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 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

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
"Campaign ROI"	Full ROI calculation with engagement value estimates

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.

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Troubleshooting

Problem	Likely Cause	Solution
Engagement rate appears unrealistically high (>50%)	Reach value is too low relative to engagements, or reach/impressions data is swapped	Verify that reach represents unique users reached (not impressions). Engagement rate = (likes + comments + shares + saves) / reach. If using Instagram data from 2025+, note that Instagram shifted from "impressions" to "views" as primary metric -- ensure you are using the correct field
Benchmark comparison shows "no_benchmark_available"	Platform name in input JSON does not match expected values	Use exact lowercase platform names: instagram, facebook, twitter, linkedin, tiktok. The analyzer matches against these exact strings
ROI calculation shows negative despite good engagement	Engagement value estimates are too conservative for your industry	The default engagement value model uses $0.50/like, $2.00/comment, $5.00/share, $3.00/save, $1.50/click. Adjust these values in calculate_metrics.py for your specific vertical. B2B companies typically have higher per-engagement values than B2C
TikTok metrics show low engagement compared to benchmarks	Using reach-based calculation on a platform where view-based metrics are standard	TikTok's 2026 benchmark engagement rate of 2.50-3.70% is calculated against views, not reach. Ensure your TikTok data uses video views in the reach field for accurate comparison. TikTok engagement rates rose 49% YoY in 2025
LinkedIn engagement appears lower than expected	Comparing against outdated benchmarks	LinkedIn's 2026 median engagement rate is approximately 3.85-6.1%, significantly higher than other platforms. Carousel/document posts earn the highest engagement (up to 21.77% median). If your rate is below 2%, focus on conversation-starting content rather than corporate announcements
Instagram metrics declining despite consistent content quality	Algorithm and metric definition changes in 2025-2026	Instagram shifted to "Views" as its primary metric across all formats (Reels, Stories, posts), replacing "Impressions" and "Plays." Carousel posts now earn the most engagement. Meta plans to replace reach with "Viewers" metric in Graph API by June 2026. Adapt your data collection accordingly
Campaign analysis has too few posts for reliable insights	Small sample size produces unreliable averages	Minimum 10 posts recommended for meaningful analysis. The analyze_performance.py script flags campaigns with fewer than 10 posts. For statistical reliability, aim for 30+ posts per analysis period

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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

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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

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Integration Points

Integration	Purpose	How to Connect
Meta Business Suite	Export Instagram and Facebook campaign data	Export post-level metrics (likes, comments, shares, reach, impressions, clicks) as JSON for calculate_metrics.py and analyze_performance.py. Note: "Views" is replacing "Impressions" in 2026
LinkedIn Campaign Manager	Export LinkedIn ad and organic performance data	Export engagement metrics per post. LinkedIn's native analytics now includes "Viewer" demographics and AI search visibility data
TikTok Business Center	Export TikTok campaign performance data	Export video-level metrics. Use video views as the reach equivalent for engagement rate calculation
Google Analytics 4 (GA4)	Track social traffic and conversions on your website	Connect social campaign UTM parameters to GA4 to measure downstream conversions. Use campaign-analytics skill for full attribution
campaign-analytics skill	Cross-channel ROI comparison	Feed social media ROI data into campaign_roi_calculator.py alongside other channels for unified portfolio analysis
content-creator skill	Content optimization based on performance data	Use top-performing post analysis to inform content strategy. Apply brand_voice_analyzer.py to ensure social content matches brand voice
marketing-demand-acquisition skill	Social as demand gen channel	Integrate social performance data into demand gen channel mix evaluation. Use CAC data from social alongside other acquisition channels

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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:

Method	Parameters	Returns
calculate_engagement_rate()	post: Dict (likes, comments, shares, saves, reach)	Engagement rate as percentage (float). Formula: (likes + comments + shares + saves) / reach * 100
calculate_ctr()	clicks: int, impressions: int	CTR as percentage (float)
calculate_campaign_metrics()	None (uses constructor data)	Dict with platform, total_posts, total_engagements, total_reach, total_impressions, total_clicks, avg_engagement_rate, ctr
calculate_roi_metrics()	None (uses constructor data)	Dict with total_spend, cost_per_engagement, cost_per_click, estimated_value (at $2.50/engagement default), roi_percentage
identify_top_posts()	metric: str = 'engagement_rate', limit: int = 5	Sorted list of top posts by specified metric. Supported metrics: engagement_rate, likes, comments, shares, clicks
analyze_all()	None	Combined dict of campaign_metrics, roi_metrics, and top_posts

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:

Method	Parameters	Returns
benchmark_performance()	None	Dict with engagement_status, engagement_benchmark, engagement_actual, ctr_status, ctr_benchmark, ctr_actual. Status values: excellent (>=1.5x benchmark), good (>=benchmark), below_average
generate_recommendations()	None	List of actionable recommendation strings based on engagement rate, CTR, CPC, ROI, and post volume thresholds
generate_insights()	None	Dict with overall_health (excellent/good/needs_improvement), benchmark_comparison, recommendations, key_strengths, areas_for_improvement