Beyond dwell time: How mobility data uncovers true retail consumer intent

Retail marketers have long been using location data to try to reveal consumer behavior. They’ve tracked where people stop, how long they stay, and what’s nearby. But here’s the problem: A single data ping isn’t the whole story. A visit may not always equal intent. And a long stop at a store next to yours doesn’t necessarily mean they were interested in your brand.

Dwell time is static. Mobility data is dynamic.

Mobility data captures how people drive, when they drive, and where they go before and after a visit. It can unlock a deeper understanding of consumer behavior. Retailers, instead of merely reacting to store visits, can proactively target audiences based on commuting behaviors and journey patterns.

That is, retailers can examine real-world intent.

Mobility data goes beyond dwell time. It can help marketers at major retail chains deliver smarter messaging, better offers, and measurable results. Here’s how.

Dwell time is static. Mobility data is dynamic.

1. Quick-serve restaurants (QSRs): Identifying the loyal drive-by customer

The traditional view: A customer visits your QSR location once a month. They spend 10 minutes at the drive-thru. Basic location data captures this visit – but fails to convey anything else.

The mobility view: You discover that this customer drives past your location every weekday morning at nearly the same time. This makes them not just a monthly visitor; they’re a daily opportunity.

How this helps: With mobility insights, you can create a segment of high-frequency “drive-by” commuters and deliver mobile ads promoting breakfast combos timed precisely to their morning route. By targeting behavior, not just visits, you can turn passive patterns into active revenue.

2. Supermarkets: Understanding errand journeys

The traditional view: A customer stops by your grocery store every Saturday afternoon. Dwell time indicates they spend 25 minutes inside. But that’s the extent of your insight.

The mobility view: You learn this customer typically visits the gym, the pharmacy, and a local pet store before coming to your store. Then she visits your competitor’s store, even though she lives 12 miles away.

How this helps: With full-journey visibility, you can identify “multi-stop errand shoppers” and offer tailored promotions (e.g., weekend bundle deals or one-stop convenience messaging) that resonate with their actual shopping behavior. You can also target them earlier in the journey – before they even decide where to shop.

3. Fuel and convenience: Timing offers to capture the impulse stop

The traditional view: A driver stops at your convenience store for six minutes. Did they need fuel? Coffee? Did they choose your brand intentionally – or were you merely the closest option?

The mobility view: Mobility data shows the driver just started a long road trip and passed three competing gas stations before choosing your location. They tend to make a stop like this every Sunday morning.

How this helps: By identifying “routine long-trip drivers,” you can create targeted offers for them. Snack bundles, premium fuel, or in-store loyalty bonuses – these promotions can be delivered 10–15 minutes before they typically stop. This is intent-based advertising, timed to influence the decision and not just observe it.

Did they choose your brand intentionally – or were you merely the closest option?

4. Home goods retailer: Distinguishing browsers from buyers

The traditional view: A person visited your store twice in the last three months and spent 30 – 40 minutes there each time. You assume that they’re a faithful customer.

The mobility view: Mobility data shows these visits followed trips to other nearby big-box competitors. They never returned to your store after visiting a rival location. They browsed but never bought.

How this helps: Instead of misidentifying this person as a loyal shopper, you now understand they’re still in the research phase – or possibly converting elsewhere. You can retarget them with personalized offers or content about your in-stock inventory, value pricing, or convenient delivery services to capture the sale next time.

5. Big-box retail: Spotting seasonal shopping behavior

The traditional view: You see a spike in visits from a segment of shoppers every December, each with long dwell times. You attribute the sudden increase of customers to holiday traffic.

The mobility view: The same group is also highly active at neighboring electronics, toy, and apparel stores during that same week, driving to five different shopping centers in one afternoon.

How this helps: This is a high-intent, high-mobility holiday shopper. You can use that knowledge to target similar audiences with competitive pricing, curbside pickup reminders, or gift-related content during peak season – not just based on past visits, but on current real-world shopping behavior.

Seeing the full picture

Mobility data shifts your perspective from reactive to proactive. It moves marketing teams from asking “Did they visit?” to “Where were they coming from?” and “Where did they go next?”

By analyzing full trip journeys, drive frequency, time-of-day patterns, and behavioral cohorts, retailers can:

Create intent-based segments (e.g., habitual commuters, weekend errand runners, long-trip drivers)
Deliver timely, relevant messaging based on where a customer is in their journey, not just after a visit
Measure online-to-offline attribution with more confidence by comparing exposed vs. non-exposed driver cohorts who follow similar patterns

If you’re still planning campaigns based only on where people stop, you’re missing where the journey really begins.

Final thought

Retailers have long relied on dwell time as a proxy for engagement. But dwell time is a pixel in a much bigger picture. Mobility data creates the full portrait, helping you see not just where your customers are but who they are, what they do, and when they’re ready to act.