Building an Account List for a Product With a Tiny Buyer Pool

Pull the last dozen closed-won deals and list what they actually had in common beyond industry: the software stack they were already running, the team size at the specific department you sell into, whether they were VC-backed or bootstrapped, even the compliance frameworks they needed to hit.

In a vertical this narrow, two or three of those details usually do more filtering work than a generic firmographic filter like employee count ever will, because everyone in a micro-vertical looks similar on the broad metrics.

Turn what you found into a worksheet with five or six rows, each one a specific, checkable fact: does this company use the specific competing or adjacent tool, does it operate in the sub-segment where your product actually fits, does it have the specific role you sell to as a named hire rather than a founder wearing five hats.

A row like "good culture fit" doesn't belong on this list. If two reps looking at the same company wouldn't give the same answer, the row needs to be more specific before you use it.

Examples of checkable worksheet rows:

• Does the company use the specific competing or adjacent tool your product replaces or works alongside?
• Does it operate in the sub-segment where your product genuinely fits, rather than in the wider industry?
• Does it employ the specific role you sell to as a named hire, not a founder covering several jobs?
• Does it meet a compliance requirement or funding profile that your recent closed-won deals shared?
• Could two people on your team look at the same company and give the same yes or no answer without guessing?

In a tight vertical, the source lists are usually smaller and weirder than a standard database export: a specific trade association's member directory, a niche marketplace's vendor page, a conference's exhibitor or attendee list, a subreddit or Slack community where your buyers hang out.

Build the raw list from those sources first, then run it through the worksheet criteria, rather than starting from a broad database and trying to filter down. A database built for breadth will always miss the smaller players that make up a real chunk of a micro-vertical.

Because the list is short, you can afford to enrich it more thoroughly than you would a thousand-account list. Apollo or Lusha can fill in the contact layer, the named decision-maker and their verified email, once you've hand-picked the company list itself.

Don't run the enrichment step first and let the tool's own filters shrink your list before you've applied your own criteria. In a niche this small, a company that a generic filter drops for looking too small or too new might be exactly the account you want.

A hyper-niche list needs more maintenance than a broad one, not less, because the whole pool is small enough that losing track of a handful of accounts is a real dent in your addressable market. Revisit the worksheet criteria every quarter against your newest closed deals, since a micro-vertical's defining traits shift as the category itself matures.

Keep a note of every company you excluded and why, so the next person building on the list isn't guessing at whether a borderline account was already considered and rejected on purpose.

Say you sell scheduling software built specifically for physical therapy clinics with more than one location. A generic filter for "healthcare, 10 to 50 employees" would return chiropractors, dentists, and single-location practices that don't fit at all.

The worksheet version instead asks: does the practice have more than one location, does it currently use a general-purpose scheduling tool rather than a PT-specific one, and does it list a practice manager or operations lead as a distinct role from the clinical staff. Those three questions, checked against a directory of multi-location PT clinics rather than a broad healthcare database, produce a shorter list that converts at a meaningfully higher rate.