Imported from DenisSoldugeev/DenisSoldugeev (claude/skills/marketing-skill/social-media-analyzer/SKILL.md). Install upstream with npx skills add DenisSoldugeev/DenisSoldugeev --skill social-media-analyzer. Copyright stays with the author.

Social Media Analyzer

Campaign performance analysis with engagement metrics, ROI calculations, and platform benchmarks.

Table of Contents

• Analysis Workflow
• Engagement Metrics
• ROI Calculation
• Platform Benchmarks
• Tools
• Examples

Analysis Workflow

Analyze social media campaign performance:

1. Validate input data completeness (reach > 0, dates valid)
2. Calculate engagement metrics per post
3. Aggregate campaign-level metrics
4. Calculate ROI if ad spend provided
5. Compare against platform benchmarks
6. Identify top and bottom performers
7. Generate recommendations
8. Validation: Engagement rate < 100%, ROI matches spend data

Input Requirements

Data Validation Checks

Before analysis, verify:

• Reach > 0 for all posts (avoid division by zero)
• Engagement counts are non-negative
• Date range is valid (start < end)
• Platform is recognized
• Spend > 0 if ROI requested

Engagement Metrics

Engagement Rate Calculation

Engagement Rate = (Likes + Comments + Shares + Saves) / Reach × 100

Metric Definitions

Performance Categories

ROI Calculation

Calculate return on ad spend:

1. Sum total engagements across posts
2. Calculate cost per engagement (CPE)
3. Calculate cost per click (CPC) if clicks available
4. Estimate engagement value using benchmark rates
5. Calculate ROI percentage
6. Validation: ROI = (Value - Spend) / Spend × 100

ROI Formulas

Engagement Value Estimates

ROI Interpretation

Platform Benchmarks

Engagement Rate by Platform

CTR by Platform

CPC by Platform

See references/platform-benchmarks.md for complete benchmark data.

Tools

Calculate Metrics

python scripts/calculate_metrics.py assets/sample_input.json

Calculates engagement rate, CTR, reach rate for each post and campaign totals.

Analyze Performance

python scripts/analyze_performance.py assets/sample_input.json

Generates full performance analysis with ROI, benchmarks, and recommendations.

Output includes:

• Campaign-level metrics
• Post-by-post breakdown
• Benchmark comparisons
• Top performers ranked
• Actionable recommendations

Examples

Sample Input

See assets/sample_input.json:

{
  "platform": "instagram",
  "total_spend": 500,
  "posts": [
    {
      "post_id": "post_001",
      "content_type": "image",
      "likes": 342,
      "comments": 28,
      "shares": 15,
      "saves": 45,
      "reach": 5200,
      "impressions": 8500,
      "clicks": 120
    }
  ]
}

Sample Output

See assets/expected_output.json:

{
  "campaign_metrics": {
    "total_engagements": 1521,
    "avg_engagement_rate": 8.36,
    "ctr": 1.55
  },
  "roi_metrics": {
    "total_spend": 500.0,
    "cost_per_engagement": 0.33,
    "roi_percentage": 660.5
  },
  "insights": {
    "overall_health": "excellent",
    "benchmark_comparison": {
      "engagement_status": "excellent",
      "engagement_benchmark": "1.22%",
      "engagement_actual": "8.36%"
    }
  }
}

Interpretation

The sample campaign shows:

• Engagement rate 8.36% vs 1.22% benchmark = Excellent (6.8x above average)
• CTR 1.55% vs 0.22% benchmark = Excellent (7x above average)
• ROI 660% = Outstanding return on $500 spend
• Recommendation: Scale budget, replicate successful elements

Reference Documentation

Platform Benchmarks

references/platform-benchmarks.md contains:

• Engagement rate benchmarks by platform and industry
• CTR benchmarks for organic and paid content
• Cost benchmarks (CPC, CPM, CPE)
• Content type performance by platform
• Optimal posting times and frequency
• ROI calculation formulas

Proactive Triggers

• Engagement rate below platform average → Content isn't resonating. Analyze top performers for patterns.
• Follower growth stalled → Content distribution or frequency issue. Audit posting patterns.
• High impressions, low engagement → Reach without resonance. Content quality issue.
• Competitor outperforming significantly → Content gap. Analyze their successful posts.

Output Artifacts

Communication

All output passes quality verification:

• Self-verify: source attribution, assumption audit, confidence scoring
• Output format: Bottom Line → What (with confidence) → Why → How to Act
• Results only. Every finding tagged: 🟢 verified, 🟡 medium, 🔴 assumed.

Related Skills

• social-content: For creating social posts. Use this skill for analyzing performance.
• campaign-analytics: For cross-channel analytics including social.
• content-strategy: For planning social content themes.
• marketing-context: Provides audience context for better analysis.