The industry is asking the wrong question. Here is what we should be asking instead.
When Oxfordshire County Council withdrew footfall figures supplied by Huq Industries from its public monitoring reports in early 2026, the predictable response rippled through the location analytics industry: how do we make footfall data more accurate?
It is a reasonable question. But is there a more useful one worth asking?
Not because accuracy does not matter – it clearly does, and we address the Oxford situation directly in a separate article [link: Oxford’s Withdrawal of Huq Data], but because even accurate footfall data – counted with perfect precision and validated against every available benchmark – still tells you surprisingly little about what is actually happening in a location.
Even perfect footfall data has fundamental limits. Here are five of them.
- Every footfall figure is, at best, an educated estimate
Physical cameras can count pedestrians at a defined point with reasonable precision – though even then, multi-counting, camera angle, and occlusion create noise. But one camera on one street tells you nothing about what is happening around the corner, in the market square, or down the side streets where independent businesses thrive or struggle.
All other models – built from mobile signals, app data, and location panels – are statistical extrapolations. Sophisticated ones, certainly, with many inputs: weather, events, car parking, transport patterns, historical trends. But extrapolations nonetheless. At the end of any model chain, there is always an assumption, a weighting decision, a confidence interval that quietly acknowledges the uncertainty.
The only way to know with absolute certainty how many people visited your town centre is to count every one of them. That is not going to happen. So the figure on the dashboard is always an estimate. The question is not whether it is perfectly accurate – it cannot be. The question is whether it is consistent, validated, and useful enough to support the decisions made on the basis of it.
- A single number hides everything that matters
Footfall goes up. Footfall goes down. Neither tells you why – or whether it is good news.
Consider an event day. The headline figure looks strong: visitor numbers are up twelve per cent. But examine the behaviour underneath. The town is full of one-time visitors who came for the festival, while regular shoppers – the sustained commercial lifeblood of local retail – stayed away because parking was unavailable. Net new visitors: close to zero. Net new trade for the high street: difficult to establish from a single counter.
Or consider the reverse. Footfall is flat. But dwell time is up, spend per visit has increased, and the proportion of visitors from the primary catchment – residents within a ten-mile radius who are likely to return – has grown significantly. By the headline metric, nothing happened. In commercial reality, something important changed.
The aggregate masks the behaviour. Behaviour is what drives retail decisions, planning strategies, investment cases, and regeneration programmes. A metric that cannot distinguish between these scenarios is not wrong, exactly. It is simply not asking the right question.
- Not all visitors are equal – but footfall treats them as if they are
A tourist killing time before a train, a local doing their weekly shop, and a first-time visitor discovering the town all count as one in a footfall figure. Their economic contribution is completely different. Their likelihood of returning is completely different. Their signal value to a retailer choosing between locations, or a local authority evaluating a transport intervention, could not be more different.
Has a change in the retail mix attracted a more affluent demographic – or pushed one away? Has a new leisure offer brought in younger visitors who will become long-term residents? Has a shift in transport links opened a new catchment or severed an existing one? Footfall registers none of this. It counts bodies. It does not understand people.
- Footfall tells you what happened. It cannot tell you what to do.
By the time a monthly footfall report lands on a desk, the moment has passed. The event is over. The campaign has concluded. The retailer has already chosen a different location. Footfall is a rear-view mirror – useful for accountability and trend analysis but limited for active decision-making.
What place managers, retail directors, and destination teams need is forward-looking intelligence: an understanding of what conditions drive the right visitors, at the right times, with the right behaviours. Which combination of weather, events, transport, and retail mix is likely to produce a strong November Saturday? Which interventions – a new anchor tenant, a change to parking charges, a Thursday morning market – will move dwell time and spend rather than just raw visitor numbers? Footfall cannot answer those questions. Predictive, behavioural intelligence can.
- Optimising for footfall creates the wrong incentives
When a town’s primary success metric is a visitor count, it optimises for a visitor count. A pop-up market drawing ten thousand browsers gets celebrated. A boutique quarter bringing two thousand high-spend regulars goes unrecognised. A one-day festival looks like a triumph; a steady week-on-week increase in catchment penetration goes unmeasured.
It is the digital equivalent of optimising for page views rather than conversions. Organisations that govern by footfall risk making decisions that look strong on a dashboard but hollow out their long-term commercial vitality, retail diversity, and community character. The goal is not more people. The goal is the right people, more often, staying longer, spending more, and coming back.
“The question worth asking is not ‘how many people came?’ It is ‘who came, why did they come, what did they do, and how do we get more of that?’”
What good location analytics actually looks like
None of this is an argument against measurement. Quite the opposite: it is an argument for measuring the right things, and being honest about what any single metric – however well-validated – is actually capable of telling you.
Consider what a complete picture of a location actually requires. Footfall tells you how many people arrived. Movement analytics tells you where they came from, how they travelled, and which parts of the place they moved through. Spend data – drawn from credit and debit card transaction intelligence – tells you what they were worth economically, which areas attracted the highest-value visitors, and how that is changing over time. Demographic and persona data tells you who those visitors actually are: their age profile, socio-economic background, and the behavioural patterns that distinguish a loyal regular from a one-time browser.
Available data consistently shows, for example, that visitors travelling from three or more miles away dwell significantly longer than those arriving locally – on average nearly twice as long. That distinction is invisible in a footfall figure but critical to decisions about parking provision, event programming, transport investment, and marketing targeting. It is the difference between understanding your location and simply counting the people in it.
A complete approach brings together GPS mobility data, anonymised credit and debit card spend intelligence, demographic and socio-economic segmentation, and overnight stay data to build this fuller picture. Consistent, validated footfall and behavioural benchmarking provides a reliable baseline. Layering in catchment analysis, spend analytics, visitor personas, and movement data reveals the full visitor journey, the economic contribution of different visitor segments, and the behavioural patterns that drive long-term location performance. Packaging that intelligence directly into board-ready reports ensures the right people have the right numbers at the right time.
The future of location analytics is not a more accurate footfall count. It is a richer, more complete understanding of the people who make a place what it is – and the intelligence to act on what that understanding reveals.
At Place Informatics, this is exactly what we build. Place360 brings together footfall, movement, spend, and persona intelligence into a single platform – so decision makers can stop counting and start understanding. Get in touch to find out more: placeinformatics.com/contact
Place Informatics provides location analytics, footfall intelligence, and behavioural insights for UK town centres, BIDs, local authorities, national parks, and tourism destinations.