xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX ____________________________________________________________ Drilling Down: Turning Customer Data into Profits with a Spreadsheet To order the complete book with customer profiling application, visit my store at Booklocker.com: http://jimnovo.booklocker.com/p/index.html?s=text For more information on the software application, see: http://www.jimnovo.com/software.htm Table of Contents - Complete Book Preface Introduction About Jim Novo Chapter 1 Jonesin' for Some ROI Chapter 2 Customer Profile or Customer Model? Chapter 3 Data-Driven Marketing and Service Drivers Chapter 4 Customer Marketing Basics Chapter 5 Customer Marketing Strategy: Friction Model Latency Metric Toolkit Chapter 6 Trip Wire Marketing Chapter 7 The Hair Salon Example Chapter 8 The B2B Software Example Chapter 9 Turning Latency Data into Profits ** Your E-mailed Chapters Will End Here ** Recency Metric Toolkit Chapter 10 Predictive Marketing Chapter 11 The Ad Spending Example Chapter 12 Turning Recency Data into Profits Chapter 13 The Online Retail Example RFM Scoring Toolkit Chapter 14 Cash Flow Marketing Chapter 15 A Tweak for Interactive Customers Chapter 16 No Customer Database? How to Set Up a Spreadsheet to Score Customers Chapter 17 How to Score Your Customers Chapter 18 The Commerce and Content Examples: Turning Scoring Data into Profits Chapter 19 Case Study: Non-Profit Scores 192% Increase in ROI using RFM Model Advanced Data-Driven Marketing Toolkit Chapter 20 Using Customer Characteristics & Multiple Scores Chapter 21 Customer LifeCycles: Tracking Scores Over Time Chapter 22 Customer LifeCycle Grids: High Performance Behavior-based Modeling Chapter 23 Straight Talk on LifeTime Value Chapter 24 Lifetime Value, I'd Like to Introduce You to the CFO Chapter 25 Fellow Drillers at Work Definitions and Background Information Customer Loyalty and Retention Customer Segmentation and LifeTime Value Professional Services Ad-Supported Content / Subscription Models Online / Offline Retailing and Catalogs Distribution / Operations / Channel Management The ROI of Online Branding Efforts Chapter 26 Predicting Campaign ROI: Set Up Chapter 27 Predicting Campaign ROI: The Model Chapter 28 Predicting Campaign ROI: Fine Tuning Chapter 29 Expense and Revenue You May Not be Capturing: Subsidy Costs and Halo Effects Chapter 30 Some Final Thoughts: Seasonality, CRM, Behavioral Inertia, Data-Driven Program Outlines APPENDIX: Software Download and ReadMe ============================================================ Chapter 1 Jonesin' for Some ROI It was a day just like any other day. The Customer Retention Clinic was open, yours truly at the helm. Both offline and online marketers trudged through, with the same old issues. One is drowning in data. The other has reports that provide no actionable information. Still others have fancy models and profiles, but don't know how to use them to increase the profitability of the company. I became aware of a fresh-faced marketer, waiting eagerly in line. Something seemed different about this one. Untouched by CRM. Never been to a Business Intelligence demo. Ignores every e-mail plea to attend "educational" webcasts from vendors. "Your question?" I ask. "Jim, how can I tell if a customer is still a customer?" was the reply. I stood there, floored by the question. I knew this marketer was special. How elegant, I thought: the summation of 20 years of my work in a single question. Nobody had ever asked it before. They always want to know about the money, you know - how can I make more money, show me the tricks. Addicted to ROI. They start off innocently enough, probably with a spreadsheet. Then maybe a simple model or two. Before you know it they're into data mining. But they don't make any money for the company. Devastating. Then they show up at my Customer Retention Clinic, looking for the magic bullet, the secret to ROI. But not this one. No, this one was special. "Why do you want to know?" I asked. "Because I want to calculate our customer retention rate and track it over time" was the answer. "You can't put a retention rate in the bank, you know" was my cynical answer. "What you really need is a formal, widely accepted definition of when a customer is no longer a customer in your company. Then you will be able to get at your precious retention rate." Silence from the fresh-faced one. Then: "In customer service, they say only 10% of customers complain and tell us they will stop doing business with the company. They say this means customer satisfaction is 90%. Does that mean customer retention is 90% too?" Well, it's all well and good to be fresh-faced, but now we're getting into naive. Still, I think, maybe there is something here, something worth saving for the future of customer marketing. "Are you saying the only defected customers are ones you have documented?" I sneer. "Ones who told you they will never do business with you again? Look, to me, a customer is a person or company you sell stuff to, who pays you for a product or service. You have identified 10% who are not going to buy from you anymore; they are definitely defected customers." "But the word "customer" implies some kind of "future activity," doesn't it? I mean, if you know they will never buy from you again - as in the above complaint example - you don't call them customers, so the opposite must be true: to be a customer, there must be expectation they will buy again. If you know they will not buy again, they're former customers, correct?" "So the definition of a customer would be someone who: 1. Purchased from you in the past, and 2. Is expected to purchase in the future." "Just because somebody bought from you in the past and did not tell you they hate your guts now does not mean they are still a customer. A customer is somebody you expect to transact with you in the future; otherwise they are a former customer, by definition." Not a bad sermon, I think. "Wait a minute," says fresh-face, "what about customers who purchased in the past that we have no expectations for? We don't have any idea whether they are likely to buy or not, there is no "expectation." What about them?" Oh, so fresh-face is going to play tough with me, I think. Probably an MBA. Wait a minute; I have an MBA. Is it getting hot in here?! "Listen, you know the answer to that question, don't you? Because you don't know crap about the people you sell to and their likelihood to buy, you simply call them all "customers." You have no more reason to call them customers than to call them former customers, but of course, you "default" to calling them all customers. They didn't call up and tell you they are not customers, so they are, right? Is that what you are saying?" It is hot in here...phew. I go on. "What if they didn't tell you they hated your guts, but they told 10 other people they would never buy from you? Are they still a customer? Do you know how many there are? How many have had a bad product or service experience and never said anything? Is it 10%, 20%, 40% of your customers?" No reply. Floor staring from the face-man. I have caused hurt feelings. But I have got to move on, there are all these people waiting for