ABM Ad Research — Mapping a Competitor's LinkedIn Footprint
How to reverse-engineer a competitor's account-based marketing footprint using the EU LinkedIn Ad Library's targeting parameters — persona tuples and vertical signal.
Account-Based Marketing rises and falls on the quality of the target-account map. Building that map from scratch is the slowest part of an ABM program. Using the LinkedIn Ad Library to reverse-engineer a competitor's ABM footprint can shortcut weeks of research.
Why LinkedIn is the only platform for this
LinkedIn is the only major platform where the EU Ad Library exposes targeting parameters — job function, seniority, industry. For EU-served competitor ads you can literally see which segments they're paying to reach. That's the closest the open web gets to seeing inside an ABM strategy.
Step 1 — Pull every EU-served ad
In the LinkedIn Ad Library, search the competitor company. Filter to EU countries (DE, FR, NL, IT, IE are the most data-rich). Pull every active and inactive ad from the last 12 months.
Step 2 — Extract the targeting tuples
Each EU ad shows the targeting parameters. For each ad, record the tuple: (job function, seniority, industry, country). A typical B2B competitor will run 5-8 distinct tuples — each corresponds to a buyer-persona segment.
| Tuple example | Likely persona |
|---|---|
| IT, Director+, Manufacturing, DE | CIO/CTO in mid-market manufacturing |
| Marketing, Manager+, SaaS, US | B2B marketing manager |
| Finance, VP+, Banking, UK | Financial services exec |
| Engineering, IC, Software, Global | Developer-led growth motion |
Step 3 — Cluster ads to tuples
For each tuple, list the creatives that targeted it. The creative count by tuple tells you which persona the competitor weighs most heavily — more creatives = more priority.
Step 4 — Read the creatives for vertical signal
Many B2B ads name specific verticals or company sizes in the copy. Compile a list of industries, company sizes and named example customers across the competitor's ads. That list is a strong proxy for their target-account list — at least at the segment level.
Step 5 — Build your own counter-footprint
For each persona tuple the competitor targets, decide whether to compete head-on or flank. Going head-on means matching their tuple and creating differentiated creative. Flanking means finding adjacent tuples (e.g., one seniority level higher, or a related industry).
Automating the research
AdScrape ingests the LinkedIn Ad Library daily and parses the EU targeting parameters into structured fields, so the persona-tuple map for any competitor is a single API call.
Put this into practice with AdScrape
Search every active Meta ad, compare brands side-by-side, and pull it all through a clean REST API. Free to start, no credit card required.
