Over the past week we've taken the pieces of an expansion decision one at a time: the housing and income numbers that predict demand, the map-pack read that catches the traps, and the operational signals that break ties. An Expansion Study puts all of that to work on one question: where do you put a new office so Google treats you as a local option for "plumber near me" (or whatever your trade is) in the towns you actually want? Below is what goes into one, and why the finished report beats picking a market on instinct.
Two ways to run it
There are two ways to start, and they land on the same report.
Targeted is for the owner who already knows the towns. You name the one to three you want to win, and the study works out which offices would cover them.
Discovery is for the owner who wants us to find the ground. We take your current "near me" field, the places you already surface for, and set the search area from that. No preset mileage band, no fixed circle drawn on a map. Where to look comes from where you already show up, and we go find the best places to plant an office ourselves.
What the study measures for each office
Whichever way it starts, the study comes back with a set of recommended offices, and it works out the same things for each one:
- The towns it puts you in contention for. Every town you're after gets a plain read for that office: in the field, on the edge, or out. That's about whether an address there gets you considered for the town's near-me searches at all. Distance is one of the three factors Google says its local results run on, alongside relevance and prominence, and it works as a gate. Near enough to be in the running, or not in the field.
- The top three near-me competitors, and how dug in they are. Not a head count. Everywhere has other shops in your trade. What matters is who owns the top of the near-me pack for that office, and whether they're beatable or have been there fifteen years with four hundred reviews.
- The Census read on the field. Owner-occupancy, the age of the housing stock, median income: the numbers that tell you whether the work around that office is real, recurring, and able to pay for more than a bare-minimum repair.
- A competitor-free suite. A real, staffable address with no same-trade rival in the building. A Google Business Profile needs a genuine address, and a P.O. box won't do.
- Priced listings. Actual office or operations-hub space you could rent, rents attached, so the recommendation is something you could sign rather than a pin on a map.
- The high-value neighborhoods it reaches. Checked the hard way, by running the near-me search from inside each neighborhood to see whether the office actually surfaces there.
An office here can be one of two things, and the choice doesn't change the price. It might be a one- or two-person suite whose job is to anchor the Google Business Profile, or a real operations hub your crew works out of. Both have to clear the same near-me bar.
The output: a ranked set of offices
The study hands you a decision, not a data dump. The recommended offices come back ranked, top to bottom, with the reasoning attached to each: the towns it reaches, the competitors already there, the demographics, the suite, the rent. Gold ($1,795) recommends three offices; Platinum ($2,995) recommends five across a wider search. You read it top-down and know which address to chase first, which is the backup, and what would change the order.
The ranking is what you're paying for. Anyone can gather numbers. The work is weighing the near-me reach, the competition, and the demographics the same way across every candidate, so the answer isn't "here are a dozen spreadsheets" but "sign here, this one's your backup, and skip that one."
Why it beats guessing
Doing this by hand is a real research project. For each candidate office you're pulling Census tables, running the near-me search from several points to see who actually surfaces, sorting the local competitors by how dug in they are, then hunting commercial listings for a suite with no same-trade rival in it. Across a handful of candidates that's several evenings, using sources you have to know where to find and how to weight. Most owners don't have that kind of time, so they fall back on the biggest town on the map, which is exactly how the expensive mistakes happen.
Guessing weighs the towns you already thought of, using the signals your gut can see. A study checks who actually surfaces for near-me, in the towns you want, from an address you could really rent, including the signals that decide the outcome but don't show up from the driver's seat. For a call that could cost you a lease and a year of a crew's time in the wrong place, $1,795 for three offices or $2,995 for five is cheap insurance on the decision.
FAQ
What's the difference between an Expansion Study and doing the research myself? Completeness and consistency. Every input is public: Census data, Google's near-me results, commercial listings. What the study adds is running the near-me search from inside each field, scoring every candidate office the same way, and handing you a ranked answer instead of a folder of half-finished tabs.
Do I have to know which towns I want? No. That's the difference between the two intakes. If you already know the towns, we run it Targeted and find the offices that cover them. If you don't, we run it Discovery: we read your current near-me field, set the search area from it, and find the best ground ourselves. Same report either way.
Does this only work for plumbing? No. The framework fits any local service business: HVAC, electrical, roofing, siding, and beyond. The near-me and demand signals stay the same, and only the trade-specific weighting changes. See the industries we work with.
What do I actually get at the end? A ranked set of recommended offices, three on Gold or five on Platinum. For each one: the towns it puts you in contention for, the top competitors and how dug in they are, the Census read, a competitor-free suite with real rent, and the neighborhoods it reaches. A decision you can act on, not a data dump to interpret.