Navigating the World of Store Location Data: A Comprehensive Guide

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Store location data refers to data on retail buildings, and it includes things like foot traffic counts, transaction data, and more..  It can include basic address or coordinate information, as well point-of-interest type information like hours, types of products sold, and location specifics like in-store shopping/dining, delivery, and curbside details.

Depending on how the store location data is sourced, it can also provide more in-depth footfall data on customers — info like foot traffic trends, where consumers came from and where they headed after they left, and even how long they spent in specific parts of the store.

In an age where companies have embraced building both online and brick-and-mortar experiences, having robust store location data about your customers and your competitors is key to strategic decision making. Before we look at just how significant store location data is and how industry leaders are leveraging it, let’s look at the various ways of sourcing store location data.

Sources of Store Location Data

Without the modern smartphone, store location data would be basic geographic data paired with likely outdated store information. Instead, with a broad array of signal types, store location data sets are complex and help deliver deep insights. Combining GPS, wifi, beacon, geofencing, and machine learning creates this rich source of data.

GPS

Beyond just basic location information for each store, GPS data gathered from customers provides the basis for traffic and footfall data — including general traffic levels, where shoppers come from, how long they stay at the location, and where they go when they leave.

WiFi

Wifi’s contribution to store location data is primarily what’s known as the wifi positioning system (WPS). Wifi access points can determine the approximate location of nearby devices, even if they don’t connect to the network. This is helpful in places where there is blockage for satellite signals — such as indoors — or busy and crowded locations that need a faster location connection than acquiring a satellite signal.

Beacons

Beacons typically broadcast bluetooth low energy signals to determine how close a customer’s device is to the beacon and for how long. By incorporating these into a brick-and-mortar location, savvy retailers pull insights like which holiday endcaps are the most engaging.

Geofencing

Like beacons, geofencing helps deliver location data about a specific area. Instead of the relative distance to a specific point, geofencing gathers data about when customers enter or leave the geofenced region. While this can help with traffic and footfall insights, it can also give companies a power trigger for communicating personalized offers and experiences when repeat customers enter defined areas.

Significance of Store Location Data

Sure, modern digital signals provide rich and complex datasets — but how useful are they?

Outside of the basic examples given above, when paired with customer and purchase information, store location data helps paint a more complete picture of consumers shopping and buying habits — both online and offline.

Customers who spend time in a particular area of the store but then make their purchase online can help find issues with product variety/availability or if additional services are needed like delivery or curbside pickup. When footfall traffic is high but purchases are low, it may indicate a need to change how products are displayed or promoted in-store. When shoppers move between competitors before making a purchase it can help guide web and marketing teams to address gaps in online content.

Insights like these are invaluable to modern businesses as they compete for shrinking attention spans to build customer loyalty.

Use Cases for Store Location Data

While many of our examples have focused on how retail companies can use store location data, the insights store location data can generate are useful across multiple industries and use cases from retail and marketing to commercial real estate and urban planning.

Store Location Data for Retail

From product placement and promotion to supply chain efficiencies — better understanding the movement and patterns of shoppers allows retailers create layouts that match how customers move. By understanding which other brands customers visit, retailers can create the right partnerships and product placements for their customers. By understanding where customers live and work, they can determine where to promote products and where to place new store locations. Retailers can also perform in-depth competitive intelligence using store location data.

Store Location Data for Marketing

A couple of big promises of the digital age for marketers has always been greater personalization and increasingly accurate targeting. With store location data, marketers can better understand shoppers’ behavior and deliver personalized and relevant ads and offers for customers.

Store Location Data for Commercial Real Estate

Store location analysis isn’t just for retail companies. Commercial real estate developers looking for their next big project can leverage store location data of existing locations to inform investment decisions and build more accurate projections for capacity and revenue for their new development. Additionally, property management benefits from ongoing store location data by being able to predict tenant renewals and plan for parking capacity and efficient facility services.

Store Location Data for Urban Planning

Whether planning future infrastructure expansions, renovating well used areas, or attempting to build connections between previously disparate locations, understanding how people currently move at scale and building accurate predictive models is critical for municipalities seeking to responsibly meet the needs of their citizens. Store location data can help meet these civil needs by providing accurate, dynamic data for urban planning.

Challenges and Privacy Concerns of Store Location Data

With such clear benefits to businesses of all sizes, it’s important to understand the challenges that store location data can present. Some of the biggest challenges are the same ones that any location intelligence effort presents — sourcing timely data, ensuring data is high quality and accurate, and ensuring that the data sourced is usable for your intended purposes.

On top of that, it’s important to use ethically sourced data that protects the privacy and anonymity of shoppers.

Timely Data

Building insights from mobility patterns requires ensuring that data is up-to-date and timely  — but that it is also indexed against accurate historical data. It’s hard to know how behaviors are changing if you can’t compare to a useful baseline.

High-quality, Accurate Data

Mobility data sourced from multiple inputs is inherently noisy and unstable. It’s important to ensure that the data used to build a location intelligence function has been cleaned, aggregated and stabilized in order to build accurate insights, plans, and models. Given the enormity of this type of data, it’s now a major advantage to work with providers leveraging machine learning and AI to ensure the quality of data.

