Know what to say to your customer next with predictive analytics
You’re a careful shopper, and you’re in the market for a new sofa.
It should be cream-colored, or maybe black. You start by casually browsing the local furniture store sites. There are some possibilities, but you’re not one to make a quick decision. You check the major department stores and the online discounters. Still more choices, and reviews to read. But it’s not yet time to pull the trigger…
You’re busy, so your search goes to the back burner. And then you notice… ads for sofas are popping up in your browser. Some of the better retailers are showing you sofas on sale. And another store with a reputation for their progressive marketing is offering a sleeper sofa that costs just a bit more than a standard one. You’re always open to a sale, but on the other hand, a sleeper sofa is so practical. It’s as if they know you and how you make a major decision like this…
Predictive analytics makes possible the kind of individualized marketing that makes the customer feel like, “Wow, you really know me.” And the experience that the retailer knows more about you than you know yourself – that’s cognitive analytics.
Predictive analytics uses data and statistical algorithms to identify the likelihood of future outcomes based on any historical data available.
Today’s empowered customers demand more personalized online experiences. And better retailers have been stepping up their customer experience game. But those higher expectations don’t just apply to interactions with luxury brands. Every business must place more focus on and improve their customer experience game.
Predictive Customer Intelligence is the IBM solution that can satisfy today’s customers’ needs for personalization. It can make use of the millions of bytes of data that businesses have on customer behavior. It can intelligently analyze data from past purchase history, location, browsing and even take into consideration millions of conversations across social networks, blogs, forums, comments, ratings and reviews. Like a human, it can make sense of lots of information, incorporating content, context and sentiment. But unlike a human, it can do this on enormous amounts of data simultaneously, while using machine learning to become even “smarter.” With predictive analytics solutions, organizations can better assess and predict purchase intent, purchase experience and purchase satisfaction.
Customer acquisition: Like the earlier example of shopping for a sofa, each click enables the system to learn more about a customer’s preferences, so the items displayed as suggestions become increasingly more customized. Analysis of similar ‘customer acquisitions’ shows what works best.
Business growth: A family visits a theme park. They receive alerts on their smartphones to special events around the park, as well as opportunities for dining and merchandise. Affinities between offers, price sensitivities and location data all come together into the optimal recommendation.
Customer retention: A long-time customer for auto insurance signs on to check her policy just weeks before her contract is up for renewal, but is tempted by a targeted ad to add on coverage for her boat.
IBM Predictive Customer Intelligence helps you develop behavior-based customer profiles and segments. Based on those insights, it generates highly personalized and optimized offers. At every touchpoint, you can predict the next best action to interact with your customer, to show them that you understand them.