Geolocation intelligence is accurate contextual information about the location and movement of people around the world.
The potential that this type of data has in the context of business decision making in the retail sector is invaluable. It includes demographic information about the retail outlets themselves, nearby businesses, competitor insights, customers, suppliers, among others. With this data it is possible to know consumers in depth: who lives in a given area, what is their socioeconomic position, what type of housing they live in, what businesses they visit, at what times, on what days, what preferences and tastes they have, among others. This data can be complemented with sociodemographic details to deepen the analysis and provide a wealth of information about the populations.
For retailers, it provides the opportunity to better understand current and potential customers, providing a critical link to the consumer.
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Five of the main geolocation intelligence models in the retail sector are:
With dynamic map visualizations that reveal populations and demographic groupings to explore possible potential locations in the context of surrounding areas and traffic patterns, ideal catchment areas can be found in a location, further understanding the potential profitability of an area, broadening the scope to better understand granular traffic patterns.
Used to better serve customers in a given area. Analyzing demographics to find out if there is a specific type of population in a given area, providing an opportunity to cater to a particular niche market by adding specialized articles or media publications that target the same audience.
Geomarketing is another opportunity for retailers to increase revenue by sending targeted advertising to selected consumers when they are nearby. Targeted advertising can be sent to digital advertising platforms based on the presence of a wider audience at a particular location and time.
Create the opportunity to link customers’ online experience with physical stores. By connecting website visits and browsing history with a person’s physical presence in the store, retailers can understand shopper behavior and better serve their needs.
Performance benchmarks are set for individual locations. In many organizations, annual targets for stores are based on a percentage increase over the previous year’s performance, challenging high performers while perpetuating poor results for stores that generate low performance.
Also read: “How does Big Data improve the relationship with consumers?“
Contact us for more information about our Geospatial data solutions for the retail sector.