Predicting and Positioning with Location Analytics
If you’ve followed the news recently you’ve likely seen the latest bit on how Target used predictive analytics to identify teen pregnancies and thus were able to market baby products to a particular customer segment before any babies were even born. All things considered, that’s pretty amazing. You might also recall that certain EMS providers are beginning to use predictive analytics tools to prevent medical emergencies before they happen. These are two prime examples of how crafty individuals are using data at hand in a creative fashion. One must consider that if you want to follow suite, you’ll need two things: a repository of data and a method for uncovering patterns hidden within that data.
A fairly common example of where data is aggregated nowadays is a Customer Relationship Management (CRM) tool. CRMs are used to track customer data and opportunities across an organization and come in many shapes and sizes. If you work for a large enterprise, you’re most likely familiar with them. Smaller organizations, on the other hand, are just beginning to see CRMs integrated into their businesses. Thanks to vendors such as Salesforce and Sugar CRM, prices of small scale CRMs have dropped considerably. Thus, if a CRM hasn’t been implemented into your business yet, chances are that it will be soon.
The second half of the predictive analysis equation is to have a means for analyzing aggregated data. Having a location analytics tool is most definitely a step in the right direction for companies that can afford it. Keep in mind, however, that the more data and relationships that need to be analyzed, the more complex and expensive a solution will be. Imagine how many Target customers there are around the nation and how many EMS organizations (public and private) there are. That means one must secure a reliable method for overlaying data on a map that is stored in a CRM for quick, visual interpretation. As an aside, many CRMs come equipped to store geographic information (such as a customer’s home address, a storefront location, or a truck delivery route), but lack the capacity to interpret the data in a visual, map-based manner.
We live in an exciting time as data is at our fingertips everywhere we go. We have places to store it and new ways to look at it. Analyzing the geographic aspects within that data has such a positive upside for business that each day we’re finding new and better ways to make decisions. From the public health sector (predicting diseases) to the economic sector (uncovering new sales territories), predictive analysis by way of location analytics is paving the way for smart, evidence based decisions.
Leave us a comment below and let us know how you plan to use location analytics to your advantage!