• April 4, 2016

Taking the Creep Factor Out of the Big Data Customer Experience

A few years ago just outside of Minneapolis, an angry man walked into a Target store and demanded to see the manager. He was clutching mailer coupons that had been sent to his high school-aged daughter. The coupons featured maternity clothes and baby furniture. “Are you trying to encourage her to get pregnant?” he yelled, according to an employee at the store.

As it turned out, the targeted coupons were the result of a predictive model developed by a brilliant statistician the retailer brought on board in 2002. Through clever Big Data analytics, he discovered that women shift their purchasing patterns just before entering the second trimester of pregnancy. They load up on unscented lotions and specific vitamin supplements. As their delivery dates draw near, they stockpile cotton balls, hand sanitizer, and washcloths.

‘Creep Factor’ Unleashed

While the pregnancy prediction model was a sales-generating success, it was a public relations disaster for Target (the high schooler was indeed pregnant). Customers like personalization but flinch when things get too personal. Welcome to the “creep factor” of predictive Big Data analytics.

Auto insurers have run head-on into this phenomenon. When offered the option of trackers in their cars to gauge driving habits in exchange for a shot at lower rates, 40 percent of the insured said, “No way in hell.”

There’s a fine line between engaging personalization and the off-putting perception of prying eyes. Consumers, for example, are often repelled by banner ads that persistently follow them around the web if they’ve clicked on a product. But how does an enterprise avoid spooking the very customers it seeks to engage? It isn’t easy.

Deep Personalization

Whether they realize it or not, customers expect engagements with organizations to mimic their interactions with other people. In these circumstances, cues and exchanges of information in the moment guide outcomes. Customers not only want companies to understand their histories; they want them to grasp their immediate situation and anticipate what they need next. But they also want trust and respect for privacy. In short, they don’t want to feel like their highly personal information is being leveraged for a marketing ploy to serve enterprise needs. They want to participate in experiences that pivot around their needs.

How can enterprises steer clear of the creep factor? Forrester Research recommends developing app and website interfaces with granular controls so customers can choose how much personalization they’re comfortable with. The firm says to test engagement designs through controlled experiments to tease out danger zones. In addition, Forrester recommends developing multiple versions of a digital experience to cater to varying personalization comfort levels. Deploy predictive analytics processes focused on individual discomfort levels as a component of the customer experience.

In the future, predictive analytics and machine learning will enable the development engagement styles tailored to each individual customer. This will be crucial for developing experiences that draw customers in and keep them engaged—instead of driving them away.

Like this story? Learn more about fine-tuning the customer experience in the financial services industry.