Location intelligence is revolutionizing the way companies establish, operate, and expand their business.
From deciding where to locate a new store to analyzing foot traffic to gauge market competition, the use of location data is growing.
One of the key components of location intelligence is data related to points of interest (POIs). POIs indicate a specific location in an area of interest to businesses: it can be a store, a hospital, a university, or a corporate building, among others, depending on the information required for the particular target, helping companies make faster, more informed and cost-effective decisions.
This data is essential to optimize the performance of marketing and expansion models, market share, or supply chain logistics.
You may be interested in: “Foot traffic Analytics: Burger King Vs. McDonald’s.”
In order to properly leverage them, companies must understand the type of data they need, the scale at which they need it, and understand the best way to represent it to fit their objectives.
How is POI data represented?
POI data, in general, is represented in two ways:
With a latitude or longitude pair.
This does not include any information about the area covered by the POI. It only indicates the reference point that represents the location. Example: Reforma 222 shopping center, is represented as 19.428372737875023, -99.16192173875073.
Although point-based POIs only carry location and do not provide any additional information related to the area, when combined with customer analytics techniques based on mobility data, they can answer questions such as:
- How many people are in the radial vicinity of a POI?
- What are the preferences of consumers living or residing near or within the POI?
- What are the time periods when more and less pedestrian mobility is observed within the POI?
- How is pedestrian mobility within the POI?
- What is the relative wealth index level of people living in or visiting the POI?
- What type of people are those who visit or inhabit the POI?
Although polygon-based POIs can provide more accurate information, the operations related to polygons are more complex than those related to points.
Also read: “How to improve the performance of physical stores.”
Geofencing
POIs, in general, are used individually to understand some of the following aspects:
- Area covered
- Possible customer reach
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This time-based visualization can help us identify exactly which people have visited the store and gain more information about their behavior. For example, once visitors have been classified, you can understand more about them such as:
- Where have they been before or where are they coming from?
- Where will they go after visiting the store?
- What is the average time customers spend in the store?
This data helps businesses make the right decisions, and all of this data depends on the quality and accuracy of POI data.
At PREDIK Data-Driven we help businesses implement this type of intelligence to optimize revenue, generate more efficient expansion models and optimize costs.
Need to implement market solution strategies based on location intelligence analytics? Contact us!
