AI Tool for Marketing Analytics — Upload Campaign Data, Ask

What this export contains

What makes this file hard to work with

• ROAS by channel requires combining ad platform exports manually

  Google Ads, Meta Ads, and LinkedIn Ads each export different formats. Comparing ROAS across all three channels means reformatting and combining three files.

  DataMimi Upload exports from all ad platforms and ask 'which channel had the best ROAS last month?' — Datamimi reads all files and ranks by the metric you name.

• Cost per lead changes week to week but you only notice at month-end

  Ad spend is tracked daily but cost per lead calculation requires dividing total cost by conversions — a formula that needs rebuilding each time the export is refreshed.

  DataMimi Ask 'what was our cost per lead by channel this week?' and Datamimi divides Cost by Conversions for each channel row and returns the comparison.

• Best-performing creative is buried in rows

  A campaign with 20 ad variants has performance data scattered across 20 rows. Ranking by ROAS or CTR requires sorting and filtering before any insight surfaces.

  DataMimi Ask 'which ad creative had the highest CTR in the Google campaign?' and Datamimi filters and ranks the creative rows by CTR in seconds.

• Attribution comparison across channels is guesswork

  Each channel claims credit for conversions. Comparing attributed revenue across Google, Meta, and email in one view requires a combined file you have to build manually.

  DataMimi Upload all channel files and ask 'how does attributed revenue compare across Google, Meta, and email for this quarter?' — Datamimi aggregates and compares.

Common questions