The best data scientists are more like marketers than you might imagine.
Since it burst onto the Australian online shopping scene in 2006, fast-growing e-commerce insurgent Kogan has been busy upending old certainties in the retail sector Down Under. Estimated to be worth $400 million by “The Wall Street Journal,” the private company is believed to have turned over more than $300 million in 2014–with no end to its steep growth curve in sight.
At the heart of this success, according to founder and chief executive Ruslan Kogan, is a commitment to world-class technology infrastructure that helps the company deliver the market’s lowest prices. The company also recognises the importance of analytics to its digital marketing program.
“When we are hiring marketing managers, we actually look for accountants,” Kogan told CMO.com. “Marketing in the digital age is based on the fact that you can measure everything: click-through rates, open rates, conversion rates. For every single cent we spend on marketing, we know what that person did on the Internet before coming to our site, where they landed, what they clicked on, what drives conversion, and what doesn’t.”
In fact, Kogan said he is confident of his company’s ability to calculate the lifetime value of a particular customer based on this initial interaction. “We have people sitting there looking at all the trends and deciding where to spend the money, working out the best way to target the consumer,” he said. “Often they come to us without any prior marketing knowledge whatsoever.”
Building On Analysis
According to Doug Campbell, chairman of the Institute of Analytics Professionals of Australia, data scientists and marketers are becoming more and more alike.
“In some ways, ‘scientist’ has connotations that don’t align with the role of a data scientist–it’s not white coats in a secret room,” Campbell told CMO.com. “Data science is business suits, market-share predictions, churn reduction, profit-and-loss statements–communication is as much a part of a data scientist’s day as models, data, and algorithms.”
Greta Roberts, founder and chief executive of United States-based Talent Analytics, said she uses data-analysis techniques to understand this emerging profession.
“Our firm actually studied data-science professionals to understand more about what makes them tick,” Roberts told CMO.com. “We scientifically measured their aptitude–that is, their natural abilities.”
One of the important insights Talent Analytics gleaned from this study was the importance of creativity to data scientists. Roberts said it was clear that data-science professionals need to approach a difficult challenge and not be satisfied with the first answer.
“They need to see it from another perspective,” she said. “So although designers, art directors, and marketing managers may not believe they have much in common with data-science professionals, they both have a deep appreciation for experimentation, an out-of-the-box approach, and for colours and texture.”
Scientists As Storytellers
Data scientists often make a smooth transition into marketing because business and communications skills are integrated into their training, said Anna Russell, director of Sydney-based analytics and strategy consultancy Polynomial.
“Traditionally, the ‘analyst’ mind-set has been one of rigid processes and cold logic,” Russell told CMO.com. “This type of thinking isn’t necessarily optimal for the type of exploratory behavioural analysis central to most commercial data-science initiatives. Real, human-generated data is messy and ... human.”
Data scientists, according to Russell, tend to be less rigid and more curious than conventional business analysts given their focus on human behaviour.
“To gain buy-in from nontechnical stakeholders, data scientists must be storytellers and weave a compelling tale,” Russell said. “The more interesting the story, the tighter their analysis is linked to business meaning, and the easier it is to draw attention and engagement from stakeholders.”