Relevant Data

There is no one-size-fits-all (or even one-size-fits-most) when it comes to store location data. Merchandising requires incredibly granular interior store location datasets whereas commercial real estate may benefit from a much broader look at footfall traffic in an area. Sourcing data that doesn’t meet your needs can be worse than just wasting needed budget — insights drawn from ill-fitting data can lead to business decisions that harm instead of improve.

Ethical Store Location Data Sourcing

Whatever your use case for store location data, it’s incredibly important to ensure you’re using ethically sourced data. This includes not just transparent sourcing — ensuring consumers opted in to the collection of their data and were made aware of how it would be used — but that their privacy was protected. Privacy protection includes not just how the data is stored, but that it’s properly anonymized and aggregated.

How to Best Leverage Store Location Data

With everything that can be done with store location data, it can be difficult to know exactly how to best make use of these data sets. Like any large project, it’s often best to break it down into a logical process.

Build your strategy first.

A retailer trying to optimize in-store layout and promotion will need a vastly different view of store location data than a company looking to expand their brick and mortar locations or running competitive analysis. By identifying your use cases, needs, and objectives first you’ll ensure you source the right data or insights tools and ask it the right questions.

Use the right tools for the job.

Analyzing and gleaning insights from huge datasets is no small feat. It’s critical to ensure you have the right personnel and technology for your identified use cases.

Finding a data provider or platform that integrates with your existing tech stack and data will help you find deeper insights faster. But if you’re integrating data — does your internal data need to be standardized and cleaned first? Once you have the data integrated, how are you analyzing it? How are you visualizing it? Do you have the location and business intelligence expertise in house or do you need to outsource it? Finding and partnering with a vendor with deep location intelligence expertise can help you find the answers to these questions.

Top Store Location Data Providers

There are multiple vendors offering POI and store location data services — but finding the right partner is critical to meeting your specific needs. Here are some of the top store location data providers and some key insights into their offerings.

Unacast

Unacast store location data is sourced from multiple inputs and cleaned and stabilized by machine learning to provide robust foot traffic data sets. With a rigorous methodology and privacy-first approach, buyers can be sure they’re working with the most trusted data provider in the industry.

The Unacast Insights platform is another great option for teams looking to leverage store location data without manually processing datasets in-house. The platform provides AI generated insights and visualizations that make it easy to understand your next right step.

Coverage

US Only

Types of Data

Foot traffic, cross-visitation, trade areas, POIs

Available POIs

5m

Historical Data

Yes, 4 years

API/Integrations Available?

Yes

Visualization Platform?

Yes

SafeGraph

SafeGraph has a mission to grant open access to point of interest data. They work to empower firms with better analytics and geolocation intelligence.

Coverage

Global

Types of Data

POIs

Available POIs

11m

Historical Data

Yes, 2 years

API/Integrations Available?

Limited API, Integrations via specific partnerships

Visualization Platform?

No

The Data Appeal Company

The Data Appeal Company focuses on using AI to help companies find insights from all kinds of data, including store location data.

Coverage

Global

Types of Data

Store Sentiment

Available POIs

200m+

Historical Data

Yes, 4 years

API/Integrations Available?

Yes

Visualization Platform?

No

Echo Analytics

Echo leverages satellite imagery, machine learning & human verification, to define the most precise boundaries of locations and provides monthly or quarterly updates to their data and analytics.

Coverage

US Only

Types of Data

POIs, Boundary

Available POIs

6.9m

Historical Data

Yes, 2 years

API/Integrations Available?

None

Visualization Platform?

No

Xtract

Xtract helps companies visualize real-time location with accurate address, city, state, zip code, latitude, longitude, and other key attributes of the location through their POI datasets.

Coverage

US, Canada, UK/Europe, Australia

Types of Data

POIs

Available POIs

5m+

Historical Data

Yes, 1 year

API/Integrations Available?

Data is available as Excel, CSV, or JSON for importing into an existing tech stack

Visualization Platform?

No

Store Location Data Future Trends

The possibilities for applying store location data are ever expanding. One of the biggest opportunities on the horizon pairs store location data with artificial intelligence (AI). By using Generative AI to identify patterns and opportunities, businesses can stay ahead of the curve in identifying their next strategic decision.

Another exciting opportunity lies in utilizing augmented reality (AR). From inches-accurate in-store navigation to virtual try-on/view-in-your-room experiences, store location data is blending and enriching both online and offline shopping to create a seamless experience for your customers. As AR becomes increasingly commonplace, savvy companies will identify and define the space the further — leveraging insights about their customers to deliver them exactly what they want before they realize it themselves.

Store Location Data for Your Business

Whether you’re planning your next development project or trying to get an edge on your competitors through hyper localized and personalized marketing, store location data plays a critical role in modern business decision making. And now is the perfect time to get started — connect with us below and we can help you plan out how to best leverage location data for your business.

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