Windsor MCP for Social Media: Analyze Performance, Find Patterns & Optimize Content

Social teams publish content daily and can quickly see top-performing posts inside each platform.

What’s harder is understanding what patterns consistently drive results across channels.

Data is split across Facebook, Instagram, LinkedIn, YouTube, Google Analytics 4, and other platforms, each reporting differently. Comparing performance, identifying trends and gaps, and tying content to conversions requires a lot of manual work.

Windsor MCP connects your social media data to AI tools like ChatGPT, Claude, and more, so you can analyze cross-channel performance, spot patterns, and make decisions based on unified data.

No spreadsheets. No code. No manual analysis. Just a comprehensive social media analysis with AI.

🚀 Try Windsor.ai for free and connect your social media data to AI in minutes: https://onboard.windsor.ai/, 

In this guide, you will find practical social media use cases for Windsor MCP + AI, plus ready-to-use prompts you can apply right away.

Social media analysis with AI and Windsor MCP: Use cases and prompts

Go from raw data to clear insights—every use case includes a copy-ready prompt and the output it delivers.

1. Content performance analysis (Facebook Organic)

The challenge:

Publishing content consistently is the easy part. Knowing which posts are resonating with your audience and why is the real challenge.

Reach, reactions, clicks, shares, and comments all tell a different part of the story, but pulling them together across dozens of posts to find patterns takes time most teams do not have. The result is content decisions driven by the last post that performed well, rather than a clear picture of what works across the board.

Windsor MCP connects your Facebook data directly to AI, so you can instantly analyze performance across all your posts, identify the formats and topics that consistently drive results, and build a content strategy grounded in real data.

Prompt: Weekly content performance digest

Use Windsor.ai to pull Facebook Organic data for [page name] for the last 30 days.

For each post include:
- post_title
- post_message_oneline
- post_created_time
- media_type
- post_impressions_organic
- post_reactions_total
- post_clicks
- post_comments_total
- post_activity_by_action_type_share

Calculate engagement rate as:
(post_reactions_total + post_comments_total + post_activity_by_action_type_share) divided by post_impressions_organic for each post.

Rank posts by engagement rate from highest to lowest.

Identify:
1. Top 5 posts by engagement rate and what they have in common across media_type, topic, and post length.
2. Posts where post_impressions_organic exceeded 1000 but engagement rate was below 1%.
3. media_type that consistently produces the highest average engagement rate.

Format as a ranked post table followed by three specific content recommendations for the next 30 days. 

Prompt: Content format performance analysis

Use Windsor.ai to pull Facebook Organic data for [page name] for the last 90 days.

For each post include:
- media_type
- post_impressions_organic
- post_reactions_total
- post_clicks
- post_comments_total
- post_activity_by_action_type_share
- post_video_views_organic
- post_video_avg_time_watched
- post_created_time

Group posts by media_type and calculate for each group:
- Average post_impressions_organic
- Average engagement rate
- Average post_clicks
- Total post_video_views_organic where applicable
- Average post_video_avg_time_watched where applicable

Identify:
1. The media_type with the highest average engagement rate.
2. The media_type with the highest average post_clicks.
3. Any media_type where post_impressions_organic is above average but engagement rate is below 1%.

Format as a format comparison table followed by a recommendation on which content formats to prioritize and which to reduce over the next 30 days.

What you’ll get:

A clear picture of which content formats and topics drive the strongest engagement on Facebook, so your team can stop guessing what to post and start building a repeatable content system backed by real performance data.

2. Post and Reels performance analysis (Instagram)

The challenge:

Instagram rewards content that stops the scroll. Yet few teams have a systematic way to know which posts did that and why. Likes feel good in the moment, but tell you very little about what is genuinely building an audience.

Reels, carousels, static images, and stories all perform differently, and the metrics that matter vary by format. Without deeply analyzing them, teams end up optimizing for the wrong signals and producing content that looks busy without growing the account.

