Social media campaign analysis and performance tracking

Social Media Analyzer

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

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Table of Contents

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

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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

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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

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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

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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.

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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

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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

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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.