Here are 9 ways retailers are using big data technology to create an advantage in the retail sector.
1. Recommendation EnginesThis is one of the classic use cases of big data tech in retail (albiet mostly in ecommerce settings). Based on a customer’s purchase history, and the histories of others like him, what is that customer likely to purchase next? By training machine learning models on historical data, the savvy retailer can generate accurate recommendations before the customer leaves the Web page.
“When you think about recommendations, everybody wants to beat Amazon,” says Eric Thorston,Hortonworks‘ GM for consumer products. “Love them or hate them–most retailers hate them–Amazon makes from 35 percent to 60 percent revenue uplift on recommendations, and everybody is saying, 'How can we get a piece of that?' ”
This is one of the “low hanging fruit” opportunities because it is implemented relatively easily and can have an immediate impact on revenue and profit. According to Thorston, retailers are finding they can improve revenue uplift by generating recommendations on Hadoop, instead of using standard recommendation engines.
“One customer was using a third-party recommendation engine,” he says. “They brought that in house, let the merchants and developers build up some logic, and deployed it. They raised revenue uplift to 24 percent. So you’re still not getting an Amazon-like response, but you’re getting a huge boost.”
2. Customer 360Forrester analyst Mike Gualtieri often talks about how consumers today want the “celebrity” treatment when they engage with companies. We all want to be treated like Taylor Swift (or perhaps Kanye West) when we enter a store. We expect companies to anticipate our needs, to have the products we want on-hand, to communicate with us in real time (via social media), and to adapt to their needs as they change.
This is a tall order for any retailer to achieve, but it would be practically impossible to do without some sort of Customer 360 initiative. And considering how many customers a retailer must interact with, and how many data sets are involved with getting there, big data technology and real-time processing is critical to making that happen.
Companies in lots of industries would like to have a 360 degree view of their customers. However, few actually obtain it. In the cutthroat world of retail, where profit margins are razor thin, developing a customer 360 system may be a matter of survival.
3. Market Basket AnalysisMarket basket analysis is a standard technique used by merchandisers to figure out which groups, or baskets, or products customers are more likely to purchase together. It’s a well-understood business processes, but now it’s being automated with big data.