Windsor MCP connects your Instagram organic data for AI-powered social media analysis, so you can analyze every format, surface the posts that drove real engagement and saves, and build a content calendar around what the data confirms is working.

Prompt: Instagram post performance analysis

Use Windsor.ai to pull Instagram data for [account name] for the last 30 days.

For each post include:
- media_caption
- media_type
- media_product_type
- media_impressions
- media_reach
- media_like_count
- media_comments_count
- media_saved
- media_shares
- media_engagement
- timestamp

Calculate engagement rate as media_engagement divided by media_reach for each post.
Rank posts by engagement rate from highest to lowest.

Identify:
1. Top 5 posts by engagement rate and what they have in common across media_type, caption length, and topic.
2. Posts where media_saved is above 50, indicating high-value content worth replicating.
3. Posts where media_impressions exceeded 1000 but engagement rate was below 2%.

Format as a ranked post table followed by three specific content recommendations for the next 30 days.

Prompt: Reels and stories performance audit

Use Windsor.ai to pull Instagram data for [account name] for the last 90 days.

For Reels include:
- media_type
- media_product_type
- media_reel_video_views
- media_reel_avg_watch_time
- media_reel_total_interactions
- media_reach
- media_saved
- timestamp

For Stories include:
- story_impressions
- story_reach
- story_exits
- story_taps_forward
- story_taps_back
- story_shares

For Reels identify:
1. Reels where media_reel_avg_watch_time exceeded 15 seconds.
2. Reels where media_reel_video_views exceeded 1000.
3. Reels with the highest media_saved count.

For Stories identify:
1. Stories with exit rate above 30% (story_exits divided by story_impressions).
2. Stories with the highest story_taps_back rate, indicating content worth rewatching.

Format as two separate tables for Reels and Stories, followed by three recommendations to improve Reels watch time and reduce Story exit rate.

What you’ll get:

A precise breakdown of which Instagram formats and posts are driving real engagement and saves, so your team can stop producing content that performs average across the board and double down on what is genuinely growing the account.

3. Company page organic performance (LinkedIn Organic)

The challenge:

LinkedIn is a unique social media platform where content can directly reach decision-makers, buyers, and future employees. Yet too many companies treat it as a broadcast channel, posting regularly without ever analyzing what is building influence and driving clicks back to the business.

The platform’s native analytics show basic impressions and reactions, but spotting patterns across posts, understanding which topics resonate with which audience segments, and knowing whether follower growth is accelerating requires analysis that goes far beyond the dashboard.

Windsor MCP connects your LinkedIn Company Page data to AI, so you can analyze post performance, follower composition, and engagement patterns across every share and turn LinkedIn into a channel that consistently delivers measurable business results.

Prompt: LinkedIn post performance analysis

Use Windsor.ai to pull LinkedIn Organic data for [company page] for the last 30 days.

For each post include:
- share_text
- share_title
- share_media_category
- share_impression_count
- share_unique_impressions_count
- share_clicks_count
- share_like_count
- share_comment_count
- share_count
- share_engagement_rate
- share_published_time

Rank posts by share_engagement_rate from highest to lowest.

Identify:
1. Top 5 posts by share_engagement_rate and what they have in common across share_media_category, topic, and post length.
2. Posts where share_impression_count exceeded 500 but share_engagement_rate was below 2%.
3. Posts where share_clicks_count exceeded 50, indicating strong intent to learn more.
4. The share_media_category that consistently produces the highest average share_engagement_rate.

Format as a ranked post table followed by three specific content recommendations for the next 30 days.

Prompt: LinkedIn audience and follower analysis

Use Windsor.ai to pull LinkedIn Organic data for [company page] for the last 30 days.

Include:
- organization_follower_count
- followers_gain_organic
- seniority_follower_counts
- seniority_follower_type
- industry_follower_counts
- industry_follower_type
- function_follower_counts
- function_follower_type
- account_analytics_impression_count
- account_analytics_engagement
- date

Calculate:
- Net follower growth per month
- Whether followers_gain_organic is accelerating or declining over the 90-day period

Identify:
1. Top 3 seniority levels among followers.
2. Top 3 industries represented in the follower base.
3. Top 3 job functions among followers.
4. Whether account_analytics_engagement grew or declined in months where followers_gain_organic was highest.

