How to Use Retail POI and Geospatial Data

How to Use Retail POI and Geospatial Data

The world of retail is changing at a staggering pace. The rapid-fire changes aren’t just about the rise of eCommerce, but also the creative ways brick and mortar retailers have adapted to new consumer expectations. To remain on the cutting edge, retailers are using point of interest (POI) and geospatial data to buy the right buildings, send the right messages and attract the right people.

Data on customer traffic, buying trends, competitor visits and more can provide immense insight into where, when and how to sell. From site selection to laser targeted marketing, high-quality data is empowering retailers to retain a competitive market share, even when Amazon is only a tap away. Read on to learn about retail POI data and how it is used by brands around the country and around the world. If you'd like to skip down, just reference the table of contents, below:

Retail Data Collection

Data-Driven Retail Decisions

Retail Analytics

Point of Interest Data in Retail

Two Types of Relevant Retail Data

Store Data

Customer Data

Geospatial Data and Retail Site Selection

Planning and Development

Consumer Data and Behaviors

Demographic Insights

POI Data for Store Performance

Marketing and Advertising

Geotargeting

Geofencing

Points of Interest Data Sets

Data Formats for POI

How to Make a Data Map

POI Data Management Platforms

Meaning of POI in Retail

Unacast: Turning Retail Data Into Revenue

Retail Data Collection

What kinds of data do retailers collect?

Retail data collection is a practice used for pricing intelligence, to improve shopper experiences and optimize operations. This broad delineation encompasses in-store data collection, online data collection, realtime reporting and more. Retail data collectors focus on numerous types of data. 

Here are a few broad categories of businesses retail data:

  • Personal data
  • Behavioral data
  • Engagement data
  • Attitudinal data

Examples of data sets within these categories include:

  • Shopper demographics
  • Demand spikes
  • Periodic sales comparisons
  • Stock replenishment cycles
  • Trend analysis
  • Shopping trends
  • Inventory movement trends


This discipline leverages all types of new data collection technology, some of which feels straight out of a sci-fi film: floor sensors, personalized ads and rapid implementation. 

Retail point of interest data and location data is an integral component of retail data collection, and one on which retailers are increasingly reliant.

If you need a level set, read the Beginner’s Guide to Point of Interest Data

Data-Driven Retail Decisions

Raw data is a starting point which can drive retail decisions. To get to a point of action, data must be organized and interpreted. Some of the ways in which retail data, especially location-based retail data, is regularly used include:

  • How does a distinct, identified population behave in a retail environment?
  • What are common traffic patterns that should inform retail store placement or design?
  • Where do customers go?
  • How long do customers stay in a location?
  • What are the natural, jurisdictional and social/lifestyle boundaries in an identified area and how does that impact the retail market?
  • What is the demographic and psychographic information for an identified area, which can be used to inform retail store placement and operations?

These are simply illustrative, as there are virtually endless application possibilities once a strong process of analysis is deployed.

Retail Analytics

Analysis, interpretation and reporting are the steps that must precede action. Once geospatial data is collected for a region or neighborhood, that data can be analyzed and used to make decisions.

Raw location data may refine a retailer’s understanding of which consumers live in the area and how they behave. That data becomes useful when applied to decisions on store placement, store hours, product placement or pricing, geo targeted ads and more. Ideally, retail analytics are integrated and used with an array of other analytics that can go beyond responding to a market. A full set of data can empower retailers to make predictive data-driven decisions for retail. This can mean making ahead-of-market decisions that future-proof retail operations.

It is useful to see how major retailers use POI and location data to make strategic decisions like this.

Point of Interest Data in Retail

Unacast regularly collects information on how point of interest data is impacting retail. In a recent Emerging Areas Report, our team reported on sectors of regional growth in 2021. Using global position system (GPS) data for the first two quarters of 2021, our team of data scientists identified three cities with measurable growth in income flow, population and rate of foot traffic recovery. Cincinnati, Buffalo and St. Louis all showed foot traffic recovery data at or above 81% and income flow at or above $500 million. What’s more, our team found identifiable cross visited brands in hospitality and retail, as well as projecting the related economic impact on each city by the end of 2021.

Well-analyzed and interpreted point of interest retail data gives brands strategic opportunities to decide where to locate/relocate, how they should target ads and what kind of growth can be predicted in various regions.

