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Blog›Marketing

One view of your social performance across every platform

LinkedIn, X, Instagram, and TikTok each lock you inside their own analytics UI. AnalityQa AI AI joins your exports, normalises engagement metrics, and tells you what is actually working and where.

Try AnalityQa AI AI free →See live examples
Marketing analytics dashboard

The problem

  • →Each social platform uses different metric definitions — LinkedIn counts 'impressions' differently from TikTok's 'views' — making cross-platform comparisons misleading without a normalisation step.
  • →Spotting a viral post requires someone to manually check each platform's analytics UI or build a custom alert, by which point the engagement window has often passed.
  • →Audience growth trends are silently skewed by follower purges, bot removals, and platform algorithm changes that appear as unexplained drops in a flat follower count.
  • →Proving ROI for social content requires linking engagement data to downstream web traffic and conversions — a join that native social analytics never provide.

Why the usual approach breaks down

Metric definitions are inconsistent across platforms

LinkedIn counts an impression when a post appears in a feed for more than 300ms. TikTok counts a view the moment the video starts. Instagram separates reach from impressions. X reports engagements as a sum of likes, retweets, replies, and link clicks. Aggregating these without a clear normalisation decision produces numbers that look coherent but are not comparable.

Export formats change without notice

Social platforms update their export column names, date formats, and metric scopes with no versioning or changelog. A pipeline built on last quarter's LinkedIn export format silently breaks when the platform renames 'Engagement rate' to 'Avg engagement rate (per impression).'

Cross-platform analysis requires a shared content taxonomy

To know whether video content outperforms carousels across all platforms, you need a content-type label applied consistently to every post. Native analytics do not share that taxonomy, so building it requires tagging posts in a spreadsheet and joining on post ID or publish date — a fragile manual step.

Viral detection needs a dynamic baseline, not a fixed threshold

A post that gets 500 likes is viral on a 2,000-follower account but unremarkable on a 200,000-follower account. Detecting genuine outliers requires calculating engagement rate relative to current follower count and comparing it against a rolling average — not a fixed like count.

How AnalityQa AI AI solves it

Upload your data — or connect it live — and ask in plain English.

01

Upload exports from every platform in one place

Drop in CSV or Excel exports from LinkedIn, X, Instagram, and TikTok. AnalityQa AI AI detects each platform's schema automatically and normalises column names so you can query across platforms without renaming anything manually.

02

Choose a normalised engagement metric and apply it consistently

Tell AnalityQa AI AI which engagement definition to use — for example, 'engagement rate = (likes + comments + shares) / impressions.' It applies that formula to every platform's data and flags where a platform does not expose the required fields.

03

Compare post-type and platform performance in natural language

Ask 'Which post format had the highest engagement rate on LinkedIn last month?' or 'Compare reach by platform over the last 8 weeks.' AnalityQa AI AI writes the query, runs it across your joined dataset, and returns a chart.

04

Detect viral posts using a rolling engagement baseline

Ask AnalityQa AI AI to flag posts where engagement rate exceeds two standard deviations above your 30-day rolling average. It calculates the baseline per platform and surfaces the outliers with their publish date, content type, and engagement breakdown.

05

Link social data to web traffic for downstream attribution

Upload your GA4 or analytics export alongside your social data. AnalityQa AI AI joins on UTM source/medium and publish date, then shows you which posts drove the most landing-page visits and downstream conversions — not just likes.

You askedGenerated in 4.2s

"Compare engagement rate by platform over the last 8 weeks."

MQLs

2,418+18.2%

CAC

€142−9.4%

ROAS

4.2×+0.6×

Line chart: normalised engagement rate by platform — last 8 weeks

Last 12 mo

Bar chart: Instagram engagement rate by post type — last 90 days

Segment ASegment BSegment CSegment DSegment ESegment F

Line chart: net follower growth by platform — weekly, last 26 weeks

A dashboard built in AnalityQa AI — from question to chart, no SQL.

Real examples

Paste your data. Ask. Ship.

You

Compare engagement rate by platform over the last 8 weeks.

