Performance Analyzer

Performance Analyzer

This skill helps you analyze influencer marketing campaign performance comprehensively. It goes beyond surface metrics to understand what truly worked and why.

Quick Start

Shortest invocation:

Analyze performance of [campaign name] influencer campaign

Common scenario — 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.
• Writes: a performance analysis to memory/influencer/performance-analyzer/YYYY-MM-DD-.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](../../track/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.
• ~~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

When a user requests performance analysis:

1. Gather Performance Data

```markdown ### Performance Data Collection

Campaign: [name] Period: [start] - [end] Influencers: [count] Platforms: [platforms]

### Data Sources

| Source | Metrics Available | Collection Method | |--------|-------------------|-------------------| | Native analytics | Reach, views, engagement | Platform export | | Influencer reports | Screenshots/exports | From creators | | Website analytics | Traffic, conversions | GA/tracking | | Sales data | Revenue, orders | E-commerce platform | | Promo code data | Redemptions | Sales system | ```

1. Analyze Core Metrics

```markdown ## Campaign Performance Overview

### Summary Metrics

| Metric | Result | Target | vs. Target | vs. Benchmark | |--------|--------|--------|------------|---------------| | Total Reach | [X] | [X] | [+/-X%] | [+/-X%] | | Total Impressions | [X] | [X] | [+/-X%] | [+/-X%] | | Total Engagements | [X] | [X] | [+/-X%] | [+/-X%] | | Engagement Rate | [X%] | [X%] | [+/-X%] | [+/-X%] | | Total Video Views | [X] | [X] | [+/-X%] | [+/-X%] | | Link Clicks | [X] | [X] | [+/-X%] | [+/-X%] | | Promo Code Uses | [X] | [X] | [+/-X%] | N/A | | Conversions | [X] | [X] | [+/-X%] | [+/-X%] | | Revenue | $[X] | $[X] | [+/-X%] | N/A |

### Performance Score: [X/10]

Assessment: [Excellent/Good/Average/Below Average/Poor]

### Key Highlights

✅ What Exceeded Expectations:

• [Highlight 1]
• [Highlight 2]

⚠️ What Underperformed:

• [Issue 1]
• [Issue 2]

```

1. Analyze by Platform

```markdown ## Platform Performance

### Platform Comparison

| Platform | Reach | Engagements | ER | Clicks | Conversions | CPA | |----------|-------|-------------|-------|--------|-------------|-----| | Instagram | [X] | [X] | [%] | [X] | [X] | $[X] | | TikTok | [X] | [X] | [%] | [X] | [X] | $[X] | | YouTube | [X] | [X] | [%] | [X] | [X] | $[X] | | Total | [X] | [X] | [%] | [X] | [X] | $[X] |

### Platform Insights

Best Performing Platform: [Platform]

• Why: [analysis]
• Key content: [what worked]

Underperforming Platform: [Platform]

• Why: [analysis]
• Improvement opportunity: [suggestion]

### Platform-Specific Metrics

#### Instagram

| Metric | Feed Posts | Reels | Stories | |--------|------------|-------|---------| | Reach | [X] | [X] | [X] | | Engagements | [X] | [X] | [X] | | ER | [%] | [%] | [%] | | Saves | [X] | [X] | N/A | | Shares | [X] | [X] | [X] |

#### TikTok

| Metric | Result | Benchmark | |--------|--------|-----------| | Views | [X] | | | Likes | [X] | | | Comments | [X] | | | Shares | [X] | | | Average Watch Time | [X]s | | | Completion Rate | [%] | | ```