Format as an audience composition table followed by three content recommendations tailored to the dominant seniority, industry, and function segments.

What you’ll get:

A clear view of which LinkedIn posts drive real engagement and clicks, combined with a precise picture of who your audience is, so your team can create content that resonates with the right people and builds influence where it matters most.

4. Social media audience growth analysis (Meta: Facebook + Instagram)

The challenge:

Follower count is the metric everyone watches, but few teams analyze properly. Growing by 200 followers one month means nothing if you lost 180 the same period, or if the new followers are entirely outside your target market.

Most teams track total follower count without understanding where growth is coming from, which audience segments are joining, and whether the people following you match the people you are trying to reach. The result is a growing number that does not translate into reach, engagement, or revenue.

Windsor MCP connects your Facebook and Instagram data to AI, so you can analyze audience growth across both platforms, understand who is following you, and build a content strategy that attracts the right people, not just more people.

Prompt: Facebook audience growth analysis

Use Windsor.ai to pull Facebook Organic data for [page name] for the last 30 days.

Include:
- page_fans
- page_daily_follows
- page_daily_unfollows
- page_fan_adds_unique
- page_fan_removes_unique
- page_fans_age
- page_fans_gender
- page_fans_country_name
- page_fans_city_name
- page_engaged_users
- date

Group by month and calculate:
- Total new followers per month (page_fan_adds_unique)
- Total unfollows per month (page_fan_removes_unique)
- Net follower growth per month
- Net growth rate as percentage of page_fans

Identify:
1. Whether net follower growth is accelerating, flat, or declining over the 30-day period.
2. Top 3 countries and cities by follower count.
3. Dominant age and gender segments among followers.
4. Whether page_engaged_users grew proportionally with follower growth or declined.

Format as a monthly growth table followed by an audience composition summary and three recommendations to attract higher-quality followers.

Prompt: Instagram audience growth and profile analysis

Use Windsor.ai to pull Instagram data for [account name] for the last 90 days.

Include:
- followers_count
- follower_count_1d
- follows_and_unfollows
- follows_count
- audience_age_name
- audience_age_size
- audience_gender_name
- audience_gender_size
- audience_country_name
- audience_country_size
- profile_views
- website_clicks_1d
- date

Group by month and calculate:
- Net follower growth per month
- Profile visit to follow conversion rate (follows_count divided by profile_views)
- Website click rate from profile (website_clicks_1d divided by profile_views)

Identify:
1. Whether follower growth is accelerating or declining.
2. Top 3 countries by audience_country_size.
3. Dominant age and gender segments.
4. Whether profile_views are converting to follows at a rate above 10%.

Format as a monthly growth table followed by an audience composition breakdown and three recommendations to improve profile visit to follow conversion rate.

What you’ll get:

A precise picture of how your audience is growing across Facebook and Instagram, who those followers are, and whether your content is attracting the right people, so your team can build a community that converts.

5. Video performance analysis (YouTube)

The challenge:

Most YouTube channels have a handful of videos quietly doing all the heavy lifting. The rest get posted, get a few views, and fade. The problem is that most teams never find out which is which until they look at the data properly.

Watch time, average view percentage, and subscriber gains tell a far more complete story than raw view count. But pulling those metrics across every video, spotting the patterns, and turning them into a content strategy requires truly deep analysis.

Windsor MCP connects your YouTube data to AI, so you can instantly see which videos are driving real watch time and channel growth, understand why they work, and use those insights to make every future video stronger than the last.

Prompt: Video performance ranking and analysis

Use Windsor.ai to pull YouTube data for [channel name] for the last 30 days.

For each video include:
- video_title
- video_category
- video_length
- views
- average_view_duration
- average_view_percentage
- estimated_minutes_watched
- likes
- comment_count
- shares
- subscribers_gained
- published_at

Rank videos by estimated_minutes_watched from highest to the lowest.

