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Problems with data bias are well-documented, from an image recognition algorithm that identified black users as gorillas to language translation services that referred to engineers as male and nurses as female.
And just as bias found its way into these data sets, so, too, can it sometimes be found in the models marketers use to make predictions about their customers.
Alex Andrade-Walz, head of marketing at location intelligence company Spatially, said segmentation via predictive analytics results in a stereotype of an ideal customer that may have higher conversion and retention rates and greater lifetime value.
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