Automation — With a Little Help From Good Machines
We should be mindful when dropping buzzwords (refer to “Why Buzzwords Suck”). As more and more people jump on the bandwagon of a buzzword, it tends to gain magical power. Eventually, some may even believe that buying into a “word” will solve all their problems.
But does it ever work out that way? Did anyone make a fortune buying into the Big Data hype yet? I know some companies did; but, ironically, the winners do not even utter such words. I’ve never seen any news release from Google or Amazon that they are investing in “Big Data.” For them, playing with large amounts of data have been just part of their businesses all along.
Now the new buzz is about AI, machine learning and automation, in general; and it will be a little different from buzzwords from the past. Whether we like it or not, that is the direction that we are already headed in the world where each decision will be increasingly more dependent on deterministic algorithms.
Some even claim that human behaviors are just algorithmic responses developed over past 70,000 years or so. Now, armed with data that we are casually scattering around, machine-based algorithms outperform human brains in most areas already, and such evolution will continue until most humans will become largely irreverent in terms of economic value, they say. Not that it would happen overnight, but the next generation may look at our archaic way of things the way we look at our ancestors who were without computers.
First, the Marketing Case for AI
If such is our fate, why are contemporary humans so willingly jumping onto this automation bandwagon where machines will make decisions for us? Because they are smarter than average humans? What does “smart” even mean when we are talking about machines? I think people generally mean to say that machines remember details better than us, and calculate a complex series of algorithms faster and more accurately than us.
Some may say that humans with experiences are wiser with visions to see through things that are not seemingly related. But I dare to say that I’ve seen machines from decades ago finding patterns that humans would never find on their own. When machines start learning without our coaching or supervision — the very definition of AI — at a continuously increasing rate, no, we won’t be able to claim that we are wiser than machines, either. In the near future, if not already.
So, before we casually say that AI-based automation is the future of marketing, let’s ask ourselves why we are so eager to give more power to machines. For what purpose?
The answer to that philosophical question in the business world is rather simple; decision-makers are jumping onto the automation bandwagon to save money. Period.
Specifically, by reducing the number of people who perform tasks that machines can do. As a bonus, AI saves time by performing the tasks faster than ever. In some cases — mostly, for small operations — machines will perform duties that have been neglected due to high labor costs, but even in such situations, automation will not be considered a job-creating force.
Making the Marketing Case for Humans Using Data
Some may ask why I am stating the obvious here. My intention here is to emphasize that automation, all by itself, doesn’t have the magic power to reveal new secrets, as the technology is primarily a replacement option for human labor. If the result of machine-based analytics look new to you, it's because humans in your organization never looked at the data the same way before, not because it was an impossible task to begin with. And that is a good thing as, in that case, we may be talking about using machine power to do the things that you never had human resources for. But in most cases, automation is about automating things that people know how to do already in the name of time and cost savings.
Like any other data or analytics endeavors, we must embark on marketing automation projects with clear purposes. What would be the expected outcome? What are you trying to achieve? For what types of tasks? What parts of the process are we automating? In what sequence?
Just remember that anyone who would say “just automate everything” is the type of person who would be replaced by machines first.
At the end of that automation rainbow, there lie far less people employed for given tasks, and only the logical ones who see through the machines would remain relevant in the new world.
Nonetheless, providing purposes for machines is still a uniquely human function, for now. And project goals would look like those of any other tasks, if we come back to the world of marketing here. Examples are:
- Consolidate unorganized free-form data into intelligent information — for further analyses, or for “more” automation of related tasks. For instance, there are thousands of reasons why consumers call customer service lines. Machines can categorically sort those inquiries out, so that finding proper answers to them — the very next logical step — can also be automated. Or, at least make the job easier for the operator on the call (for now). Deciphering image files would be another example, as there has been no serious effort to classify them with sheer manpower in a large scale. But then again, is it really impossible for humans to classify large numbers of images? How about crowdsourcing? Or let an authoritarian government force a stadium-full of North Koreans to do it manually? We’d use machines, because it would be just cheaper and faster to do it with machine learning. But who do you think corrected wrong categorization done by machines to make them better?
