Several years ago, there were reports about how Target uses predictive analytics to identify customers who are pregnant and then markets to them accordingly. A New York Times article included the story of a father who was upset that his teenage daughter received coupons for baby clothes and cribs … until his daughter confirmed that she was expecting.
Therefore, I was not taken by surprise when, soon after reading about it, I received an email from Target congratulating me on my new baby. Except I wasn’t pregnant. I still sometimes wonder which combination of purchases it was that put me in the wrong bucket. I laughed it off and told the story a few times. But I can easily imagine how upsetting that message could have been be for a different woman in a different situation.
That is an extreme example of just how bad inaccurate data can be. But bad data isn’t always wrong data. As our cover story explains, bad data is also incomplete or outdated information about your members.
“Bad data happens more often than we think,” says Karan Bhalla, CEO of CUES Supplier member CU Rise Analytics, Vienna, Virginia. “Everybody is having a lot more information thrown at them every day. Managing all that data requires a big culture shift and new expertise.”
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