Big Data Science Opportunities in Retail
Machine learning seems to be at the forefront of everyone’s mind. The buzz at NRF Retail’s BIG Show 2017 was no different. See what we learned about the intersection of retail and machine learning. Machine learning, combined with strong datasets and shoppers and trends, is already helping retailers create a customized, seamless, and predictive experience for their shoppers. As the predictive capabilities of computers improve, so will both online and […]
Machine learning seems to be at the forefront of everyone’s mind. The buzz at NRF Retail’s BIG Show 2017 was no different. See what we learned about the intersection of retail and machine learning.
Machine learning, combined with strong datasets and shoppers and trends, is already helping retailers create a customized, seamless, and predictive experience for their shoppers. As the predictive capabilities of computers improve, so will both online and offline shopping user experiences. With this advancement come opportunities for data scientists and more.
Data science: the key to personalization
One of the many machine learning technologies that we saw at NRF 2017 revolves around the idea of detailed personalization. As the amount of data on each individual shopper increases, so do the opportunities for data scientists to crunch the data. Algorithms that predict what users might like best, or what they might not yet know they like, are already in use and will continue to improve.
In another application, facial-recognition technology can be used on shoppers in brick-and-mortar stores to identify them, pull up the existing data on them, and guide them to the items that they are more likely to like and ultimately purchase. This kind of facial recognition technology can be used to reward loyal shoppers coming back for another visit. It is also bridging online and offline shopping by matching faces to online shopper personas and search or purchase history.
Testing out facial recognition technology that can tell gender, mood, and age range
New realities and interactive possibilities
Machine learning has interactive applications, too. Imagine being able to test-drive a new sofa, seeing exactly how it will look in your living room and debating whether it matches the color of your walls or rug. Retailers are eager to apply both virtual reality (VR) and augmented reality (AR). With VR experiences, virtual stores are places you can “visit” from the comfort of your home with the use of a headset. Imagine virtually browsing aisles of a grocery store without having to navigate through crowds with a shopping cart that inevitably has that one wobbly wheel.
On the other end of the [x]-reality spectrum is AR, which would help shoppers in the earlier sofa example. With AR, one can see both real “reality” and an augmented portion of it – in this case the potential sofa. This kind of technology would also allow shoppers to “try-on” clothes without working up a sweat shimmying into and out of too many pairs of jeans. At NRF we saw retailers using these new technologies to make shopping experiences more seamless and enjoyable for shoppers.
Using VR to design a new living room
On top of these, artificial intelligence can add yet another interactive layer. Chatbots – computer programs powered by artificial intelligence that are able to improvise and converse like humans – are being deployed online to chat with shoppers about what they’re looking for and help them find it. Though still in their infancy, and with examples to show that there is room for improvement, chatbots could be the future of everything from personalized shopping to customer service.
Taken together, machine learning’s many applications have a lot to offer to retail companies both online and offline.
But the implications of this growth go further still.
As dealers in data, we look at these trends and see a parallel rise in the need for massive data storage, processing, and analysis. Everyone from cloud technology architects to data scientists to data security experts should keep their eyes on the rapidly growing application of ML to retail and ecommerce.