Picture Sofia at her kitchen table on a Tuesday night. The kids are down, the dishwasher is running, and she has a dozen browser tabs open trying to settle one question: which town gets the second truck. She owns a plumbing company, she's ready to expand, and she's decided to do the homework herself rather than guess.

By eleven she has four tabs of Census tables, two commercial listing sites, a Google Maps search for every plumber in a forty-mile spread, and a spreadsheet she's already renamed twice. It's genuine research, and most owners who start it never finish. An adjacent-market study is that same night of homework, handed back to you and run properly for the towns you want to win, not just the two you had a hunch about.

I wrote a general walkthrough of what's in an expansion study a while back. Think of this as the narrower cut: the specific homework behind choosing your next adjacent town, and the evenings it takes to do by hand.

The towns you want, read the hard way

Sofia's list starts with two names, the towns she drives through and already pictures a truck in. The question she can't answer from the driver's seat is the one that matters: if she opens there, does Google actually show her when someone in that town searches "plumber near me"? An address in a town, or close enough to it, is what puts her in the running for that town's near-me searches. A line on her website saying she serves it doesn't. Distance is one of the three things Google says its local results run on, next to relevance and prominence, and it works as a gate. Near enough to be considered, or not in the field.

A study reads that for every town she's after, and marks each office it finds as in the field, on the edge, or out. Which adjacent town to pick stops being a hunch and becomes something she can see.

The housing and income read for each one

For every office the study puts on the table, the same three numbers get pulled from public Census data for the field around it: how old the housing stock is, how much of it is owner-occupied, and the median household income. Old homes owned by the people living in them, with enough income to pay for real repairs, is the profile that predicts steady work. Reading a local market off housing and income goes deeper on why those particular numbers matter.

Doing this by hand is where Sofia's evening disappears. The data is free and public. Finding the right table, pulling it for a handful of candidates, and lining the results up so they actually compare is the slow part.

The competitors already at the top

Now the map search. For each candidate office, the study pulls the top three competitors in that town's near-me results and reads how dug in they are: how many reviews they carry, how long they've clearly been at it, how close they sit. Everywhere has other plumbers, so a raw head count tells Sofia almost nothing. What she needs is whether the top of that pack is beatable or already locked up, and that takes judgment applied the same way across every town. No market is empty. The only question is whether the shops already at the top are beatable.

A real office with open ground around it

A Google Business Profile needs a real, staffable address, so the study finds one. Not just any address: a private suite with no same-trade rival in the building. In the studies we've run, those suites have tended to rent somewhere between about $400 and $850 a month, and some come furnished and month to month. Either way it's a fraction of an ad budget. Read that as the kind of range we see, not a price anyone is promised.

The office can be one of two things, and the choice doesn't change what the study costs. It might be a one- or two-person suite whose only job is to anchor the Google Business Profile, or a real operations hub the crew actually works out of. Both have to clear the same near-me bar. Finding an office with open ground around it is its own search, and drive time from the existing base is the kind of tiebreaker that doesn't show up in the Census at all.

Covering the towns with more than one office

Sofia already runs one location, so the question isn't only which single office scores best. It's which addresses, taken together, put her in contention across the towns she wants with the least wasted overlap. That's the shape of the study: it doesn't hand back one pin, it hands back a ranked set of offices chosen to cover the ground together. Gold comes back with three, Platinum with five across a wider search. By hand that means sketching near-me reach on a map and eyeballing where it doubles up. The study works it out and keeps the overlap small, so the offices reach as far as they can without stepping on each other.

The done-for-you version

Run all of it (the near-me read for every town she wants, three kinds of Census data per field, the top competitors and how dug in they are, an office search, and the coverage math) and Sofia has spent several weeknights on what a study returns as one ranked set of offices. That is the done-for-you version of her homework: same inputs, same public data, the near-me search actually run from inside each field, and every candidate scored the same way, so the result is a decision rather than a folder of tabs. Strip the label off it and an adjacent-market study is just that night at the kitchen table, finished properly and run for the offices that would actually get her found.

FAQ

Can't I just do this research myself? Yes. Every input is public: Census housing and income tables, Google's near-me results, commercial listing sites for office space. The cost isn't access, it's time and consistency. Reading the near-me field for a handful of towns, sorting the competitors in each, and scoring every candidate office the same way is a real project most owners start and don't finish.

How is this different from a general expansion study? It's the same engine pointed at one decision. The general expansion study explains the full method for any local service business. This piece is about the adjacent-market call: the towns just outside your current reach, and the office that gets you found in them.

Do I have to name the towns first? Either way works. If Sofia already knows the towns she wants, the study runs Targeted and finds the offices that cover them. If she'd rather we find the ground, it runs Discovery: we read her current near-me field, set the search area from it, and find the best offices ourselves. Same report at the end.