their magic bullet, people needing a customer marketing fix, they're jonesin' for some ROI... "Look, I'm sorry" I say half-heartedly. "Let's come at this from a different direction that will perhaps be more helpful. Let's take all the customers who you think are customers, and ask just one question - when was the last time you had contact with these people?" "For example, the last time you had any contact with a customer was 3 years ago. Are they still a customer? With no activity for 3 years?" "Maybe" says fresh-face. "OK, fine. What about if the last contact with the customer was 5 years ago? Is this person or business still a customer?" "Maybe" is the reply. "10 years ago?" I ask, sweating. "Maybe." That worked like gangbusters, I think. No wonder nobody knows how to sell more to current customers while reducing costs. All customers are customers for life - unless they tell you they aren't anymore. Sometimes it seems as if today's marketing people have no sense of reality. They are thinking every person or business that ever transacted with them is still a customer! "All right, one more try," I say impatiently. "Take two customers - the last contact with one was 10 years ago, the last contact with the other was 2 years ago. Would you be willing to go out on a limb and say the "customer" you last had contact with 2 years ago is more likely to still be a customer than the customer you last had contact with 10 years ago?" "Yes," says the face. "Finally," I gasp. "And if the customer you last had contact with 2 years ago is more likely to still be a customer than the customer you last had contact with 10 years ago, is the customer you last had contact with 2 years ago more likely to purchase good or services from you today than the customer you last had contact with 10 years ago?" "Sure." "More likely to purchase goods or services now, and in the future, from you?" I wheeze expectantly. "Yes" is the reply. "So, let me get this straight - when comparing two customers, the customer you have had contact with more recently is more likely to purchase, relative to the other customer?" "I would think so" is the answer. "What???" I gurgle, starting to lose my balance, eyes becoming glassy... "I mean yes, Jim..." "Then, if I was to define a customer as someone who: 1. Purchased from you in the past, and 2. Is expected to purchase in the future, you would say the customer you last had contact with 2 years ago was more likely to still be a customer than the customer you last had contact with 10 years ago? Would you say that?" I ask breathlessly. "Yes!" the face shouts triumphantly. "I get it!" "So for any two "customers," the one you had contact with more recently, relative to the other, is more likely to still be a customer and keep purchasing goods or services from you, now and in the future?" "Yes!!!" fresh-face screams. "So as a marketing genius, you would then go out and treat these two customers exactly the same, spend the same amount of money marketing to them and servicing them, even though one is more likely to still be a customer and purchase than the other?" I scream back. The trap was set. "Yes!!" face blurts out. "That's what we do! We spend the same amount of money and resources on every "customer," regardless of their likelihood to still even be a customer!" "I know, your company and most other companies out there. The question is why do you do this, when it is so darn easy to tell which customers are more likely to purchase goods or services relative to the others?" And that, Dear Driller, is what this book is about. You are going to learn some very simple techniques for tracking which customers are more likely to purchase goods or services from you, and then you will learn precisely what to do with this information to increase your sales while cutting your marketing costs. Because I don't want to see you down at the Clinic, the line is too long already. First, we're going to talk a little bit about customer models - what they are and are not. Then we'll put a little background in place so you understand the basic objectives and strategy behind High ROI customer data-driven marketing. Next, we'll take a look at the simplest model of all - Latency - because it is the most intuitive model and often the easiest to implement for those just getting started with customer behavior models. Then it's on to the Recency and RFM models. Often used in tandem with the Latency model, Recency and RFM are "smarter" than the Latency model but a bit less intuitive. And finally, we'll jump into the whole Customer LifeCycle marketing methodology and show you how to use what you will know about simple customer models to really drive the profitability of your customer marketing / retention / CRM programs. By understanding what the customer is likely to do even before they do it, you can use your modeling intelligence to craft the most profitable customer marketing programs you probably have ever been a witness to. The Customer LifeCycle is the key to the fabled "right message, to the right people, at the right time" marketing kingdom. By the end of this book, you should be able to very clearly answer some basic marketing and service questions about your customer base. Questions you no doubt have asked many times yourself, such as the following: Who do I provide marketing or service programs to? When? How often? Should I contact some customers more often than others? (Yes, you definitely should.) How much and what kind of incentives should I provide to get a customer to do something I want them to? Can I predict which customers will be responsive to the program? (Yes, you can) How can I tell when I'm losing a customer or when service has failed? How can I put a value on my different customers and the business as a whole now, and project this value into the future? Is my business strong and healthy, or becoming weaker? What can I expect in future sales from existing customers? So what do you say, fellow Driller? Are you ready to cut that line at the Clinic? ============================================================ Chapter 2 Customer Profile or Customer Model? Many people think using your customer data for marketing efforts is about creating a customer "profile." It's a hot topic. Everybody wants to do it. But what is a customer profile? Here are 2 kinds of customer profiles: * Customer is married, has children, lives in an upscale neighborhood, and reads Time magazine * Customer visited the web site or business every day for 2 months, but has not visited at all in the past 2 weeks The first profile is demographic, a set of characteristics. The second profile is behavior-based, involving what the customer is actually doing. It's about customer activity. Which seems more important to you? They're both important in their own ways. For someone selling advertising, or deciding on content for a website, the first profile could be important, because it defines the market for ad sales and provides clues to editorial direction. These are important considerations in attracting customers and generating revenue in the first stages of an online project. The second profile is about action, behavior, and for anybody concerned about