customer science

  • March 12, 2015
  • W. Edwards DemingCRM makes several promises to its users including selling more or faster, resolving service issues faster or at least quickly, and generating more leads. But if you do root cause analysis you can quickly conclude that at least in some cases, you are looking through the wrong end of the telescope.

    For instance, the best way to resolve service issues is to avoid them in the first place. Figuring out how to head them off is both highly cost effective and better for raising your profile with your customers. Heading problems off is beyond the scope of CRM though a close second in this derby is deflecting customers to other less expensive channels. Although these things have a passing resemblance, deflecting is worlds away from heading off.

    Much the same can be said of selling faster. I am not a fan of faster selling because we’ve reached the point where we are pushing on a string. You need only recall that the customer buying process is in direct competition with traditional sales processes to see this.

    This leaves marketing and if you look at the strides made by marketing in the last few years you might come away wondering how they did it. Marketers really are generating more leads and they are better qualified by the time they get to sales people. So how did they do it?

    One of the big differences I see between marketing and the rest of CRM is that marketers constantly ask the customer for feedback. They might not launch questionnaires every minute, but the data stream coming back from numerous marketing initiatives provides them with great insight into what’s going on in their respective patches and it tells them what needs to be done next.

    This feedback loop is not new; it goes way back to W. Edwards Deming and his regimen of statistical analysis. Deming was all over manufacturing like a cheap suit. He collected data and ran stats on manufacturing so that he could know what worked and what didn’t. He then went the extra mile and tried to weed out what didn’t work and reward or encourage what did. In the process manufacturing got better. There were fewer defects, better tolerances between parts, and manufacturing costs declined because there was less waste.

    Japanese manufacturers were, famously, the first big adopters of Deming’s ideas and we all know what that led to. They have a word that’s useful to remember, kaizen, which roughly means continuous improvement.

    So how does this apply to CRM? Quite simply, modern marketing has its own kaizen thing happening and I think it’s worth asking how we can apply these principles to sales and service. In service, we have metrics that look at speed like time in queue, time in resolution process, and lots more metrics that measure speed but not necessarily effectiveness of the encounter. It’s great to get customers on their way quickly, but what does this do for handling cross-sell and up-sell opportunities?

    But it’s in sales that I am most intrigued by the possibilities of a kaizen strategy. A slim majority of sales people makes or exceeds quota every year. Jim Dickie, Managing Partner of CSO Insights puts the number at 58 percent in most recent years, plus or minus a little. The interesting thing about selling for me is that there’s usually only one evaluation point — did you close the deal? But what if we used a more incremental approach to assess sales like marketers do when they capture customer input throughout the nurturing process?

    That might be a scary proposition for a lot of sales people. If there was a natural point in the process when everybody stopped swimming for a moment to look up and determine if they’re still on track, the feedback might be very useful. For example, how was the demo? It’s a very important part of the sales process and good reps will ask during and after if the demo answered the customers questions and concerns or “Was it alright?”

    Customers will often play along but saying that the demo answered all questions is not the same as saying that the demo showed me that this solution would work in my shop. A customer running a buying process wants to be able to have alternatives at decision time so it’s not good to eliminate all contenders lest there be no leverage when discussing price. So everybody keeps swimming until most of the sales people are surprised at the end when their solutions aren’t selected. After all, everything in the process went according to plan.

    That’s why it might be useful to go up a level of abstraction in the sales process by sponsoring a mid-process questionnaire. But rather than asking about nebulous things like the rep’s professionalism or even if the five major points of the demo were articulated well, ask open ended questions about fit. On a scale of 5 or 10, how would you rate this solution’s suitability for your needs?

    There are a lot of questions you could ask and they will give you a better picture of your position in the account than relying on traditional process milestones that are completely vendor oriented — i.e. if we’ve done the demo we must be X percent of the way through the process.

    If we take a kaizen approach and don’t simply wait until the process is over to assess our performance we may not be able to impact an ongoing sales process (though we might) but we will do two other useful things. We’ll improve selling over a short time and we’ll be able to safely weed out deals that won’t go anywhere. Eliminating bad deals saves resources and is the ultimate form of sales acceleration.

