Mostly no, not for the job it usually gets hired to do. Walk-by volume predicts impulse categories like coffee and convenience. A gym, a trampoline park, or a vet clinic is a planned trip decided at home days earlier, so passing counts measure a step that never happens. The data still earns its place, on comparison work rather than prediction.
This comes up in almost every conversation we have with an operator outside standard retail. The real estate lead has been handed a site packet full of traffic charts, they cannot make themselves care about any of it, and they assume the problem is them. It is not. The default site-selection model was built for businesses that capture people already walking past, and a good share of the formats expanding right now do not work that way at all.
Two site problems that look like one
Every physical location converts demand into revenue, but there are two very different mechanisms behind it, and they need different evidence.
In impulse capture, the customer is already in motion for some other reason. They are commuting, shopping the center, leaving the grocery store. Your storefront enters their field of view and the decision to walk in takes about three seconds. Coffee, convenience, quick service, and mall apparel all live here. For these formats, the number of qualified people moving past the door is close to the whole model.
In planned destination, the customer decides at home. They search, ask a friend, compare three options against how long the drive takes, and then make a dedicated trip to an address they chose in advance. Gyms, trampoline parks, climbing walls, indoor playgrounds, pet resorts, and vet clinics all live here. Almost nobody joins a gym because they happened to be walking by.
The distinction sounds obvious written down. It stops being obvious the moment a site packet arrives, because the packet looks identical for both. Same trade area map, same traffic chart, same co-tenant list. The template does not know which business you are in.
Where the number comes from, and why that matters more here
Foot traffic estimates are not counts. They come from a panel of mobile devices that share location through opted-in apps, matched to points of interest, then extrapolated up to the full population. The provider comparison covers how each vendor does that differently. What matters for a destination category is how thin the sample gets as the question gets smaller.
A 2024 PLOS ONE analysis of SafeGraph data (published January 2024) put the average U.S. sampling rate at 7.5% of devices across 2018 to 2022, with yearly rates between 6.6% and 8.5%. At the county level, sampled device counts tracked population very closely, above 0.97 correlation in urban counties and above 0.91 in rural ones. The researchers also found that representativeness weakens materially at the census tract and block group level.
Read that last sentence as a site selector rather than a statistician. The data is strongest at the geography you are not asking about and weakest at the geography you are. A single building in a single block group is the finest resolution in the whole system, and it is the one you are trying to make a seven-figure lease decision on.
Attribution compounds it. Smartphone horizontal position error can run from about 5 meters on a good ping to 50 on a poor one, the accuracy range SafeGraph itself uses as its worked example, which is wide enough to cover several units in the same strip center. SafeGraph's own visit attribution guide describes the spatial logic vendors build to resolve which venue a ping belongs to, which is a fair acknowledgment that the raw signal cannot do it alone. The vendors are quiet about the rest of it. Placer.ai's current data page describes the panel as "tens of millions of mobile devices" forming a representative sample, with no published device count and no accuracy figure. The third-party device counts circulating online disagree with each other.
None of this makes the category worthless. It makes it a relative instrument, useful for ranking one place against another and unfit for an absolute number. We treat it that way in our own scoring, and we say so on the pages where we sell it.
The frequency problem nobody prices in
Sampling rate is only half the story. The other half is how often your customer shows up at all, because a panel can only see visits that happen.
Quick service is at the dense end. A YouGov survey of 5,000 U.S. adults, reported by Nation's Restaurant News in June 2024, found nearly 9% of QSR customers order at least fifteen times a month, and among households with two adults and a child under 18 that share rises to 42%. Fifteen visits a month is roughly 180 observable events a year from one customer.
Gyms are respectable but a step down. The Health & Fitness Association reported in January 2026 that U.S. commercial fitness facilities averaged more than 184,000 visits per location in 2025, up 4.2% year over year, and Placer.ai's September 2025 analysis found the share of gym-goers visiting at least four times a month rising through 2025. Call an engaged member roughly weekly.
Then there is the deep end. A family visits a trampoline park or a full-featured entertainment center a handful of times a year, and most operators we work with plan around something in that range. We could not trace that figure to a primary industry study, so treat it as the operator consensus it is rather than a statistic. The direction is not in doubt even if the decimal place is.
Stack those three up. The same panel that observes a QSR customer more than a hundred times a year observes an entertainment center household a handful of times. Cut that by a single-digit sampling rate and the venue-level signal for a low-frequency destination is a rounding error wearing a chart.
What actually predicts a destination site
None of the inputs that matter here are exotic. They are just different from the ones in the packet.