what his or her customers are doing, is more important than the first. Will they visit again? Will they buy again? These are the questions answered by looking at behavior. Customer behavior is a much stronger predictor of your future relationship with a customer than demographic information ever will be. You have to look at the data, the record of their behavior, and it will tell you things. It will tell you "I'm not satisfied." It will tell you "I want to buy more, give me a push." It will tell you "I think your service is awful." I'd argue the second type of profile is more important longer term, because if the customer stops buying from or visiting the site, you're not going to have much of a chance to serve up the customized pages or ads based on any "profile" given to you. You could customize the heck out of the site based on demographics or self-reported survey data but customers would never see the results if they never come back. So for the long haul, if you had to choose the more important profile, the profile based on action and behavior would be more critical to you than a demographic one. Customer behavior profiling is critical to a company interested in selling more to current customers while at the same time reducing costs. Marketers who use data often talk about "customer modeling" instead of customer profiling. Modeling is kind of like profiling, but it is action oriented. Models are not about a static state, like "Customer is 50 years old." Models are about action over time, like "If this customer does not make a purchase in the next 30 days, they are unlikely to come back and make any further purchases." It sounds so mystical, and it is. To see a mathematical model predict customer behavior is astonishing, to say the least. The model says, "Do this to these people and they will likely do this." The marketer or service provider goes out and does what the model says, and like magic, a good bunch of the customers do exactly what the model said they would. It works like a charm - usually. Building heavy-duty models is expensive, because it requires an awesome amount of talent and experience. There are many mathematical techniques used to build models, each with their own pitfalls and gotchas. Success depends a lot on the type of business, the kinds of data available, and the experience of the modeler / analyst in building models for a particular business. What is a model? Simply, it looks at customers who are engaging in a certain behavior and tries to find a commonality in them. The marketer might say to the modeler, "Here's a list of our very best customers, and here's a list of our former best customers. Is there any behavioral signal a best customer gives before they stop being a customer? What does the data say to you?" So here's what's in it for you, what this book is about. You can do your own models, based on the decades of experience Data-Driven marketers and service provider have already invested. And while they won't be as good as the heavy-duty models done by Ph.D. analysts, they'll be pretty darn good. Plus, they will help you increase profits while cutting marketing and service costs. This book will show you how to do it, with just a spreadsheet. Ph.D. not required. By the way, once you figure out your behavioral models, you can use them in combination with demographics and characteristics to produce an even richer picture of the customer. But the behavior comes first, because it is behavior you want to influence. Knowing the following about a customer is not very actionable; there is not much you can do with this information: * Customer is married, has children, lives in an upscale neighborhood, and reads Time magazine But if you add behavior to this demographic profile: * Customers who are married, have children, live in upscale neighborhoods, and read Time magazine appear to be disappointed with our site, because a high proportion of them haven't visited the site in the last 30 days you can start deciding what (if anything) you want to do about it, because you know these customers are engaging in a specific behavior. The combination of behavior and demographics can be very powerful indeed. But without the behavior, demographic characteristics don't tell you much. You will learn how to use both in building your models. First we'll talk about customer behavior, and then add customer demographics later on. ============================================================ Chapter 3 Data-Driven Marketing and Service Drivers I came up with the phrase "Data-Driven" because I needed one name for the process happening in the background of all the marketing and business optimization approaches where customer data is used. As soon as you say "Relationship Marketing" or "Loyalty Marketing" or "1-to-1 Marketing" or "Permission Marketing" or "CRM," all kinds of extra ideas creep in, obscuring what's really going on in the background of all these concepts. These approaches differ in how they are positioned to the customer, and how they are communicated. But back in the pits where the data analysts are, where customer profiling and modeling take place, they're much the same. Data-Driven marketers and service providers generally have two objectives with customer value management, which is what the above approaches are all about: 1. Hold on to the most valuable customers 2. Try to make less valuable customers more valuable So whether it's relationship marketing, a loyalty program, permission based, or 1-to-1, you still have to accomplish these goals, and to do it, you have to create marketing or service programs and execute them. This means you have to know the value of your customers and their likelihood to respond to a program, whether the program is customized based on books already purchased, uses loyalty points, or is service-oriented. The marketing and service programs named above are all "wrappers" around what is really going on - you want the customer to do something, or perhaps not do something. This means you have to reach out to the customer and communicate your marketing and service programs. When you're going to execute the communication, you need answers to 3 questions - WHAT will you say, WHO will you say it to, and WHEN will you say it. It doesn't matter what you call your program, what "wrapper" you put it in for the customer - you always have to answer these 3 questions (and maybe a few more). In addition, you probably care about how much you spend on these marketing and service programs. Ideally, instead of blasting out expensive stuff to every customer, you would want to spend money on the customers most likely to do whatever you want them to, and not waste money on those who are not. You want customers to do something, to take action. You want them to visit your website, make a purchase, sign up for a newsletter, add new services. And once they do it for the first time, you usually want them to do it again, especially since you probably paid big money to get them to do this "something" the first time. You don't want to pay big money the second time. The data can tell you how to accomplish this, no matter what kind of front-end marketing or service program you are running or