does).

## Example

**User**: "Analyze performance of our summer skincare campaign with 10 influencers"

**Output** (abridged — full version in [references/analysis-templates.md](references/analysis-templates.md)):

```markdown
# Summer Skincare Campaign Performance Analysis — Above Average (7.5/10)

| Metric | Result | Target | Status |
|--------|--------|--------|--------|
| Total Reach | 2.4M | 2M | ✅ +20% |
| Engagement Rate | 4.2% | 3.5% | ✅ +20% |
| Conversions | 1,847 | 2,000 | ⚠️ -8% |
| Revenue | $142,500 | $150,000 | ⚠️ -5% |
| ROI | 2.8:1 | 3:1 | ⚠️ -7% |

**Top 3**: @skincaresarah (ROI 4.2:1), @glowwithgrace (ER 6.8%), @beautyreview (reach/$).
**Key learning**: TikTok beat Instagram (3.5:1 vs 2.1:1 ROI) — shift 20% of IG budget to TikTok.
**Recommendation**: Renew top 5; replace bottom 2 with TikTok-native creators.
```

## Reference Materials

- [references/analysis-templates.md](references/analysis-templates.md) — the eight fill-in step templates plus the full worked example.
- [skill-contract.md](../../../references/skill-contract.md) — shared contract and handoff format.
- [state-model.md](../../../references/state-model.md) — memory tiers and save-path conventions.
- [CONNECTORS.md](../../../CONNECTORS.md) — verified free/keyless data recipes per connector category.
- [measurement-protocol.md](../../../references/measurement-protocol.md) — preregistered readback windows, outcome unit, alpha, practical-effect boundary, multiplicity/sequential policy, guardrails, and decision owner. Report statistical and practical flags separately; use `experiment.py` for deterministic `Calculated` evidence, and never substitute a universal p-value/lift rule or attribute a business action to the helper.
- The STAR benchmark at [references/star-benchmark.md](../../../references/star-benchmark.md) — scoring architecture when a structured score is needed.
- Sibling skills: [roi-calculator](../roi-calculator/SKILL.md), [report-generator](../report-generator/SKILL.md), [fit-scorer](../../scout/fit-scorer/SKILL.md), [campaign-planner](../../target/campaign-planner/SKILL.md).

## Next Best Skill

**Primary**: [roi-calculator](../roi-calculator/SKILL.md) — convert measured performance into dollar-level ROI, cost-per-result, and payback math.

**Alternates** (same Report family):

- [report-generator](../report-generator/SKILL.md) — package the analysis into a formal stakeholder report.
- [fit-scorer](../../scout/fit-scorer/SKILL.md) — feed proven performers back into creator scoring for the next round.

**Termination note**: Maintain a visited-set. If a skill has already been invoked this session, stop and report chain-complete rather than re-running it. Cap the chain at max-depth 3 hops; if results are inconclusive after that, surface the open loops to the user instead of continuing.

More from aaron-he-zhu/aaron-marketing-skills

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ad-creative-builderUse when the user asks to "write ad copy", "generate RSA headlines", or "build ad creative at volume"; produces ad units — RSA headlines/descriptions, hooks, and an angle matrix — message-matched to the destination landing page. Not for scoring an ad account — use ad-account-auditor; not for the post-click page — use landing-optimizer; not for organic articles — use content-writer. 广告创意/广告文案/RSA标题
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audience-belief-mapperUse when the user asks to "map what our buyers believe", "capture the objections we keep hearing", or "find the switching forces that move the beachhead"; produces a belief map of the beachhead — held beliefs and mental models, the recurring objections and their reframes, and the JTBD four forces (push of the problem, pull of the new, anxiety of switching, habit of the present) — each item sourced from interviews or win-loss notes (User-provided) and labeled Measured / User-provided / Estimated, with any unverified quote or comparative claim marked "[needs source]" and routed to the claims candidates, never adjudicated here. Not for demographic or persona profiling — use audience-mapper; not for the positioning canvas — use positioning-truth-tracer. 受众信念/异议地图/切换四力/流失语言
audience-mapperUse when the user asks to "analyze my target audience", "build an audience profile for influencer targeting", "research a niche community", or "deep-dive a subculture before partnering with creators"; in audience mode produces demographic/psychographic profiles, a platform-priority matrix, named personas, and an influencer-selection criteria set, and in niche mode produces a community map, culture decode (language/norms/taboos), key-voice tiers, a Brand Fit Score, and a phased entry strategy. Not for finding specific creators to contract — use influencer-discovery; not for scoring a shortlist on Suitability — use fit-scorer. 目标受众画像/人群分析 · 细分社群/亚文化调研
audience-segment-builderUse when the user asks to "build audience segments from my customer list", "make value-based / lookalike seed lists", "set up exclusion / suppression segments", or "map audiences to funnel stages across platforms"; turns the user''s OWN customer/CRM/GA4 export into seed audiences, value-based lookalike SEED lists, exclusion/suppression segments, and a cross-platform funnel-stage targeting map, informing the ROAS A (Audience) dimension. Not for building account structure or match types — use campaign-architect; not for organic SERP intent — use keyword-research. 付费广告受众分群/种子人群/排除人群/相似人群种子
bid-strategy-plannerUse when the user asks to "pick a bid strategy", "set a tCPA/tROAS target", or "plan the learning-phase entry"; produces a bid-strategy choice (tCPA / tROAS / max-conversions / manual CPC), the starting target math, a portfolio grouping map, and a learning-phase entry plan. Not for splitting the budget across campaigns — use budget-optimizer; not for in-flight pacing/scale moves — use budget-pacing-monitor; not for scoring the account — use ad-account-auditor. 出价策略/tCPA目标/tROAS/学习期
brand-language-codifierUse when the user asks to "codify our brand voice", "define naming rules for our products and tiers", or "write the tone-of-voice guide with banned phrases"; produces the brand-level voice canon (register, tone spectrum, banned-phrase list, few-shot examples drawn only from the brand''s own published material) and the naming tax (product / feature / tier naming rules plus approved and banned terms) that seeds the narrative-registry canon and that every channel''s voice adaptation points up to. Not for per-platform voice adaptation — use channel-registry''s voice-dossier; not for finished copy or blog posts — use content-writer; not for the message hierarchy itself — use message-system-architect; not for claim adjudication — use offer-claims-registry. 品牌语气/词汇表/命名税/禁用词/品牌语言规范