When Madison Reed first launched its product recommendation engine in 2015, it was coded by humans. While it factored in a shopper’s current and desired hair colors, it didn’t include elements related to consumers’ reactions to the brand and its products.

For machine learning and artificial intelligence (AI) to work, there needs to be enough information fed into the system to interpret new information and adjust its algorithm, says Dave King, the retailer’s chief technology officer. In early 2016, after more than 1 million consumers took the hair color quiz and Madison Reed had their “hair profiles,” the retailer upgraded its human-written code to a recommendation engine fueled by AI.

The tool has helped Madison Reed recommend a better product, a complementary product, an application tool or signal to Madison Reed to provide the shopper with additional instructions for application, King says. This feedback has spurred Madison Reed to develop …

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