how you "wrap it up" and present it to the customer. As long as you have the data, you can interpret it for clues as to what steps to take next, and how to save precious marketing dollars in the process. When you understand the fundamental ideas behind Data-Driven Marketing and Business Optimization, you will understand how to execute all of these customer retention-oriented programs, no matter what they are called. Here are the four primary ideas driving all of these programs: 1. Past and Current customer behavior are the best predictors of Future customer behavior. Think about it. Any entity you can define as a customer - external, internal, distributors, manufacturers, suppliers - they all pursue certain routines, and changes in these routines often indicate an opportunity or challenge is ahead in your relationship with them. When it comes to action-oriented activities like interacting with a web site, this concept really takes on a very important role. You can predict future behavior based on an understanding of past behavior, and use this knowledge to improve the performance of marketing or service programs. We are talking about actual behavior here, not implied behavior. Being a 35-year-old woman is not a behavior; it's a demographic characteristic. Take these two groups of potential buyers who surf around the 'Net: * People who are a perfect demographic match for your business, but have never made a purchase / subscribed to a service online * People who are outside the core demographics for your business, but have repeatedly purchased / subscribed to a service online If you sent a 20% off promotion to each group, asking them to visit and make a first purchase, response would be higher from the buyers (second bullet above) than the demographically targeted group (first bullet above). This effect has been demonstrated for years with many different Data-Driven programs. It works because actual behavior is better at predicting future behavior than demographic characteristics are. 2. Customers want to win at the customer game. They like to feel they are in control and smart about choices they make, and they like to feel good about their behavior. Marketers and service providers take advantage of this attitude by offering programs and communications of various kinds to get customers to engage in a certain behavior and feel good about doing it. Customers like to "win" through these programs, whether they are consumer customers taking a discount, B2B customers getting enhanced attention or service, distributors receiving volume-based perks, or manufacturers partnering on supply chain issues. Communication programs encourage behavior. If you want your customers to do something, you have to do something for them, and if it's something that makes them feel good (like they are winning the customer game) then they're more likely to do it. 3. Data-Driven programs are about allocating resources. All businesses have limited resources, even the dot-coms (eventually). When you spend $1.00 on a program, you are looking to make back more than $1.00 in PROFIT (not sales). If you can't make back $1.00, the dollar is not worth spending. Given multiple places to spend the program dollar, if you can get back $2.00 in one place and only $.50 in another, wouldn't you rather spend it where you get $2.00 back? This approach is called Return on Investment, or ROI, and is the reason why you want to do Data-Driven programs in the first place. Data-Driven marketing and service programs are among the very few allowing you to accurately measure ROI. It's about knowing you will make a $2.00 for every $1.00 you spend. If you know this for sure, wouldn't it be foolish not to spend every $1.00 you had in the budget to get $2.00 back? If you always migrate and reallocate program dollars towards higher ROI efforts, profits will grow even as the program budget stays flat. This idea is at the center of ROI thinking - reallocating capital with low return to higher return projects or programs, generating higher profits in the process. ROI is often a difficult concept to understand because there are so many people using ROI in the wrong context and measuring it incorrectly. You will learn the correct way to calculate and use ROI later on in the book. If you have a financial background, you probably know that what people nowadays call ROI is really ROME (Return On Marketing Expense), but I'll use ROI to keep things from getting too confusing. 4. Action - Reaction - Feedback - Repeat. Data-Driven marketing and service programs are driven by creating continuous communications and interactions between the business and the customer, and analyzing these interactions for challenges or opportunities. Marketing and service programs are conversations, as the ClueTrain Manifesto (www.cluetrain.com) and Permission Marketing (www.permission.com) have pointed out (if you have not read these books, do so, they are not just dot-bomb fantasies). At a high level, service is just another form of marketing - and an extremely important one. Marketing and service provision using customer data is a highly evolved and valuable conversation, but it has to be back and forth between the program operator and the customer, and you have to L-I-S-T-E-N to what customers are saying through their actions and data these actions create. That's why I will sometimes talk about the data "speaking to you." The data is, in effect, speaking for the customer, telling you by its very existence (or non-existence) that there has been an action (or not) that is waiting for a reaction. An action or inaction is a raising of the hand by the customer, and the Data-Driven marketer or service provider not only sees the raised hand, but also reacts to it, then looks for the hand to be raised again by the customer. For example, if a customer visits your web site every day and then just stops, something has happened. They are unhappy with the content or service, or they have found an alternative source. Or perhaps they're just plain not interested in you anymore. This inaction on their part is the raising of the hand, the flag telling you something has happened to change the way this customer thinks about your site. You should react to this and then look for feedback from the customer. If you improve the content, e-mail them a notice, and the customer starts visiting again, the feedback has been given. The cycle is complete until the next time the data indicates a change in behavior, and you need to react to the change. Let's say this same customer then makes a first purchase. This is an enormously important piece of data, because it indicates a very significant change in behavior. You have a new relationship now, a deeper one. You should react and look for feedback. You send a welcome message, thank the customer for the trust they have displayed in your site, and provide a 2nd purchase discount. Then you await feedback from the customer, in the form of a second purchase, or increased visits. Perhaps you get negative feedback, a return of the first purchase. React to this new feedback and repeat the process over again. The Data-Driven model of marketing / service provision is 2-way, as opposed