Using population to rank candidate towns for your next opening

To rank towns for an opening, measure each one the same way from its centre: how many people live within the distance your customers travel, how many of them are in the age group you serve, and how many businesses like yours already serve them. Turn those into one score with weights you write down in advance, rank the towns, and visit the top three. The ranking will not pick the town for you, but it tells you which visits are worth the drive and gives the board a reason for the order.

This is for chains and franchise systems moving beyond their home city: a gym brand looking at regional towns, a veterinary group, a childcare operator, a bakery chain. It covers choosing what to measure, weighting it, a worked example around Lyon, and having an AI employee refresh the ranking each quarter.

Why towns need a different method from city districts

In a city you compare districts that sit next to each other. Towns are separate places of very different sizes, often with open country between them. Two things follow. Measure from each town's centre, where people go to shop, rather than from its official boundary, which may include farmland. And choose a distance that covers the town but not the next one: often 3 to 5 km for a small town, wider for a large one.

The three measures

Residents within reach

How many people live within your chosen distance of the centre. This sets the size of the pool. Use the same distance for every town on the list, so a big town wins on size honestly.

Residents in your age group

Demographics gives nine age groups. Add up the ones that buy what you sell. For a children's activity business that might be under-10s and their parents' ages; for a gym, roughly 20 to 49; for hearing or eye care, 60 and over.

Competitors already there

How many businesses of your kind lie within the same distance. Divide your target residents by that count, plus one, to get people per competitor: the share of the pool each existing business is working with.

Turning the measures into a score

1. Give each measure a rank among the towns: 1 for the best, 2 for the next, and so on. Ranks are easier to explain than raw numbers on different scales.
2. Decide the weights before you look at the results, and write them down. For example: target residents 50%, people per competitor 35%, total residents 15%.
3. Multiply each rank by its weight and add them up. The lowest total is the best town.
4. Check the top three by hand: a map, a look at the high street, a call to a local agent.

A worked example around Lyon

The figures below are made up. A climbing-gym operator with two sites in Lyon wants a third in a regional town. It measures residents aged 20 to 39 within 4 km of each town centre, and counts climbing walls and gyms within the same distance.

Invented figures, for illustration only.

With the weights written down beforehand (target residents 50%, people per gym 35%, total residents 15%), Villeurbanne ranks first, mostly on size. But it is next to Lyon and would draw members from the operator's own two sites. The team removes it, a step the numbers could not take on their own. Bourg-en-Bresse comes next: a much smaller pool than Saint-Etienne, but the least crowded of all four, and far enough from Lyon to be its own market.

Mistakes to avoid

• Using official town boundaries. They differ wildly in size and include fields and forests. Measure from the centre at one distance.
• Overlapping circles. When two towns on the list are close, their circles share people, and both look bigger than they are. Note the overlap, or shrink the distance.
• Forgetting your own sites. A town that steals customers from your existing sites is not a new market. Measure the distance to your nearest site too.
• Mixing countries. Population figures exist in some countries only, among them France, Canada, India, Thailand, Egypt and Australia, and not the United Kingdom or the United States. A list that crosses into a country without figures cannot be ranked on residents.

Doing it with an AI employee

Rankings go stale as competitors open. An AI employee can redo the measures every quarter and tell you when the order changes.

1. Schedule, every quarter.
2. My data with your shortlist of towns, each with the position of its centre. Your own sites can go in a second table.
3. Demographics, Each incoming place, at your chosen distance. Each town gets population and every age group.
4. Nearby places, Each incoming place with counts, for your kind of business at the same distance. Each town gets nearby_count.
5. Find nearest with your own sites, so each town gets the distance to the closest one.
6. Calculated field for the target residents and people per competitor, then Sort and limit.
7. xMap LLM on the whole list, told to apply your written weights and explain the top three in plain words using only the figures given, then Email.

What lands in the inbox each quarter is the ranked list of towns with their figures, how far each is from your nearest site, and a short note on why the top three lead. When a new competitor opens in one of them, its people per competitor drops, and the next quarter's email shows the change.