Gas prices impact spending: What mobility data reveals early

When gas prices spike, consumers don’t change where they shop; they simply go out less.

Between early March and late April 2026, fuel prices rose sharply. Across nine states*, gas prices increased by an aggregate average of approximately 33%, with variation across individual markets. At Arity, we analyzed driving behavior data to understand whether and how U.S. drivers changed where they went, how often they traveled, and how far they drove as prices climbed.

Mobility data is essential because it reveals shifts in consumer behavior before they fully materialize in business results. Traditional datasets — sales reports, foot traffic counts, and consumer surveys — are inherently lagging indicators. By the time revenue declines appear in dashboards, opportunities to adjust marketing and operations have often passed. Mobility data turns real-world driving behavior into early, actionable insight, enabling leaders to make strategic decisions sooner.

Why don’t sales dashboards immediately capture the dip in spending?

Rising gas prices suppress discretionary spending quickly — not only through direct budget pressure, but as a perceived loss. As a frequent, unavoidable expense, fuel prices become a catalyst, prompting consumers to cut back weeks before those changes are reflected in reported sales data.

Sales dashboards rely on lagging inputs such as monthly performance reports and credit card funds transfer. By the time these metrics register a downturn, consumers may have already adjusted their routines, reduced trips, and reallocated spending. The behavioral shift has occurred well before it becomes visible in traditional performance metrics.

Why is mobility data such a powerful consumer behavior signal?

Traditional data is designed to measure completed transactions — not the full set of behaviors that lead up to them. It shows when a purchase occurs, but offers limited visibility into the routes consumers take, how often they pass a location, or whether a visit is part of a broader routine. Mobility data fills that gap.

By capturing how people move over time, mobility data reveals patterns in routes, routines, and visit frequency — insights that are especially critical for brands that depend on in‑store engagement. It provides visibility into both sustained traffic and early behavioral shifts, including when visits begin to decline well before those changes appear in sales data.

That earlier visibility matters. When leaders can identify weakening demand sooner, they are better positioned to adjust pricing, planning, and operations before those shifts translate into missed revenue. Mobility data enables organizations to move from reacting to past performance to identifying change as it emerges.

When gas prices rise, drivers go out less

As fuel costs increase, drivers don’t just spend more to refuel — they also reduce visits to retail points of interest (POIs).

Looking at our mobility dataset from March 2, 2026 to April 20, 2026, we focused on the impact of the gas price increase in nine states: California, Colorado, Florida, Massachusetts, Minnesota, New York, Ohio, Texas, and Washington. As we suspected, movement shifts took place at the same time as the gas price spike.

On average, for every 1% increase in state‑level weekly gas prices, stops per user declined across key categories:

• Gas: – 0.74%
• Restaurants: – 0.67%
• Grocery stores: – 0.66%
• Retail (excluding gas): – 0.57%
• Entertainment venues: – 0.52%

At scale, even a sub-1% drop can translate into meaningful traffic and revenue loss for multi-location retailers.

This response is broad, affecting both discretionary and essential trips. One clear exception is the highest income quartile, which largely maintains typical year‑over‑year activity levels — indicating a segment that is materially less sensitive to rising fuel prices.

For most consumers, however, the pattern is consistent: As cost pressure increases, movement declines — and in‑store demand contracts alongside it.

What retail leaders should do now

These shifts aren’t isolated or category‑specific. They point to a broader change in consumer behavior with direct business implications:

• Don’t wait for sales data: Declines in trips and stops act as leading indicators of reduced foot traffic and spend.
• Watch movement trends weekly: For multi‑location brands and regional networks, < 1% declines can translate into meaningful volume loss.
• Act while demand is still forming: This goes beyond typical belt‑tightening and signals a broader pullback in shopping activity.

What does this mean for retail marketing and operations leaders?

Mobility data serves as a critical early warning signal for retail marketing and operations leaders. By surfacing changes in movement patterns sooner, it allows teams to anticipate demand risk and plan proactively rather than reactively.

• Marketing teams can reallocate media and promotions earlier using geo‑targeting and daypart optimization, while trips are still occurring.
• Operations leaders can make more precise staffing and inventory decisions, helping protect margins as demand softens.

The impact of rising gas prices will vary by region, income mix, and car dependence — making localized insight essential. Together, these early mobility signals enable faster adjustments to pricing, promotions, and media strategy before revenue impacts fully materialize.

How Arity can help retailers

Arity’s mobility intelligence gives leaders a real-time view of how demand is changing at a local level. By monitoring driving behavior and patterns continuously, teams can identify shifts earlier, act faster, and make more precise decisions across their business.

Conclusion

When gas prices pressure spending, changes in movement and visitation appear first. Mobility data turns those early shifts into a strategic advantage, giving leaders clarity before traditional metrics respond. Retailers that act on mobility signals sooner can recalibrate marketing and operations in time to protect performance — and stay aligned with how consumers are actually moving.

Methodology

*Our analysis was comprised of eight weeks of Arity driving behavior data, from March 2 through April 20, 2026. The study focused on nine states: California, Colorado, Florida, Massachusetts, Minnesota, New York, Ohio, Texas, and Washington. This mix of states captures a diverse mix of regional, economic, and driving patterns. We analyzed visitation trends across key points of interest (POIs), including gas stations, restaurants, grocery stores, retail locations, and entertainment venues. To contextualize changes in mobility, fuel price trends were sourced from the U.S. Energy Information Administration and used as an external benchmark alongside observed movement behavior.