to the 1-way approach of media advertising or "data-blind" service. It is give and take, an exchange, a communication process. Using a lot of customer communications can be costly in the offline world. But communication costs are generally low on the Internet so the Data-Driven model is ideally suited for use there. That's not to say this model doesn't work offline; the initial development and implementation of ideas has been happening in the offline world for decades. How is this exchange accomplished? Can the data really "speak"? It can and does, but you need to know its language and learn how to listen. It's not very hard, and I'm going to teach you how to do it. But first we're going to run through an overview of how these four driving forces of data-driven marketing are turned into actionable campaigns and programs that drive your sales higher while cutting marketing expenses. ============================================================ Chapter 4 Customer Marketing Basics No question about it, the constant drumbeat of the CRM machine over the past several years has confused the heck out of people. I've been doing this stuff for almost 20 years now, and I can tell you it is not as difficult as it is often portrayed. Sure, you can make it very, very complicated if you want to. But if you don't start with the basics, you're going to end up wasting a ton of money. Let's start simple, shall we? In this chapter I'm going to explain in a general sense how High ROI Customer Marketing campaigns and programs are developed and implemented, and in particular, address some of the misconceptions people have regarding customer value-based and relationship marketing techniques. Much of what is now called "CRM" from a marketing perspective is based on these fundamental ideas. Remember, CRM is an approach to managing a business, not a technology. You do not need to live on the bleeding edge of technology to take advantage of a customer-based management philosophy. Generally, CRM / Relationship Marketing / Database Marketing attempts to define customer behavior and then looks for variances in behavior. When you hear people talk about "predictive modeling" or looking for "patterns" using data mining, they are essentially taking a behavioral approach using the latest tools. Once you know how "normal" customers behave, you can do two things with your business approach: * Formally document "normal" customer behavior and internalize it systemically, leveraging what you know o improve business functionality and profitability. * Set up early warning systems, triggering events, or "trip wires" to alert you to customer behavior outside the norm. This variance in behavior generally signals an opportunity to take action with the customer and increase their value - online or offline. What is most important to measure in CRM is change. People spend way too much time worrying about "absolute" numbers, like LifeTime Value - the cumulative value of the customer now and in the future. What they should really be looking at is "relative" numbers - change over time. It's not nearly as important to know the absolute or exact value of a customer as it is to know whether this value is rising or falling over time. Customer behavior also changes over time, and these changes in behavior typically precede a change in customer value. That means if you track these changes in behavior, you can forecast a change in value, and if you can forecast a change in value, you can get your campaign or program out there and do something about it. This is the core idea behind Relationship Marketing, and these changes in customer behavior and value over time are called the Customer LifeCycle. Knowing and understanding the Customer LifeCycle is the most powerful marketing tool there is; you will learn how to track the customer LifeCycle and use it to increase the ROI of your customer marketing later in the book. Segments of customers tend to follow similar behavioral patterns, and when any single customer deviates from the norm, this can be a sign of trouble (or opportunity) ahead. For example, if the average new cellular customer first calls customer service 60 days after they start, and an individual customer calls customer service 5 days after they start, this customer is exhibiting behavior far outside the norm. Is there a potential problem, or opportunity? Does the customer having difficulty understanding how to use advanced services on the phone? Or is the customer happily inquiring about adding on more services? In either case, there is an opportunity to increase the value of the customer, if you have the ability to recognize the opportunity and react to it in a timely way. Understand, there is no "average customer," and a business will have many different customer groups, each exhibiting their own kind of "normal" behavior. The tools available to identify and differentiate customer segments using behavioral metrics are discussed at length in this book. For example, the type of media or offer used to attract the customer can have a dramatic effect on long-term behavior, and customers who come into the business on the same media and offer at the same time will tend to behave in similar ways over time. In the cell phone case above, number of days from sign-up to the customer service call serves as the "trip wire," and detects a raising of the hand by the customer, which should say to the marketer, "I'm different. Pay attention to me." It is then up to the marketing behaviorist to determine the next course of action. Trip wire metrics like these provide the framework for setting up the capability to recognize the opportunity for increasing customer value. This raising of the hand by customers, and the reaction by marketers, is the feedback loop at the center of Relationship or LifeCycle-based Marketing. It's a repeating Action - Reaction - Feedback cycle. The customer raises the hand, the marketer Reacts. The customer provides Feedback through Action - perhaps they cancel service, or perhaps they add service. The marketer reacts to this Action, perhaps with a win-back campaign, or with a thank you note. It's a constant (and mostly non-verbal) conversation, an ongoing relationship with the customer requiring interaction to sustain itself. It is not a relationship in the "buddy-buddy" sense. Customers don't want to be friends with a company, they want the company to be responsive to their needs - even if they never come out and state them openly to the company. This relationship continues to cycle over and over as long as there is value in the relationship for both the customer and the marketer. If the customer takes an Action and there is no Reaction from the marketer, value begins to disappear for the customer, and they may defect. When value disappears for the marketer (the customer stops taking Action / providing Feedback), marketers should stop spending incremental budget on the customer. Notice I did not say "fire the customer" or any of the related drivel thrown around in some of the CRM venues. All customers deserve (and pay for) a certain level of support. The real question is this: for each incremental, or additional dollar spent on marketing