Drive time, not a radius. A ten-minute circle drawn on a map and ten actual minutes at 6pm on a Tuesday are not the same shape, and the gap is worst in exactly the suburban corridors these formats lease in. The trade area has to be built from the road network and the time of day your customer travels.
Household composition against the real member file. Not generic demographics. A trampoline park needs households with children in a specific age band; a strength studio needs a different profile than a recovery studio. Franchisors usually hold this data and will share it if asked directly, which is the point we make in the boutique fitness franchise breakdown.
Competitive draw inside the catchment, including independents. Independent operators are missing from most franchisor packets and they take the same members.
Your own nearby units. Moving members between two of your locations and booking it as growth is the most expensive way to learn about cannibalization.
The unglamorous physical facts. Parking count, turn-in from the main road, ceiling height, floor loading, and how a stroller gets from the car to the front door. For a destination venue these do more work than any traffic chart, and none of them are in the data feed.
The four jobs foot traffic data still does well
The honest version of this argument is narrower than "the data is bad." What decides whether foot traffic applies is the question you point it at, and there is a clean line through the middle.
The pattern behind the split is that foot traffic works when you point it at places that already exist and pool enough of them. The Health & Fitness Association's own visitation reporting is a good example of the data used properly: roughly 11,000 U.S. facilities analyzed together with Sports Marketing Surveys USA, powered by Placer.ai. Pooling thousands of venues smooths out the thin-sample noise that wrecks any single low-frequency location. Their October 2025 quarterly reported average visits per facility near 47,000 for the quarter and budget gyms running 22% above pre-pandemic levels. Those are real, useful numbers, and none of them tell you whether to sign the lease at 4412 Commerce Drive.
Trade area work is the other legitimate use. Mapping where the visitors to an open location actually live, rather than assuming a ring, is genuinely valuable, and it is the methodology Placer.ai markets as True Trade Area. It requires an operating venue. You cannot run it on an empty box, which is why it validates decisions rather than making them.
How to weight it instead of arguing about it
Nobody has to throw the input away. It just has to stop carrying weight it did not earn.
A site score should be a set of named inputs with visible weights, not a single number handed down. GrowthFactor scores every site across five configurable lenses: demographics fit, market potential, competition analysis, visibility, and accessibility. Each returns its own grade with a plain-English justification for why it came out where it did, and the weights are yours to change.
Notice where foot traffic actually sits in that. It has no lens of its own. It feeds market potential and visibility, which is exactly where a destination format wants it dialed down. Accessibility is the lens that carries vehicles per day on the adjacent streets, parking adequacy, and ingress and egress quality, and for a family arriving with a stroller and a car seat that lens decides more than any traffic chart in the packet.
The forecast is a separate job from the score, and it should run on your own performance data rather than a panel. GrowthFactor customers report forecast error roughly half the industry norm once that workflow is in place (JAN2026 customer survey). Use the panel to compare places that already exist. Use your own numbers to predict the place that does not yet.
Teams get burned on this without ever running short of data. They accept a scoring model built for a different business and never ask which of its inputs are doing the work. If your customer plans the trip, the highest-scoring site in the packet may be scoring the wrong thing entirely.
Frequently Asked Questions about Foot Traffic Data for Destination Categories
Does foot traffic data work for gyms?
Not for the job most teams hire it for. Walk-by volume at a candidate site tells you how many people pass a building they were never going to enter, and a gym member decides to join at home, not on the sidewalk. Foot traffic data does work for gyms in comparison mode: benchmarking rival clubs that are already open, mapping the real catchment of a location you already run, and checking overlap between your own units.
What data actually predicts a good gym or trampoline park location?
Drive-time catchment rather than a radius circle, household composition matched against the brand's real member file, the draw of competing venues already operating in that catchment, and the practical stuff a map will not show you: parking count, turn-in from the main road, and ceiling height for the buildout. Passing volume sits near the bottom of that list, not the top.
How accurate is mobile foot traffic data for one specific location?
Less accurate than it is for a whole market. A 2024 PLOS ONE analysis of SafeGraph data found sampled device counts track population very closely at the county level but that representativeness weakens as the geography gets smaller, which is exactly the resolution a single site sits at. Attribution also blurs inside multi-tenant buildings, because typical smartphone position error is wide enough to cover several neighboring units.
How does GrowthFactor compare to Placer.ai for destination-category site selection?
Placer.ai is a foot traffic specialist and the depth of its visit panel is the category benchmark, which is why trade associations use it for industry-wide visitation reporting. GrowthFactor is a site scoring and deal platform where foot traffic is one input feeding five configurable lenses rather than the headline number, and every lens weight is yours to change. For a destination category that matters, because you can weight passing volume down and drive-time catchment up instead of reading a traffic number that was never measuring your customer.