Koen Pauwels is one of the foremost marketing scientists in the world and the author of “It’s Not the Size of the Data, It’s How you Use It”.
If you can make one broad generalization about marketers it is that they probably hated math and science in high school.
Even today, with the business world awash in performance data of all kinds, marketers tend to fall back on long-held marketing truisms or heuristic rules in the decisions they make. Anything to avoid number-crunching. The right split between brand building and activation? Of course, it has to be 60:40! Isn’t that what Binet and Field recommend? The optimal media budget? Let the media agency decide! The ROI of that last product launch campaign? Uh, not sure exactly, but we did see a short-term sales spike. The synergistic effect of offline and online advertising? No clue, actually, just know that our brand awareness scores are higher than ever.
No wonder the finance people scoff at the budget proposals that come out of marketing. Whenever they demand to see a clear link to business value – for some (any!) proof of effectiveness – all they ever get are performance forecasts built on a pile of dubious assumptions. In part, that is due to the abstract nature of marketing. There are many interdependent variables that come into play in any assessment of spending effectiveness. There is so such thing as “spend this much, get this much in return”. The approximate answers lie somewhere between what has happened in the past and what might happen in the future. And so a certain amount of educated guesswork is to be expected. But that crucial job of estimating marketing effectiveness based on known historical data needs to be far more rigorous, far more fact-based, and backed as much as possible by scenario modeling.
For brands with larger media budgets, the usual approach has been to lean on market-mix modeling and multitouch attribution tools to come up with the right budget allocations. And while those automation solutions do help to calibrate the media mix, there are many other thorny questions that require a working familiarity with statistics to answer.
Marketing has become a fiendishly complex business, with a myriad of media channels to consider, and a slew of direct and indirect drivers of market behaviour that have to be taken into account. Too many in fact for marketers to figure out on their own, no matter how good they may be at pivot tables.
So the time has finally arrived for marketing science to emerge from the halls of academia and come to the rescue of practitioners. Unlike data scientists, who apply statistical methods to customer data analysis, a marketing scientist is a social and behavioural expert trained in answering the toughest marketing questions. Need to know the optimal pricing strategy? Which market segments offer the greatest profit potential? The right balance between ad reach and frequency? Whether it is worth the trouble to pursue light category users? The best promotional timing? The most important drivers of market share? A marketing scientist can build simulation models that get marketers a lot closer to the truth. Or at the very least, to a defensible answer.
Perhaps the best known marketing scientist in the world is the slightly subversive Byron Sharp of the Australia-based Ehrenberg-Bass Institute whose best-selling book “How Brands Grow” won him a lot of fame for busting many cherished marketing beliefs such as “differentiate or die” and “perception drives behaviour”. A lesser known but equally esteemed marketing scientist is the Belgium-born Koen Pauwels who is Vice Dean of Research at Northeastern University and heads up the DATA Initiative there. In fact, Marketing Week’s Mark Ritson calls him “the best marketing academic on the planet”. He has written a number of books of his own, one of which, “It’s Not the Size of the Data, It’s How You Use It”, remains an indispensable guide to marketing dashboard design. He has also duelled occasionally with Professor Sharp over some of Ehrenberg-Bass’ more contentious findings.
I started by asking Professor Pauwels to first define marketing science and explain how it differs from data science.
Koen Pauwels (KP):: So, I would say it is, analyzing exchange behaviour on markets using the scientific method. That’s marketing sites, right? So, I originally got interested in marketing when I was a teenager, and I tried to figure out why people would wear, you know, brand name ski jackets in my native Belgium when it was not cold. And it was just, for me, clothes were functional. So it was really weird for me that people would wear brand names to express themselves or anything like that, right? So that really intrigued me.
And so marketing seemed to me, for me as a teenager, the perfect combination between economics, right? That typically assumes rational human behaviour and deals with profits and costs, and the psychology, sociology, basically the social sciences. So, marketing, you know, aims to influence people, right, to kind of, enable, profitable transactions for win wins, basically. So marketing science is really investigating that whole process with a more scientific lens. So why do people buy what they do? What stops them from reading a book that they bought, for instance? Right? So marketing science is really bringing the scientific methods to some of these very basic, questions. It’s really being intrigued by human behaviour as it relates to the marketplace. How do competitors relate to each other? How do manufacturers deal with powerful retailers? How do consumers, you know, trade off privacy and convenience when they go online? And so, all of these questions are basically interesting to me as a scientist because they touch on human behavior.