You might remember Garry Kasparov, the last chess grand master to beat a computer. That was about 20 years ago when he went up against Deep Blue, the IBM megaframe that is the direct ancestor of Jeopardy!-winning Watson. A re-match between Deep Blue and Kasparov a year later did not go so well for the human and that was the end of an era. You might even recall the Jeopardy! tournament in which two of the greatest players of all time, including Ken Jennings, matched wits and lost.
It would be a mistake to say that we’ve arrived on the threshold of artificial intelligence based on the Jeopardy! and chess performances but we may have reached an accommodation point. When computer science quit (for the moment) trying to emulate human thinking, it began to make real progress.
Watson, and it’s predecessor did as well as they did because they were loaded with up to 200 million pages of information that they could retrieve and sort through very quickly to winnow out possible wrong answers, stack rank the good ones, and offer up the most likely possibility. There was no thinking involved it was all raw horsepower. Watson was tuned for jeopardy! just as other versions are being tuned for things like medical diagnosis.
Watson isn’t always right but the combination of speed and accuracy enabled it to win Jeopardy! Interestingly, when asked how he would attempt to beat the computer one of Kasparov’s not so lucky colleagues, offered this advice—bring a hammer to the competition. Kasparov did the next best thing — he invented chess moves that hadn’t been written down yet and thus could not be in Deep Blue’s database. As a result the machine was forced to puzzle through (let’s not call it thinking) its options and it didn’t always choose wisely. It lost the tournament.
This bit of chess history has direct applicability to CRM and front office business. Like Kasparov’s strategy, business comes at you randomly, chaotically and the best you can do is to prepare for the well-known eventualities, even if they don’t happen to everyone all the time. Think of it like insurance. When a moment of truth hits, you’re glad to be covered.
From his experience with computers and chess, Kasparov penned an algorithm that serves as a blueprint for us. It says that you can’t rely on a brilliant player or a great computer alone, but that the key to success was, “Weak human + adequate computing + great process.” Did you note that Kasparov’s formula is the same people, process, and technology in a different order that we’ve all been preaching for many years?
You’d probably take umbrage at the notion of a weak human and rightly so because in business you have to be able to play with the team you have, not the one you wish for. So for business purposes let’s think in terms of empowered humans since we always want to empower our employees to do great things, at least we should. And computing is no longer a big deal. You can buy a lot of compute power for very little today so adequacy in the Kasparov sense can be assumed. So what separates great companies from the also-rans?
People haven’t changed in the last couple of decades; it takes millions of years for evolution to work its magic. Computers evolve before our eyes though and what was not possible yesterday is taken for granted tomorrow. Such is the case with process and the rapid advance of technology is one reason that new processes are coming on-stream all the time.
The MIT professors Erik Brynjolfsson and Andrew McAfee take Kasparov as the jumping off point in their book The Second Machine Age (yes, I have written about them before). They tell us that even for expert jobs we need to heed Kasparov, eschewing chasing success with only one or two of those resources. With computing being a level playing field and the human capital market being a coin toss process becomes the differentiator.
I think those ideas are essential to CRM success these days and I have written about how it all comes together in my new book, Solve for the Customer. Customer-facing processes are now undergoing a massive change simply because customers are increasingly demanding more sophistication in the processes they participate in — well beyond the purchase transaction and especially our uneven attempts at customer experience. Also, it is dead solid easy to figure out better processes, if you want to commit to change, that is.
In this case, change simply means checking your assumptions about customers and “the customer experience” at the door. Instead, you need a method, which I call Customer Science, to interrogate customers, capture their data, and apply some adequate computer analysis. The result is amazing. Like an insurance company you’ll be able to figure out the probabilities of a host of customer facing issues — called moments of truth — so that you can prepare for them. This beats relying on faulty memory and “experience” to speculate what a customer experience is all about.
As a matter of fact, if you embrace Customer Science you’ll discover that customer experience is no longer a noun, it’s a verb, as in customers experience a moment of truth. Knowing that you’ll compete so much better in the second machine age. It’ll be like winning Jeopardy! every day.
