Demand Gen Teams Are DIY-ing Competitive Ad Intelligence. Here's What a Real System Looks Like.
Scott Schnaars
Demand Gen Teams Are DIY-ing Competitive Ad Intelligence. Here's What a Real System Looks Like.
The Exit Five community surfaced a marketer building a workflow to scrape Google and Meta ad libraries and run competitive analysis through an LLM. Good idea. The DIY version breaks regularly and produces inconsistent output without a structure to hold it together.
Here is what a functional competitive ad intelligence system requires that most DIY workflows miss.
Consistency: the system needs to run on the same schedule for the same set of competitors in the same format. Ad intelligence that runs when someone has time is not intelligence. It is trivia.
Structure: define what you are tracking before you build the workflow. Minimum viable structure:
- Which platforms each competitor is running on
- What ad formats they are using
- What their message emphasis appears to be
- Whether activity is increasing or decreasing
Actionability: the output of each run should answer specific questions. What changed since last week? What new messaging are they testing? Are they entering a new platform?
History: log every run in a consistent format so you can look back six months and see patterns. A competitor that tested LinkedIn for three months and then pulled back is meaningful. Consistent logs make that pattern visible.
Tools that make this workable: Google Ad Transparency Center, Meta Ad Library, and LinkedIn Ad Library are all publicly accessible. Clay or a custom Zapier workflow can automate collection. An LLM prompt with a defined output template structures the analysis into a consistent format each time.
The difference between doing this once and doing it consistently is the difference between a data point and a competitive advantage.
Yirla automates competitive ad monitoring so your team gets consistent weekly intelligence without the DIY maintenance burden. (https://www.yirla.com/integrations)
