The location of a store can determine its success, therefore, rich data analysis and modeling techniques involving demography, environment, accessibility, market spectrum and trade area analysis need to be taken into consideration. Geospatial data analytics helps in making such strategic decisions.
“Location Intelligence enriches property information by providing valuable geospatial context. It integrates sociodemographic data, real estate market trends, environmental risk assessments, surrounding infrastructure, and POI information. This enables professionals to target specific buyer segments, forecast property values, assess environmental risks, and evaluate the desirability of a location,” says Yanqing Zeng, Lead Data Scientist, JLL, France.
On top of the static geospatial data, dynamic mobile data are now being used for retailers to gain a rich, detailed view of the consumers’ profile who visit a particular location, the frequency and timing of their visits, and how long they stay. With this complementary data, retailers can forecast seasonal trends, analyze variations in daily or weekly foot traffic, and observe human migration patterns in the surrounding area. Accordingly, the most ideal site selling the most suitable product lines can be selected based on the catchment characteristics and preferences.
“We see a trend where retailers are looking at a combination of Floating Car Data, to understand Site Selection Analysis traffic patterns in the area around their store, and spending behavior data to understand the business potential,” says Pierre Maere, CTO, Geo Mobility, Belgium.
“Retailers are also looking for innovative ways to attract customers and improve conversions by e.g. being easier found or improving the check-out experience with address validation.”
Here’s how optimal site selection and merchandising help in better retail practices:
Catchment Area Identification
By creating a buffer area on a map, measured in walking/driving time from the nearest residential area, retailers can identify where their target shoppers will come from.
With geospatial data, retailers will be able to visualize how many people lived within a location, distance to potential retail sites, the time of day people were typically near the area and how close the sites are to existing franchises and local competitors. The analytics will enhance predictions as to which potential sites could drive the most volume.
A proposed retail site location also needs to support various amenities such as parking spaces, drive-through window, signage visibility to the main street, and many others. High-resolution satellite data, complemented by geospatial and location analytics can provide the detailed context for retailers.
“When choosing new store locations, we rely on Location Intelligence to analyze population density, traffic patterns, and competitor presence. This data-driven approach helps us identify high-growth potential areas and ensure optimal store placement,” shares Mayank Singh, Chief Digital Officer and VP – Marketing, Digital Business & IT, Domino’s Indonesia.
“Compute time capability is changing the application of technology. It used to be that only a limited amount of locations could be scored with a simple forecasting model. Today we can score thousands of locations at any given time with changing parameters as we select new sites for market prioritization,” says Jan Kestle, President and CEO, Environics Analytics, Canada.
“We used to be confined to the office and a desktop to do spatial analysis. Now, we can run a sales forecasting model to trade area analysis from the field using a phone, tablet or laptop and by entering specific proposed site characteristics that we observe standing at the location of a new store.”
Store Merchandising
By understanding the demographics of the retail area, such as population, age, professions, etc.; commercial indicators such as household incomes and spending patterns; and nearby establishments such as hospitals, restaurants, parks, etc.; retailers can decide which product lines are most profitable for a specific retail site. A goods retailer might use human mobility data, anonymized, aggregated information about how people move based on their mobile network locations, to determine what types of products to stock and store locations to focus on. “In the past, sales data was helpful making these decisions, but it couldn’t reveal anything else, such as how many people walk by your store and their demographic profile,” underscores Bas Jansen Venneboer, Managing Partner, Local Eyes, The Netherlands.
“The other key themes we see in retail are promotions and consumer savviness. More and more we are seeing consumers take advantage of promotions and patiently wait for the right deals. This creates interesting incentives retailers to be extremely strategic about how and when to promote and offer discounts on merchandise,” says Marc Carolus, SVP, Insights & Analytics, Location Products, Mastercard, Germany.
Footfall & In-Store Analytics
Indoor positioning technologies, such as beacons, Wi-Fi, RFID, and geomagnetic, complemented by various cameras, sensors and mobile technologies; have provided retailers with great analytical capabilities.
“Retailers increasingly rely on data analytics and Business Intelligence tools to gain insights into customer behavior, optimize pricing strategies and improve operational efficiency. Advanced analytics techniques, including predictive analytics and real-time data analysis, help retailers make data-driven decisions and identify trends to stay ahead of market demands,” says Morten Brøgger, CEO, MapsPeople, Denmark.
In-store analytics collect and analyze data about customer behavior and interactions within a physical retail space. The analytics capability makes physical stores just as measurable as e-commerce – helping brick-and-mortar stores compete and remain relevant in the digital-first world.
The data collected from such infrastructure can generate insights on foot traffic and movement patterns, popular in-store product areas, the busiest time of the day, and typical dwell times. This information helps retailers optimize store layouts, improve product placements, organize employee schedules, create in-store marketing strategies, and enhance the overall shopping experience.
Carl Barker, VP, Global Omni Programs at Lululemon, a global athletic apparel retailer said “With online RFID solution, we can leverage real-time, accurate data to enable our in-store educators to spend even more time engaging and connecting with our guests. RFID continues to elevate our digital experience, offering accurate real-time store inventory visibility and powering key fulfillment experiences such as BOPIS.”
Footfall Monitoring & Measurement
Infrared/thermal sensors placed at the entrance of a store and geolocation hits from mobile devices are used to monitor and measure footfalls, i.e. the number of visitors entering a store at a particular time. The analytics are done hourly, daily, weekly and monthly, to generate patterns. By understanding footfall patterns, retailers can ensure more employees are working on the sales floor during busy hours, schedule flash sales or other special promotions for specific times. The footfall data can also be used to calculate retailers’ conversion ratio, i.e. how many visitors entering their stores turn into paying customers.
Store Layout & Product Placement
Using a combination of indoor positioning systems, high-precision tracking devices on shopping carts and baskets, and wall-mounted infrared/thermal sensors, retailers will be able to map the full customer journey, from the moment the customer enters a store to the moment they exit.
The system generates heat maps to analyze customers’ behavior and engagement time within the store. Understanding this pattern can help retailers design a better store layout for effective traffic flow, and validate its product placements. New arrivals and high-demand products can be placed in eye-catching and prominent areas where shoppers spend the most time at.
It’s all about automation, integration, and interoperability
Just as diverse ecosystems thrive when interconnected, businesses will flourish when location insights seamlessly merge with other data sources, empowering retailers with a holistic view of their operations.
On top of geospatial and location data, advanced analytics, AI and Cloud native tools will become the backbone of decision-making systems that will help retailers stay agile in the industry.
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