Read the white paper: Downtowns Going Uptown

Both in urban and suburban areas, drug stores are a mainstay of retail. In another study, our team of data analysts ranked foot traffic recovery for drug stores in New York. The goal of assessing foot traffic recovery is to better estimate revenue projections based on the trend of where people go. This is a very basic retail POI data dive, and it yielded valuable findings:

  • Duane Reade foot traffic: -52.9%
  • Good Neighbor Pharmacy: -2%
  • Rite Aid: -1.1%
  • CVS: +0.8%
  • Walgreens: 10.2%
  • Kinney Drugs: 30.3%

Health spending overall was down in the U.S. by about 1.5% in December 2020. Even so, in 2020, 17.7% of people in the U.S. said they would not buy drugs online. This means that there is still a large market share of medicine purchases that occur in person. Brick and mortar drug stores have a high level of opportunity, as long as they are collecting the right retail data to make wise, market-conscious decisions.

Read the white paper: Drug Stores in New York Foot Traffic Recovery

Point of interest data for foot traffic can illustrate areas of growth (and potential growth) in various sectors. The data scientists at Unacast collected data based on a national study of 300 brands, and their month-to-date visits from customers. Comparing February 2021 to March 2021, the following report was generated:

  • Healthy Hearing health offices saw a 35.6% increase in foot traffic
  • ARCO gas service stations saw a 34.7% increase in foot traffic
  • Follett bookstores saw a 32.9% increase in foot traffic
  • Dutch Bros Coffee saw a 24.9% increase in foot traffic
  • 76 gas stations saw a 20.5% increase in foot traffic
  • 99 cent stores saw a 20.1% increase in foot traffic
  • Del Taco restaurants saw a 19.6% increase in foot traffic
  • Stater Bros. Markets grocery stores saw a 18.4% increase in foot traffic
  • Smart & Final grocery stores saw a 17.8% increase in foot traffic
  • Maverick petroleum products saw a 17.5% increase in foot traffic

This growth could be the result of numerous efforts—from marketing to placement to outreach—but the outcome is sure to match: increased profits. 

In fact, a study by the Mays Business School at Texas A&M University states that the ability for retailers to attract traffic to stores, then convert that traffic, is one of the most vital activities for financial performance.

Read the white paper: 10 Retail Brands With Fastest Growing Foot Traffic

Two Types of Relevant Retail Location Data

As stated above, there are numerous types of retail data that can add meaningful insight to a cycle of analysis and application. For location-based data, two of the most relevant types of data are collected about the store and about the customer.

Store Data

Here are some broad types of store retail data:

  • Market data
  • Business listings data
  • Supply chain data
  • Merchandising data
  • Operations data

Store location data gets more granular, focusing on geospatial information, which may include:

  • Neighborhood traffic
  • Retail foot traffic
  • POI visitation data/store visit tracking
  • Competitor visit tracking
  • Customer catchment areas

Sometimes, the activity of applying this kind of data is discussed in a broader sense as retail intelligence.

Here are some ways in which this data can be meaningfully used:

  • Tracking store traffic impacts labor planning and scheduling models, which is often a high expense and cost-cutting opportunity for retailers.
  • Understanding foot traffic and patterns help retailers identify key selling periods.
  • Applying traffic data helps multi-site retailers better determine key performance indicators (KPIs) and then benchmark those between different locations.

Forecasting retail traffic has immense benefit and can make a difference in the long-term success of a retail store. Store data is just one side of the equation. The other way to approach retail POI and geospatial data collection is to track customer journeys and behaviors.

Customer Data

Here are some broad types of retail customer data:

  • Point of sale data
  • Loyalty card data
  • Demographics and qualitative data
  • Behavioral or descriptive data
  • Customer sentiment

Customer location data also dives deeper, focusing on the behaviors and trends of location-based traffic and transactions:

  • Foot traffic analytics by personal device
  • Population and real estate data
  • Geo-targeted ad data

As consumers travel with a GPS-enabled smartphone in their pocket or purse, it is easier than ever to track behaviors and norms down to an individual level. This data can be aggregated to extrapolate large-scale insights into how cohorts of a population will behave in a retail environment. Using both store and customer geospatial data is the most powerful way to make informed decisions about retail operations.

Geospatial Data and Retail Site Selection

Before ground is even broken at a new retail site, the value of POI data is clear. As far as years in advance, analysts in commercial real estate use geospatial data for retail site selection. Knowing the retail behaviors of consumers in a location, and how they are likely to evolve, provides an immense advantage. 