AI

AnalityQa AI AI normalises engagement rate across your LinkedIn, X, Instagram, and TikTok exports using a consistent formula, aggregates by platform and week, and plots the result.

Line chart: normalised engagement rate by platform — last 8 weeks
You

Which post types perform best on Instagram — reels, carousels, or static images?

AI

AnalityQa AI AI groups your Instagram export by the content type column, calculates average engagement rate and reach per type, and returns a comparison ranked by engagement rate.

Bar chart: Instagram engagement rate by post type — last 90 days
You

Show me audience growth by platform over the last 6 months.

AI

AnalityQa AI AI extracts follower count by date from each platform's export, normalises to a weekly data point, and plots net follower change per platform on a single chart.

Line chart: net follower growth by platform — weekly, last 26 weeks
You

Flag posts that went viral on any platform in Q1.

AI

AnalityQa AI AI calculates a 30-day rolling average engagement rate per platform, then flags posts where the post's rate exceeded the rolling average by more than two standard deviations, sorted by magnitude of the spike.

Table: viral post outliers by platform — Q1 2026, ranked by engagement rate delta
You

Which LinkedIn posts drove the most website traffic last month?

AI

AnalityQa AI AI joins your LinkedIn post export to your GA4 UTM report on source and publish date, sums sessions attributed to each post, and returns the top 10 by session count.

Table: LinkedIn posts ranked by attributed web sessions — March 2026

What teams get out of it

✓Social teams that consolidate four platform reports into one shared dashboard, cutting weekly reporting time from several hours to under 20 minutes.
✓Content strategy shifts informed by actual cross-platform engagement data rather than intuition or whichever platform's native dashboard was checked last.
✓Viral posts identified and responded to within hours rather than days, capturing the engagement window while the algorithm is still amplifying the content.
✓A defensible link between social activity and web conversions that gives social teams a revenue number to present in budget discussions.

Frequently asked questions

Which social platforms does AnalityQa AI AI support?+

AnalityQa AI AI works with CSV or Excel exports from LinkedIn, X (formerly Twitter), Instagram, TikTok, Facebook, Pinterest, and any other platform that provides a data export. If the platform produces a file, AnalityQa AI AI can analyse it. Direct API integrations are on the roadmap.

Engagement rate is defined differently on each platform. How does AnalityQa AI AI handle that?+

You define the formula once — for example, '(likes + comments + shares) / impressions' — and AnalityQa AI AI applies it consistently to every platform. Where a platform does not expose a required field (e.g., TikTok does not separate shares from other interactions in some export formats), AnalityQa AI AI flags the gap in the chart footnote rather than silently substituting a different number.

Instagram and TikTok exports can include personal data about post audiences. Is that safe?+

Your data is stored in your isolated tenant, encrypted in transit and at rest, and is never used to train models. Audience demographic data in social exports is aggregated and does not contain individual user identifiers.

Can I track hashtag or keyword performance across platforms?+

If your social export includes a caption or hashtag column, you can ask AnalityQa AI AI to group posts by hashtag presence and compare engagement rates. Platform-level hashtag reach data (how many non-followers saw the hashtag) is only available through paid third-party listening tools, which you can also export and upload.

How do I keep the dashboard current without exporting manually every week?+

Today the workflow is to export from each platform weekly and upload the files. AnalityQa AI AI appends new data and refreshes all pinned dashboard charts automatically. Scheduled API pulls from social platforms are on the product roadmap.

Can AnalityQa AI AI tell me the best time to post on each platform?+

Yes. Ask 'Show me average engagement rate by hour of day for Instagram posts in the last 3 months.' AnalityQa AI AI extracts publish hour from your export timestamps and groups engagement rate by that dimension. The result is based on your own audience's behaviour, not industry benchmarks.

We manage multiple brand accounts. Can we analyse them separately and together?+

Upload exports labelled by account and ask AnalityQa AI AI to keep them separate or aggregate them as needed. You can compare performance across accounts with a single question — 'Show me engagement rate for Brand A vs. Brand B on LinkedIn last quarter' — without merging files manually.

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