Innovation

Guessing shopper behavior online

Seattle-based Cleverset is bringing out a next-generation recommendation engine to figure out shopper behavior.

Seattle-based Cleverset is bringing out a next-generation recommendation engine to figure out shopper behavior.

An excerpt from Technology review:

Many systems just match products to people by looking at the products that others have bought. For instance, if you are looking at a blender, and people who bought the blender also bought a toaster oven, then the system would suggest a toaster oven to you. The problem here, says D'Ambrosio, is that all this analysis of purchases happens offline, and the system has no awareness of what a consumer is trying to accomplish at that specific point in time.

Cleverset's founder Bruce D'Ambrosio believes that a statistical relational model does the best job at taking in all factors into consideration, no matter how small, to drive the recommendations to the customer.

The relational model factors in the relation among several datasets pertaining to the customer while statistics are deployed to ascertain the importance of the relationships.

What's your experience with recommendations at several retailers? Has it worked for you?

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