Lately location data has received a lot of press and attention – and rightly so. Organizations that utilize location data have shown it to be effective in enhancing marketing efficiency, reducing risk, improving medical research, and facilitating urban planning. Location data combines information from diverse sources in new ways to create knowledge, make better predictions, and tailor services.
But what exactly makes the spatial so special?
In an interview with Forbes, Javier de la Torre, CEO at CARTO, explained, “Eighty percent of data has a location component….But only about ten percent of organizations are really taking that location data to drive meaningful insights to help them optimize and make better decisions.”
A year later and organizations are just beginning to realize that data doesn’t heed artifical boundaries, everything is related to everything else, relationships change from place to place, and movement matters.
But the delay in getting on the location data train is understandable. In a short period of time, businesses have had to adapt and navigate on an unfamiliar and slightly non-traditional commerce terrain. The integration of new digital infrastructure, like the Internet of Things, machine learning, artificial intelligence, big data, and predictive analytics, in consumer products has become ubiquitous and essential for all competitive businesses. A very foundational component to these various technological evolutions is location data. And fundamental to using location data for business optimization, and improving the decision making process, is applying analytic methods.
Location intelligence can be used in all four types of analytic methods.
However, like any famous foursome, the four types of analytics all have a reputation for performing distinct actions that yield different results depending on a given business challenge. Let’s take a look at how location data operates within each of the specific analytical models illustrated by the following site planning examples.
Descriptive analytics creates a summary of historical data to yield useful information and possibly prepare data for further analysis. Using descriptive analytics you can calculate X using the number of Y in a given area.
Jet, an online retailer recently acquired by Walmart for $3 billion, assessed historical location data trends and found that a store that’s constantly changing and includes interactive events at select locations could signal the future of brick-and-mortar retail.
Even though data showed that thousands of mall-based stores closed around the U.S. in 2016, by the second quarter of 2017, Jet opened Fresh Story on Manhattan’s Upper West Side. Open for only six weeks, Jet’s larger goal is to raise awareness around its food delivery service as well as its more niche artisanal offerings.
Diagnostic analytics is a deep examination of data to understand the causes of events and behaviors. It is characterized by the use of drill-down functionalities and correlations that use geographically weighted regression to determine the factors that cause X and use these factors as parameters for calculating Y.
J.C. Penney is experiencing severe financial losses, highlighting the downward spiral for many department stores around the world. The loss isn’t as bad as many retail analysts have estimated but sales at the retailer have fallen for the third consecutive quarter plunging the company’s stock to 42 percent in 2017.
After diagnosing the potential cause for the decline, J.C. Penney is working on modernizing its locations in an attempt to postpone the liquidation of 138 locations.
Predicitive analytics is the use of data, statistical algorithms, and maching learning techniques to identify the likelihood of future outcomes based on historical data. It is currently the most recognized type of analytic method used in business solutions. In a nutshell, it is the analysis of patterns and trends to predict the number of X in the coming year(s).
It is the Yoko Ono of the group.
It is estimated that Amazon has around 60 percent in market share of book sales through its website. So why does it need physical stores at all?
It is hard to discern the motivations for Amazon’s latest go at brick-and-mortar. It is clear that the retail titan has a lot of ideas about how physical retailing can be improved, ideas that come from its data-centric approach in online retailing.
Amazon forecasts that its physical stores will be an important way to introduce the public to new and unfamiliar devices. While the technologically savvy are comfortable purchasing artifical intelligence devices online, like the Echo, there are considerable chunks of the population that still need to see the new tech upclose first, which means books are probably not the main focus of Amazon’s demographically unique and consumer data-driven stores.
Prescriptive analytics uses optimization and simulation algorithms and is dedicated to finding the best course of action for a given situation. It is the powerful, but quiet, brother (i.e. a George Harrison type) of descriptive and predictive analytics. Using prescriptive analytics you can offer X at variable rate based on Y (determining Y from descriptive analytic methods) and focus Z efforts on low-risk areas where you can better the price of your competitors.
El Corte Inglés, the largest Spanish department store chain and global leader in distribution, is piloting Situm’s indoor positioning technology to improve the shopping experience inside of its department store locations.
Relying on smartphone devices and without the need for beacons, El Corte Inglés hopes to reduce deployment, future maintenenace costs, and extend the location data technology to other stores in the future.
Among the challenges location intelligence proposes to solve for retail, grocery stores, and other brick-and-mortar establishments, is the ability to unlock the power of geospatial data, enabling you to apply geographic contexts to business data. It allows for a deep study and analysis of collected data, the formation of descriptive, diagnostic, predictive, and perscriptive models, as well as optimal visual communication.
Location intelligence is not just another information system for geographers or data analysts., but a pwerful set of capabilities that can augment other business intelligence solutions.
Like a chameleon, location intelligence fits in different places. It can form part of the BI infrastructure by providing location data, take the shape of a BI-like application or provide tools to embed in other BI solutions.
Is your organization using these four types of analytics with its location data? Do decision-makers have the necessary resources to perform their own analysis or are they dependent on IT staff or external consultants? Read our Really Good Guide to Location Intelligence Implementation and download the templates to evaluate whether your organization is extracting value from its location data and start using location intelligence today!
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