Why mobility data is key to interpreting customer behavior  

Key takeaways 

Mobility data reveals intent beyond destinations
By showing how journeys start, unfold, and connect over time, mobility data helps businesses move from isolated location signals to a deeper understanding of customer intent. 

Journey-level context turns observation into anticipation
High-frequency, privacy-safe mobility data makes it possible to see patterns and routines, enabling brands to anticipate what consumers are likely to do next rather than reacting after the fact. 

Movement patterns improve relevance and timing
Understanding routes, cadence, and trip context allows brands to align outreach with how people actually move through their day, making engagement feel more helpful and less interruptive. 

Predictive insights support stronger performance and attribution
When outreach reflects real-world movement behavior, brands can improve return on ad spend, clarify performance attribution, and engage earlier in the decision cycle. 

Respecting context builds trust and long-term value
Using mobility data to engage only when relevant — and avoiding moments when engagement would be disruptive — helps brands respect consumer context and build trust over time. 

Introduction 

Most businesses can tell you where their customers are, but far fewer can explain how they got there or what their movement could signify. 

When businesses see only destinations, they are limited to isolated moments rather than the sequence of decisions that led there. When they can understand the full journey — how a trip starts, unfolds, and connects to what comes next — they can better understand customer intent and move from observation to anticipation.  

That’s where mobility data comes in. By capturing high-frequency, privacy-safe, aggregated and anonymized driving behavior over time, it adds a critical layer of information about customer behavior and can help shift reactive strategies into predictive ones.  

What is mobility data and location data?  

Location data helps businesses understand the places people visit at specific moments in time. Mobility data shows how, when, and where those people move through the world.   

When used alongside location data, mobility data fills in a missing layer, adding signals such as: 

• Trip origin 
• The route   
• Trip duration  
• Subsequent movement patterns 

Mobility data does not replace location data; it augments it by adding journey-level context.  

Why do you need mobility data to understand customer intent? 

If a business can only see where someone ends up, but not how they got there or where they go next, they are optimizing based on a single visible data point and may miss broader behavioral signals.  

By showing customer journeys at scale, mobility data can uncover intent:   

• Whether a visit was purposeful or incidental  
• Whether a location was a destination or a pass‑by  
• Whether exposure occurred before, during, or after a decision to stop (or not) 

Taken together, these signals contribute to a more complete picture of likely consumer intent, rather than isolated actions. 

How does understanding customer movement reveal intent? 

Mobility data makes visible the routes that customers took – including those that may have passed you by. 

Consider a weekly food shopping routine that includes stops at three retailers: a lower-priced store (your business), a neighborhood standby, and a store known for kid-friendly snacks.  

Location data will register a visit to your store, but it only provides limited information about the context in which that visit happened. You won’t know if your visit was the first one, or where the consumer went next.   

Mobility data can add context that further illustrates customer behavior. For example, the stop at the neighborhood standby may have occurred first because it is closest to the trip origin. If that first visit captures the largest share of spend, understanding this pattern can inform how to continue to position your outreach, such as encouraging a visit before the journey has begun and before spending has already occurred.  

“By collecting this higher frequency data, you really get a good understanding of the movement, the journeys, the context, the intent of why somebody is doing what they’re doing.” — Jeff Schlitt, Director of Engineering, Arity

How does understanding the customer journey support predictive intent? 

Seeing journeys over time can enable a second layer of understanding: anticipating what someone might do next.  

For example, a parent who drives to evening sports practices twice a week, or a board member who attends late meetings every other Wednesday, may follow routines that differ from a typical weekday pattern. Mobility data can help identify these recurring behavior changes and suggest the higher likelihood that the tired parent or board member may grab a convenient meal on the way home.  

With this type of insight, a business can intersect with consumers in ways that reduce friction and respect context, such as sharing a relevant offer when it aligns with established patterns and not simply because someone happens to be nearby.  

How does understanding customer intent reveal predictability? 

Arity data of trips taken in one week around a retailer’s locations, noted in yellow. Teal indicates drivers that passed but did not stop at locations. 

 

The inverse of one-off behavior is pattern recognition. Mobility data can help identify repeated routes and routines over time.   

When businesses understand how journeys typically start, unfold, and conclude, including timing and frequency, they can better contextualize outreach. This might include recognizing common locations of interest, such as workplaces, schools, or regular retail stops, without relying on single-moment signals alone.   

Understanding trip origins, cadence, and timing can help businesses estimate likely intermediate stops, expected dwell time, and what may come next. This supports more thoughtful engagement rather than reactive messaging.  

How does mobility data help with return on ad spend (ROAS)? 

Understanding routes and routines can support more relevant and timely engagement. For example, if a business is introducing a new energy drink and knows that a particular consumer segment regularly visits a fitness center before driving past your retail location, mobility data can help align messaging and timing with those established patterns.   

When outreach reflects how people already move through their day rather than interrupting it, it can support more efficient spend and clearer performance attribution.   

Case study: How a tequila brand used Arity driving data to gain measurable results 

Suerte Tequila used mobility data to inform targeted outreach across three different audience groups. By combining mobility data with behavioral insights, they aligned ads with moments such as proximity to retail locations or engaging with relevant content at home.  

The results: a 320% stronger performance compared to control audiences, with display ads delivering the best efficiency at $9.50 per visit with a 589% incremental lift.   

How do you move from reactive to predictive insights? 

At scale, mobility data surfaces journeys over time – and that’s when patterns emerge, such as:  

• Repeated routes 
• Consistent departure windows 
• Predictable movement behaviors 

For example, a restaurant with a competitor across the street may want to better understand how to influence drive-by traffic. Understanding the traffic flow past the locations and patterns for when drivers choose one location over another can help inform more proactive decision-making.  

The roadway itself is also an important factor. Are the locations on a two-lane road where it may be relatively easy to turn into a parking lot, or along a highway or divided road that can make certain turns more difficult?  

These insights can help the restaurant assess how road design, traffic flow, and proximity shape behavior, and where adjustments to messaging, placement, or timing may support more relevant engagement with nearby drivers.  

As patterns become clearer, outreach can shift from speculative timing towards predictive engagement that reflects observed behavior. Mobility data can help businesses better anticipate where and when engagement may be most relevant – and just as importantly, when not to engage, such as avoiding distraction while someone is driving.  

Case study: How an automotive service brand increased brand awareness and drove measurable lifts for in‑store visits 

An automotive maintenance and repair brand sought to improve brand recognition and attract likely customers to its locations. They used driving behavior signals to: 

• Identify characteristics that may indicate readiness to visit a location, such as accumulated mileage or risky driving behaviors (hard braking, high speed)   
• Understand customer routes and routines to align outreach with more relevant timing  

The result: Increased brand awareness by 15.2% and achieved a 196% incremental lift in store visits. Read the case study. 

Conclusion 

When businesses understand the full journey, they reduce reliance on assumptions and one‑off signals.  

Mobility data can help brands connect with consumers by aligning engagement with real-world movement patterns. This helps businesses to: 

• Anticipate consumer needs 
• Respect consumer context 
• Engage earlier in the decision cycle 
• Improve the relevance and timing of their outreach 

When messaging reflects how people actually move through their day, marketing is more likely to feel helpful, and over time, that consistency can help businesses build customer trust and loyalty.