One of the highlights of last week’s Content2Conversion conference was a keynote by the always-stimulating Tim Riesterer of Corporate Visions, who argued that an effective sales presentation should (1) start with an unfamiliar factoid that shows why change is essential (a concept similar to the CEB “challenger sale”) (2) show that you have a solution and (c) contrast your solution with other approaches to clarify how it’s different and better. Riesterer’s own talk followed exactly that template, a bit of consistency I always admire. This in turn got me thinking about my presentation on machine learning systems at the MarTech conference in March. I pretty much finished drafting it last week, but it was still an interesting exercise to imagine recasting it along the lines Riesterer proposed.
Linear thinker that I am, this meant first looking for an appropriate factoid about why the growth of machine intelligence poses a threat that can’t be ignored. This led to several hours of research into what’s being written about machine intelligence, which is something I like to do anyway. It seemed like a reasonable starting question was how many marketing jobs are threatened with replacement by intelligent systems. A quick bit of Googling led to an article that quoted economist W. Brian Arthur as estimating that machines could replace 100 million U.S. jobs by 2025.* That’s pretty scary but a second, even more intriguing article quoted a study by Oxford economists Carl Benedikt Frey and Michael A. Osborne that estimated the probability of 702 specific job categories being replaced by “computerisation” (British spelling). This offered the possibility of showing how much employment risk is faced by marketers in particular.
The Frey and Osborne paper lists just two categories with “marketing” in their title:
- “Marketing Managers” with a 1.4% probability of computerization, and
- “Market Research Analysts and Marketing Specialists” with a 61% probability.
That’s a pretty sharp divergence but it eerily matches my observations last week about the landscape of machine intelligence systems for marketing: virtually no systems attempt to manage marketing strategy and planning, but a great many systems can automate supporting tasks such as trend analysis and data extraction. A similar pattern emerges in the Frey and Osborne data if you look at a broader set of job titles: managers will remain employed but many analysts, researchers, and other types of assisting jobs will go away. Looking at other business functions relevant to marketing, the study sees most types of creative jobs as relatively safe and most types of sales jobs at risk. The forecast is mixed for technology jobs, again with the more senior jobs being relatively secure but run-of-the-mill computer programmers and support specialists likely to be replaced. Since there are many more people in the lower-level jobs than in management, it’s safe to conclude that anything from half to two-thirds of marketing, content creation, sales, and technology jobs are at risk.
Frey and Osborne did their calculations based on the assumption that it will be hardest to computerize jobs that require manual dexterity, creativity, and social perceptiveness. These assumptions may already be obsolete – the paper was written in 2013 and this 2014 video by machine learning guru Jeremey Howard suggests that new developments in “deep learning” are making machines more powerful than anticipated, especially in areas relating to creativity and perceptiveness. I also suspect that Frey and Osborne conclude that management jobs are relatively safe in part because managers need social perceptiveness to motivate their staff – a need that will diminish if the staff is largely replaced by machines. So I’d say there’s a good chance that all but the most senior jobs are less secure than Frey and Osborne suggest.
That’s all interesting, but what does it mean for my presentation? Let’s go back to Riesterer’s three-part template.
- The first step was showing that change is necessary. I think convincing marketers that half to two-thirds of their jobs will vanish in the next ten years should do the trick.
- The second step was offering a solution. I’m proposing that learning to manage intelligent machines will be the key to future success. My MarTech presentation will offer some specific suggestions on how to do that.
- The third step was contrasting the proposed solution with other approaches. That’s easy if the alternative is to continue with traditional marketing methods. It’s a bit harder if the alternative is making other types of changes, if only because you’d have to list what those alternatives might be. I can think of a few approaches I can easily out-argue, such as random experimentation or buying new technology without addressing organizational and process issues. A more challenging competitor is to focus on optimizing the customer experience rather than use of machine intelligence. The problem is that customer experience sounds strategic and customer-centered while machine intelligence sounds narrowly mechanical. Still, the ultimate question is which approach will give better results, and I suspect machine intelligence – by making marketers more productive and thus freeing them to do more new things – will eventually win out. It would certainly be an interesting debate.
As I say, I won’t actually be structuring my MarTech presentation to match this template, since it’s already locked in. But it’s an interesting approach to the topic – and one I’m sure I’ll have a chance to use in the future, since this is a subject that won’t go away.
*What Arthur actually said is machines in the U.S. could produce output equal to the 1995 U.S. economy, which employed 100 million people. Close enough. As a point of reference, the current U.S. total of jobs is around 150 million.