Where Should We Open Next? How to Find Retail Whitespace

Every expansion team has had this meeting. A shortlist of possible cities or catchments is on the table, someone has a hunch about a postcode that “feels underserved”, and the honest answer to “why here?” is a mix of instinct, a competitor’s recent closure, and whichever broker called first. The site selection process, checking footfall, demographics and the lease terms on a specific unit, only starts once a location has already been chosen. The harder question, the one that determines whether that shortlist is even worth building, comes earlier: where should we be looking at all?

That’s what whitespace analysis answers. This guide sets out what retail whitespace actually is, why “an empty spot on the map” is a dangerous way to define it, and how a data-led approach finds gaps that are genuinely worth pursuing, rather than ones that simply look empty.

What is retail whitespace?

Retail whitespace is a market or catchment where demand for a brand exists, but isn’t currently being served, by that brand or a close competitor, and where the local economics support opening profitably.

It’s a more precise idea than it sounds. A whitespace opportunity has to satisfy three conditions at once:

• Your customer is actually there. The catchment contains enough people who match the demographic and behavioural profile of your best-performing stores, not just a large population in general.
• Nobody’s meeting that demand. Neither your own network nor a close competitor already has a presence capturing that catchment.
• The economics work. Rent, fit-out cost, labour and realistic sales density support a profitable unit, not just a theoretically underserved population.

Miss any one of those three and it isn’t whitespace, it’s just a gap. A large population with no spending power isn’t whitespace. A demographically perfect catchment that a stronger competitor already dominates isn’t whitespace. A textbook-perfect catchment with no viable unit at a rent you can justify isn’t whitespace either, it’s an interesting fact with no action attached to it.

Whitespace analysis vs site selection: two different questions

Whitespace analysis and site selection get used almost interchangeably, and the confusion causes real problems, because they’re solving different problems at different stages.

Whitespace analysis asks where to look. It scans markets, towns and catchments at a broad level to identify where a genuine gap between demand and supply exists. The output is a shortlist of areas worth investigating further, not a decision on any specific property.

Site selection asks which unit to take. Once whitespace analysis has narrowed the field to a handful of promising catchments, site selection evaluates the specific available properties within them, footfall past the door, the exact demographic mix, the commercial terms, to decide which one to sign.

Both sit inside a wider location strategy: the plan for which markets to prioritise, at what pace, and in what format. Get the sequencing wrong, run site selection before whitespace analysis, and you end up doing rigorous due diligence on a location that was never worth pursuing in the first place. Skip whitespace analysis altogether, and expansion becomes a reactive process: chasing whichever unit becomes available, rather than deciding where the brand should actually be.

Why “the map looks empty” is the wrong test

The oldest method of finding whitespace is also the least reliable: pull up a map of existing stores, yours and your competitors’, and look for the gaps. It’s intuitive, it’s fast, and it’s wrong often enough to be genuinely expensive.

The problem is that raw whitespace treats every empty town, every gap on the map, as an equal opportunity. In practice, most of them are empty for a reason. A town with no representation from your category might have too few on-profile customers to support a unit. It might already be served by a strong independent operator who doesn’t show up on a competitor map because they’re not a chain. It might have exactly the right demographic profile but rents that no realistic sales density could justify.

A gap on a map is not evidence of unmet demand, it’s an absence of information, and treating the two as the same thing is how expansion budgets end up funding stores that should never have opened.

The alternative isn’t a better map. It’s better data, layered against a clear framework, so a catchment gets ruled in or out for reasons that can be defended rather than a visual impression of empty space.

How data-driven whitespace analysis actually works

A proper whitespace analysis answers the same handful of questions for every candidate catchment, from the same consistent dataset, so that markets can genuinely be compared against each other rather than assessed one at a time on different evidence.

• Catchment and footfall: how many people are in and around the area, and how many actually move through it, not just how many live there on paper.
• Demographic fit: whether the people in that catchment match the profile of your brand’s best-performing existing stores, on age, income and household type, not a generic population count.
• Competitive presence: whether your brand, or a close substitute, is already capturing that demand, and how strong that presence actually is rather than simply whether a competitor exists nearby.
• Spend and category behaviour: what people in that catchment actually spend, and on what, so the opportunity is sized in revenue terms rather than footfall alone.
• Benchmarking: how the candidate catchment compares with catchments where your brand is already trading successfully, so a shortlist can be ranked rather than eyeballed.

