Foot Traffic: The Definitive Guide

Foot traffic, also known as footfall or footfall data in business settings, refers to the flow of people entering, exiting, or moving through a physical location. While it once simply meant counting how many people walked through a door, today, foot traffic data delivers far more than a basic headcount. This data and its sources can be broken down into two categories: macro level and micro level foot traffic data.

Thanks to advancements in location intelligence, foot traffic has evolved into a tool that reveals who your visitors are, how they behave, where they came from, how long they stay, and where they go next. These insights power real-world business strategy across industries including retail, real estate, investment, and urban planning, and many others.

Modern foot traffic data goes beyond raw counts. It can reveal:

• Who your visitors are (demographics, repeat vs. new customers)
• Where they came from
• How long they stayed
• What path they took through a space
• Where they went next

Foot traffic describes the movement of people from place to place, and it helps businesses understand visits to a location or area of interest.

The foot traffic and location intelligence ecosystem depends on high-quality location data, but its real power emerges when combined with other forms of big data. Together, they generate a stream of real-world insights that can predict revenue, detect change early, and measure performance for things like advertising efforts, site performance, and many others.

How to Measure Foot Traffic

Today, foot traffic measurement is powered by anonymized GPS signals and other sensor data from smartphones and connected devices. From there, raw data is enriched using machine learning models that blend contextual inputs like venue types, weather, time of day, demographics, and historical patterns to deliver reliable insights into real-world customer behavior.

Unacast’s machine learning model goes beyond basic GPS tracking by drawing from a wide range of data sources, making it more stable and less susceptible to disruptions in any single data supply. The process starts with pseudonymized first-party location and traffic data. This is then enriched with contextual signals such as demographics, industry trends, venue attributes, points of interest, and historical patterns. Our model is powered by over 120 features that help it identify relationships between these diverse inputs—linking real-world mobility behaviors with broader contextual and historical insights.

This machine-learning powered foot traffic data allows us to create curated foot traffic datasets that make it easy to work with location analytics. When validated against ground truth data, Unacast’s models recorded an R-squared value of up to .92, widely considered to be best-in-class.

How does Points of Interest (POI) data influence foot traffic analysis?

Accurate point-of-interest (POI) data is the foundation for reliable foot traffic analysis. POIs serve as the spatial anchors that define where visits begin and end. If these anchors are imprecise, foot traffic insights will be incomplete, incorrect, or misleading.

Because foot traffic measures the flow of people in and around physical locations, every insight, visit counts, dwell time, and even competitive comparisons, depends on the accuracy of POI boundaries. Clean, granular POI data ensures visits are attributed to the right business, storefront, or venue.

For example, GPS coordinates alone might show high dwell time near a location, but without knowing whether that location is a bank branch or a quick-service restaurant, the insight is meaningless. Metadata such as brand affiliation, venue type, or whether the location is part of a multi-tenant property adds necessary context that elevates the analysis.

Mislabeling or outdated POI records can throw off entire datasets, making high-quality POI data one of the most critical components in trustworthy location intelligence.

Foot traffic data is a powerful lens into real-world behavior—but like any data source, it has limitations that must be addressed to ensure a clean, reliable end product.

Low foot-traffic data volume

One common challenge is low data density, particularly in rural locations or niche commercial zones. Foot traffic data is generally sourced from mobile devices. Thus, un areas where fewer users have opted into location sharing, sample sizes may be too small to yield statistically significant conclusions. This can result in underreported visit trends or misleading fluctuations.

Foot traffic is always an estimate

Even the most advanced location intelligence providers deal with signal noise and coverage issues. Devices can be powered off, GPS can drift, and urban canyons can interfere with signal precision. Additionally, each provider uses different map-matching logic and data normalization techniques. That’s why it’s important to understand the methodology behind any dataset—it provides necessary context when interpreting results.

