Leading dollar stores in the United States, Dollar General and Dollar Tree, have sustained foot traffic growth during most months over the past four years, compared to January 2020. Each December, visits surged and, in 2023, they increased by over 80 percent at Dollar Tree and over 45 percent at Dollar General, compared to the base month.

For the week ended May 9, 2020, aggregate consumer foot traffic in the United States was up by nine percent when compared to the previous week.
For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Aurora:GeoStudio® is a premier geospatial analysis platform that excels in supporting foot traffic data through its sophisticated Population Dynamics® analytic. Foot traffic data encompasses information about the number of people visiting specific locations or establishments, providing deep insights into customer behavior, patterns, and trends. This data is crucial for businesses looking to understand their audience and make data-driven decisions.

Core Features:

  1.    Data Collection Methods:
  •    Passive Sensors: Aurora:GeoStudio® integrates data collected from passive sensors deployed at various locations. These devices count the number of visitors, track their movement paths, and record the duration of their visits.
  •    Mobile Devices: The platform also leverages data from mobile devices, providing additional insights into foot traffic patterns through location-based services and applications.
  2.    Population Dynamics® Analytic:
  •    Aurora:GeoStudio®’s Population Dynamics® analytic processes foot traffic data to deliver comprehensive insights. This analytic tool helps visualize and understand visitor behavior, peak visiting times, and movement trends within specific areas.
  3.    Visualization and Mapping:
  •    The platform offers advanced visualization capabilities, displaying foot traffic data on customizable maps from providers like Google, Esri, Open, and Stamen. These visualizations help users understand spatial patterns and relationships, facilitating informed decision-making.

Applications:

  1.    Customer Behavior Analysis:
  •    Businesses can analyze foot traffic data to understand customer behavior, such as the number of visitors, the duration of their visits, and the paths they take within an establishment. This information is crucial for tailoring services and improving customer satisfaction.
  2.    Store Layout Optimization:
  •    Foot traffic data helps businesses optimize store layouts by identifying high-traffic areas and bottlenecks. By understanding how customers move through a space, businesses can rearrange products and displays to enhance flow and maximize sales opportunities.
  3.    Marketing Strategy Enhancement:
  •    Aurora:GeoStudio® enables businesses to refine their marketing strategies by providing insights into peak visiting times and customer demographics. This data supports targeted marketing campaigns, ensuring promotions reach the right audience at the right time.
  4.    Operational Efficiency:
  •    Understanding foot traffic patterns allows businesses to optimize staffing levels, manage inventory more effectively, and improve overall operational efficiency. By aligning resources with actual customer demand, businesses can enhance service delivery and reduce costs.
  5.    Urban Planning and Public Spaces:
  •    Foot traffic data is invaluable for urban planners and managers of public spaces. It helps in designing public areas that accommodate pedestrian flow efficiently and ensures that amenities are accessible and well-placed.

Aurora:GeoStudio®’s support for foot traffic data through the Population Dynamics® analytic offers businesses and urban planners a powerful tool for understanding and optimizing visitor behavior. By leveraging data from sensors, cameras, and mobile devices, the platform provides detailed insights into customer movements and trends. These insights enable businesses to enhance their marketing strategies, optimize store layouts, and improve operational efficiency. For urban planners, foot traffic data facilitates the design of more effective and accessible public spaces. Aurora:GeoStudio®’s advanced features empower users to make informed decisions and achieve a comprehensive understanding of foot traffic dynamics, leading to better strategic outcomes.

The average monthly footfall in physical stores steadily grew over 2021, peaking in July, with an increase of almost 44 percent compared to January of that year. In January 2022 growth dropped to just 15 percent, but by March 2022 it had recovered to over 34 percent.

Overview

Huq Industries offers a robust and precise Daily Footfall Data feed, tailored to meet the diverse needs of investors, government bodies, retailers, and real estate professionals. Our data, which includes real-time foot-traffic data, location data, and mobility data, serves as a trusted resource for informed decision-making, with over 600 satisfied clients across various sectors.

Key Features and Specifications

• Hex 12 (19m) resolution • Valued by 600+ customers • Unique visitor foot traffic data • Accuracy backtested & verified • Daily foot-traffic statistics • Global coverage

Unique Visitor Foot Traffic Data: Gain access to detailed data on unique visitors, providing clear insights into foot traffic trends.

