To predict customer buying behavior, stop, look, listen, analyze

By gathering customer data from multiple channels of communication and creating a centralized view, companies will “have access to everything they need to know about customers’ actions and preferences,” said Gerard Szatvanyi, president and CEO of e-commerce consultancy OSF Global Services.

Good sources for mining customer wants and needs include data collected from sales orders, products, page visits, wish lists, abandoned carts, review history, tickets and accumulated loyalty points. This information is critical to providing personalized marketing, support and sales experiences.

“By coupling this data with analytics, or even taking it a step further to include AI,” Szatvanyi said, “organizations are able to make informed decisions” and provide a completely seamless online and offline experience.

A single view of the customer allows the salesperson to know the customer on a deeper level across a variety of touchpoints. For example, a sporting goods saleswoman would be able to know if a customer she greeted in the store had earlier considered and deleted a particular fishing rod purchase from his online shopping cart. The saleswoman could offer him to try the same rod in store and perhaps present a 10% discount coupon to be used in the store that day.