How to Analyze Social Media Data
Knowing how to analyze social media data starts with exporting platform-level performance files from LinkedIn, Instagram, Twitter, and TikTok — each of which uses different column names for the same metrics and different date format conventions, making cross-platform comparison impossible without normalization. Most teams spend more time reconciling these differences than actually analyzing the content.
See the exact steps to normalize platform exports into a single comparable dataset and identify which content types drive the most reach and engagement.
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What this export contains
| post_id |
| publish_date |
| platform |
| content_type |
| impressions |
| reach |
| engagements |
| engagement_rate |
| clicks |
| video_views |
What usually goes wrong with it
Each platform uses different column names
LinkedIn exports 'Impressions'; Twitter uses 'impressions'; TikTok uses 'Views' — merging the three files into one table requires manual column mapping before any cross-platform comparison runs.
DataMimi Datamimi maps each platform's column names to a shared schema automatically when multiple exports are uploaded together.
Engagement rate defined differently by platform
Instagram calculates engagement_rate on reach; LinkedIn calculates it on impressions — comparing the two figures directly overstates Instagram's engagement relative to LinkedIn.
DataMimi It recalculates engagement_rate on a consistent denominator — reach or impressions — across all platforms so cross-platform comparisons are valid.
Date formats differ across platform exports
LinkedIn exports dates as 'YYYY-MM-DD'; Facebook as 'MM/DD/YYYY'; TikTok as Unix timestamps — sorting or filtering by publish_date across merged platforms fails without conversion.
DataMimi It converts all date formats to ISO standard before merging so publish_date sorting works correctly across the combined dataset.
Organic and paid metrics mixed in the same export
Boosted posts appear in organic exports with inflated reach figures that include paid distribution, making organic content performance appear stronger than it is.
DataMimi It flags boosted posts using the content_type column so organic and paid performance are separated before benchmarks are calculated.
Common questions
How do you analyze social media data across platforms?
Export performance data from each platform; normalize column names and date formats to a common schema; calculate engagement rate on a consistent denominator; then pivot by platform and content_type to compare performance.
What are the most important social media metrics to track?
The core metrics are impressions, reach, engagements, engagement rate, clicks, and (for video) completion rate; reach-based engagement rate is the most comparable metric across platforms.
How do I calculate engagement rate from a social media export?
Divide the engagements column by the reach column for each post; multiply by 100 to express as a percentage; use the same denominator across all platforms for valid comparison.
How do I find my best-performing social posts?
Sort posts by engagement rate (not raw engagement count) to account for reach differences; filter by content_type to compare like-for-like post formats; then look at the top 10% to identify patterns.
How much does Datamimi cost?
Datamimi offers a Free plan at $0/month (40 credits, no credit card required), Lite at $9/month (400 credits), Starter at $24/month (1,500 credits, up to 3 simultaneous files), Pro at $59/month (5,000 credits with rollover), and Team at $199/month (20,000 credits, 5 users).
Try it with your own file
DataMimi reads the file you actually have — merged cells, headers below row one, totals pasted at the bottom — and shows which rows and columns every number came from.
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