Daily Foot Traffic Statistics: Updated daily, our real-time foot-traffic data ensures you have the most current information.

High-Resolution Data: With a Hex 12 (19m) resolution, our location data offers detailed geographic insights.

Global Coverage: Comprehensive coverage across the United Kingdom, ensuring no region is left unmonitored, which is essential for mobility data.

Accuracy and Validation: Our data's accuracy is rigorously backtested and verified, correlating with known benchmarks such as DCMS / British Museum entrants and sales data from Walmart, Petco, and Boot Barn.

Why Choose Huq Industries' Foot Traffic Data?

Investors

For investors, our foot traffic data is crucial for local economic forecasting and consumer trend analysis. By analyzing location data and foot traffic patterns, investors can make informed decisions about resource allocation, identify emerging market trends, and assess the potential performance of retail investments. The granularity and frequency of our mobility data enable detailed analyses necessary for high-stakes investment decisions.

Government Bodies

Government agencies can leverage our real-time foot-traffic data and mobility data for urban planning and infrastructure development. Daily updates allow for real-time monitoring and assessment of public spaces, aiding in resource allocation and policy implementation to improve urban mobility and public safety. Our location data ensures that planning efforts are tailored to the specific needs of local communities.

Retailers

Retailers benefit from our foot traffic data through enhanced analytics and consumer behavior insights. Understanding footfall patterns helps optimize store locations, tailor marketing strategies, and improve customer engagement. The data can highlight peak shopping times, popular areas, and customer movement trends, providing actionable insights to boost sales and satisfaction.

Real Estate Professionals

Real estate professionals can utilize our mobility data and location data to assess the viability of retail locations, understand market demand, and make data-driven decisions about property development and investment. Detailed foot traffic data offers a clear picture of how different areas perform, aiding in the identification of high-potential properties and the evaluation of existing assets.

Data Schema and Cadence

Our data schema is designed for clarity and ease of use, with properties including:

Datestamp: The date of observation. Polygon ID: The ID of the CDRC defining the retail center. Centre Name: The name of the CDRC retail center. Centre Type: The classification of the CDRC retail center. Centre Region: The NUTS2/UK2 value for the region. H3 Key: The H3 ID at level 12 for the geographic unit. Latitude and Longitude: Geographic coordinates of the H3 unit centroid. Footfall Value: The number of unique population member observations, adjusted for geographic sampling bias. Our data's daily update cadence ensures users receive the most current and actionable insights, enabling timely decision-making and rapid response to changing conditions—a critical advantage in today's fast-paced environment.

Conclusion

Huq Industries' Daily Footfall Data feed is an essential tool for any organisation looking to leverage mobile location data and foot traffic data for strategic advantage. Whether for economic forecasting, urban planning, retail optimisation, or real estate investment, our data delivers the quality, frequency, and granularity needed for informed decisions. Join over 600 satisfied customers and unlock the full potential of location data and mobility data with Huq Industries.

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market. Veraset Movement (Mobile Device GPS / Foot Traffic Data) offers unparalleled insights into footfall traffic patterns across North America.

Covering the United States, Canada and Mexico, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's Movement data helps in shaping strategy and making data-driven decisions.

Veraset’s North American Movement Panel: - United States: 768M Devices, 70B+ Pings - Canada: 55M+ Devices, 9B+ Pings - Mexico: 125M+ Devices, 14B+ Pings - MAU/Devices and Monthly Pings

Uses for Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

The difference in consumer foot traffic between the week ending March 28 and May 2, 2020, was 18 percent in both Louisiana and Tennessee.
For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

As the coronavirus (COVID-19) pandemic begins to subside over the course of 2021, optimism among convenience store retailers in the United States begins to rise. While only about a quarter of retailers were optimistic about shopper foot traffic in c-stores in the first quarter of 2021, nearly 70 percent believed it would improve by the fourth quarter of the year.