Manual collection is inconsistent

Before the advent of GPS and smart device tracking, businesses relied on hand counters, infrared door sensors, or staff observations. Some still do. These legacy methods are time-consuming and often inaccurate. They also lack the scale and consistency required for enterprise analysis. GPS-based systems, while far more scalable, still rely on well-modeled POIs and rigorous signal filtering to produce clean data.

Once businesses understand the common pitfalls, the next step is learning how to unlock value from foot traffic insights. Here are a few key ways our customers are using foot traffic data to gain a competitive edge:

Cross Visitation

Cross visitation tracks where consumers go before or after visiting a location, offering valuable information about customer habits and brand affinities. For example, a retailer might discover that many of its shoppers also visit a nearby coffee shop or competitor. Using retail analytics like that can fuel smarter partnerships, ad targeting, marketing efforts, or even location selection for new stores.

Ranking

Footfall data enables comparative performance analysis by ranking locations based on visit volume, dwell time, or frequency. This helps businesses and professional across industries benchmark themselves against competitors, spot underperforming assets, or identify top-tier properties worth reinvestment. Rankings can also reveal seasonal trends or event-driven spikes that may not be obvious through sales data alone.

Customer Retention

Retention serves a larger role than a key performance indicator. Foot traffic data can reveal how often customers return, how long they stay, and whether visit frequency is increasing or declining over time. These insights help businesses gauge brand stickiness and experiment with initiatives to boost in-person engagement, from loyalty programs to in-store events.

Foot traffic analytics transform everyday movement into actionable business intelligence. From understanding competitors to optimizing store layouts and evaluating real estate, location data fuels smarter decisions across industries.

Competitive Intelligence and Market Positioning

Foot traffic data helps businesses understand how they stack up against the competition. Using visit volume, peak hours, dwell times, and visitor origin neighborhoods, companies can gain a clear picture of competitor performance.

More advanced strategies involve identifying shared customer journeys. For example, a movie theater in Portland might discover that many of its visitors also frequent nearby restaurants. This insight opens the door to co-marketing partnerships like “dinner and a movie” promotions—creating value for both businesses.

We believe we've set ourselves up for success to weather the pending storm regardless of its status as a recession or correction. We feel confident knowing we have the location data we need." - Market Intelligence Director, National Home Building Company

Investment Decisions and Commercial Due Diligence

For investors and commercial real estate professionals, foot traffic data reveals real-world activity in and around properties. This helps identify high-growth zones or rising areas of consumer demand.

More advanced use cases include using visitation data to verify seller claims, assess tenant viability, and reduce risk during acquisition. Location intelligence adds an objective layer of insight to the due diligence process—one that traditional financials can’t provide alone.

Measuring Macro Trends and Societal Change

Foot traffic data offers a real-time view into human behavior on a broad scale. During the COVID-19 pandemic, mobility data revealed a mass exodus from urban centers well before government surveys or reports caught up.

Businesses that monitored these patterns were able to pivot quickly—adjusting location strategies, reallocating resources, and planning for what came next. Whether tracking urban flight, economic recovery, or consumer demand surges, foot traffic data helps organizations stay ahead of macro shifts.

Use Case: Hospitality Pricing and Foot Traffic Data

Hotels are increasingly turning to real-time foot traffic data to optimize room pricing and anticipate demand surges during local events. By tracking the movement of people near event venues, entertainment districts, or convention centers, hotels can dynamically adjust rates to reflect actual on-the-ground activity—not just historical trends. This approach helps maximize revenue and occupancy, keeps pricing competitive, and ensures guests are charged fairly based on current demand. In today’s fast-paced hospitality market, using live mobility data for pricing isn’t just smart—it’s a strategic advantage.

CPG Product Launches and Geotargeted Activation

Consumer packaged goods (CPG) brands are using foot traffic data to take the guesswork out of product launches and promotional events. By analyzing where their target audiences live, shop, and spend time, brands can identify high-traffic zones that align with their ideal customer profiles. This enables smarter deployment of pop-up experiences, sampling stations, and geotargeted digital ads in locations with proven footfall—not just assumed potential.