Here are some illustrations of geospatial data and how it enhances the retail site selection process:

Trade area analysis—Mapping analytics provide this primary service in which existing customers are mapped in relation to store locations. Customer distance and drive times to store locations are variables that impact a defined trade area.

Neighborhood patterns data—Census block data and anonymized mobile location data feeds can provide footfall data for a very specific regional target. This is supplemented by demographic data to determine optimal placement.

Consumer foot traffic patterns—The value of this type of geospatial data has already been emphasized, and its importance begins as early as retail site selection.

Models and visualizations—Models can be built whereby complex data analysis provides location intelligence for developers and investors. Using spatial data science for site selection ensures that a retail location is in the right spot to attract the right target audience. What’s more, it ensures that a location is not only accessible to the right people, but that they are highly likely to travel to it on a regular basis.

Unacast has two tools that directly support these types of CRE planning functions:

  • Migration Patterns datasets can identify potential commercial hubs and areas of future growth, as well as other key insights for real estate investors.
  • Retail Impact Scoreboard, activated as a tool during the COVID-19 pandemic, this scoreboard provides foot traffic comparisons for retail analytics groups and more.

Explore them both using the links above or schedule a demo to learn more.

Planning and Development

Large brands use POI data throughout the investment cycle. New stores are planned in advance using this kind of information. 

Franchising and New Stores

Location intelligence maximizes the potential of franchise locations and new stores by streamlining market planning and capitalizing on areas of future growth.

Real Estate Investments/Evaluating Opportunities

Investors have a better chance of making the right site location decision when they evaluate opportunities next to POI data.

POI Data Through the Investment Lifecycle

From deal origination to ongoing monitoring of an investment, investors, planners and developers are collecting POI data for a particular location to manage acquisitions. 

Financial Metrics

POI retail data includes massive sets of historical content and quantifiable information. Knowing the kind of buyer, buyer behavior, neighborhood changes and transportation trends all speak to how profitable a retail location will be. 

Consumer Data and Behaviors

The “where” of site selection should not be analyzed in isolation. The “who” of consumer data and behaviors is its essential counterpart. Here is some of the retail intelligence that can be gleaned from POI retail data:

  • Census block groups
  • Shopper demographics
  • Visitation patterns
  • Regional preferences
  • Outside visitors v. native visitors


This insight drives everything from where to put a store to how to word signage to how to deploy digital ads. Using this data enables decision makers to achieve retail growth with the greatest amount of knowledge possible.

POI Data for Store Performance

For many retailers, POI data highlights points of improvement for store performance. Numerous metrics exist that can add insight to standard KPIs and meaningfully guide store owners and operators.

Here are some types of POI data for store performance:


  • Dwell time, or the time a shopper stays in a store, easily found in GPS data.
  • Distance traveled, or how far an average shopper will travel to reach a store location.
  • Footfall by hour, monitoring day-to-day averages and norms by hour.
  • Competitive insights, and how closest competitors’ retail locations are performing in comparison.

Competitive insights are particularly salient for marketing and advertising.

Geospatial Data for Marketing and Advertising

The relationship between geographic information systems (GIS) and marketing is well-established. GIS technology generates geospatial data. Spatial data and analytics can be used to deploy marketing campaigns that feel personal, which is the utmost goal for any  marketer.

Here are some of the ways GIS adds value to marketing:

  • Segmenting customers based on location can refine messaging 
  • Push notifications based on location enhance the timeliness of ad delivery
  • Geo-targeted digital ads feel highly relevant to the recipient
  • Personalized recommendations of nearby shops create suggestive messaging that can drive consumer response

The latter two are particularly popular among savvy digital marketers in the retail space.

Geotargeting

Through geotargeting, advertisers can send ads to targeted locations or a set of locations. A very basic data set in Google Trends and similar platforms enable advertisers to use location extensions to tie up the conversion cycle of an ad. Location targeting sends ads to users in a specific area, and is good for a broad strategy to drive foot traffic to regional locations.

Geofencing

Location based marketing gets the most on-demand delivery with a practice called geofencing. Lately used a lot for in-app experiences, proximity targeting lets advertisers show promotions to people who are near targeted locations. This is a great way to target people physically near a retail location, or even to target people near competitors’ stores.

Points of Interest Data Sets

Most retailers are well-convinced of the value of POI and geospatial data. The larger question becomes how to source and use this data. The first issue—where to find retail POI data or geospatial POI data—is fairly easy to parse out.