Identify:
1. Top 5 videos by estimated_minutes_watched and what they have in common across video_category, video_length, and topic. 
2. Videos where average_view_percentage exceeded 50%, indicating strong content retention.
3. Videos where views exceeded 500 but average_view_percentage was below 25%, indicating strong click-through but weak content retention.
4. Videos that generated the most subscribers_gained.

Format as a ranked video table followed by three specific recommendations covering video length, topic, and hook style for the next three videos.

Prompt: Channel growth and subscriber analysis

Use Windsor.ai to pull YouTube data for [channel name] for the last 30 days.

Include:
- video_title
- published_at
- views
- subscribers_gained
- subscribers_lost
- videos_added_to_playlists
- shares
- card_click_rate
- card_clicks
- date

Group by month and calculate:
- Total views per month
- Net subscriber growth per month (subscribers_gained minus subscribers_lost)
- Average card_click_rate across all videos
- Total videos_added_to_playlists

Identify:
1. Whether subscriber growth is accelerating or declining over the 30-day period.
2. Videos that drove the highest subscribers_gained in the 7 days following published_at.
3. Videos with card_click_rate above 5%, indicating strong audience interest in related content.
4. Any month where subscribers_lost exceeded subscribers_gained.

Format as a monthly channel growth table followed by three recommendations to improve subscriber retention and card click performance.

What you’ll get:

A clear picture of which videos are genuinely growing your channel and which are costing you watch time and subscribers, so your team can stop producing content that underperforms and double down on what the data confirms is working.

6. Cross-channel organic content comparison (Facebook + Instagram + LinkedIn)

The challenge:

Facebook, Instagram, and LinkedIn each have their own dashboard, their own metrics, and their own definition of what KPIs look good. Managing all three without a unified view means making decisions in silos, with no way to compare what each platform is delivering.

Without comparing performance across channels on the same metrics, teams keep distributing effort equally across platforms that deliver very unequal results. Time gets spent maintaining a presence on a channel that barely moves, while the one driving real engagement gets the same attention as the rest.

Windsor MCP connects all your social media platforms to AI, so you can see exactly how each network performs side by side and make resourcing decisions backed by the full cross-channel picture.

Prompt: Cross-channel content performance comparison

Use Windsor.ai to pull Facebook Organic, Instagram, and LinkedIn Organic data for the last 30 days.

From Facebook Organic include:
- media_type
- post_impressions_organic
- post_reactions_total
- post_clicks
- post_comments_total
- post_activity_by_action_type_share
- page_engaged_users

From Instagram include:
- media_type
- media_impressions
- media_engagement
- media_like_count
- media_comments_count
- media_saved
- media_shares

From LinkedIn Organic include:
- share_media_category
- share_impression_count
- share_clicks_count
- share_like_count
- share_comment_count
- share_count
- share_engagement_rate

For each platform calculate:
- Average engagement rate per post
- Average reach per post
- Average clicks per post
- Total shares or saves

Identify:
1. Which platform delivers the highest average engagement rate per post.
2. Which platform drives the most clicks per post
3. Whether any platform shows declining average reach over the 30-day period.

Format as a side-by-side platform comparison table followed by a resource allocation recommendation identifying where to increase and where to reduce effort.

Prompt: Content format comparison across platforms

Use Windsor.ai to pull Facebook Organic, Instagram, and LinkedIn Organic data for the last 30 days.

From Facebook Organic group by media_type and include:
- Average post_impressions_organic
- Average post_reactions_total
- Average post_clicks

From Instagram group by media_type and include:
- Average media_impressions
- Average media_engagement
- Average media_saved

From LinkedIn Organic group by share_media_category and include:
- Average share_impression_count
- Average share_clicks_count
- Average share_engagement_rate

For each platform identify:
1. The content format with the highest average engagement rate.
2. The content format with the highest average clicks.
3. Any format consistently underperforming the platform average by more than 20%.

