Using information collected during a customer’s first purchase, a new marketing tool that leverages machine learning technology can provide firms with valuable predictions about the customer’s future behavior, says Eva Ascarza, a marketing researcher and associate professor at Harvard Business School.
By incorporating data most companies discard, Ascarza and her co-researcher devised an algorithm capable of quickly analyzing more than 40 variables to create a “first impression” of the customer after the initial transaction. The algorithm then predicts which customers will become repeat buyers and which will be most responsive to email campaigns, information that firms can use to improve marketing strategy and return on investment.
“Companies are leaving money on the table,” says Ascarza, because they don’t know how to use most of the customer data they collect, especially when trying to manage newly acquired customers, for whom there is no historical data—something known in computer science as “the cold-start problem.”
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