• January 27, 2015
  • End of a long day looking out a window with near zero visibility due to a raging blizzard. We’re home and safe. But scanning the New York Times, provides some unintended humor such as:

    “Massachusetts state officials used electronic signs on highways to speak to Boston drivers in their native language: ‘Wicked Big Storm Coming. Pahk Ya Cah!’”

    Yes, we really, really talk like that though we also freely sprinkle in expletives. Watching the Super Bowl with natives will be a real treat.


    “In the category of hell-freezes-over, Roger Carroll of The Telegraph of Nashua, in New Hampshire, sent out this Tweet: ‘Here’s how you know storm is serious: NH is closing liquor stores on Tuesday. #nhpolitics #hellfreezesover’”

    Years from now people will tell their grandchildren that the storm of ’15 was so bad that… . Young people will wonder why their iWatch17’s didn’t just dispense a drop of alcohol directly into the blood stream through a micro-pore in the skin.


    “Gov. Maggie Hassan of New Hampshire told New England Cable News that she had closed state government, which she said was an ‘unusual event’ in hardy New Hampshire, because snow was coming down at four inches an hour and visibility was dangerously low. The governor did not order a travel ban, she said, because she did not want law enforcement officials distracted by having to enforce it. By and large, she said, residents were cooperating and staying at home.”

    In addition to being hardy, New Hampshire is notoriously tight-fisted—they would say frugal as only New Englanders can be. Leave it to them to think that a travel ban in a blizzard with snow falling at 4 inches per hour and 40+MPH winds would need enforcement and the expenditure of police budget dollars.



    Published: 2 days 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: 3 days 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: 2 weeks ago

    Many thanks to Chris Bucholtz and CRM Buyer for including me in the Top 20 CRM Blogs of 2014. Your can see the first ten in the list here and next week the next group of ten will be announced.

    It’s always a treat to be recognized for something you do and this mention is especially welcome. I write as a way of organizing my thoughts and as Chris correctly points out, “You never know what you’ll find on Denis’ blog.” But I am glad that you, and he, take the time.

    Published: 2 weeks ago

    We are now through almost 15 years of the century and for all of that time I have been analyzing the CRM industry as it has evolved. This year, rather than simply reviewing some of the progress we made in the industry for the last 12 months, I think taking a broader view of the decade and a half might be more interesting. It certainly gives us a great perspective on how far we’ve come.

    CRM was already a thing at the turn of the century and it had been gathering steam throughout the 1990s. But it suffered from the same troubles that ERP was having at the time. The on-premise software was expensive and it usually cost somewhere between 2 and 3 times the license fee to get the products to work in your business. Part of the high cost of integration was that few vendors had all of the products under one roof — i.e. well integrated. You could buy SFA from one vendor but you’d need to buy call center and service products from others and subscriptions were embryonic.

    Marketing automation as we know it today didn’t exist except in the mind of a Canadian entrepreneur, Mark Organ. It was largely an accounting system designed to help manage marketing spending. It is worth noting today that marketing spending only came under control once we were able to apply analytics and capture great heaps of customer data. We got costs under control because we were able to be smarter about where we spent our resources, not because we watched the pennies.

    In this scheme there was no thought of using software to support business processes beyond being able to cover actual transactions, which is not the same. CRM grew up in a time when transaction management was all you needed so no one knew the difference. But try applying 15-year-old sales or call center software transaction management tools to today’s process oriented business world and you’ll have your clock cleaned by your customers.

    Process orientation is, to me, the greatest difference between then and now. Even though our businesses for the most part still do a middling job of process support today, the tools are much better and any company that wants to take on the challenge can bring together a suite of end-to-end processes supported by modern software.

    So why don’t we do a better job of supporting front office business processes today? I suspect it’s because we don’t know what it is. For instance, consider the customer onboarding process. I don’t think it existed 15 years ago and if it did in your company, it was completely manual. On boarding was well served in cases where there was a big need for installation and training — recall that 2x to 3x multiplier. But onboarding for smaller products was overlooked either because products were assumed to be intuitive (though many weren’t) or because a manual came with them. At any rate, because there was no such thing as a subscription as we know it now, once the sales transaction was completed a vendor simply went on to the next opportunity.

    Onboarding became a real issue when vendors noticed an avalanche of calls to the service center. With some products you could sense customer resentment, especially as third party sentiment sites and communities gained traction. Manuals went on line and FAQs proliferated and that satisfied enough people though it barely solved the problem.

    Today, vendors treat onboarding more seriously, in part because so many things are now bought as subscriptions. With subscriptions if you don’t onboard customers successfully they can easily leave you taking their revenue streams and any investment you’ve made in them out the door. So vendors chase customers today to get them involved with their products after the purchase. They have databases and procedures designed to get customers up to speed and happily involved so that frustration doesn’t rise and lead to attrition.

    That was just onboarding. How many other front office business processes need the same treatment? There are two answers to that question — a lot of them and all of them. There are a lot of business processes that the front office engages in and they all need to come under the jurisdiction of a unified system for many of the same reasons onboarding is so important.

    You might be able to automate support for a single process like onboarding through a Herculean programming effort but if you try to duplicate it for all of your front office processes you’ll go crazy and broke. It’s hard to say which happens first. Yet here we are on the eve of 2015 still talking about individual apps as if it was the eve of the new century. We have apps that will shave a few minutes off a sales rep’s day or apps that will enable a call center rep to get off one call and on to another 10 seconds faster but we still don’t get the results we want.

    Those apps make about as much sense as installing an accounting function in marketing did a long time ago. The route to better front office business processes runs through data and analytics. But the findings must be incorporated into the next customer encounter through machine learning and other nifty tools. Doing this brings us up to the present and reveals the importance of platform, the thing that incorporates all of our data, analytics, social, and mobile technologies into a powerful tool that enables our businesses to change as quickly as our markets.

    That’s the biggest difference between today and 15 years ago. Back then you could run a business based on the old mass production and mass marketing paradigms because few things changed much. Today change is constant and our ability to keep up resides not in our systems but in our platforms. What makes up our platforms and how they’re brought into business challenges is what the future of CRM is about.

    Published: 4 weeks ago