1. Analyze by Influencer

```markdown ## Influencer Performance

### Influencer Ranking

| Rank | Influencer | Reach | ER | Conversions | ROI | Score | |------|------------|-------|-------|-------------|-----|-------| | 1 | @[handle] | [X] | [%] | [X] | [X]:1 | ⭐⭐⭐⭐⭐ | | 2 | @[handle] | [X] | [%] | [X] | [X]:1 | ⭐⭐⭐⭐ | | 3 | @[handle] | [X] | [%] | [X] | [X]:1 | ⭐⭐⭐⭐ | | 4 | @[handle] | [X] | [%] | [X] | [X]:1 | ⭐⭐⭐ | | 5 | @[handle] | [X] | [%] | [X] | [X]:1 | ⭐⭐ |

### Top Performers Deep Dive

#### #1: @[handle]

| Metric | Result | vs. Campaign Avg | |--------|--------|------------------| | Reach | [X] | [+/-X%] | | Engagement Rate | [%] | [+/-X%] | | Video Completion | [%] | [+/-X%] | | Click-through Rate | [%] | [+/-X%] | | Conversion Rate | [%] | [+/-X%] | | Cost per Conversion | $[X] | [+/-X%] |

Why They Performed Well:

• [Reason 1]
• [Reason 2]
• [Reason 3]

Content Analysis:

• Format: [what they posted]
• Hook: [how they opened]
• Message: [how they communicated]
• CTA: [what they asked viewers to do]

Recommendation: [Renew/Expand/Ambassador potential]

### Underperformers Analysis

#### @[handle]

Results: [summary] Why Underperformed: [analysis] Learning: [what to do differently] ```

1. Content Performance Analysis

```markdown ## Content Performance

### Top Performing Content

| Rank | Creator | Platform | Format | Reach | ER | Key Feature | |------|---------|----------|--------|-------|-------|-------------| | 1 | @[handle] | [platform] | [format] | [X] | [%] | [why it worked] | | 2 | @[handle] | [platform] | [format] | [X] | [%] | [why it worked] | | 3 | @[handle] | [platform] | [format] | [X] | [%] | [why it worked] |

### Content Format Analysis

| Format | Pieces | Avg Reach | Avg ER | Best Performer | |--------|--------|-----------|--------|----------------| | Video (Reels/TikTok) | [#] | [X] | [%] | @[handle] | | Static Images | [#] | [X] | [%] | @[handle] | | Carousels | [#] | [X] | [%] | @[handle] | | Stories | [#] | [X] | [%] | @[handle] | | YouTube Videos | [#] | [X] | [%] | @[handle] |

### Content Theme Analysis

| Theme | Pieces | Avg ER | Conversion Rate | Notes | |-------|--------|--------|-----------------|-------| | Product demo | [#] | [%] | [%] | [notes] | | Lifestyle | [#] | [%] | [%] | [notes] | | Tutorial | [#] | [%] | [%] | [notes] | | Review | [#] | [%] | [%] | [notes] | | Unboxing | [#] | [%] | [%] | [notes] |

### Winning Content Patterns

Hook Patterns That Worked:

• [Pattern 1]: [examples]
• [Pattern 2]: [examples]

Messaging That Resonated:

• [Message type 1]: [why it worked]
• [Message type 2]: [why it worked]

Visual Elements That Performed:

• [Element 1]
• [Element 2]

```

1. Engagement Quality Analysis

```markdown ## Engagement Quality

### Engagement Breakdown

| Type | Volume | % of Total | Quality Assessment | |------|--------|------------|-------------------| | Likes | [X] | [%] | Passive | | Comments | [X] | [%] | [quality] | | Saves | [X] | [%] | High intent | | Shares | [X] | [%] | High value | | Link clicks | [X] | [%] | Direct action |

### Comment Sentiment Analysis

| Sentiment | % | Examples | |-----------|---|----------| | Positive | [%] | "[example]", "[example]" | | Neutral/Questions | [%] | "[example]", "[example]" | | Negative | [%] | "[example]", "[example]" |

Key Themes in Comments:

• [Theme 1]: [frequency] mentions
• [Theme 2]: [frequency] mentions
• [Theme 3]: [frequency] mentions

### Purchase Intent Signals

| Signal | Count | Examples | |--------|-------|----------| | "Where to buy" questions | [#] | | | Price questions | [#] | | | Code requests | [#] | | | "Just ordered" | [#] | | | Tagged friends | [#] | |