to the customer, is there a Return On the Investment? If I have the ability to choose between spending $1 on a customer returning $.50, and $1.00 on another customer returning $2.00, I would be nuts not to choose the customer returning $2.00. I have not "fired" the customer returning only $.50; I have just chosen not to spend incremental money doing any special marketing or service programs with them. Do you see the difference? In fact, much of the profitability typical of High ROI Customer Marketing techniques comes from knowing who *not* to spend on. Most of the decreased profitability in any marketing program is a result of over-spending on unsuitable targets with lowered returns. But because marketers tend to look at results in the aggregate, or they are looking at demographically-based segments to measure a behaviorally-based outcome like purchases, they miss important details. For example, certain segments in the campaign or program may return $5.00 for each $1.00 spent while others may lose $5.00 for every $1.00 spent, even though the campaign as a whole may return $2.00 for each $1 spent. When you are trying to encourage a customer to buy something, you are looking for a behavior to occur. To measure the results of such a marketing campaign using only demographic segmentation without any behavior-based metrics is misleading at best, and just plain lazy otherwise. If you are trying to create behavior, use behavior as your measurement yardstick to define success. Why is all of this important to understand? Customers who are in the process of changing their behavior - either accelerating their relationship with you, or terminating their relationship with you - are the highest potential ROI customers from a marketing perspective. They represent the opportunity to use leverage, to make the highest possible impact with your marketing dollar. You may make some money marketing to customers who are just cruising along the LifeCycle, acting like an "average customer." But when you can predict the likelihood of an average customer to turn into a best customer, and you successfully encourage this behavior, or you can reverse a customer defection before it happens, then there are tremendously profitable longer-term implications for the bottom line. You will discover these opportunities by understanding behavior and setting up trip wires to alert you to deviations from normal behavior by a customer. What about all the rest of the customers, those who are not either accelerating or terminating the relationship? Leave 'em alone. Whatever background marketing you are doing (advertising, branding, service campaigns, etc.) is serving them just fine. High ROI Data-Driven marketing techniques are best used (and create the highest returns) when they are used to surgically strike at a trend in behavior, not when customers are comfortably plodding along. However, there are not nearly as many comfortable plodders as you think; in fact, from 40% to 60% of your customer base is either in the process of accelerating or terminating their relationship with you right now. So the real question is this: how do you find out who these customers are, and take advantage of the situation? Latency, Recency, RFM, and all the other customer behavior metrics and models described in the Drilling Down book are simply tools for recognizing the opportunity to take an Action in Reaction to the customer raising their hand. If you don't have some kind of system to recognize customers in the process of changing their behavior, you will miss out on most of the highest ROI customer marketing opportunities you have. And don't count on the customer to e-mail or call you when they're thinking of changing their behavior - we both know that is not typically going to happen. A more likely scenario: they will just stop taking Action and providing Feedback. And by then, it's too late for you to do anything profitable about it. Set up your trip wires and predict the behavior, folks. It's the only way to sense when an average customer is ready to become a best customer. And reacting to a customer defection after the fact with a "win-back" campaign is a truly sub-optimal way to "manage" a relationship. For example, a win-back program is triggered when the customer defects. Have you switched long distance or cellular providers lately? Did you get inundated with win-back calls begging you to reconsider? "Jim, we just wanted you to know we have lowered our rates." Yeah, well, thanks for telling me after over-charging me for the past six months! But could they have known I was about to switch by looking at my behavior? Sure. If they had looked at the calling patterns of previously defected customers like me, they would have seen a common thread in the behavior. These patterns create the "trip wires" for initiating high ROI marketing campaigns before the defection. The proper profit maximizing approach is to wait until I look like I'm going to defect, and then call me and offer a lower rate before I defect. I would humbly submit marketing to the customer after they defect is a sub-optimal approach; the decision has already been made. If you can market to them when they appear likely to defect, you optimize your marketing resources by not applying them too soon or too late in the Customer LifeCycle. Based on a national survey, 50% of marketing managers do not know their customer defection rate, and the other 50% underestimate the true defection rate. After reading this shocking statistic, I figured it was time write the book on using Customer LifeCycles to both track customer defection and define high ROI opportunities to retain customers *before* they defect. If you understand the Customer LifeCycle, you can predict the primary defection points and react to them before customers leave you. This is the highest ROI marketing you can possibly do; it's much cheaper than "win-back" (after the customer defects, response is much lower) and preserves the investment and profits you have in the customer already. ============================================================ Chapter 5 Customer Marketing Strategy: The Friction Model You have probably heard or read references to the "portfolio" approach to managing customers and their value. I think it's a sound idea and one I have used over the years because it's generally quite easy to understand in theory, though the actual implementation is always left for you to figure out on your own. So we're going to take a look at this portfolio approach for managing customers and I am going to supply you with the implementation tools you need to actually make it work. This is an important chapter, because understanding these concepts will provide you with the very foundation needed for developing all of your Data-Driven marketing campaigns and programs. The general idea behind the portfolio approach to customer value management is this: your customer base is a business asset. Businesses can have lots of different assets, for example, real estate holdings, buildings, inventory, and common stock, along with other financial instruments. Each of these assets has a value to the business. This collection of assets is an "asset