A worked example of a zone study, step by step

The best way to understand how a zone is studied before opening a business is to see it from start to finish with a concrete case. So let's imagine one. Let's imagine an illustrative, invented case in a medium-sized European city, with no name, not located in Barcelona or anywhere real, and with round figures we have made up so the method comes across clearly. It isn't a real client or a project that actually happened: it's just an example to teach the five steps. In a real study, population and income come from official sources, not from made-up figures like these.

Let's set the scene. Someone wants to open a small fresh-produce shop on a high street. They've found two vacant units in different neighbourhoods of this invented city and don't know which one makes more sense. Instead of deciding on the unit that "feels best", they're going to study each zone with the same method. We follow along with them, step by step.

Step 1: define the catchment area with isochrones

The first thing isn't to look at who lives in the neighbourhood, but to map out which area each unit actually reaches. And that isn't a circle on the map: it's an isochrone, the area you can reach in a given time following the real streets.

For a neighbourhood shop, the day-to-day customer arrives on foot. So we draw a 10-minute walking isochrone around each unit. In our invented example, it comes out like this: the walking isochrone of the first unit covers a good part of a dense neighbourhood; the second, closer to an avenue, stretches less because there are fewer streets crossing it.

Since the person also wants to capture big weekend shops, they add a 10-minute driving isochrone. Here the picture changes a lot: the unit next to the avenue, worse on foot, reaches by car a considerably wider area than the first. That contrast is already information: each unit serves a different customer.

Step 2: read the demographics of that zone with official sources

We now know how far each unit reaches. The next question to ask is who lives within that area. And the rule here is clear: no making things up. The data on population, income, age or education level comes from each country's official statistics institute. The tool crosses the geometry of the isochrone with that data by census tract or neighbourhood and returns the portrait of the zone.

In our invented case, the portrait might look like this (remember: round, fictional figures, purely to illustrate):

• Unit 1 (dense neighbourhood, on foot): around 18,000 inhabitants within the walking isochrone, income somewhat above the city average, a fairly young population and plenty of churn among residents.
• Unit 2 (next to the avenue, by car): fewer residents within walking distance, around 9,000, but the driving isochrone brings it a more family-oriented profile with greater spending power on the big shop.

Notice the important point: it isn't "how many people are in the neighbourhood", but how many people who match your customer fall within the real area you reach. That's the population you can genuinely serve.

Step 3: locate competitors and measure saturation

Population alone isn't enough. A zone can be full of your ideal customer and still be a bad idea if there are already ten businesses just like yours fighting over them. That's why the third step is to put the competitors on the map within each isochrone and measure how many there are per inhabitant.

In the invented example, we count the similar businesses within each area:

• Unit 1: within its walking isochrone there appear around 12 direct competitors. Plenty of people, but plenty of competition too: the zone is fairly saturated.
• Unit 2: within its isochrone there appear only 3 competitors. Fewer potential customers, but much less noise too.

The figure that really helps you compare is competitors per 1,000 inhabitants. A neighbourhood with a large population but a great many rivals can leave each business with fewer real customers than a quiet, underserved neighbourhood. Without this step, saturation is discovered too late, once the lease is already signed.

Step 4: estimate the real incremental reach

We reach the step people skip most and that saves the most decisions. The isochrone population isn't your customer base: it's the theoretical ceiling. The real incremental reach is the share of that population you can genuinely expect to attract, once you've subtracted what the competitors already keep and what simply won't buy from you.

In our example we reason it like this, with round, invented figures:

• Unit 1: 18,000 inhabitants, but spread very thinly across 12 competitors. Even though there are more people, the new share you can win over is modest, because almost everyone already has their trusted shop nearby.
• Unit 2: 9,000 inhabitants, but only 3 competitors and a higher-spending profile. Here the customer you capture is usually a customer who previously had no convenient option: cleaner incremental reach.

The right question isn't "where are there more people?", but "where am I going to add customers that nobody nearby has today?". Very often the zone with the largest population is the worst on incremental reach, because that market is already carved up.

Step 5: score and decide

With the previous four steps we now have what we need to decide without hunches. The last step is to sum everything up in a comparable score for each zone. In POINOVA, with the Pro plan, the NovaScore scores the zone's conditions from 0 to 100: income, higher education and density, compared with the rest of the country. It doesn't predict sales, and you weigh up the incremental reach yourself alongside it, so the two zones are no longer compared "from memory".

In our invented case, the final table might look like this:

• Unit 1: a large population, but saturated. Middling score. Good for volume, risky on competition.
• Unit 2: fewer people, but little competition, better average spend and better incremental reach. A higher score for this particular business.

With those numbers in front of them, the person in our example leans towards Unit 2. And the nice part isn't the conclusion, it's that they can now explain why: not because it gave them a good feeling, but because the incremental reach and the low saturation outweigh the raw population. The decision is written down and defensible.

What we take away from this example

Although the case is invented from start to finish, the lessons apply to any real business:

• The catchment area isn't a circle: it's walking and driving isochrones, because each mode of travel draws a different customer.
• Demographics are only worth it if they're official: population, income and profile come from the official statistics institutes, never from guesswork.
• Population isn't the customer base: without measuring competition per inhabitant, you mistake a crowded neighbourhood for a profitable one.
• Incremental reach rules: the best zone is usually where you add customers no one serves, not where the most people live.
• Deciding with a comparable score turns a hunch into an argument you can show your bank, your partner or your future self next year.

That's the whole trick. You don't need to be an analyst: you need to ask the questions in the right order and let the official data answer. The example is fictional; the method works in any European city.