Aurora:GeoStudio® stands out in geospatial analytics by effectively utilizing passive sensor data to provide detailed mobile location metadata. This passive data collection method captures a wealth of information about mobile devices without requiring active user engagement, offering an extensive overview of device interactions and movements. Here’s a breakdown of the specific data collected and how it enhances various applications:

Data Collected by Passive Sensors:

  1.    Identification and Metadata:
  •    Wireless ID: A unique detection ID associated with each data value.
  •    Device ID: The name of the sensor collecting the data.
  •    Sensor ID: An ID generated for each sensor upon creation.
  •    Department ID: An ID generated for each department upon creation.
  2.    Temporal Data:
  •    Date and Time: The precise date and time when the detection occurred.
  3.    Detection Details:
  •    Value: Sensitive data or data value collected.
  •    Signal Type: Type of detection signal (e.g., WiFi, BLE, 4G LTE).
  •    Brand and Model: The brand (e.g., Samsung, Apple) and model (e.g., Samsung A22) of the detected device.
  •    Status: Indicates if the detection occurred within the sensor’s working time (“Was Working” or “Was Not Working”).
  •    Provider: The network provider associated with the detection (e.g., Vodafone), represented by the MNC.
  •    Country: The country where the detection occurred, represented by the MCC.
  •    Role Color ID: Assigned role color IDs for devices (e.g., 6 for unauthorized, 1 for trusted coworkers).
  •    Type: Type of detection data (e.g., TMSI, IMSI, MAC).
  •    Trusted: Indicates if the detection is trusted (default is “No” for new detections).
  •    Signal Strength: Indicates the range within which the device was detected.
  •    Company: The company name determined based on the sensitive data.
  4.    Database and Location Information:
  •    Created At and Updated At: Timestamps for record creation and updates in the database.
  •    Location Details: Name, city, state, country, street, and zip code of the location tagged with the sensor.
  •    Coordinates: Latitude and longitude of the tagged location.

Applications:

  1.    Customer Behavior Analysis:
  •    By analyzing foot traffic data, businesses can understand customer behavior, including visit frequency, duration, and movement paths. This information is crucial for tailoring services and improving customer satisfaction.
  2.    Store Layout Optimization:
  •    Passive sensor data helps identify high-traffic areas and bottlenecks within stores, allowing businesses to optimize product placement and store layouts to enhance customer flow and maximize sales opportunities.
  3.    Marketing Strategy Enhancement:
  •    Businesses can refine their marketing strategies using insights from peak visiting times and customer demographics, enabling targeted campaigns that reach the right audience at optimal times.
  4.    Operational Efficiency:
  •    Understanding foot traffic patterns allows businesses to optimize staffing levels, manage inventory more effectively, and improve overall operational efficiency. Aligning resources with actual customer demand enhances service delivery and reduces costs.
  5.    Urban Planning and Public Spaces:
  •    Urban planners can use foot traffic data to design public areas that accommodate pedestrian flow efficiently and ensure that amenities are accessible and well-placed.

Aurora:GeoStudio®’s integration of passive sensor data provides a comprehensive view of mobile location metadata, offering detailed insights into device movements, customer behavior, and spatial dynamics. The extensive data collected, including identification, temporal, detection, and location information, supports a wide range of applications from retail optimization to urban planning. By leveraging this data, Aurora:GeoStudio® enables businesses and planners to make informed decisions, optimize operations, and enhance customer experiences, thereby setting a new standard in geospatial analytics and location-based services.

dataplor’s foot traffic product addresses a significant gap in location intelligence by combining precise POI data with accurate foot traffic counts:

• Historical visitor counts back to 2019 on every single commercial/physical location in the US, Canada, and Mexico (excluding any sensitive places)

• Much more accurate scaling of visits than the typical approaches in the industry

• High quality polygon dataset scaled through AI/ML

• Fully GDPR compliant with no PII

• Built on the highest quality point of interest/places data available

This product includes:

(1) POIs: Over 40 million meticulously verified and accurately geocoded Points-of-Interest across North America, forming the foundation of all mobility metrics.

(2) Building Polygons: Detailed building footprints to enhance spatial accuracy and understand foot traffic distribution within specific structures or complexes.

(3) Mobility Data: Aggregated and anonymized device movement information to analyze where, when, and how populations travel.

Use Cases:

(1) Retail Expansion: Identify high-traffic areas for store openings. (2) Marketing Optimization: Target areas with high consumer engagement. (3) Real Estate Decisions: Evaluate potential commercial properties based on visitor trends. (4) Competitor Analysis: Understand customer flows to competitor locations. (5) Urban Planning: Inform city planners about pedestrian dynamics.

