Their Pitch
Marketing reporting that unlocks growth.
Our Take
A data janitor for marketing teams. Takes messy campaign data from Facebook, Google, LinkedIn and everywhere else, cleans it up, and puts it in one place so you stop living in Excel hell.
Deep Dive & Reality Check
Used For
- +**Your team spends every Monday morning copying ad spend from 8 different platforms into one report** → Automated pulls happen overnight, your Mondays are free
- +**Campaign names are inconsistent chaos across platforms** → Standardizes "FB_Campaign_X" and "Google-CampaignX" into unified reporting
- +**Client wants to know which channels actually drive signups, not just clicks** → Connects ad data to your actual conversion events
- +500+ connectors to every ad platform you've heard of (and some you haven't)
- +AI chat feature lets you ask "which campaign performed best last month" in plain English
Best For
- >Marketing teams drowning in spreadsheets from 10+ ad platforms
- >Agencies tired of manually pulling Facebook and Google data for client reports
- >You've got budget for tools but no data engineer to build custom pipelines
Not For
- -Small teams under 10 people — you're paying enterprise prices for connectors you'll never touch
- -Anyone wanting simple plug-and-play — expect 1-2 weeks learning curve even for basic transforms
- -Teams needing white-label reports for clients — not supported, you'll need workarounds
Pairs With
- *Google Sheets (where you'll still export final reports because old habits die hard)
- *Looker Studio (for prettier dashboards than Funnel's built-in ones)
- *Slack (where you get alerts about connector failures at inconvenient times)
- *HubSpot (to connect ad spend data with actual lead conversion tracking)
- *Snowflake (if you want to warehouse the cleaned data for custom analysis)
- *Facebook Ads Manager (one of 500+ sources it pulls from automatically)
The Catch
- !Pricing starts around $200-300/month for basics and scales to thousands — budget accordingly
- !Connectors break or pull incomplete data regularly, forcing manual fixes that defeat the automation purpose
- !Setup looks easy in demos but data transformation gets complex fast if you want anything beyond basic reporting
Bottom Line
Saves you 10-20 hours a week of manual data wrangling, but costs like you're buying a small car every year.