Machine Learning is here, and it affects our daily lives in such subtle ways as to often not be noticeable.
Have you ever been called by your bank and alerted to a possible at-risk transaction of your credit card? That’s Machine Learning, and it’s a simple example of day-to-day existence in the age of advanced, analytics-driven machine intelligence.
It might not be the things of Terminators and Skynet, but it’s still pretty cool (and far less terrifying).
Machine Learning allows a computer to immediately and automatically understand a particular data point based on the collective understanding of data points it has observed in the past.
Machine Learning helps to more effectively and efficiently prompt an organization into action. In a utility company, a sensor in a power station can detect output variability in a key component, and its automatic alerts to operators can help avoid a major power outage. In the “old days,” the fix would have to wait until a component failed. Today, the failure – and subsequent power outage – is prevented.
In retail, the applications of Machine Learning are different, of course, but the idea is very much the same. In the video below, George Shaw (@kngpengwin), head of R & D at RetailNext, provides a quick overview of Machine Learning and an example of how it’s used in a retail setting.
Intro into Machine Learning from RetailNext on Vimeo.
In George’s example, Machine Learning is the enabler of real-time, relevant store communications to shoppers, and as such, is an integral part of a retailer’s “technology stack” in customizing and personalizing appropriate communications. And, that’s just one example of hundreds, maybe thousands, that retailers are using to design and deliver better shopping experiences and improved store results.
For more looks by George into technology and its application in retail environments, be certain to watch the Tech Talk series.
Join the #retail conversation on Twitter @RetailNext @kngpengwin and @RayHartjen, as well as at www.facebook.com/retailnext.