How to rank locations by data, not by eye

When you have three or four candidate areas on the table, the easiest thing is to settle on the one that "looks busiest". The problem is that the eye plays tricks: it mistakes a noisy street for a profitable one, and falls for the first unit it visits. Ranking locations by data isn't red tape, it's the way to compare like with like and put down in writing why one area beats another. In this article you'll see which criteria to weigh, how to turn them into a transparent, comparable score, how to weight that score for your business and which mistakes lead to the wrong choice.

Why "by eye" almost always fails

Choosing a location on instinct has three well-known traps. The first is the bias of the last unit you saw: the area you walked round yesterday weighs more in your head than the one you visited two weeks ago, even if it's worse. The second is mistaking activity for demand: a street with heavy footfall can be full of people just passing through, not your customer. The third is that a decision made "in your head" can't be defended or handed down: when a partner or a committee comes in, there's nothing to show beyond "it felt right".

The alternative isn't to overcomplicate things, it's to bring order. You define a set of criteria, measure them the same way for every candidate and get a comparable score. That turns a hunch into an auditable decision.

The criteria that really count

You don't need twenty indicators. With four well-chosen ones you can already separate the good areas from the bad. These are the ones that tend to move the needle most:

1. Population and spending power in the catchment area

Not the population of the whole town, but the share that falls within your real catchment area. How many people live or work within reach, and on what income. An area with lots of people but low income may not fit if you sell a premium product, and vice versa. These figures come from each country's official sources, not from a guess by eye.

2. Competition and saturation

It's not enough to count competitors: you have to look at them per inhabitant. Ten competitors in a heavily populated area can still leave room; three in a small neighbourhood can mean it's already saturated. The ratio of direct competitors per thousand inhabitants tells you whether you're entering a market with space or a fight.

3. Accessibility and reach within an isochrone

Two units can have the same radius on the map and a very different real reach. What matters isn't the distance as the crow flies, but how many people genuinely arrive within X minutes on foot, by bike or by car. That's the reading an isochrone gives you, and it's the only honest way to compare accessibility between areas with different streets and transport.

4. Rental cost

The best area in the world stops being so if the rent eats your margin. Cost isn't looked at on its own: it's looked at against the potential. A slightly worse but much cheaper area can beat the star area if the rent on the latter is out of all proportion.

From loose criteria to a comparable score

The trick to making the criteria comparable is to bring them all onto the same scale. Income, population, competition and cost come in different units: euros, people, units, euros per square metre. If you add them up as they are, the big number crushes the rest. The solution is to normalise: turn each criterion into a score from 0 to 100 based on how it sits against the other candidates.

Once normalised, you give each criterion a weight according to how important it is for your business, and add everything up. The result is a single score per area that you can put in a table and sort (in POINOVA, with the Advanced plan, you can build a score like this as your own metric, with the data and weights you choose; the NovaScore is something else: it measures the area's conditions with weights you don't choose). What matters isn't the number itself, but that every area has been measured against the same yardstick.

How to weight by type of business

Not every criterion weighs the same for everyone. That's where the intelligence of the method lies: in sharing out the weight according to what really drives your revenue.

• Passing trade (coffee to go, kiosk): accessibility and footfall rule. Income matters less than the volume within reach.
• Premium or destination product (jewellery, private clinic): the spending power of the catchment area rules. People travel to you, so the footfall past the door counts for little.
• Local service (hairdresser, neighbourhood gym): nearby resident population and competition saturation weigh heavily.
• Tight margins (groceries, low cost): rental cost carries more weight, because every euro of rent comes straight out of the margin.

Set the weights before you see the scores, not after. If you adjust the weights so that the area you already wanted wins, you've gone back to "by eye" dressed up as a table.

An illustrative example: four areas in a mid-sized European city

Let's suppose an illustrative case. The figures are made up and rounded, purely to show how to read a scoring table; the real ones always come from each city's official sources. A neighbourhood café brand compares four candidates in a mid-sized European city (not Barcelona) and weights them like this: accessibility 40%, population and income 30%, competition 20%, rental cost 10%.

• Area A, established centre: accessibility 90, population/income 80, competition 40 (very saturated), cost 30 (high rent). Weighted score: 71.
• Area B, mid-sized commercial high street: accessibility 75, population/income 70, competition 75, cost 70. Weighted score: 73.
• Area C, neighbourhood in transition: accessibility 60, population/income 65, competition 50 (already plenty of cafés), cost 80. Weighted score: 61.
• Area D, residential outskirts: accessibility 40, population/income 50, competition 90, cost 90. Weighted score: 58.

The ranking in this example puts Area B at the top, not Area A. By eye we'd have chosen the established centre for its buzz, but its saturation and rent penalise it. Area B wins because it balances good reach, competition with room, and a reasonable cost. To be clear: the numbers are made up. What isn't made up is the lesson, that the flashiest area isn't always the one that scores highest once you put everything in the same table.

Common mistakes when scoring areas

• Comparing gross reach instead of incremental reach. If you already have nearby units, what counts is the new people you capture, not the total that falls within the isochrone. An area can have a huge reach yet bring in customers who were already yours.
• Ignoring saturation. Looking only at population and forgetting how many competitors share that population is the fastest way to open in a market with no room.
• Trusting apparent traffic. A very busy street isn't a very profitable street. Passing footfall isn't demand until you cross-reference the figure with your customer profile.
• Tweaking the weights so your favourite wins. The moment you adjust the formula after the fact, the score stops being objective and goes back to being a hunch with the appearance of method.

The POINOVA approach

The idea is simple: instead of visiting areas and going on a hunch, you measure each candidate with the same data and rank them. You see the population and income of the catchment area and the real isochrone reach for free; competition per inhabitant and the NovaScore with Pro; and, with Advanced, your own score built from the data and weights you choose. You rank them from best to worst score, decide which ones to tackle first and put in writing why you rule out the rest.

That turns the choice of location into something you can explain to a partner, defend before a bank or repeat every time you open. It stops being a stroke of luck and becomes a process.

A practical checklist for ranking your locations

• A closed list of candidates. Define the areas you're weighing before you look at any data, so you don't add favourites halfway through.
• The same criteria for all. Choose your four or five indicators and measure them the same way in every area.
• Weights set before you see the scores. Share out the importance according to your type of business and don't change it so that your favourite wins.
• Incremental reach, not gross. If you already have a network, count only the new audience in each area.
• Competition per inhabitant. Never count competitors without dividing by the population that shares them.
• Data from official sources. Every number with its origin, so the table can be audited.
• A written reason on the discarded ones. Note why each area falls out; it'll save you repeating the analysis a year from now.