Introduction to Location Analytics Part I: A Great Source of Value

Introduction to Location Analytics Part I: A Great Source of Value

This week to shake things up a bit I’ll be writing an introduction on location analytics, which can be used by many departments such as Sales, Marketing, and Operations to increase revenue, lower costs, and have a higher ROI on certain marketing efforts.

Location analytics is the group of analysis techniques on sets that include geographic data that may consist of both spatial and tabular data.

-Spatial data: location and shape of the respective elements.

-Tabular data: information expressed as tables concerning the corresponding spatial data.

Spatial data:

  • Points: they define geographic locations too small to show as lines or areas (ex. stop signs, traffic lights lamp, etc.) or they may also represent addresses, GPS coordinates and mountain peaks.
  • Lines: they represent the form and location of geographic objects too narrow to show up as areas (such as streets, roads, and rivers). They also serve to represent elements that have longitude but no areas such as administrative borders.
  • Polygons: shapes with many sides that represent the shape and location of homogeneous entities such as states, counties, land use types, and terrain type.
  • Images: aerial and satellite pictures, cells, digital cartography (made up of pixelated information and vectors).

Tabular data:

In conjunction with the objects mentioned above, maps may represent descriptive information through map symbols, colors, and labels; example: bodies of water being blue, triangles to represent mountains, streets having labels with their names and associated location, a special id for hospitals, schools, airports, etc.

Regarding descriptive data, it is best organized in tables to ensure compatibility with any data analysis application.

Obtaining the data:

So how is the raw data obtained before it is worked on and connections are made, which lead to insights? There are many sources for the data, one of them is the one users provide willingly.

Data is provided freely by users through inputs on apps, forms, quizzes, etc. Physical location can be explicitly given when the user indicates his country, state, and zip code. Other valuable data depending on its final use are where the user works, if dealing with local businesses what franchise he visited or if he’s interested in an area in particular based on his search.

Then there’s what is gathered through the devices (GPS, wifi, data collected by apps that were allowed to track the user). Data collected may be about the places the user visited and sometimes even his trajectory.

The data can be enriched with external data that provide a context for the “points” and “lines” accumulated such as interest points (restaurants, businesses, offices, landmarks) already on the map. This also applies to static points such as a building. Without context all the company has is its geographical location, but when you add data such as nearby schools, businesses, roads, etc. you can now create a profile around the information. 

Extracting Value through Location Analytics

Profiling can apply to both users and locations, which will lead to being able to work with location analytics. Using business analytics on the data gathered can not only make it possible to indicate the behavior, traits and the present consumption; but also, what the subject is likely to spend or consume in the nearby future and how he is likely to respond to certain situations. Prescriptive analysis would lead to higher revenue since the business can adjust the offerings of a certain location based on the characteristics of the targeted population.

Another form of value provided by location analytics is improvement, which is something often seen in smart cities when it comes to transportation for example. Using location analytics, it could be observed that there are public transport lines that could be merged, which would reduce costs and pollution. Another example, bus stops could be set based on population density and/or high traffic zones (observed from analyzing the trajectory of public transportation users, workers, etc.).

Marketing of course can tailor the company’s products or services based on the traits of the person, predictions of his needs derived from where he has been recently or made his previous purchases. The value of this would be reflected on the ROI of the different marketing efforts. This allows the company to measure which ones had the best results and which ones didn’t.

Finally, it shouldn’t be a surprise that the data itself holds value not only for the company but for others too. Anonymized data can be sold to different businesses so that they in turn can produce their own insights, sometimes increasing its value with open data. Depending on your country, open data can be a great complement as it contains demographic information (sometimes narrowed down to zip code if you’re lucky) in tabular form.

Miguel Morales

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