    Published: 7 years ago

    Knight-Rider-in-Chess-gameYou 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.

    Published: 7 years ago

    Land-Drill-rigNo, this is not an article about fracking, drilling for gas and oil in shale. This is about drilling down into big data. We’ve been using the term for a long time and it provides a useful metaphor for data analysis. But we’ve conditioned ourselves to think of drilling down only to a superficial degree and that needs a rethink.

    When data wasn’t big and analytics relied on less robust hardware we were only able to scratch the surface of our data, a practice that survives to this day. Scratching often means looking for insights only at the end of business processes. So for example, we look for signs of churn next week or the next best offer today, or to forecast the next sales deadline. All of this is valuable but not enough. If we’re doing our jobs right, we should be using powerful analytics to perform root cause analysis to better forecast events so that we can either avoid them entirely or further enhance our likelihood of success.

    What if you could go further into your data so that rather than simply discovering someone or some business that was about to leave your service (churn, non-renewal) you could find those moments of incipient danger and correct a problem at the source? You can but it requires change, not more hardware or better software though those things are always welcome, but a different way of framing the challenge in front of you.

    Too often we make assumptions about some aspect of business and then collect and analyze data about it. That’s a good approach as long as the assumptions are valid and accurate but too often they are not. When we assume something we are building an ad hoc model of what we believe reality is and that’s a good thing. Modeling is the heart of all kinds of progress in any number of fields of human endeavor but it’s not something we do particularly well in business with some exceptions.

    As Nate Silver writes in The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t, “We need to stop and admit it: we have a prediction problem. We love to predict things — and we aren’t very good at it.” You might recall that Silver called 49 out of the 50 states correctly in the 2012 presidential election. This man does not have a prediction problem.

    Retailers might be an exception; they model heavily and they do a good job. They collect customer and store data so that they can model the ways they set up stores and plan the assortments that they stock. Those models mirror very closely the clientele and traffic for an individual store. When it comes to online business and B2B business we aren’t there yet because it’s both a different and a harder challenge.

    Finding a solution in the online world starts with figuring out your model before you make any assumptions and before you implement something. (This is not your business model but your approach to customers, which is part of the business model.) It’s surprisingly easy to do if you take a two-step approach to analytics.

    Step one, build a realistic model of your business by asking your customers. I call this discovering your moments-of-truth and I write about it in my new book, Solve for the Customer, which will be available shortly. As you know if you read this space often, a moment-of-truth is simply any time your customers expect you to live up to a promise whether that’s a product, company, or brand promise irrespective of whether the promise is expressed or implied.

    Knowing your moments-of-truth you can build customer facing processes in Step Two. Your processes and supporting software will meet customers where they live, so to speak. The best way to do this is with journey mapping software because it lets you examine all of the contingencies and define sub-processes as appropriate. It’s also the logical place to define metrics that will tell you if you are meeting your goals in your moments-of-truth.

    For example, customer onboarding is a good example of a moment-of-truth and there are many analytics vendors that focus on customer health as a function of how quickly customers get down your learning curve. People at Scout Analytics, for instance, tell me that there is a direct correlation between customer longevity and how fast they onboard. Knowing this, smart vendors deploy customer success managers to ensure that onboarding is swift and trouble free.

    You can identify moments-of-truth like this throughout your customer lifecycle and often those moments do not automatically require expensive human intervention. But having a moments-of-truth approach plus good analytics, rather than assumptions, enables a vendor to deploy resources where they’ll be most beneficial to both the customer and to the vendor.

    None of this is hard. In fact once you change your frame of reference (a.k.a. your paradigm) from ad hoc assumptions to dedicated and conscientious modeling, it flows. When we move from random approaches to modeling, which incorporates a bit of statistics (and that is what analytics is about to a great degree) we pass from a framework of art to one of science. That’s what’s happening right now in many areas of front office business and it’s why I’m saying we’ve arrived at a new science, Customer Science.



    Published: 8 years ago

    Customer journeyPart 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.