Rather than relying solely on demographics or historic sales data, brands can now base activations on actual human movement, ensuring every dollar spent reaches engaged, on-the-ground audiences. Whether launching a new beverage, snack, or personal care item, this strategy helps CPG marketers improve reach, relevance, and ROI.

Insurance Risk Models and Predictive Foot Traffic Analysis

Insurance providers are increasingly incorporating foot traffic data into their underwriting and pricing models to better assess location-based risk. By mapping visitation patterns around commercial properties, venues, or high-traffic corridors, carriers gain a clearer understanding of potential risk exposure—especially for property, casualty, and liability lines.

For example, a retail location with consistently high foot traffic may present a greater risk of slip-and-fall claims, while low-traffic areas could correlate with lower exposure but also slower emergency response times. By correlating mobility patterns with historical claims data, insurers can create more nuanced pricing models, set premiums more accurately, and reduce claim surprises.

Ultimately, using foot traffic data in risk modeling helps insurers transition from reactive coverage to proactive risk intelligence—resulting in better margins and fairer pricing for policyholders.

Foot traffic analytics are no longer reserved for big enterprises. Thanks to accessible platforms and intuitive tools, small and medium-sized businesses (SMBs) can now tap into location intelligence to make smarter, data-driven decisions.

One key benefit is the ability to identify true competitors. For instance, a local pizzeria might assume its main competition is the pizza place across town only to discover, through foot traffic patterns, that a nearby chicken wing restaurant is capturing the same "Friday night takeout" crowd.

Foot traffic data can also uncover insights about operating hours. A boutique or café may notice a consistent spike in visitors on Tuesday evenings, suggesting an opportunity to extend hours and capture additional sales with minimal staffing changes.

Lastly, SMBs can improve the effectiveness of local advertising by identifying the top neighborhoods their most loyal customers frequent. Instead of spreading marketing efforts thin, they can focus flyer drops, local partnerships, or geo-targeted social ads where they’ll make the biggest impact.

In short, location intelligence gives small businesses the clarity they need to compete smarter, without the need for a massive marketing budget.

Accessing foot traffic data can be tailored to different technical needs and business goals.

• Insights Platform: A user-friendly, web-based software (SaaS) platform with interactive dashboards and automated reports. It's ideal for business analysts, marketers, and real estate professionals who need actionable answers quickly without coding.
• CSV / Flat File Delivery: Clean, aggregated foot fall data delivered in a simple format like a CSV. This is for data analysts and BI teams who want to import location data into their own tools (like Tableau or Power BI) to join it with internal datasets for custom analysis.
• API (Application Programming Interface): A direct data feed for data scientists and software engineers who are building proprietary models or integrating location intelligence directly into their own products for maximum flexibility and power.
• Enterprise Data Processing & Geospatial Integration: For the most sophisticated users, advanced solutions are available. This includes integrating data with powerful GIS software like Arc GIS Pro from Esri or spatial analytics platforms like CARTO.
• Furthermore, for entities like telco companies that own massive first-party customer data, a data processing engine can transform their raw data into valuable, privacy-friendly mobility insights at scale, often within their own secure cloud environment.

Foot traffic is a foundational layer of real-world intelligence that connects digital behavior with physical movement. Whether you're a small business owner, a national retailer, a real estate investor, or a city planner, the ability to track and interpret human mobility unlocks a meaningful competitive edge.

By leveraging high-quality, privacy-compliant location data through advanced analytics platforms, businesses can:

• Predict trends before they impact revenue
• Validate assumptions using real-world movement
• Personalize offerings based on actual behavior
• Mitigate risk across investments and operations

Expect the future of foot traffic analytics to include:

• Predictive modeling that not only explains the past but guides what to do next
• Real-time alerts when traffic spikes near competitors or drops at your key sites
• Unified analytics that connect movement data with transactions, digital behavior, and outcomes across the entire customer journey

Organizations that invest in location intelligence today are positioning themselves for greater agility, profitability, and long-term resilience tomorrow.