POI Analytics: Uses and Applications

POI analytics has become a vital business intelligence process for any company, since, by aggregating multiple data layers, a much more representative view of customer behavior patterns at a point of interest is generated.

POI analytics allows you to select a point or area of interest that, when related to different data layers, allows businesses to generate enriched and detailed information with which they can obtain valuable insights to make better-informed business decisions.

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By using spatial data and intersecting it with large volumes of information on consumer behavior, tastes and interests, the analyses reach such a deep level of detail that it allows us to answer questions such as:

  • How many people transit a particular area on a daily basis?
  • What is the relative wealth index of these consumers and of those who live in the area?
  • What is the foot and vehicular traffic flow over a given period of time?
  • What are the most and least visited locations?
  • What is the potential market in the area?

Example of use and applications: POI analytics of potential customers in an area

At PREDIK Data-Driven we conducted a detailed analysis of the behavior and mobility of people in the municipality of Karisandra, in BangaloreIndia.

As shown in the image, the heat map represents the mobility recorded in the area during the month of July. The red dots detail the locations where the highest concentration of foot traffic is found and from this, the most visited points of interest were identified.

This analysis generates various insights on how foot traffic is distributed over a period of time and how many visits are reported in a given location.

Using data from the Wealth Index Global, collected by Facebook, it’s possible to estimate the socioeconomic levels of the inhabitants, their age, and their profile. In parallel, by applying location intelligence analytics, corporate buildings, residential areas, schools, etc., are located. The combination of these two analyses makes it possible to classify the habitants as students, workers, residents, or “floating or instant population”, i.e. people who only pass through that area to get to another location.

The points of interest identified in the area were also categorized to understand the behavioral patterns of the inhabitants and thus identify potential markets and places that generate more interest.

Also read: “Location analytics can drive retailers to success: Case Study Home Depot Vs. Ace Hardware

This type of analysis is very useful for retail companies, as it provides interesting data on visit rates per store, dwell time, and customer behavior patterns in competing stores, among others.

This information is also a key input to finding potential locations to open a new point of sale (site selection), among many others.

At PREDIK Data-Driven we analyze our clients’ needs and implement all types of location intelligence to maximize their revenues, generate solutions and foresee future problems.