How to build an M&A buyer list, and why AI data buyers sit on a separate track

How do you build an M&A buyer list?

Build an M&A buyer list in three passes: a broad long list drawn from the seller's goals and the deal's size, a scored list that ranks every name on the same criteria, and a tiered short list that sets who hears first. The scoring pass does most of the work, because a name that looks right on industry codes often fails on check size, on a recent acquisition that already fills the gap, or on the owner's refusal to talk to a competitor.

The list covers four groups of acquirers: strategic buyers, financial sponsors, PE-backed platforms making add-ons, and smaller-capital buyers such as independent sponsors, family offices and search funds. AI labs and data buyers are not a fifth group. They license records rather than buy companies, so if a client's records interest them, that value is captured on a separate track, outside the auction.

What to settle before you build the list

Agree these points with the seller first so the list is built once.

• Transaction type: full sale, majority recapitalization, minority investment, or open to all three.
• Owner priorities: price, certainty, timing, the team's future, the owner's role after closing and how much rollover equity they will accept.
• Size metrics: trailing and run-rate revenue and EBITDA or seller's discretionary earnings, which set the check size every name must be able to write.
• No-contact list: competitors, customers or suppliers the owner will not allow, and anyone with a known confidentiality problem.
• Positioning: whether the business reads as a platform, an add-on or a tuck-in, since that changes which sponsors care.
• Sign-off rule: who approves the list and each later addition.

Step by step: from long list to tiered short list

1. Write the buyer thesis. Two sentences on why someone would pay a premium for this business: the capability, customer base, geography or talent it adds.
2. Pull strategic acquirers. Direct competitors, adjacent providers, suppliers or customers integrating vertically, and out-of-region players seeking a foothold. Association member lists, exhibitor lists, trade-press deal announcements and the owner's own knowledge are the best inputs.
3. Pull financial sponsors. Funds whose published criteria match the EBITDA range and sector, plus their recent platform investments. Check whether the firm is still investing from its current fund.
4. Pull PE-backed platforms. These are often the most motivated buyers because add-ons are their growth plan. Bain's Global Private Equity Report 2026 puts buyout holding periods at exit at around seven years, counts about 32,000 unsold portfolio companies and says general partners are holding assets longer to buy time to grow EBITDA. Platforms under that pressure keep looking for bolt-ons.
5. Pull smaller-capital buyers. Independent sponsors, family offices, holding companies and search funds can suit owners who care about legacy, but financing certainty varies, so note how each would fund the deal.
6. Score every name. Use the FFF score below, with one line of rationale per score so the seller can follow your reasoning.
7. Tier the list. Tier 1 gets a tailored call from the lead banker, Tier 2 gets the teaser in the first wave, and Tier 3 is a reserve wave if the first round is thin.
8. Get the seller's sign-off. Walk the owner through Tier 1 name by name and record approvals and exclusions.
9. Track every touch. Log teaser sent, NDA status, CIM access and contact person per name, so no buyer hears from two people on your team.

The FFF score: fit, funding and friction

Score each name 1 to 3 on three questions and add them up. Anything under 5 drops to Tier 3 or off the list.

Friction is scored inversely, so a 3 means low friction. Keep the notes: they become the answer when a seller later asks why a name was or was not called.

What each buyer group pays for

Where do AI data buyers fit on a buyer list?

They do not go on it. AI labs and data buyers acquire rights to use defined records for an agreed term; they are not bidding for the operating company, and sending them a teaser or a CIM widens the confidentiality circle for no purpose.

Public filings show the structure. Reddit's February 2024 registration statement disclosed data licensing arrangements with an aggregate contract value of $203.0 million and terms of two to three years. That is a contract for access to data over a set period, not a change of ownership. The explainer on what an AI data buyer is covers who these buyers are and what they evaluate.

Because the tracks are separate, the advisor adds proceeds without adding bidders, NDAs or timeline risk to the auction; the dual-track M&A process guide covers running both. If a data buyer approaches the client directly mid-process, read what to do when an AI company offers to buy a client's data during a sale.

How to sequence a data license with the sale

Timing is a decision for the seller and deal counsel. Three patterns cover most cases:

• License before marketing. The signed agreement goes into the data room and every bidder prices a known fact, including the exclusivity term.
• License after closing. The acquirer decides, which suits buyers who want full control of the records.
• License in parallel. Workable when the license is ring-fenced and disclosed in both rounds; the guide to addressing AI risk in a CIM shows how to present it.

Avoid surprise: a license bidders discover after the letter of intent hands them a reason to reopen price.

Common buyer-list mistakes

Illustrative example

Illustrative only; the company and every figure are fictional. A sell-side advisor represents a 140-employee field-service software company. The long list starts at 160 names across all four groups. The FFF score cuts it to 55, the owner strikes two competitors, and the advisor ends with 18 Tier 1 names, 25 in the first teaser wave and 10 in reserve.

During preparation the advisor notices nine years of support tickets with resolutions, product specifications and code review history in the company's own systems. Instead of adding data buyers to the list, the advisor introduces the owner to SourceX, and the owner completes the data inventory before marketing so any resulting license is disclosed in the CIM, not discovered in diligence.

How advisors introduce a client to the license track

1. Raise it with the owner privately, never with bidders.
2. Submit the company through the referral form, or send the owner your referral link, which opens sourcex.si/apply with your code attached.
3. SourceX checks the baseline with the owner: 50+ full-time employees at peak (contractors excluded), several years of documented operations, rights to license the data and an authorized sponsor. The who qualifies page lists every criterion.
4. The company builds its data inventory and agrees price and terms with SourceX; nothing is binding until it signs.
5. You keep running the auction and never touch the data itself.

Partners earn 25% of the eligible platform fees SourceX actually collects from the referred company's licensing deals, capped at $100,000 per referred company. The reward is payable only after the buyer pays and SourceX receives its fee, it is never deducted from the client's proceeds, and no reward is guaranteed. If you are a registered representative or hold a state license, check your firm's and your regulator's rules on referral compensation before you register. This is general information, not legal, tax or financial advice.

Next step

Run your current mandate through the company fit checker for a preliminary, non-binding read. If the client looks like a fit, register as a partner and make the introduction, or send the owner to sourcex.si/apply with your referral link. The M&A advisor partner page covers the program from the sell-side advisor's point of view.