Social Media Performance Analyzer is a marketing claude skill built by Alireza Rezvani. Best for: Marketing managers and social strategists analyze campaign performance across platforms to identify top performers and optimize spend..

What it does
Calculate engagement rates, ROI, and platform benchmarks from social media campaign data with validated metrics.
Category
marketing
Created by
Alireza Rezvani
Last updated
Claude Skillmarketing GitHub-backed CuratedintermediateClaude Code

Social Media Performance Analyzer

Calculate engagement rates, ROI, and platform benchmarks from social media campaign data with validated metrics.

Skill instructions


name: "social-media-analyzer" description: Social media campaign analysis and performance tracking. Calculates engagement rates, ROI, and benchmarks across platforms. Use for analyzing social media performance, calculating engagement rate, measuring campaign ROI, comparing platform metrics, or benchmarking against industry standards. triggers:

  • analyze social media
  • calculate engagement rate
  • social media ROI
  • campaign performance
  • compare platforms
  • benchmark engagement
  • Instagram analytics
  • Facebook metrics
  • TikTok performance
  • LinkedIn engagement

Social Media Analyzer

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


Table of Contents


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

| Field | Required | Description | |-------|----------|-------------| | platform | Yes | instagram, facebook, twitter, linkedin, tiktok | | posts[] | Yes | Array of post data | | posts[].likes | Yes | Like/reaction count | | posts[].comments | Yes | Comment count | | posts[].reach | Yes | Unique users reached | | posts[].impressions | No | Total views | | posts[].shares | No | Share/retweet count | | posts[].saves | No | Save/bookmark count | | posts[].clicks | No | Link clicks | | total_spend | No | Ad spend (for ROI) |

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

| Metric | Formula | Interpretation | |--------|---------|----------------| | Engagement Rate | Engagements / Reach × 100 | Audience interaction level | | CTR | Clicks / Impressions × 100 | Content click appeal | | Reach Rate | Reach / Followers × 100 | Content distribution | | Virality Rate | Shares / Impressions × 100 | Share-worthiness | | Save Rate | Saves / Reach × 100 | Content value |

Performance Categories

| Rating | Engagement Rate | Action | |--------|-----------------|--------| | Excellent | > 6% | Scale and replicate | | Good | 3-6% | Optimize and expand | | Average | 1-3% | Test improvements | | Poor | < 1% | Analyze and pivot |


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

| Metric | Formula | |--------|---------| | Cost Per Engagement (CPE) | Total Spend / Total Engagements | | Cost Per Click (CPC) | Total Spend / Total Clicks | | Cost Per Thousand (CPM) | (Spend / Impressions) × 1000 | | Return on Ad Spend (ROAS) | Revenue / Ad Spend |

Engagement Value Estimates

| Action | Value | Rationale | |--------|-------|-----------| | Like | $0.50 | Brand awareness | | Comment | $2.00 | Active engagement | | Share | $5.00 | Amplification | | Save | $3.00 | Intent signal | | Click | $1.50 | Traffic value |

ROI Interpretation

| ROI % | Rating | Recommendation | |-------|--------|----------------| | > 500% | Excellent | Scale budget significantly | | 200-500% | Good | Increase budget moderately | | 100-200% | Acceptable | Optimize before scaling | | 0-100% | Break-even | Review targeting and creative | | < 0% | Negative | Pause and restructure |


Platform Benchmarks

Engagement Rate by Platform

| Platform | Average | Good | Excellent | |----------|---------|------|-----------| | Instagram | 1.22% | 3-6% | >6% | | Facebook | 0.07% | 0.5-1% | >1% | | Twitter/X | 0.05% | 0.1-0.5% | >0.5% | | LinkedIn | 2.0% | 3-5% | >5% | | TikTok | 5.96% | 8-15% | >15% |

CTR by Platform

| Platform | Average | Good | Excellent | |----------|---------|------|-----------| | Instagram | 0.22% | 0.5-1% | >1% | | Facebook | 0.90% | 1.5-2.5% | >2.5% | | LinkedIn | 0.44% | 1-2% | >2% | | TikTok | 0.30% | 0.5-1% | >1% |

CPC by Platform

| Platform | Average | Good | |----------|---------|------| | Facebook | $0.97 | <$0.50 | | Instagram | $1.20 | <$0.70 | | LinkedIn | $5.26 | <$3.00 | | TikTok | $1.00 | <$0.50 |

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

| When you ask for... | You get... | |---------------------|------------| | "Social media audit" | Performance analysis across platforms with benchmarks | | "What's performing?" | Top content analysis with patterns and recommendations | | "Competitor social analysis" | Competitive social media comparison with gaps |

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.

Use this skill

Most skills are portable instruction packages. Claude Code supports SKILL.md directly. Other agents can use adapted files like AGENTS.md, .cursorrules, and GEMINI.md.

Claude Code

Save SKILL.md into your Claude Skills folder, then restart Claude Code.

mkdir -p ~/.claude/skills/social-media-performance-analyzer && curl -L "https://raw.githubusercontent.com/alirezarezvani/claude-skills/HEAD/marketing-skill/social-media-analyzer/SKILL.md" -o ~/.claude/skills/social-media-performance-analyzer/SKILL.md

Installs to ~/.claude/skills/social-media-performance-analyzer/SKILL.md.

Use cases

Marketing managers and social strategists analyze campaign performance across platforms to identify top performers and optimize spend.

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Stats

Installs0
GitHub Stars13.3k
Forks1765
LicenseMIT License
UpdatedMar 25, 2026