Replacing Unskilled Data Marketers With AI
People react to words like “machine learning” or “artificial intelligence” very differently, depending on their interests and levels of understanding of technology. Some get scared, and among them are smart people like Elon Musk or the late Stephen Hawking. Others, including data marketers who lack strategic skills, may react based on a vague fear of becoming irrelevant, thinking that a machine will replace them in the job market soon.
On the contrary, I find that most marketers welcome terms like machine learning. Many think that, in the near future, computers will automatically perform all the number-crunching and just tell them what to do. In marketing environments where “Do more with less” is the norm, the idea of machines making decisions for them may sound attractive to many marketers. How great it would be if some super-duper-computer would do all of the hard work for us? The trouble is that the folks who think like that will be the first ones to be replaced by the machines.
Modern marketing is closely tied into the world of data and analytics (the operative word being “modern,” as there are plenty of marketers still going with their gut feelings). There are countless types of data and analytics applications influencing operations management, R&D or even training programs for world-class athletes, but most of the funding for analytical activities is indeed related to marketing. I’d go even further and claim that most of data-related work is profit-driven; either to make more money for organizations or to cut costs in running businesses. In other words, without the bottom-line profit, why bother with any of this geeky stuff?
Yet, many marketers aren’t interested in analytics and some even have fears of lots of numbers being thrown at them. A set of numbers that would excite analytical minds would scare off many marketers. For the record, I blame such an attitude on school systems and jock cultures that have been devaluing the importance of mathematics. It is no accident that most “nerdy” analysts nowadays are from foreign places, where people who are really good at math are not ridiculed among other teenage students but praised or even worshiped.
The joke is that those geeky analysts will be replaced by machines first, as any semi-complex analytical work is delegated to them already. Or will they?
I find it ironic that marketers who have a strong aversion to words like “advanced analytics” or “modeling” would freely embrace machine learning or AI. Because that is like saying you don’t like music, unless it is played by machines. What do they think machine learning is? Some “thinking-slave” that will do all of the work without complaint or asking too many questions?
Machine learning is one of many ways of modeling, whether it is for prediction or pattern recognition. It just became more attractive to the business community as computing power increased over time to accommodate heavy iterations of calculations, and because words like neural net models were replaced by easier sounding “machine learning.”
To wield such machines, nonetheless, one must possess “some” idea about how they work and what they require. Otherwise, it would be like a musically illiterate person trying to produce a piece of music all automatically. Yes, I’ve heard that now there are algorithms that can compose music or write novels on their own, but I would argue that such formulaic music will be a filler in a hotel elevator, at best. If emotionally moving another human being is the goal, one can’t eliminate all human factors out of the equation.
Machines are to automate things that humans already know how to do. And it takes ample amounts of “man-hours” to train the machine, even for the relatively simple task of telling the difference between dogs and cats in pictures. And some other human would have decided that such a task would be meaningful for other humans. Of course, once the machines are set up to learn on their own, a huge momentum will kick in and millions of pictures will be sorted out automatically.
And as such evolution goes on, a whole lot of people may lose their jobs. But not the ones who know how to set the machines up and give them purposes for such work.
Let’s Take a Breath Here
Dialing back to something much simpler: Operations. In automating reports and creating custom messages for target audiences, the goals must be set by stakeholders and machines must be tweaked for such purposes at the beginning. Someday soon, AI will reach the level where it can operate with very general guidelines; but at least for now, requesters must provide logical instructions.
Let’s say a set of reports come out of the computer for the use of marketing analysis. “What reports to show”-type decisions are still being made by humans, but producing useful intelligence in an automated fashion isn’t a difficult task these days. Then what? The users still have to make sense out of all of those reports. Then they must decide what to do about the findings.
There are folks who hope that machine will tell them exactly what to do out of such intel. The first part may come close to their expectation sometime soon, if not already for some. Producing tidbits like “Hi, human: It looks like over 80% of your customers who shopped last year never came back,” or “The top 10% of your customers, in terms of lifetime spending level, account for over 70% your yearly revenue, but about half of them show days between transactions far longer than a year.” By the way, mimicking human speech isn’t easy, but if all these numbers are sitting somewhere in the computer, yes, it is possible to expect something like this out of machines.
The hard part for the machines would be picking five to six of the most important tidbits out of hundreds, if not thousands of other “facts,” as that requires understanding of business goals. But we can fake even that type of decision-making by assuming most businesses are about “increasing revenue by acquiring new valuable customers, and retaining them for as long as possible.”
Then the really hard part would be deciding what to do about it. What should you do to make your valuable customers come back? Answering that type of question requires not only an analytical mindset, but a deep understanding in human psychology and business acumen. Analytics consultants are generally multi-dimensional thinkers, and the one-trick ponies who just spit out formulaic answers do not last too long. The same rule would apply to machines, and we may call those one-dimensional machines “posers” too (refer to “Don’t Hire Data Posers”).
But let’s say that by entering thousands business cases with final solutions and results as a training set into machines, we finally get to have such machine intelligence. Would we be free from having to “think” even a bit?
The short answer is that, like I said in the beginning, such folks who don’t want to analyze anything will become irrelevant even sooner. Why would we need illogical people when the machines are much cheaper and smarter? Besides, even future computers shown in science fiction movies will require “logical” inquiries to function properly. “Asking the right question” will remain a human function, even in a faraway future. And the logical mindset is a result of mathematical training with some aptitude for it, much like musical abilities.
The word “illiterate” used to mean folks who didn’t know how to read and write. In the age of machines, “logic” is the new language. So, dear humans, do not give up on math, if self-preservation is an instinct that you possess. I am not asking everyone to get a degree in mathematics, but I am insisting that we all must learn about ways of scientific approaches to problem-solving and logical methods of defining inquiries. In the future, people who can wield machines will be in secure places — whether they are coders or not — while new breeds of logically illiterate people will be replaced by the machines, one-by-one.
So, before you freely invite advanced thinking machines into your marketing operations, think carefully if you are either the one who gives purpose to such machines (by understanding what’s at stake, and what those numbers all mean), or one who can train machines to solve those pre-defined (by humans) problems.
I am not talking about some doomsday scenario of machines killing people to take over the world; but like any historical events that are described as “revolutions,” this machine revolution will have real impact on our lives. And like anything, it will be good for some, and bad for others. I am saying that data illiterates who would say things like, “I don’t understand what all those numbers mean,” may be ignored by machines — just like they are by smartass analysts. (But maybe without the annoying attitudes.)
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is principal and chief product officer at BuyerGenomics. Previously, Yu was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, he was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at firstname.lastname@example.org.