How an embedded build delivered a qualified M&A target list in two weeks — 155 priority targets out of 2,000+ exhibitors, shipped with a fraction of the usual research overhead.
A mid-market European manufacturer was preparing for a major industry trade show with over 2,000 exhibitors. The corporate development team saw this as a prime opportunity to identify acquisition targets, but faced an impossible task: manually researching thousands of companies in just two weeks.
Client Background
The client is a family-owned manufacturer with a clear M&A strategy: acquire complementary businesses to expand geographic reach and product capabilities. Trade shows represent concentrated opportunities to find targets, but the team had never been able to systematically work through exhibitor lists before events.
Previous approaches involved ad-hoc research during the show itself, leading to missed opportunities and unprepared conversations. They needed a way to arrive at the event with a prioritized target list and pre-built outreach materials.
Challenges
- Scale of Research: 2,000+ exhibitors to evaluate, each requiring company size, ownership structure, product portfolio, and strategic fit assessment
- Time Constraint: Only 2 weeks until the event, making manual research physically impossible
- Data Quality: Exhibitor lists contained only basic information (company name, booth number, category) with no structured business data
- Team Bandwidth: The corporate development team had ongoing deal work and couldn’t dedicate full-time resources to research
Solution
I built an AI-powered enrichment pipeline that automated the entire research and qualification process:
- Data Ingestion: Parsed the raw exhibitor list and normalized company names for matching
- Multi-Source Enrichment: Pulled company data from multiple APIs and public sources, including revenue estimates, employee counts, ownership type, and product descriptions
- AI Classification: Used language models to evaluate strategic fit based on the client’s acquisition criteria, producing Class A (high priority), Class B (interesting), and Class C (not a fit) ratings
- Outreach Generation: Created personalized introduction templates for each Class A and B target, incorporating specific talking points based on strategic fit analysis
The system processed 373 companies from the exhibitor list, enriching each with 15+ data points and generating fit scores.
Results
In two weeks, the team went from nothing to a complete target list for the show:
- 373 companies enriched with structured business data
- 155 Class A targets identified as high-priority acquisition candidates
- 130 Class B targets flagged for exploratory conversations
- Outreach templates ready for each qualified target
- Strategic fit rationale documented for every classification decision
The corporate development team arrived at the trade show with a clear action plan: which booths to visit, what to discuss, and how each conversation could lead to deal flow. What would have been an impossible manual task became a systematic, repeatable capability.
Key Takeaways
The system now lives inside the corp-dev team’s operating stack. What it demonstrates:
- Speed matters. Compressing weeks of research into days is the whole point.
- Classification drives focus. Not all targets are equal; systematic scoring keeps the team off the wrong companies.
- Prep changes the conversations. Arriving with a qualified list and talking points turns a booth visit into a deal lead.
- The pipeline compounds. It runs again for the next show, and the target database grows each time.