Run consistently across every candidate market, this turns “where should we open next?” from a debate about impressions and hunches into a ranked shortlist, with the reasoning behind each ranking visible and defensible.

What good whitespace analysis catches that gut instinct misses

The value of a rigorous process shows up most clearly in the mistakes it prevents. A handful of errors recur often enough across expansion teams that they’re worth naming directly:

• Cannibalisation risk. A catchment can look like whitespace on every measure and still be a poor decision, if it draws footfall from an existing nearby store rather than generating genuinely new sales.
• Stale reference data. A demographic snapshot from the last census, or a footfall report that’s eighteen months old, doesn’t reflect how a catchment behaves today, particularly after a new transport link, a competitor opening or closing, or a local development completes.
• Confusing the population with customers. A large resident population isn’t the same as a large customer base. Whitespace analysis has to filter for people who match the brand’s actual profile, not just people who exist nearby.
• Ignoring the “why is this empty” question. Every unclaimed catchment has a reason nobody’s there yet. Sometimes it’s a genuine opportunity. Sometimes it’s a planning restriction, a structurally weak pitch, or an operator who tried and failed for reasons that would apply to you too.

These aren’t hypothetical failure modes, they show up repeatedly across expansion teams once the data is looked at properly.

Turning whitespace analysis into a repeatable process

The theory is straightforward enough. The harder part is applying it to a real shortlist of markets under real time pressure, where the data doesn’t always point in one direction and a judgement call still has to be made. A one-off whitespace study is useful for a single expansion decision. It’s less useful the second time round, because most retail teams aren’t evaluating one market once, they’re running an ongoing expansion programme, revisiting the same questions every quarter as new units become available and new markets open up.

That’s where a scoring model earns its keep: a consistent, weighted way of scoring every candidate catchment against the same criteria, catchment, demographic fit, competitive presence, spend, so that markets can be ranked against each other rather than argued about individually, and so the same framework can be rerun as new candidates come up rather than rebuilt from scratch each time. The precise weightings matter less than the consistency: what makes the model useful is that every catchment is judged on the same basis, every time the question comes round.

Where Huq fits in

Finding whitespace means running the same evaluation across dozens of catchments at once, not building a deep report on a single site. That’s a different job from what most location tools are set up to do, since they’re built to go deep on one market or one property at a time, which is precisely what breaks down when the task is comparing thirty candidate towns against each other rather than validating one.

Huq’s platform is built around that comparison holding up. Catchment and footfall, dwell time and visit frequency, and benchmarking against your existing network all come from one consistent, validated dataset covering the UK, Europe, US and the Middle East, with demographic profiling across most of those markets and spend data for the UK, so a catchment in Leeds and a catchment in Lyon get scored on exactly the same basis, rather than whichever report happened to cover each one. And because the footfall estimates are validated against external, authoritative figures rather than taken on trust, a catchment that tops the shortlist still holds up when someone in the room asks how confident you are in that number.

The result is a shortlist that means the same thing from top to bottom, built once and rerun every time a new market comes into scope, rather than reassembled from a different set of reports each time the question comes round again.

Frequently asked questions

What is retail whitespace?

Retail whitespace is a market or catchment where demand for a brand exists but isn’t currently being met, by that brand or a close competitor, and where local rents, costs and realistic sales support a profitable store. A catchment only qualifies if all three conditions, unmet demand, an underserved position and viable economics, are true at the same time.

What’s the difference between whitespace analysis and site selection?

Whitespace analysis identifies which markets or catchments are worth pursuing in the first place. Site selection evaluates the specific available properties within a market that’s already been identified as promising. Whitespace analysis narrows the field; site selection chooses the unit.

How does location analytics improve retail site selection?

Location analytics replaces assumptions about a catchment, how many people live nearby, whether they match the brand’s customers, whether a competitor already serves them, with measured, validated data. Applied to site selection, this means every candidate location is assessed against consistent footfall, demographic, spend and benchmarking evidence rather than a broker’s pitch or a site visit impression, which makes the resulting recommendation both more accurate and easier to defend to a board or investment committee.

How do you find retail locations that are actually worth pursuing, not just empty ones?

Start from the customer, not the map. A catchment is only worth pursuing if it contains enough people who match your best-performing stores’ profile, if that demand isn’t already being captured by you or a close competitor, and if the local economics, rent, fit-out and realistic sales density, support a profitable unit. Screening candidate markets against all three filters, rather than simply looking for gaps between existing store locations, is what separates genuine whitespace from an empty-looking postcode.