How to Eliminate Bias in Data-Driven Marketing

A common misstep is not being upfront about limitations in methodology

Build your own seamless purchase experience at Commerceweek, February 28-29. Gain strategies for consumer retention with immersive touchpoints, AI innovation and storytelling. Register now at 35% off.

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.



Subscribe today!

To Read the Full Story Become an Adweek+ Subscriber

View Subscription Options

Already a member? Sign in

Adweek magazine cover
Click for more from this issue

This story first appeared in the April 16, 2018, issue of Adweek magazine. Click here to subscribe.