Format as a format performance table per platform followed by three recommendations on which formats to prioritize on each channel over the next 30 days.

What you’ll get:

A clear side-by-side view of how Facebook, Instagram, and LinkedIn compare on engagement, reach, and clicks, so your team can stop spreading effort equally across unequal platforms and invest where the data says it matters most.

7. Organic vs paid media performance comparison (Meta: Facebook Organic + Facebook Ads)

The challenge:

Organic and paid social rarely get compared on the same metrics. Paid teams optimize for CPM, CPC, and conversions while organic teams track reach and engagement, and nobody ever asks which one is delivering more value per dollar and hour invested.

Running both without comparing them means budgets get allocated based on assumptions rather than evidence. Paid gets scaled because the spend is visible and the dashboards look active. Organic gets maintained out of habit, even when it outperforms paid on engagement at zero cost.

Windsor MCP connects your Facebook Organic and Facebook Ads data to AI, so you can compare both on the same metrics and make decisions about where to invest based on what the data shows.

Prompt: Organic vs paid engagement comparison

Use Windsor.ai to pull Facebook Organic and Facebook Ads data for the last 30 days.

From Facebook Organic include:
- post_impressions_organic
- post_reactions_total
- post_clicks
- post_comments_total
- post_activity_by_action_type_share
- page_engaged_users
- media_type

From Facebook Ads include:
- campaign_name
- impressions
- reach
- clicks
- spend
- actions_post_engagement
- actions_post_reaction
- actions_comment
- CTR
- cpc

For each channel calculate:
- Average engagement rate per impression
- Average clicks per post or campaign
- Cost per engagement for paid (spend divided by actions_post_engagement)

Identify:
1. Which channel drives a higher average engagement rate.
2. Paid campaigns where cost per engagement exceeds $2.00.
3. Organic posts where engagement rate exceeded 5% that could be boosted for paid amplification.

Format as a side-by-side comparison table followed by a recommendation on where to shift budget and effort based on engagement efficiency.

Prompt: Paid campaign reach vs organic reach analysis

Use Windsor.ai to pull Facebook Ads data for the last 30 days.

For each campaign include:
- campaign_name
- campaign_objective
- impressions
- reach
- spend
- clicks
- CTR
- cpm
- cpc
- actions_link_click

Identify:
1. Campaigns where cpm exceeded $15 and CTR was below 1%, indicating high cost for low audience interest.
2. Campaigns where cpc exceeded $3.00 and actions_link_click was below 50.
3. Campaigns where reach is growing but CTR is declining, suggesting audience fatigue.
4. The campaign_objective delivering the lowest cpc.

Format as a campaign efficiency table ranked by cpc, followed by three recommendations to reduce paid spend waste and improve organic content to reduce reliance on paid amplification.

What you’ll get:

A clear picture of how organic and paid Facebook performance compare on engagement and cost efficiency, so your team can stop running both in isolation and start building a strategy where each channel amplifies the other.

8. Content planning (Facebook Organic)

The challenge:

Content ideation often comes down to gut feel and whatever performed well last month. Teams recycle formats and topics without a systematic way to identify the patterns that consistently spark engagement versus the ones that consistently fall flat.

Windsor MCP connects your Facebook Organic data directly to AI, so you can instantly identify content patterns worth repeating and build an ideation process grounded in what your audience has already told you it wants.

Prompt: Content pattern analysis and content planning

Use Windsor.ai to pull Facebook Organic data for [page name] for the last 30 days.

For each post include:
- post_message_oneline
- post_created_time
- media_type
- post_impressions_organic
- post_reactions_total
- post_clicks
- post_comments_total
- post_activity_by_action_type_share

Calculate engagement rate as:
(post_reactions_total + post_comments_total + post_activity_by_action_type_share) divided by post_impressions_organic for each post.

Identify:
1. Top 10 posts by engagement rate and the content patterns they share across topic, format, and tone.
2. Posts where post_activity_by_action_type_share exceeded 20, indicating highly shareable content.
3. Posts where post_comments_total exceeded 15, indicating content that sparks conversation.
4. Content themes that consistently underperform with engagement rate below 1%.