Part of my New Year routine has been ordering new business cards. In this electronic age they are the only things I actually print, and I’m a writer! Well, actually, in a few weeks I’ll publish a book, Solve for the Customer, in paperback, and the two are related. Printing cards requires a special diligence because the process is akin to carving something in stone while electrons are nearly free and inspire an iterative process that’s too expensive for the printing press; so you really need to think about what’s on them.
I made a discovery while writing the book that fundamentally changes what I do for a living and the title I use, hence my need to reprint. The discovery is this: The front office and its practices have transitioned from art to science before our eyes.
Now many writers including me, have recognized the need for such a conversion over the last decades and CRM more than once gave us hope that the change was happening. But we’ve been disappointed more times than I can count. Writing the book forced me to examine the panoply of technologies we now use, or at least offer, to front office practitioners to pursue their business efforts and for the first time, it appears that they all can work together in support of end-to-end efforts, not just individual transactions.
But that doesn’t make a science. For my definition of science, I looked to a major authority, Thomas Kuhn author of the 1962 landmark work, The Structure of Scientific Revolutions. I know what you’re thinking, this is way out in the weeds, but if you have a minute, it gets interesting.
Kuhn examined how the sciences got started how, astronomy and physics gradually separated from astrology; how chemistry removed itself from alchemy. His observations were distinct and consistent and amounted to this: when we stopped assuming things and began experimenting and using a little math to understand our observations, sciences crystallized. Newton (and Leibnitz) invented calculus and with it went on to describe mechanics and modern physics for instance.
But hard sciences like physics and chemistry are only the most obvious examples for most people. The social sciences from economics to sociology also rely heavily on math, statistics to be precise, and their prominent symbol is the bell curve. In field after field, statistics have enabled us to understand the social world providing us with probabilities of aggregate action if not exact formulaic certainty. When we hear than particular lifestyle choices can result in maladies later in life, it is not because there is a certainty but because there is both causation (the behavior choice) and correlation (a high number of occurrences) and that insight is provided by statistics.
The front office is like that and now that we have big data and analytics we can derive the causes and correlations to come up with the best practices, processes, and requirements vendors can take. But big data and analytics alone are not enough to call a science. In Customer Science (my term for it) I make an important distinction that revolves around customer experience and it requires the concept of a moment-of-truth.
We use customer experience as a noun in CRM (i.e., the customer experience), and for a long time it has been a noun. But once we begin to think in terms of customer-facing processes situated around a moment-of-truth, experience becomes a verb. You experience a moment of truth. That’s an important difference and one that significantly helps vendors and customers.
A traditional customer experience is subjective and when you consider the multitude of customers and the totality of their experiences, you are dealing with a big number. Because all experiences are subjective they are also unique — there are billions and billions of them. With so many unique experiences you can see that dealing with them, and trying to build software to accommodate them, is impossible.
But assessing customers’ experiences with a moment-of-truth approach is a more manageable problem. True, the experience is still subjective and customers are still unique. But there’s a limited number-of-moments of truth in any business, which your customers will be glad to verify, and these moments-of-truth are linked in cascades with each step setting up the next until you reach a desired conclusion. Without Customer Science, too often these cascades can end abruptly and leave customers frustrated.
Succeeding in moments-of-truth and successfully navigating a cascade is Boolean — on or off, up or down, true or false — it worked or it didn’t. If it all works you have a happy customer; if the moment-of-truth doesn’t work and the cascade gets broken, you can pinpoint the problem and know exactly what to do to make it right. As a matter of fact, you can develop contingency plans in advance for all of the things that could go sideways. Of course you’d do this in your journey builder application, which brings up the need for a multifaceted software platform.
The modern software platform is the tool of Customer Scientists. Well-constructed platforms offer the needed technologies used to capture and analyze customer data and the social tools to communicate with customers. Most importantly, the software platform also provides the data gathering, analytic, social, journey mapping, workflow, and code generating facilities that can turn customer insights into running apps that support moments-of-truth.
For decades CRM vendors have delivered point solutions to support front office business. Now through Customer Science, we can bring all of the components together in a strategy that supports the customer lifecycle while efficiently and cost effectively positioning customer-facing resources to address customer needs. In a nutshell that’s Customer Science and it’s why, from now on my business cards read: Customer Scientist.