portfolio," just as you may hold your own personal portfolio of stocks. The assets in a portfolio have a current value, which is what they can be sold for today. As we know, there can be changes in the current value of an asset portfolio over time, as what you can sell assets for changes almost daily. Assets also have an "expected" or future value, which can be rising or falling as well, depending on the market for an asset and the type of asset it is. For example, real estate generally appreciates in value over time, but machinery generally declines in value over time. This means at any point in time, an asset has a current as well as a potential or future value. The customer base can be viewed as such an asset as well, and in fact, each customer has a current and a potential value. The current value is whatever the customer has created in value for the business as of today. Current value could be the cumulative profits for the customer since they became a customer, or the cumulative advertising value of all the visits made to a web site since the first one. Potential value is the future stream of profits expected from the customer as long as they continue to be a customer. If the customer terminates the business relationship, the potential value of the customer drops to near zero; this is the end of the customer LifeCycle, the defection by the customer. The sum of Current Value and Potential Value is equal to the LifeTime Value of the customer; it's the Total Value contributed by the customer to your business. If customers in your customer portfolio have both current and potential value, then you can set up a 2 X 2 chart describing the value of your customer base in terms of current plus potential value (LifeTime Value): (click the link below to see chart) http://www.jimnovo.com/images/value-model.jpg Figure 1: The Customer Value Portfolio Customers having both high current value and high potential value (upper right corner of chart) are the "rocket fuel" customers; these are the 10% - 20% of your customers generating 80% - 90% of your profits. You very much want to keep these customers and should be paying special attention to keeping them happy; these are your best buyers, heaviest visitors, and so forth. In the lower left corner of the chart, you have the opposite situation; these customers have low current and low potential value. This group probably includes most of your 1X buyers, accidental visitors to the web site, and so on. For the most part, though it's nice to have these customers and they perhaps contribute to paying overhead costs, you probably should not go out of your way to spend a lot of resources trying to grow their potential. In fact, this group likely contains every customer you have already spent too much money marketing to - those that never respond. This is also the group customer "win back" programs often focus on. The upper left and lower right corners of the chart hold customers with a mix of current and potential values. In the upper left, you have high current, low potential value customers. This area is populated mostly by defecting best customers - they were best customers at one time (by current value) but for whatever reason have slowed their profit-generating activity with you and are probably destined to fall into the lower left corner of the chart by defecting. If you're smart, you'll come up with programs that drag them back across to the upper right corner. Customer retention programs should be focused on this group, but more often than not, are not really focused on any group in particular, and that is why they have a high failure rate. In the lower right corner, you have customers with high potential value and low current value. Who are these people? It's likely they are fairly new customers who have not had a chance to create a lot of value for you yet, but are expected to create value in the future. If they do, they will rise into the upper right hand corner of the chart and become "rocket fuel" customers. If they don't, they will fall back across the chart into the lower left corner and contribute very little. Customers in this corner should be the targets of programs designed to increase customer value, though as with the retention programs mentioned above, these "grow the customer" programs are often not focused on this specific group and tend to actually lose a lot more money than they make. That's the portfolio approach to managing customers and their value, or at least my definition of it. There are others, which for the most part use lifestyle or demographic metrics to allocate the customers. But we're on to that charade, right? Demographics tell you nothing about the current or potential value of the customer, and if you're in a real business, what you care about is the money. For this reason, my approach uses actual spending or value-generating behavior to allocate customers into the quadrants of the customer portfolio. You say, "Yea, but wait a minute Jim, you're pulling a fast one here. I get how current value is derived, I mean, it's the actual transactional value of the customer - sales, visits, whatever behavior is monetized by the business. But how do you do this "potential value" allocation, how do you measure potential value? I guess future behavior will create value in the future, but how do I measure behavior that has not happened yet? What kind of behavior indicates the potential value of the customer? I was with you until now, but this idea sounds..." Relax. Can you take the pebble from my hand, grasshopper? When you can take the pebble from my hand, it will be time for you to leave. If you didn't get the reference above, you're not up on your 70's TV shows. Try a web search on "pebble grasshopper Kung Fu" if you really need to know. But you are right. This whole potential value measurement issue is, of course, the big problem embedded in the preaching you hear on LifeTime Value, CRM, and these portfolio models of customer value. How do you deal with this whole "potential value" question, how do you actually measure it and act on it? Well, fellow Driller, would it surprise you to learn that the specific answers to those questions are what the rest of this book is about? I'm not going to give you a conference lecture about all these wonderful things you should be doing with customer value management and then not tell you how to actually do them. Oh no. You will find out exactly how to measure potential value, and as a bonus, you will be surprised how easy it is. In fact, there are specific metrics for potential value and you will learn what they are and exactly how to use them. Recall this passage from the previous chapter: It's not nearly as important to know the absolute or exact value of a customer as it is to know whether this value is rising or falling over time. Customer behavior also changes over time, and these changes in behavior typically precede a change in customer value. That means if you track these changes in behavior, you can forecast a change in value, and if you can forecast a change in value, you can get your campaign or program out there and do something about it. This is the core idea behind Relationship Marketing, and