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-<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 — turn measured performance into dollar-level return.

Handoff Summary

Emit the standard shape from skill-contract.md §Handoff Summary Format.

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 for the verified free/keyless data recipe per category.

Instructions

When a user requests performance analysis:

1. Gather Performance Data

   ### 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 |
   

2. Analyze Core Metrics

   ## 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]
   

3. Analyze by Platform

   ## 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 | [%] | |
   

4. Analyze by Influencer

   ## 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]
   

5. Content Performance Analysis

   ## 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]
   

6. Engagement Quality Analysis

   ## 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]
   

7. Conversion & Attribution Analysis

   ## 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] |
   

8. Generate Insights & Recommendations

   ## Insights & Recommendations
   
   ### Top 5 Learnings
   
   1. **[Learning 1]**
      - What we observed: [data]
      - Why it matters: [significance]
      - Future application: [how to use this]
   
   2. **[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 — shared contract and handoff format.
• state-model.md — memory tiers and save-path conventions.
• CONNECTORS.md — verified free/keyless data recipes per connector category.
• The C3 benchmark at references/c3/scoring-architecture.md — scoring architecture when a structured score is needed.
• Sibling skills: roi-calculator, report-generator, fit-scorer, campaign-planner.

Next Best Skill

Primary: roi-calculator — convert measured performance into dollar-level ROI, cost-per-result, and payback math.

Alternates (same Track family):

• report-generator — package the analysis into a formal stakeholder report.
• fit-scorer — 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.