Format as a content pattern summary table followed by 10 specific content ideas for the next 30 days based on the top-performing patterns identified.

What you’ll get:

A precise picture of what your audience is responding to, so you can build a content calendar driven by evidence rather than instinct.

9. Social media impact on website traffic (Facebook Organic + GA4)

The challenge:

Social media teams spend hours creating content that drives people to click. What happens after that click is a question most teams cannot answer because social data and website data live in completely separate places.

Without connecting the two, teams optimize for reach and engagement while having no visibility into whether that engagement is sending the right people to the right pages or converting into any meaningful business outcome.

Windsor MCP connects your Facebook Organic and GA4 data to AI, so you can trace exactly how social content drives website sessions, engagement, and conversions, and build a content strategy around posts that move the needle beyond the feed.

Prompt: Social media traffic and engagement analysis

Use Windsor.ai to pull Facebook Organic and GA4 data for the last 30 days.

From Facebook Organic include:
- post_message_oneline
- post_created_time
- media_type
- post_impressions_organic
- post_clicks
- post_clicks_by_type_link_clicks

From GA4, filter by default_channel_group = "Organic Social" and include:
- sessions
- active_users
- engaged_sessions
- engagement_rate
- bounce_rate
- average_session_duration
- conversions
- source_medium
- date

For each Facebook post, match GA4 sessions to the 3-day window following post_created_time and identify:
1. Posts where post_clicks_by_type_link_clicks exceeded 50 and the corresponding GA4 sessions drove engagement_rate above 60%.
2. Posts with high post_clicks but bounce_rate above 70% in GA4, indicating a mismatch between content and landing page.
3. Whether social sessions have an average_session_duration above 60 seconds, indicating quality traffic.

Format as a post-to-traffic table followed by three recommendations to improve landing page alignment and post-click engagement.

What you’ll get:

A clear picture of exactly how your social media content drives website traffic and engagement, so your team can stop measuring success at the like and stop optimizing for outcomes that reach all the way to revenue.

10. Social media reporting for stakeholders 

The challenge:

Leadership wants to know if social media is worth the investment. Engagement rates and follower counts rarely answer that question. What they want is a clear story about reach, growth, content performance, and whether the numbers are moving in the right direction.

Pulling that story together manually across all channels, like Facebook, Instagram, and LinkedIn, takes hours every month. By the time the report is ready, it is already outdated, and the team has moved on to the next week of content without acting on a single insight from it.

Windsor MCP connects your social media data directly to AI, so you can generate a complete cross-platform stakeholder report in minutes and walk into every review with a clear, data-backed narrative instead of a deck full of screenshots.

Prompt: Monthly cross-platform performance report

Use Windsor.ai to pull Facebook Organic, Instagram, and LinkedIn Organic data for the last 30 days compared to the previous 30 days.

From Facebook Organic include:
- page_impressions_organic
- page_engaged_users
- page_fan_adds_unique
- page_fan_removes_unique
- post_reactions_total
- post_clicks
- post_comments_total
- post_activity_by_action_type_share

From Instagram include:
- media_impressions
- media_engagement
- media_reach
- follower_count_1d
- follows_and_unfollows
- media_saved
- media_shares

From LinkedIn Organic include:
- account_analytics_impression_count
- account_analytics_engagement
- followers_gain_organic
- share_clicks_count
- share_engagement_rate

For each platform calculate period over period changes for:
- Total reach or impressions
- Average engagement rate
- Net follower growth
- Total clicks or link clicks

Structure the report as:
1. Executive summary (3 sentences maximum)
2. Platform performance table with period over period changes
3. Top performing content per platform with supporting data
4. One key issue or decline per platform with supporting data
5. Three recommended actions per platform for next month

Write in plain language suitable for a CEO or client who does not work in social media day to day.

What you’ll get:

A clear, executive-ready view of how each social channel is performing: what’s growing, what’s declining, and where to focus next, so you can make faster, data-backed decisions without manual reporting.