Around 68 percent of convenience store retailers in the United States expected that in-store foot traffic within their stores would increase in 2020. In-store sales in this channel have increased steadily in recent years.

PlaceSense offers a robust and precise foot traffic data and mobility data solution, designed to empower businesses and organizations with actionable insights into location-based behavior.

Our dataset spans 3 years of historical data with a weekly resolution, ensuring detailed trend analysis and long-term strategic planning. Covering key markets across Germany, Netherlands, Austria, Switzerland, and Italy, PlaceSense data is ideal for companies operating in retail, commercial real estate, urban planning, and market research.

What Makes Our Foot Traffic Data Unique? PlaceSense stands out due to our commitment to data accuracy, scale, and privacy. We source data from over 22,000 apps, providing a diverse and representative sample that spans multiple industries and demographics.

Our advanced machine learning algorithms ensure a data accuracy of over 90% for our foot traffic values. Additionally, PlaceSense data is GDPR-compliant, guaranteeing that all insights are fully anonymized and aggregated, safeguarding user privacy while delivering precise and actionable information.

Data Sourcing: PlaceSense sources its data from a large and diverse panel of mobile applications, including weather, navigation, entertainment, and utilities apps, among others. These apps contribute billions of location signals daily, allowing us to construct a comprehensive and up-to-date view of human mobility patterns. This extensive data collection, combined with rigorous data cleansing and enrichment processes, ensures that our clients receive high-quality, reliable data tailored to their specific needs.

Primary Use-Cases: 1. Retail and Commercial Real Estate: PlaceSense data is invaluable for site selection, store performance analysis, and competitive benchmarking. Retailers and real estate developers can assess foot traffic trends, understand consumer behavior, and optimize their strategies based on real-world data. 2. Urban Planning and Smart Cities: City planners and local governments use our data to analyze pedestrian flow, optimize public spaces, and enhance infrastructure planning. Our insights help in understanding how people interact with urban environments, enabling data-driven decisions that improve city livability and sustainability. 3. Market Research and Consulting: Market researchers leverage our data to gain deep insights into consumer behavior, track emerging trends, and segment populations by demographics and location. This data supports accurate forecasting and strategic planning across various industries.

Integration with Broader Data Offering: This foot traffic data and mobility data product is a cornerstone of the PlaceSense suite, which also includes solutions for demographic analysis, catchment area insights, and visitor journey mapping. By integrating multiple data streams, PlaceSense provides a comprehensive view of how people move, where they go, and what drives their decisions. Whether used alone or in conjunction with our other data products, this dataset provides unparalleled insight into the dynamics of modern commerce and urban life.

PlaceSense’s Foot Traffic Data and Mobility Data is more than just numbers—it’s a strategic tool for understanding and predicting human movement across key European markets. With high accuracy, extensive coverage, and a focus on privacy, our data is trusted by leading brands, real estate developers, and city planners. Unlock the power of location data and drive your business forward with PlaceSense.

In-Store Analytics Market Valuation – 2024-2031

In-Store Analytics Market was valued at USD 1532.7 Million in 2024 and is projected to reach USD 5213.2 Billion By 2031, growing at a CAGR of 18.24% during the forecast period 2024 to 2031.

In-Store Analytics Market: Definition/ Overview

In-store analytics refers to the collection, measurement, and analysis of data related to customer behavior and store operations within a retail environment. Utilizing technologies such as sensors, cameras, and data analytics platforms, it provides insights into how customers navigate the store, their interaction with products, and overall shopping patterns. This data helps retailers understand shopper preferences, optimize store layouts, and enhance the shopping experience.

In-store analytics is applied to various aspects of retail operations. For example, it can optimize store layouts by analyzing foot traffic patterns to place high-demand products in strategic locations. Retailers also use it to monitor real-time inventory levels, ensuring popular items are stocked appropriately and reducing out-of-stock scenarios. Additionally, the data helps in personalizing marketing efforts by tracking customer behavior and tailoring promotions to increase engagement and sales.