- Find the next, best product for the buyer. This one is quite a popular task for machines, but even a simple “If you bought this, you would like that, too” type of product recommendation would work far better if input data (i.e., product descriptions and product categories) were well-organized — by machines. Machines work better in steps, too.
- Predict responsiveness to channel promotions and future value of a customer. These are age-old tasks for analytics teams, but with sets of usable data, machines can update algorithms and apply scores, real-time, as new information enters the system. Call that AI, if algorithms are updated automatically, all on its own. Actually, this would be easier for a machine to pick up than fixing messy data. Not that they will know the difference between easy and difficult, but I’m talking about in terms of ease of delegation, from our point of view.
- Then ultimately, personalize every interaction with every customer through every touch channel. I guess that would be the new frontier for marketers, as approaching personalization on such massive scale can’t be done without some help from good machines. But I still stand by my argument that each component of personalization efforts is something that we know how to do (refer to “Key Elements of Complete Personalization”). By performing each step much faster with machines, though, we can soon reach that ultimate level of personalization through consolidation of services and tasks. And the grand design of such a process will be set up by humans — at least initially.
This Human's Final Thoughts on AI
These are just some examples in marketing.
If we dive into the operational side, there will be an even richer list of candidates for automation.
In any case, how do marketers stay a step ahead of machines, and remain commanders of them?
Ironically, we must be as logical as a Vulcan to control them effectively. Machines do not understand illogical commands, and will ignore them without any prejudice (but it would “feel” like disrespect to us).
Teaching Humans to Automate
I heard that some overzealous parents started teaching computer programming to 4- or 5-year old children, in addition to a foreign language and piano lessons. That sounds all Cool and the Gang to me, but I wondered how they would teach such young kids how to code.
Teaching Machines to Human
If you try it, you will find that the task of writing a spec for a machine is surprisingly tedious.
Just for a little grilled cheese sandwich, you have to:
- instruct it on how to get to the breadbox,
- how to open it,
- how many slices of bread should be taken out,
- how to take them out without flattening them (applying the right amount of pressure),
- how to open the refrigerator,
- how to locate butter and cheese in the mix of many food items,
- how to peel off two slices of cheese without tearing them,
- how to ignite a stove burner,
- how to find a suitable pan (try to explain “suitable,” in terms measurements and shape),
- how to preheat the pan to a designated temperature (who’d design and develop the heat censor?),
- how to melt butter on the pan without burning it,
- how to constantly measure and monitor the temperature,
- how to judge the right degree of “brown” color of grilled cheese,
- etc. etc..
If you feel sick reading all of this, well, I didn’t even get to the part about serving the damn sandwich on a nice plate yet.
Anyway, Human Marketers, Here's the Conclusion
I am not at all saying that all decision-makers must be coders. What I am trying to emphasize is the importance of breaking down a large task into smaller “logical” steps. Smart machines will not need all of these details to perform “known” tasks (i.e., someone else taught it already). And that is how they get smarter. But they would still work better in clear logical steps.
For humans to command machines effectively, we must think like machines — at least a little bit. Yes, automation is mostly about automating things we already know how to do. We use machines to perform those tasks much faster than humans. To achieve overall organizational effectiveness, break down the processes into smaller bits, where each step becomes the stepping stones for the next. Then prioritize which part would be the best candidate for automation, and which part would still be best served by human brains and hands.
For now, that would be the fastest route to full automation. As a result of it, many humans may be demoted to jobs like reading machine-made scripts to other humans on the phone, or delivering items that machines picked for human consumers in the name of personalization. If that is the direction where human collectives are headed, let’s try to be the ones who provide purposes for machines. Until they don’t even need such instructions from us anymore.
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 email@example.com.