🇺🇸English

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

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

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 to replicate.
• Follower growth stalled -- Content distribution or frequency issue. Audit posting patterns and content mix.
• High impressions, low engagement -- Reach without resonance. Content quality or relevance issue needs addressing.
• Competitor outperforming significantly -- Content gap detected. Analyze their successful posts for format and topic insights.

Output Artifacts

Communication

All output passes quality verification:

• Self-verify: source attribution, assumption audit, confidence scoring
• Output format: Bottom Line first, then What (with confidence), Why, How to Act
• Every finding tagged with confidence level: verified, medium confidence, or assumed

Related Skills

• campaign-analytics : For cross-channel analytics including social alongside other channels.
• content-creator : For creating social media content optimized by analysis findings.
• marketing-demand-acquisition : For integrating social media into broader demand gen strategy.
• marketing-strategy-pmm : For aligning social content with product marketing positioning.

Troubleshooting

Success Criteria

• Engagement Rate by Platform (2026 benchmarks) : Instagram 0.50-0.70% (average), 3-6% (good), >6% (excellent). Facebook 0.06-0.09% (average), 0.5-1% (good). LinkedIn 3.85-6.1% (average), >6% (good). TikTok 2.50-3.70% (average), 8-15% (good), >15% (excellent). Twitter/X 0.03-0.05% (average), 0.1-0.5% (good)
• Click-Through Rate : Instagram >0.5% (good), Facebook >1.5% (good), LinkedIn >1.0% (good), TikTok >0.5% (good). Featured snippets and carousels drive highest CTR across platforms
• Cost Per Click : Facebook <$0.50 (good), Instagram <$0.70 (good), LinkedIn <$3.00 (good, but averages $4-5+ for B2B), TikTok <$0.50 (good). LinkedIn CPC has risen 89% since 2023
• ROI Threshold : Target minimum 200% ROI on paid social. Campaigns above 500% are excellent and should be scaled. Campaigns below 100% need immediate creative or targeting revision
• Content Format Performance : Prioritize high-engagement formats per platform -- carousel/document posts on LinkedIn (21.77% median engagement), Reels on Instagram, short-form video on TikTok. Test at least 3 content formats per month
• Posting Frequency : Maintain consistent posting cadence: LinkedIn 3-5x/week, Instagram 4-7x/week, TikTok 3-5x/week. The LinkedIn algorithm favors content that generates meaningful engagement in the first 90 minutes
• Analysis Cadence : Run full performance analysis weekly for active campaigns. Compare month-over-month trends to identify growth or decline patterns. Update benchmark baselines quarterly as platform norms shift rapidly

Scope & Limitations

In Scope:

• Post-level and campaign-level engagement metrics (engagement rate, CTR, reach rate, virality rate, save rate)
• ROI calculation with engagement value estimates and cost efficiency metrics (CPE, CPC, CPM, ROAS)
• Platform benchmark comparison for Instagram, Facebook, Twitter/X, LinkedIn, and TikTok
• Top/bottom performer identification and ranking
• Actionable recommendations based on benchmark assessment

Out of Scope:

• Real-time API connections to social media platforms (scripts analyze static JSON data you export)
• Social media scheduling, publishing, or content creation
• Follower growth tracking or audience demographics analysis
• Competitor social media monitoring (use dedicated social listening tools)
• Influencer identification or collaboration management
• Social commerce and shopping metrics
• Video-specific analytics (watch time, completion rate, drop-off points)
• Sentiment analysis on comments or mentions (use the app-store-optimization skill's review_analyzer for text sentiment)
• Cross-platform identity resolution or deduplication

Platform API Changes (2025-2026):

• Meta/Instagram is replacing "reach" with "Viewers" metric in Graph API by June 2026
• Instagram shifted to "Views" as primary metric across all formats, replacing "Impressions" and "Plays"
• TikTok tightened API access approval process in 2025
• LinkedIn added AI-powered conversational search; algorithm now favors people-first content over polished corporate updates

Integration Points

Tool Reference

calculate_metrics.py

Type: Python library (imported, not CLI)

Classes:

• SocialMediaMetricsCalculator(campaign_data: Dict)

Constructor Input: {"platform": "instagram", "total_spend": 500, "posts": [{"post_id": "str", "content_type": "str", "likes": int, "comments": int, "shares": int, "saves": int, "reach": int, "impressions": int, "clicks": int}]}

Key Methods:

analyze_performance.py

Type: Python library (imported, not CLI)

Classes:

• PerformanceAnalyzer(campaign_metrics: Dict, roi_metrics: Dict)

Built-in Benchmarks: Engagement rate and CTR benchmarks for facebook, instagram, twitter, linkedin, tiktok.

Key Methods:

Weekly Installs

73

Repository

borghei/claude-skills

GitHub Stars

51

First Seen

Feb 23, 2026

Security Audits

Gen Agent Trust HubPassSocketPassSnykPass

Installed on

github-copilot59

gemini-cli59

cline59

codex59

opencode59

cursor59