Global Positioning Systems (GPS)

GPS are a primary source of data, and the volume of content is supported by smart devices and mobile devices, which means it is robust and current.

Vehicle Tracking Systems

Vehicle tracking systems are increasingly used in vehicles equipped with smart systems, but GPS for fleet vehicles dates back to 1978. Ongoing data is collected as vehicles move, which makes it possible to track consumer visits to a retail location and know how far customers travel to get there.

Digital Cameras

Digital cameras can have a good amount of location information saved to the cloud, which can in turn be collected and analyzed. Point of interest data supports digital mapping from speed cameras and other geotagged images.

Satellite Images

Satellite images provide comprehensive data on everything that can be seen from above the atmosphere: topography, land masses, construction and even traffic patterns. Additionally, multisource remote sensing provides images, now being used as ancillary data in urban studies. 

All of this data may be useful for making retail decisions. The challenge is in sourcing clean data, then sorting through it and making sense of it. Unacast provides clean, reliable and decision-ready location data, and is a trusted partner of retailers worldwide. Without a source you can trust, all of the valuable insights and strategic positioning afforded by POI retail data is lost. Access a demo of Unacast Now to see one of our customizable tools in action.

Data Formats for POI

Locational retail data will arrive in numerous formats, some of which may include:

  • Older reference systems (like UTM or grids)
  • Readability - txt editable
  • ASCII Text (.asc .txt .csv .plt)
  • Google Earth Keyhole Markup Language (.kml .kmz)
  • Garmin Mapsource (.gdb)
  • Topografix GPX (.gpx)
  • Pocket Street Pushpins (.psp)
  • Maptech Marks (.msf)
  • OpenStreetMap data (.osm)
  • Maptech Waypoint (.mxf)
  • Microsoft MapPoint Pushpin (.csv)
  • TomTom Overlay (.ov2) and TomTom plain text format (.asc)
  • OziExplorer (.wpt)

It can be quite difficult to achieve apples to apples comparisons, or even organize this array of data in a meaningful way. Other challenges to making sense of POI data are:

  • Licensing issues with sources like Google Places API, which can make using the data on the platform a challenge.
  • Software vendors often sell large banks of data that may yield inaccurate POI data, come from questionable sources, and may be outdated or too disorganized to use.
  • Customization Is almost impossible to achieve without a third-party platform, like the one offered by Unacast. Without this, you don’t see data in an organized dashboard, with the prioritization of those that matter most.

Because of these and additional challenges, retailers who wish to use POI and geospatial data must find a platform to facilitate the practice.

POI Data Management Platforms

As you look for a POI data management platform, know that they are not all created equal, and not all created for the same kind of user. Some location intelligence tools only serve professional data scientists or data analysts. These require significant knowledge and will not be useful to the average non-professional data user.

Whether you want to buy retail location data, find a data API, compare the best retail datasets, here are the criteria you should measure a platform against:

  • What is the policy related to data governance, ie, how do they ensure data quality?
  • What are the data assessment tools: are they easy to use and is there any customization possible?
  • Are all of the categories of retail location data you need available within this platform?
  • What are the delivery options in terms of frequency and format?
  • Does the stated accuracy of data match your use case? For retail, this is particularly important, as an allowable margin may compromise your findings and subsequent application.

Retailers want results. Oriented towards goals, it can be helpful to consider the possible outcomes of using POI data in retail.

Meaning of POI in Retail

Point of interest data in retail, and the use of geospatial data for retail decision-making, aligns well with mission critical KPIs. With point of interest GIS data, retailers can understand how local consumers behave, and how to best attract them to a retail location.

POI in retail can make the following possible:

  • Accurate, reliable understanding of spatial contexts, traffic patterns and buyer behavior.
  • An ahead-of-the-curve understanding of up and coming retail locations.
  • The ability to build, and appeal to, audience segments based on where they live, what they buy, and what they are most likely to do.
  • Gaining access to, and results from, the mobile location advertising ecosystem.
  • Streamlined, personalized messaging that applies to users in a specific location, at the right moment in time.

Collecting and normalizing POI data for retail is the only way to yield results like these.

Unacast: Turning Retail Data Into Revenue

The possibilities listed above are not exhaustive. In fact, increasingly high-quality data and high-performing data intelligence tools are unlocking never-before-imagined possibilities for retailers. Making strategic, proactive decisions is the number one way to dominate a market. With Unacast, retailers unlock that potential, and gain a trusted partner at the front of the field. 

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