11. Best posting time analysis

The challenge:

Most teams rely on generic “best time to post” benchmarks or intuition when scheduling content. But optimal timing depends on your audience, platform, and content, and it changes over time.

Platform dashboards show how individual posts perform, but they don’t clearly reveal when your audience is most likely to engage. Finding patterns by day and time usually requires manual exports and analysis, so it rarely gets done. As a result, strong content often underperforms simply because it’s published at the wrong time.

Windsor MCP connects your social media posting data directly to AI, so you can quickly identify the days and times that consistently drive the highest engagement and schedule content accordingly.

Prompt: Best posting time analysis

Use Windsor.ai to pull social media data for the last 30 days from [Facebook Organic].

For each post:
1. Extract the day of week and hour of posting.
2. Calculate engagement metrics (engagement rate, clicks, and total interactions).

Then:
1. Group results by day of week and hour.
2. Calculate average performance for each time slot.

Identify:
1. Top 3 time slots (day + hour) by engagement.
2. Top 3 time slots by clicks.
3. Time slots with consistently low performance.

Format output as:
- A ranked list or table of best-performing time slots
- Followed by 3 recommended posting windows for the next 30 days
- Include a short explanation of why these time slots perform best

What you’ll get:

A clear understanding of when your audience is most active and responsive, so you can publish content at the right time, increase engagement, and get more results from the same posts.

Pro tips to get better insights from social media data with AI

Learn how to structure prompts, avoid common analysis mistakes, and extract insights your dashboards don’t show.

1. Analyze your top 10 posts every month, not just the latest ones

Recency bias is one of the biggest traps in social media analysis. A post from six weeks ago might be your best-performing piece of content and hold the clearest signal for what to replicate. Always pull your top 10 posts by engagement rate across the full 90-day window, not just the last two weeks.

2. Use reaction breakdowns to measure emotional resonance, not just volume

Total reactions hide more than they reveal. A post with 500 reactions that are 40% anger is a very different signal from one with 500 love reactions. Try to break down post_reactions_love_total, post_reactions_wow_total, and post_reactions_anger_total separately to understand how your audience feels about specific content types.

3. Cross-reference follower growth with your top content periods

Follower spikes rarely happen randomly. When page_fan_adds_unique jumps in a specific week, something drove it. Always pair follower growth data with post-performance data from the same period to identify exactly which content or campaign triggered the spike and reverse-engineer it for future use.

4. Treat video completion rate as your primary video metric, not views

Views measure who started watching. Completion rate measures those who cared enough to finish. A video with 200 views and a 60% completion rate is outperforming one with 2000 views and a 5% completion rate. Always calculate total_video_complete_views_organic divided by post_video_views_organic before drawing any conclusions about video performance.

5. Use one platform per prompt, unless you’re doing cross-channel analysis

For most analyses, keep prompts focused on a single platform. Combining Facebook, Instagram, and LinkedIn in one prompt can slow down processing and increase the risk of incomplete or messy output.

Only combine platforms when your goal is true cross-channel analysis. Even then, it’s better to first pull and analyze each platform separately, then run a final prompt to synthesize the findings.

This approach keeps results faster, cleaner, and easier to act on.

6. Track your posting frequency alongside engagement rate

Teams often assume posting more will grow reach. The data tells a different story. High posting frequency with declining engagement rate is a warning sign that content quality is dropping to meet volume targets. Always include post count per week alongside average engagement rate when analyzing monthly performance, so the relationship between the two stays visible.

Conclusion

Social media isn’t short on data; it’s short on clarity.

Reach, engagement, follower growth, retention, sentiment, and conversions are all there, scattered across platforms like Facebook, Instagram, LinkedIn, and YouTube, but rarely connected in a way that helps you make decisions quickly.

Windsor MCP brings that data together and makes it instantly usable through AI. Instead of manually building BI reports, you can ask natural questions and get clear answers on what’s working, what’s not, and what to do next.

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