BestPlace is an innovative retail data and analytics tool created explicitly for medium and enterprise-level CPG/FMCG companies. It's designed to revolutionize your retail data analysis approach by adding a strategic location-based perspective to your existing database. This perspective enriches your data landscape and allows your business to understand better and cater to shopping behavior. An In-Depth Approach to Retail Analytics Unlike conventional analytics tools, BestPlace delves deep into each store location details, providing a comprehensive analysis of your retail database. We leverage unique tools and methodologies to extract, analyze, and compile data. Our processes have been accurately designed to provide a holistic view of your business, equipping you with the information you need to make data-driven data-backed decisions. Amplifying Your Database with BestPlace At BestPlace, we understand the importance of a robust and informative retail database design. We don't just add new stores to your database; we enrich each store with vital characteristics and factors. These enhancements come from open cartographic sources such as Google Maps and our proprietary GIS database, all carefully collected and curated by our experienced data analysts. Store Features We enrich your retail database with an array of store features, which include but are not limited to: Number of reviews Average ratings Operational hours Categories relevant to each point Our attention to detail ensures your retail database becomes a powerful tool for understanding customer interactions and preferences.

Extensive Use Cases BestPlace's capabilities stretch across various applications, offering value in areas such as: Competition Analysis: Identify your competitors, analyze their performance, and understand your standing in the market with our extensive POI database and retail data analytics capabilities. New Location Search: Use our rich retail store database to identify ideal locations for store expansions based on foot traffic data, proximity to key points, and potential customer demographics.

GapMaps Mobile Location Data by Azira provides actionable insights on consumer travel patterns at a global scale empowering Marketing and Operational Leaders to confidently reach, understand, and market to highly targeted audiences and optimize their business results.

In this paper, we quantify the implications of this improved delivery convenience for consumers’ online shopping behavior and offline store visits. We exploit the staggered roll-out of Amazon’s network to quantify the impact of local network proximity and same-day availability on two measures of demand: online transactions from Amazon, which we obtain from the comScore Web Behavior database, and offline retailer foot traffic from SafeGraph.

In September 2024, Dollar Tree was the discounter with the highest number of average visits in the United States, at over 23 thousand. Dollar General and Family Dollar came close in second and third place, with about 14 thousand visits each.

The 3D people counter market is experiencing robust growth, driven by the increasing need for accurate and real-time foot traffic analysis across various sectors. Retail businesses leverage this technology to optimize store layouts, staffing levels, and marketing campaigns based on precise customer flow data. Similarly, commercial real estate utilizes 3D people counters to understand space utilization and improve operational efficiency. The market's expansion is fueled by advancements in sensor technology, providing more accurate and reliable data, and the integration of people counting systems with other analytics platforms for comprehensive insights. Wireless systems are gaining traction due to their ease of installation and flexibility, while wired systems continue to be prevalent in high-security environments demanding reliable data transmission. The market is segmented by application (residential, commercial) and type (wired, wireless), with the commercial segment currently dominating due to higher adoption rates. North America and Europe represent significant market shares, driven by early adoption and technological advancements. However, Asia-Pacific is expected to witness substantial growth in the coming years, fueled by rising urbanization and increasing retail infrastructure development. While the initial investment cost can be a restraint, the long-term return on investment (ROI) through improved operational efficiency and data-driven decision-making makes 3D people counters an attractive proposition for businesses across diverse sectors. Competition among established players and emerging companies is driving innovation and offering various feature sets and pricing models. The forecast period (2025-2033) anticipates a sustained upward trajectory, with the market expected to be significantly larger by 2033. This growth is further underpinned by the increasing adoption of advanced analytics and the integration of 3D people counters with other business intelligence tools. The convergence of technologies, such as AI and machine learning, will further enhance the capabilities of 3D people counters, resulting in more insightful data and improved decision-making processes. Challenges remain in addressing concerns regarding data privacy and ensuring accurate counting in high-traffic environments. However, advancements in technology and robust data security measures are steadily mitigating these challenges, paving the way for widespread adoption across a broader range of industries. The market’s success depends on continued innovation, affordable pricing, and effective marketing strategies that highlight the benefits of using this technology to gain competitive advantages.

In the second week of March 2020, foot traffic in Sam's Club stores increased by 66.9 percent when compared to the same period in 2019. After weeks of increased foot traffic in their stores, Costco, Target, and Walmart Supercenter all experienced a decrease in foot traffic in the third week of March. This represented the first drop in traffic since the beginning of the pandemic in the United States.
For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.