How to Analyze Paid Advertising Data
How to analyze paid advertising data starts with getting a clean export from your ad platform — Google Ads, Meta Ads Manager, LinkedIn Campaign Manager, and TikTok Ads all use different column names and attribution windows. Before comparing performance across platforms, you need to normalize the data to a common structure: one row per campaign per day, with consistent columns for spend, impressions, clicks, and conversions.
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What this export contains
| Date |
| Campaign Name |
| Ad Set |
| Platform |
| Spend |
| Impressions |
| Clicks |
| Conversions |
| Revenue |
| Quality Score |
What usually goes wrong with it
Attribution windows differ by platform and affect conversion counts
Google Ads defaults to a 30-day click attribution window; Meta defaults to 7-day click + 1-day view. The same conversion may be counted by both platforms under their own windows, inflating total reported conversions.
DataMimi Datamimi flags campaigns that appear in multiple platform exports and marks them as potentially double-counted, so you can choose to deduplicate or review attribution window settings.
Quality Score and relevance metrics are platform-specific
Google Ads exports Quality Score (1-10); Meta exports Relevance Score or Ad Relevance Diagnostics; LinkedIn has none. These can't be directly compared across platforms.
DataMimi It reports platform-specific quality metrics alongside each other without forcing a comparison — Quality Score for Google rows, Relevance diagnostics for Meta rows.
Campaign naming conventions vary across platforms and teams
A 'Brand Search' campaign on Google and a 'Brand Awareness' campaign on Meta may serve similar strategic purposes but have different names — grouping by intent requires manual tagging.
DataMimi It extracts intent labels from campaign names using keyword patterns and tags campaigns by type (branded, non-branded, retargeting, prospecting) across all platforms.
Common questions
How do I calculate ROAS and CPA from my ad platform export?
ROAS = Revenue divided by Spend. CPA = Spend divided by Conversions. Both require the Revenue (or Conversion Value) and Spend columns. If your export doesn't include a Revenue column, export it separately from your attribution tool or add it via a VLOOKUP against your CRM's closed revenue data.
How do I combine Google Ads and Meta exports into one spreadsheet?
Add a Platform column to each export, standardize column names (Spend, Clicks, Impressions, Conversions, Revenue), and stack the files vertically. Be aware that conversion counts overlap between platforms — don't sum them as if they're unique conversions.
What metrics should I focus on for paid advertising performance analysis?
Focus on ROAS (revenue efficiency), CPC (traffic efficiency), CTR (creative relevance), CPA (acquisition efficiency), and Impression Share (share of eligible auctions won). Track these by Platform, Campaign Type, and Audience segment. ROAS and CPA are the most important for budget allocation decisions.
What does Datamimi cost?
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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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