Want to Really Understand Your Customer Data? Try Hiring a Scientist

The next digital transformation battleground is the war for this talent

Top retailers and consumer brands will hire nearly 50 percent more data scientists over the next three years.
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Anyone who follows the NBA knows what shoes their favorite athletes are wearing, whether it’s Lebron James (Nike), James Harden (Adidas) or Steph Curry (Under Armour).

Now add Peter Fader to that list.

Fader isn’t a world-class baller. He’s a marketing professor at the University of Pennsylvania’s Wharton School of Business who invented a way for brands to calculate customer lifetime value. He’s also a co-founder of Zodiac, a consumer data analytics startup acquired by Nike in March for an undisclosed sum.

Data scientists like Fader may soon be as important as star athletes in ensuring the success of companies like Nike, which is in a footrace with Adidas and Under Armour for using data to enhance the customer journey.

“Acquiring Zodiac brings a team of world-class data science talent, along with the best-in-class tools they’ve developed,” says Sean Bruich, vp of global member services and consumer knowledge for Nike. The $34 billion sports apparel company is relying on data to power one-to-one relationships with customers and help personalize its offerings.

Nike is hardly alone. In the same month, Nordstrom acquired two digital startups that use artificial intelligence to personalize communications with customers. Other companies are staffing up internally. Top retailers and consumer brands are planning to hire nearly 50 percent more data scientists over the next three years, according to a March 2018 survey by Salesforce and Deloitte.

“Data scientists are still relatively sparse resources, and companies are falling all over themselves trying to attract these folks,” says Brandon Purcell, a principal analyst for Forrester Research.

The love vote

In 2016, Accenture surveyed more than 27,000 consumers in the U.S., U.K. and Brazil, asking which brands they truly love. Those that ranked highest on the Love Index—Apple, Google, Microsoft, Netflix, YouTube—are also among the most advanced in applying data science to the customer experience.

That’s no accident, says Nan Nayak, managing director of design strategy for Fjord, a division of Accenture Interactive.

“These companies are [measuring] their key moments of customer experience and have data scientists constantly looking for patterns,” she says. “One of the reasons digital-first brands did really well on the Love Index is that they are tapping into experience nuances most traditional brands do not.”

By applying data science, brands are trying to answer three fundamental questions, says Chris Paradysz, co-CEO of digital marketing agency PMX: Who are my customers? What do they want today? How can I evolve to meet their needs in the future?

Researchers at the University of Tennessee analyzed Facebook data to identify bars and restaurants that were good candidates for purchasing Coke products. The more check-ins and likes an establishment’s Facebook page received, the better prospect it was, allowing the beverage giant to prioritize its sales efforts.

The answers aren’t as simple as they may seem, in part because customer needs and desires evolve so rapidly.

“You can’t hope to use legacy information and be right,” Paradysz says. “You really have to use data. You need to have direct feeds of information into some kind of internal analytical tool or acquire the talent yourself.”

Today, most brands are just beginning their data science journey, says Michel Ballings, assistant professor of business analytics at the University of Tennessee’s Haslam College of Business.

There are three levels of maturity for companies using data analytics, he explains. The first stage is descriptive, using data to figure out what’s happening now. That’s where the vast majority of companies are. The second level is predictive; a smaller number of brands are beginning to use analytics to figure out what’s likely to happen. The third stage is prescriptive, using data models to make decisions on what to do next. That’s where digital natives like Amazon and Google live, he says.

Reaching level three requires major investments, says Ballings, but it’s worth the effort.

“It will help you be creative in ways you couldn’t imagine and point you in directions you wouldn’t think of,” he says. “You want to personalize your services, recommendations and products and to provide the best possible customer experience. For that you need data.”

Tech talent crunch

But data by itself is worthless unless you have the ability to understand it, manipulate it and apply it to real business problems. That takes talent. And right now, data science talent is in great demand.

This story first appeared in the May 14, 2018, issue of Adweek magazine. Click here to subscribe.

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