    Published: 8 years ago

    2a8baf6The journey map and the tools used to make one might be the sleeper part of CRM in the year or two in front of us. The reason is simple, journey maps enable you to figure out your processes and they are useful in much more than just marketing.

    Recently in this column I’ve discussed the emerging science of the customer, which is probably not the term that will ultimately stick in anyone’s head. But the key element of the idea is that the random and highly reactive approach to front office business is rapidly being eliminated because big data and analytics are showing us where the opportunity clusters are and where we shouldn’t spend our precious time.

    There is a virtuous circle nature to this. We need better processes because our analytics tell us rather convincingly what our customers think is a waste of effort and those things that we really ought to pay attention to. But knowing the things is not understanding process. A customer might want an end result but it’s the vendor’s obligation to deliver all the things that lead up to the result a.k.a. the processes.

    Take customer onboarding as an example. It’s an idea familiar to a lot of us but how many of us take responsibility for it? There’s glamour in sales, less in marketing and once the customer crosses what we consider the finish line, too often that’s it. But customer onboarding is the most important part of the whole relationship, other than securing the John Hancock because it builds the relationship.

    Onboarding can include everything from getting the product out of the box and registered to correctly entering your metadata. The point is that every business has a set of rituals that must be observed but too often there’s no high priest. Maybe there should be since onboarding is one of the key moments of truth that a vendor-customer relationship has.

    Not to get too far down a single path, but onboarding is just one example of a customer journey that looks like a dotted line dirt road on your map and that’s where journey mapping becomes important. There should be zero dirt roads and vendors need journey mapping to plan out all of the possible ways a customer can take a wrong turn so that they can make the situation right well before frustration sets in and bad things follow.

    So journey maps are really important in marketing because we want to establish an absolutely fool-proof path from interest to closed deal just as we want to get the product out of the box and a smile on the customer face. You might recall when I introduced the idea of customer science that I likened it to a form of sociology whose major point of interest is how societies erect structures for people to work within. In sociology the opposite of structure is agency as in taking responsibility for going it alone. When customers carve their own paths too often bad things like attrition are the result that’s why structure is so important.

    So you can look at the journey map and its supporting tools as the business’ structure making socio-customer-science effort to keep revenues flowing in one direction and customer happiness percolating in the other and that would be a good thing.

    Journey maps will be found all over your business in a few years, starting with marketing’s effort to corral customers to sales’ attempt to get them branded, but there’s more. We’ve looked at onboarding already but I think the biggest impact journey maps will have will be in service and support. Currently too many businesses have one or maybe a small handful of service and support processes and few specialists. That’s about all you can have with manual processes or dirt roads in this parlance.

    But at worst, a business ought to be able to segment services and support into maybe 8 or 10 service processes each with its own specialists and SLA. A how-to request might come into the same place as a request for enhancement but the two ought to take very different trajectories from there. Currently that’s too manual and mapping these separately might be the first step to speed up both and deliver greater satisfaction.

    In doing that mapping, we’re finally able to concretely describe the engagement we want in ways that don’t sound like an ad for a new vitamin. Better still, and this is huge, we can ensure that the end result of the engagement is not an accidental hang-up or worse.

    Some people might say, well, that doesn’t happen any more but if you think that, you’re just not getting involved in the vendor-customer give and take enough. I got thinking about all this recently when I called my local gas and electric utility to report a dead tree hanging off some power lines like a barfly on a barstool. I made the unwitting mistake of getting to the gas side of the house rather than the sparks.

    The very nice and efficient person on the line told me she couldn’t help me and couldn’t even transfer me. Worse, the website behaved as if it was designed by whoever built Fort Knox and I repeatedly bounced off. All I could do was send a DM to what I thought was their hash tag along with an editorial comment on the efficacy of their service solutions. This being Twitter I had to compress it all into one word.

    Had this energy company thought through its moments of truth and built its journey maps rather than operating a very efficient customer service site circa 1995, things could have been different. The point is that we’ve trained customers to reach out to vendors in various ways but we are still receiving in manual mode. Time to create journey maps that will take us to the next level.

    Published: 8 years ago