The birth and life of marketing science with Professor Gary Lilien
Marketing speaks with marketing science founding father Professor Gary Lilien about marketing analytics and the skepticism still hindering its universal adoption.
Hailed as one of the founding fathers of marketing science, Penn State’s Distinguished Professor Gary Lilien presented insights on how and when customer analytics help organisations to outperform competition at Melbourne Business School late last year.
After completing a Masters in operations research at Columbia University, Lilien learned marketing and marketing models in practice working at Mobil Corporation’s operations research department in the late 1960s and early 1970s. He became a Distinguished Professor at Penn State in the 70s, which was a favourable role as it requires “little teaching,” and “a lot of time allocated to building and implementing marketing models.”
From there, he made this his career objective. He’s developed software which is used at hundreds of schools worldwide for understanding and implementing marketing models.
In his ‘Performance Implications of Marketing Analytics’ presentation – where he was also named a Distinguished Fellow of the Centre for Business Analytics, Melbourne Business School – he demonstrated that the true key to the effective deployment of marketing analytics is top management team advocacy. Without this, even firms who do implement it, will not see its positive effects.
Marketing spoke with Lilien afterwards about ongoing skepticism among corporates surrounding marketing science and analytics, and how the successful marketers of the future will use both sides of their brains.
M: In your presentation you demonstrated the lingering skepticism surrounding marketing science and analytics, in spite of proven results.
GL: What I spoke about today perhaps began with a paper that John Little wrote in 1970 called ‘Models and Managers: the concept of a decision calculus’. He wrote that paper because he had a consulting company called Management Decision Systems, that was designed to implement marketing models that he and his colleagues at MIT had built.
He was seeing the struggle: ‘we’re building excellent models, and in many cases we can demonstrate that they’re effective, but somehow or other there’s organisational, or corporate or mental inertia that prevents that implementation.’
I believe that’s probably specific to marketing, because traditionally marketing has been such a qualitative intuition, gut-feel based discipline.
We saw the striking figures from Chris Mormon’s CMO survey done through Duke – and traditionally in cooperation with McKinsey but they’re doing it now with Deloitte – the continuous skepticism of CMOs about the performance implication of marketing analytics in spite of the evidence that we have to the contrary.
It’s just striking. Our most recent research indicates that if you really don’t believe in it as a CMO, it is going to be a waste of money. It’s really bizarre.
So where does this come from? If you believe in it, and you develop a culture for it, it’ll be an investment that pays off. If you don’t believe in it, and you’re skeptical about it, the money will be a waste of time.
M: Why do you think there is still so much skepticism surrounding it in top management teams?
GL: Basically, insecurity. Many of the folks who’ve evolved into the CMO top level positions do not have the technical background to understand what’s going on inside the box, and the literature on implementation indicates that ‘if you don’t understand it, you don’t trust it.’
I think this will go away in the next generation of CMOs. However, the question for companies is, ‘Are you going to be around for that next generation of CMOs?’
M: What portion of a good marketing organisation is made up by an analytics framework?
GL: We have research to indicate that pure intuition in marketing is flawed, and pure analytics in marketing is flawed.
So, the marketer of the future has to use both halves of his or her brain at the same time. You’ve got to basically do the analytics, and then you’ve got to have the intuition, because analytics does not make sense.
Remember, analytics is backward focused. That’s the key thing. Analytics based on data is backwards focused. In fact, in many cases relying on analytics is like driving while looking through the rear-view mirror. That’s fantastic if the road is straight. But the road is going to turn. That’s the other side of your brain, that’s the intuition side of your brain that says ‘wait a minute, we can’t rely on the data from the past, because there’s going to be a market disruption’.
That, if you will, will be the ideal marketer of the future. The one who is quite comfortable with intuition, and also is comfortable in merging that intuition with the analytics in order to make better decisions.
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M: You’ve had many years experience in the field. Over the last five to 10 years, marketers have had much more access to real time data. What implications does this have?
GL: In a sense, what’s going on right now is a disruption in many businesses, moving them into real-time businesses. In a traditional business, you would post a price, and you’d wait. You’d wait for some time to see ‘gee, are they buying it?’ Or ‘gee, are we out of stock?’
‘They’re not buying it? The price is probably too high’.
‘We’re out of stock? The price is too low.’
You’d wait until the end of the week, the end of the month, the end of the quarter, to get the data to make the adjustment.
What we’re seeing right now is real-time demand adjustment. Uber is an example everybody knows. You dial Uber, they say ‘here’s your price…. oops, no, you’re in surge’. You see all of a sudden the price going up instantly. There’s nobody sitting in the back room making that decision. That intelligence is embedded into the system. For many decisions, the operational decisions, many of the operational decisions in marketing now are being transferred over to technology. Many people are afraid of that, but leading businesses are not.
That’s where the major change is. That the operational decisions, in many cases are being taken out of the hands of individual decision makers. They’re monitored, they’ve been taken out of the hands of individual decision makers, and putting in the hands of data and software.
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