How a pharmacy chain picks its next site

A pharmacy chain picks its next site by asking three questions about each candidate address, in the same way every time: how many people live within a short walk or drive, and how many of them are older; how many clinics and doctors nearby will send prescriptions its way; and how many pharmacies already serve that area. A site with plenty of residents, several clinics and few rivals per head goes to the top of the list. Rent, lease terms and any licensing rules come after, as a filter on a list that the data has already ranked.

This article is for the expansion, property and network-planning teams of pharmacy chains, and for independent pharmacists weighing a second shop. It covers the decisions the team makes, the signals that matter, a worked comparison, an AI employee that scores every candidate, and the traps that lead to a good-looking site that never fills.

The real job of the expansion team

Most chains have more candidate sites than they can open: landlords offer units, brokers send lists, regional managers push for their towns. The team's job is less about finding sites and more about ranking them fairly, so that a unit offered on Monday is judged by the same yardstick as one offered in June. That means a short set of figures for every address, kept in one table, refreshed when the market moves. Without it, the loudest regional manager or the cheapest rent wins.

The decisions that follow from that table are: which three sites to visit this quarter, which towns to stop looking at, where a second branch would only take customers from the first, and where an independent pharmacy might be worth buying instead of opening next to it.

The signals that matter

• Residents nearby, by age. Older residents fill more prescriptions. The Demographics block counts the people living within a distance of each site, with nine age groups and seniors aged 60 and over. Population figures exist only in some countries: among them Canada, France, India, Thailand, Egypt and Australia, but not the United Kingdom or the United States.
• Prescribers nearby. Medical clinics, doctors' practices and hospitals within a few hundred metres send prescriptions to the closest pharmacy. Nearby places counts them around each site.
• Rival pharmacies. Counted the same way, split into chains and independents, with the distance to the nearest one.
• Daily life around the site. Supermarkets and transport stops bring passing trade. They are worth a count, but weight them below residents and clinics.
• Reach. A 1 km circle is a start. The Travel area block draws what can actually be reached in, say, 10 minutes on foot, which matters wherever rivers, railways or motorways cut a neighbourhood in two.

Reading a city's pharmacy market

City-wide figures show what kind of market you are entering. xMap's places database lists 519 pharmacies within 10 km of the centre of Toronto; of the 438 nearest the centre, 119 belong to a chain (27%), rated 4.3 on average. The same circle around Sydney holds 346 pharmacies, and 38% of them (132) belong to a chain. London lists the most, 866; of the 438 nearest its centre, 91 belong to a chain (21%), rated 3.9 on average.

Within 10 km of each city centre, xMap's places database, November 2026. Chains, independents and ratings are worked out from the pharmacies nearest the centre (all of them in Sydney).

A lower average rating in a dense market can mean customers who would switch to a better-run shop. A high share of independents can mean pharmacies that might sell. Neither is a decision on its own; both are reasons to look more closely at particular districts.

A worked comparison

Take three made-up candidate units in Sydney, A, B and C. The flow below adds the same figures to each. Say A has 14,000 residents within 1 km, four pharmacies and nine clinics; B has 9,000 residents, one pharmacy and three clinics; C has 16,000 residents, seven pharmacies and twelve clinics. Residents per pharmacy are 3,500 for A, 9,000 for B and about 2,300 for C. B looks emptiest, but it also has the fewest prescribers. A has a healthy balance of both. The team would visit A and B, and drop C unless the rent is exceptional. These numbers are invented for the example; the point is that each site is judged on the same three figures.

The AI employee, block by block

1. My data: your table of candidate sites, with an address or a position for each. Add rows as landlords and brokers send them.
2. Nearby places, Each incoming place, with counts: pharmacies within 1 km. Each site gains nearby_count, the nearest pharmacy and its distance, and how many are chains.
3. A second Nearby places, with counts: medical clinics and doctors within 500 m.
4. Demographics, Each incoming place: residents within 1 km, with seniors.
5. Calculated field: people per pharmacy, and seniors per pharmacy.
6. xMap LLM, on the whole list: rank the sites and write two sentences on each, using only these figures.
7. Make a report: a PDF with the ranking, the figures and a map, emailed to the expansion team.

Run it whenever new candidates arrive, or schedule it monthly to see whether a rival opened near a site you were saving. What lands in the inbox is a ranked report of every candidate, not a pile of addresses. For two or three finalists side by side, the app's Compare sites tool measures them on the same rows in one go.

Mistakes that open the wrong pharmacy

• Counting rivals but not prescribers. A gap in pharmacies with no clinics nearby is often a gap for a reason.
• Circles across water or motorways. Use a walking Travel area when the street plan is broken up.
• Cannibalising your own branch. Add your own pharmacies to the count, or a Find nearest step against your branch list, so a new site is never ten minutes from an old one.
• Assuming population data everywhere. In countries without it, lean on clinics, supermarkets and rival counts, and say so in the report.
• Ignoring the rules. In some countries the law sets how close a new pharmacy may be to another, or how many may open per head of population. Check yours before ranking.

Templates to start from: where to open next, competitor density around each site and districts with no pharmacy close by. The healthcare and pharma page has more for pharmacy and clinic networks.