these changes in customer behavior and value over time are called the Customer LifeCycle. So the following may not surprise you: there are LifeCycle Metrics you can use to forecast future changes in value by tracking behavior in the present. Pretty handy, huh? And just in time, it seemed like you were getting kind of unruly... These LifeCycle metrics are where the idea of Friction comes into play. They measure Friction so that you can track and manage it. And if you can track and manage Friction, you can actually put the concept of the customer portfolio management from above into action. Friction is really about the likelihood a customer will continue to do business with you. The actual causes of friction are created on the business side, and manifest themselves on the customer side as impatience, frustration, and lack of loyalty. Customers encounter varying degrees of this friction in their business relationships, and become more or less likely to do business with you as this friction changes. They already have low tolerance for poor customer service, processes that don't work as they should, pricing that changes unexpectedly or is confusing, interfaces that make it difficult to accomplish tasks, communications that are sloppy, not delivered in a timely way, or irrelevant. All of these friction points tend to create increasing levels of frustration and ill will, which over time mutate into dissatisfaction and defection. Friction accumulates to the point the customer simply decides to start seeking alternatives, and once alternatives are found, the customer terminates the prior business relationship. Now, none of this may sound new to you, but here is something that is new. The friction effect is especially true and is more pronounced as "customer control" of the business relationship increases. Customers are demanding and taking more control of business relationships themselves, as is true with web retail, or have been forced to take control, as with the practice of pushing customers to serve themselves though the web or a telephone interface. As the ability for the customer to exert control in the business relationship increases, customers become less and less tolerant of friction. And, as friction rises, the customer becomes less and less likely to do business with you in the future. If a customer is becoming less and less likely to do business with you, the value you could realize from the business relationship with the customer in the future has to be falling. In other words: Rising friction = falling potential value; Falling friction = rising potential value So, if you can measure friction, you can measure potential value. And measuring friction is exactly what LifeCycle Metrics do. By measuring friction, these metrics also measure the likelihood of a customer to do business with you in the future, and so also measure the potential value of the customer. Visitors and customers will "signal" their friction levels through their own behavior; LifeCycle Metrics organize and codify this behavioral data for you, and allow you to create reports and trip wires that flag increasing or decreasing friction. And how do you reduce friction? By applying the grease, my fellow Driller - your innovative selling and service campaigns are the grease that will hopefully reduce friction and increase the potential value of the customer. Fortunately, you will have your LifeCycle Metrics to tell you precisely who needs the grease, when it should be applied, and even when it should be applied a second time. Your potential value metrics will also tell you when your relationship with the customer has already "seized up" and it's too late for the grease. You only have so much grease and the grease is expensive, so you want to apply it only when and where you think it is likely you can reduce friction and prevent the relationship from seizing up. By the way, customers are not the only folks who experience friction, people trying to become customers experience it also. An easy way to measure this want-to-be-a-customer friction is to look at the visitor conversion rate on your web site. Navigational design and layout determine "physical" friction and copy elements determine "emotional" friction. Design and layout testing will reduce physical friction; persuasive copywriting will reduce emotional friction. Success at reducing want-to-be-a-customer friction is measured by an increased rate of visitor conversion to goal on the web site. But back to customers. With our first LifeCycle Metrics, Latency and Recency, we're going to be looking at the tracking of potential value only, and how you can use changes in potential value to trigger High ROI Customer Marketing campaigns or programs. After the Latency and Recency metrics we will cover the RFM model, which uses both Current Value and Potential Value metrics to really juice up your results and drive even higher profits to the bottom line of your company. xxx To order the complete book with customer profiling application, visit my store at Booklocker.com: http://jimnovo.booklocker.com/p/index.html?s=auto5 For more information on the software application, see: http://www.jimnovo.com/software.htm Table of Contents -Remaining Chapters Latency Metric Toolkit Chapter 6 Trip Wire Marketing Chapter 7 The Hair Salon Example Chapter 8 The B2B Software Example Chapter 9 Turning Latency Data into Profits Recency Metric Toolkit Chapter 10 Predictive Marketing Chapter 11 The Ad Spending Example Chapter 12 Turning Recency Data into Profits Chapter 13 The Online Retail Example RFM Scoring Toolkit Chapter 14 Cash Flow Marketing Chapter 15 A Tweak for Interactive Customers Chapter 16 No Customer Database? How to Set Up a Spreadsheet to Score Customers Chapter 17 How to Score Your Customers Chapter 18 The Commerce and Content Examples: Turning Scoring Data into Profits Chapter 19 Case Study: Non-Profit Scores 192% Increase in ROI using RFM Model Advanced Data-Driven Marketing Toolkit Chapter 20 Using Customer Characteristics & Multiple Scores Chapter 21 Customer LifeCycles: Tracking Scores Over Time Chapter 22 Customer LifeCycle Grids: High Performance Behavior-based Modeling Chapter 23 Straight Talk on LifeTime Value Chapter 24 Lifetime Value, I'd Like to Introduce You to the CFO Chapter 25 Fellow Drillers at Work Definitions and Background Information Customer Loyalty and Retention Customer Segmentation and LifeTime Value Professional Services Ad-Supported Content / Subscription Models Online / Offline Retailing and Catalogs Distribution / Operations / Channel Management The ROI of Online Branding Efforts Chapter 26 Predicting Campaign ROI: Set Up Chapter 27 Predicting Campaign ROI: The Model Chapter 28 Predicting Campaign ROI: Fine Tuning Chapter 29 Expense and Revenue You May Not be Capturing: Subsidy Costs and Halo Effects Chapter 30 Some Final Thoughts: Seasonality, CRM, Behavioral Inertia, Data-Driven Program Outlines APPENDIX: Software Download and ReadMe To order the complete book with customer profiling application, visit my store at Booklocker.com: http://jimnovo.booklocker.com/p/index.html?s=text For more information on the software application, see: http://www.jimnovo.com/software.htm