Why use a method for site selection?
A store in the wrong place is hard to fix. A mid-size store costs US$1–5 million to open, and leases run 5 to 20 years.
Without a method, every option gets evaluated differently: one on a broker’s recommendation, another on a manager’s site visit. That makes options hard to compare and the decision hard to explain to the investment committee.
A method does not replace your team’s judgment; it organizes it. Everyone looks at the same data and knows what matters most. These are the seven steps:
- Demand: are there enough customers with your profile in the trade area?
- Competition: who will you compete with, and who wins the visits?
- Access and visibility: is the site easy to reach and to see?
- Costs and lease: do the expected sales cover them?
- Cannibalization: how many customers would it take from your other stores?
- Scenarios and risk: is it still a good option if it sells less than expected?
- Validation: what should you check before you sign?
Step 1. Is there enough demand in the trade area?
Start by defining the site’s trade area: how far the store reaches, measured in travel minutes rather than meters. A circle on a map ignores rivers, ravines, and roads with no U-turn.
Within that area, measure two kinds of demand. The residential population is who lives nearby and their socioeconomic profile. The floating population is who passes through the area to work, study, or shop.
A quick-service restaurant in an office district can live off the floating population; a neighborhood supermarket depends more on residents. Compare each site with your best stores of a similar format, not with the chain average.
Step 2. Who will you compete with?
List your direct competitors (same format and price range) and indirect ones (other ways to meet the same need) within the trade area.
Counting them is not enough. Look at who wins the visits in the area, who opened recently, and which projects are coming. That visit share tells you more than the number of stores.
A strong competitor nearby is not always bad news: sometimes it confirms there is demand. What matters is whether there is room for you.
Step 3. Is the site easy to reach and to see?
Check these points on the map and on a site visit, on your format’s busiest days and hours:
- Access: which side of the street it is on, whether there are U-turns nearby, and whether drivers can get in and out without crossing traffic.
- Visibility: whether the store can be seen from the main road, and from how far away.
- Parking and transit: how many spaces there are and which bus stops are nearby.
- Inside a shopping center: which level it is on, which anchor stores are nearby, and where people come in.
- Hours: whether the area is busy at the times when your store sells the most.
Steps 4 and 5. What does it cost, and which of your stores does it affect?
Add up everything it costs to open and operate: rent, maintenance, fit-out, permits, and local taxes. Review the lease just as carefully: term, exit clauses, annual increases, and exclusivity against competitors.
Higher rent can be worth it if the expected sales justify it: that is why costs are evaluated together with the sales forecast.
Cannibalization happens when a new store takes customers from another store in your chain. To measure it, look at how much of the trade area the two stores share and how many visitors they have in common. That lets you estimate how much the existing store’s sales could drop.
Sometimes it makes sense to open despite cannibalization, for example to keep a competitor from taking the area. What matters is knowing before you sign.
Steps 6 and 7. What if it sells less, and what should you validate before signing?
A sales forecast should be read as a range, not as an exact figure. Build at least three scenarios (low, expected, and high) and ask whether the store is still profitable in the low one.
Also note the risks the data does not show: road work that will change access, a competitor that has announced an opening, or a shopping center that is not yet fully leased. Before you sign, check:
- That you visited the site on the days and at the times your format sells the most.
- That you compared it with two or three similar stores of yours that already perform well.
- That the forecast assumptions are reasonable: average ticket, opening hours, and format.
- That someone else on the team reviewed the analysis with the same criteria.
- That you wrote down why you chose the site and how much you expect it to sell, so you can measure it later.
Which data measures each criterion?
Each criterion on the checklist, with the data best suited to measure it:
| Criterion | Question it answers | Data that measures it |
|---|---|---|
| Residential demand | How many people with your profile live nearby? | Population and socioeconomic profile within the trade area |
| Floating demand | How many people pass through, and when? | Aggregated mobility: visits by day and by hour |
| Competition | Who do you compete with, and who wins the visits? | Business map and visit share |
| Traffic generators | What draws people to the area? | Points of interest: offices, schools, hospitals, transit stops |
| Access and visibility | How easy is it to reach and see the site? | Drive-time and walk-time analysis, plus a site visit |
| Cost | Do the rent and the investment pay off with expected sales? | Lease, opening budget, and sales forecast |
| Cannibalization | How much does it take from your stores? | Shared trade area and shared visitors |
| Future change | What will change in the area? | Building permits and announced openings |
Data availability varies by country and city.
What are common site selection mistakes?
The most frequent ones:
- Choosing the cheapest rent instead of the best expected sales.
- Measuring with one-kilometer circles instead of travel minutes.
- Forgetting about cannibalization of your own stores.
- Relying on a single sales figure, with no scenarios or margin of error.
- Letting urgency decide: if there is no time to evaluate properly, that is already a risk signal.
Pulster
How Pulster helps with site selection
With Pulster NOVA, you compare sites against the same criteria: who lives nearby, who passes by, who you would compete with, and how much each option could sell. You also see which of your stores it would take customers from.
Estimated sales come with a margin of error and are not a guarantee. As a reference: 96% accuracy forecasting first-year sales at existing stores, validated in a blind test with a leading quick-service restaurant chain. The model was not trained on those stores. The average error per store was under 4%. This test was run on existing stores and does not guarantee the same accuracy for new stores.
Validating a new store site usually takes 2 to 6 weeks. With Pulster, it takes hours for defined analyses, after initial setup. Based on our experience in the region.