### Engagement Quality Score: [X/10] ```

1. Conversion & Attribution Analysis

```markdown ## Conversion Analysis

### Conversion Funnel

`` Reach [XXXXXXXXXX] 1,000,000 (100%) ↓ Engagements [XXXXXXX ] 150,000 (15%) ↓ Link Clicks [XXX ] 25,000 (2.5%) ↓ Site Visits [XX ] 20,000 (2%) ↓ Add to Cart [X ] 5,000 (0.5%) ↓ Purchases [X ] 2,000 (0.2%) ``

### Conversion Metrics

| Metric | Result | Benchmark | Status | |--------|--------|-----------|--------| | Click-through Rate | [%] | [%] | ✅/❌ | | Landing Page CVR | [%] | [%] | ✅/❌ | | Overall CVR | [%] | [%] | ✅/❌ | | Cost per Click | $[X] | $[X] | ✅/❌ | | Cost per Conversion | $[X] | $[X] | ✅/❌ |

### Attribution by Method

| Method | Conversions | Revenue | % of Total | |--------|-------------|---------|------------| | Promo codes | [X] | $[X] | [%] | | UTM tracking | [X] | $[X] | [%] | | Direct attribution | [X] | $[X] | [%] | | Estimated influence | [X] | $[X] | [%] |

### Promo Code Performance

| Code | Influencer | Uses | Revenue | AOV | |------|------------|------|---------|-----| | [CODE1] | @[handle] | [X] | $[X] | $[X] | | [CODE2] | @[handle] | [X] | $[X] | $[X] | | [CODE3] | @[handle] | [X] | $[X] | $[X] | ```

1. Generate Insights & Recommendations

```markdown ## Insights & Recommendations

### Top 5 Learnings

1. [Learning 1]

• What we observed: [data]
• Why it matters: [significance]
• Future application: [how to use this]

1. [Learning 2]

• What we observed: [data]
• Why it matters: [significance]
• Future application: [how to use this]

[Continue for top 5]

### What Worked

| Element | Performance | Recommendation | |---------|-------------|----------------| | [Element 1] | [metric] | Do more of this | | [Element 2] | [metric] | Expand this approach |

### What Didn't Work

| Element | Performance | Recommendation | |---------|-------------|----------------| | [Element 1] | [metric] | Adjust or eliminate | | [Element 2] | [metric] | Test alternatives |

### Optimization Opportunities

| Opportunity | Expected Impact | Effort | Priority | |-------------|-----------------|--------|----------| | [Opportunity 1] | [impact] | [effort] | High | | [Opportunity 2] | [impact] | [effort] | Medium | | [Opportunity 3] | [impact] | [effort] | Low |

### Influencer Roster Recommendations

| Influencer | Recommendation | Rationale | |------------|----------------|-----------| | @[handle1] | Renew/Ambassador | Top performer | | @[handle2] | Renew at same level | Solid results | | @[handle3] | Don't renew | Below expectations | | @[handle4] | Increase investment | High potential |

### Future Campaign Recommendations

1. Platform Mix: [recommendation]
2. Influencer Tier: [recommendation]
3. Content Format: [recommendation]
4. Messaging: [recommendation]
5. Budget Allocation: [recommendation]

```

Example

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

Output:

# Summer Skincare Campaign Performance Analysis

## Executive Summary

**Campaign Performance**: 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 Performers

1. **@skincaresarah** - ROI 4.2:1, highest conversions
2. **@glowwithgrace** - Best engagement (6.8%)
3. **@beautyreview** - Highest reach per dollar

## Key Learning

TikTok outperformed Instagram significantly (3.5:1 ROI vs 2.1:1). Recommend shifting 20% of Instagram budget to TikTok for future campaigns.

## Recommendation

Renew partnerships with top 5 performers. Replace bottom 2 with TikTok-native creators.

Reference Materials

• [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.
• The C3 benchmark at [references/c3/scoring-architecture.md](../../references/c3/scoring-architecture.md) — scoring architecture when a structured score is needed.
• Sibling skills: [roi-calculator](../../track/roi-calculator/SKILL.md), [report-generator](../../track/report-generator/SKILL.md), [fit-scorer](../../map/fit-scorer/SKILL.md), [campaign-planner](../../plan/campaign-planner/SKILL.md).

Next Best Skill

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

Alternates (same Track family):

• [report-generator](../../track/report-generator/SKILL.md) — package the analysis into a formal stakeholder report.
• [fit-scorer](../../map/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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: aaron-he-zhu
• Source: aaron-he-zhu/aaron-marketing-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.