Strong loyalty marketing means anticipating needs every mile of the way — not just when someone is within range of a storefront.
Retail marketers often rely on location data to help them understand consumer behavior. Typically, they focus on footfall data, also known as footfall data: counts of people entering or passing a location, commonly measured by sensors, cameras, or Wi‑Fi around stores. This signal is valuable as a baseline — but it’s inherently store-centric. It tells you who showed up (or passed by) but fails to offer context about consumers’ overall journeys: where they were before that visit and where they went next.
Mobility data doesn’t replace footfall — it extends your location data strategy. Collected with permission through consumer mobile apps and used in anonymized, aggregated form, mobility adds trip-level context: routes, timing, frequency, and recurring behaviors like commutes, gym runs, or weekend shopping loops. Footfall answers “did they visit?” and mobility helps answer “how does your brand fit into their lives?”
When you understand movement patterns, you can design repeat visits and relevance rather than simply optimize for the moment someone crosses the threshold.
Knowing when and where someone purchased is useful. Knowing how their day moves is what lets you influence decisions earlier.
Fred Dimesa, Head of Product, Marketing Solutions at Arity, explains how brands use trip context to understand demand: “We work with fuel companies to help them understand patterns that impact demand in market areas they’re interested in. We can provide accurate volumes of traffic, what times of day that traffic surges, the direction of travel, where that traffic is coming from just before they arrive, and where that traffic originates at the start of the day.”
If you understand driving patterns, you can reach people before they typically head out — like promoting healthy snacks before a usual gym drive, or offering food and beverage discounts ahead of common road-trip windows.
Think of it as adding a journey layer to your location dataset: footfall shows the destination signal; mobility shows the path, cadence, and intent window.
Go beyond generic time-of-day targeting. Once you see where customers tend to intersect your network and how those visits fit into routines, you can infer why the moment matters—and personalize from there.
Understanding “why now” helps you predict specific needs and translate them into highly relevant offers, instead of only A/B testing promos and hoping something sticks.
Post-work commute
Weekend morning drive
Long highway trips
Make your brand feel like part of the day — not a detour.
Dimesa notes a common misconception: “People don’t really drive as much as we think they do… they’re also not driving on the highways, they’re driving on local streets.” If customers are nearby more often than assumed, mobility helps you stay relevant between visits — not only when footfall surges.
Tailor offers around recurring trip contexts: morning commute, afternoon school pickup, post-workout, weekend errands, road trips. As Dimesa puts it, “It’s taking something that traditionally has been the purview of pure brand — pure upper-funnel advertising — and making it much more localized and much more actionable.”
Footfall, location signals, and mobility data work best together. Store-level measurement tells you proximity and conversion moments. Mobility data supplies the missing piece: the ongoing movement context that connects visits into routines — so you’re not guessing customer life patterns from snapshots alone.
When you combine destination signals with journey signals, you can engage customers every mile of the way — with programs that feel timely, local, and useful.