Monthly Archives: April 2007

As if BI, BA, BPM, BAM and CPM Were not enough…

If you’re wondering:

BI = Business Intelligence
BA = Business Analytics (this is really a distinct category?)
BPM = Business Performance or Process Management or Monitoring, 
          depending on who you talk to
BAM = Business Activity Monitoring or Management, depending on
          who you talk to
CPM = Corporate Performance Management

Now we can add these “disciplines” to the Deconstruction of Marketing:

MOM = Marketing Operations Management – apparently, the idea here is the “Creative” side of Marketing can continue to do what they do while the easy stuff – you know, things like execution and measurement – are taken care of by machines

MDM = Marketing Decision Making
MCM = Marketing Content Management
DAM = Digital Asset Management (apparently subset of  MOM)
MRM = Marketing Resource Management (cross between MOM
           and DAM? – Thanks, Ron)
EMM = Enterprise Marketing Management ( rollup of all? –
           Thanks Jacques)

Question: What is the value of this microsegmentation of Analytics and Marketing?  Is it simply to sell software, in which case it should be questioned immediately – wouldn’t we be better off if all this stuff was bolted together seamlessly?  Remember CRM without any Analytics, which is just absolutely nutty on the face of it?  Can you believe all these subdisciplines actually have their own Conferences?  No wonder the CFO thinks Marketing is nuts…

Please explain to me why this is happening and why it makes sense…


Recency Defines Engagement: Campaigns

The Measuring Engagement series starts here.  For a clickable index of the 5 part Measuring Engagement series, look here

OK, now that you have (way too much?) background, let’s get to the “how to” on this Recency / Engagement stuff.

Recall that you can plot value-creating visitor / customer actions on a common platform and create a “customer engagement map”.  Decide what visitor variable or action representing value to the firm you want to map – this is Current Value.  Number of Blog posts or Comments, Visits, purchases, page views, visits to a certain content area, sign-ups for a newsletter, opens, clicks, you name it – any action that either creates value directly or represents value to to the firm.

Then take a look at how Recent the visitors or segments are in terms of accomplishing the action; this is Potential Value (if you’re not following this reasoning, see here).  Plot Current Value and Potential Value of each segment and you have a very easy to understand “map” of where you are – and where you could go – in terms of increasing customer value / engagement.  Each quadrant of the map has a general marketing objective in terms of allocating resources – Keep the Customer, Grow the Customer, Question Spend on the Customer.

Why should you do this kind of mapping?  Two reasons – consistency and repeatability.

Consistency, meaning the world of web analytics is messy enough and you want to introduce some rigor to the thought process and decision making when looking at various aspects of optimizing visitor / customer value.  Having a simple model to compare the various aspects of engagement ensures a level playing field for all – including the comparison of offline and online efforts.  This is not a web only model; it works in the “real world” as well.

Repeatability, meaning Recency is an incredibly stable behavioral metric that delivers time and time again, in the exact same way, over and over.  You can bet your marketing or operational dollar with confidence every time on Recency.  The more Recently a segment has accomplished any of your goals from above, the more likely they are to accomplish that goal again – either by themselves or due to stimulation on your part.

Let’s take a look at what this looks like with Campaign data.  Here we have a series of new campaigns that all started at the same time (Campaign names have been blanked out to protect the client) with Frequency of Visit data as the Current Value and Average Recency of Visit in each Campaign as Potential Value.  In this case, we are looking at the First or Initial Campaign the visitor was exposed to:

Initial Campaign 

For this web site, Visits are the action that generates value for the firm.  These 16 Campaigns are sorted by Frequency of Visit – the Current Value of visitors generated by each Campaign.  The second value is the Average Recency of visitors from that Campaign – the Potential Value of the Campaign.  You can see that Campaign 4 generates visitors with High Frequency and very Low Recency – this Campaign is generating Visitors that not only add value to the firm, but are highly likely to add value in the future.  In other words, they are both high value and engaged.

What would these campaigns look like on the Current Value / Potential Value map?  Glad you asked; here they are ranked on a relative basis to each other:

Since the ID numbers 1 – 16 for the campaigns were sorted by Frequency / Current Value to begin with, campaign ranking on Current Value starts at the top of the chart and falls to the bottom.  Potential Value grows as you move from left to the right.  Each Quadrant has a red underlined number ranking the relative desirability of investing more capital in the campaigns from that Quadrant.

In Quadrant 1, we have the “rocket fuel” campaigns.  These campaigns are generating visitors who both keep coming back Frequently and are Engaged – visitors with high value to the firm now and in the future.  These visitors are likely in the “20% of visitors generating 80% of visits” component of the visitor base.  In Quadrant 4, you have campaigns generating visitors who don’t have much value now and won’t have much value in the future.  In general, you want to reallocate spend on Campaigns in Quadrant 4 towards spend on Campaigns in Quadrant 1.  This will optimize your Campaign throughput and longer term ROMI; you want to “gun” Campaigns 2, 4 and 6.

In Quadrant 2 we have campaigns that don’t generate a lot of Current Value but have high Potential Value; these visitors are “potential up and comers”.  Please note: due to the lower Frequency / Current Value, people often kill these Campaigns before they have a chance to blossom.  This is usually a very bad idea and often can gut the profitability of the overall effort.  The Recency / Potential Value component tells you these visitors are engaged; they simply have not delivered much Current Value so far; if at all possible, let these campaigns run.

In Quadrant 3 we have the “early bloomers”; high Current Value and low Potential Value.  Ironically, these are the campaigns people tend to invest more money in due to the early activity, often robbing from Campaigns in Quadrant 2.  This can be misguided, depending on what your end objective is.  If you want to build an engaged visitor base, this is not the place to invest.  If you just want to generate short-term “activity”, then these are the campaigns you want.

But budget allocation isn’t the only thing going on here.  Knowing how these campaigns are mapping across the visitor value grid, you want to ask yourself these additional questions:

1.  What is similar about campaigns in Quadrants 1 & 2 that is different from Campaigns in Quadrants 3 & 4?  In other words, what is it that generates engaged visitors with high Potential Value?  Is it campaign media, copy, offer, channel, product, content area of the site they are sent to?  What are the drivers of this behavior?

2.  What is similar about campaigns in Quadrants 1 & 3 that is different from Campaigns in Quadrants 2 & 4?  In other words, what is it that generates high Frequency visitors with high Current Value?  Is it campaign media, copy, offer, channel, product, content area of the site they are sent to?  What are the drivers of this behavior?

This is where the ideas of Consistency and Repeatability driven by the Current Value / Potential Value platform come into play.  If you can distill why certain campaigns generate visitors that end up in each Quandrant, you can:

1.  Rely on the same campaigns to generate visitors that will always land in a certain Quadrant – you get results that are Consistent instead of wondering why things happen the way they do, which often is the case when using segmentation based on demographics, product affinity, etc.  Behavior predicts Behavior, as long as you are tracking actual behavior.

2. Know that when you take these drivers of behavior out of the current campaigns and create new campaigns, your results should be as expected.  You will be able to Repeat your success in other types of campaigns as well as in related ideas like web site copy, landing page copy, merchandising, and so forth.

You can also use these drivers to tweak and improve the behavioral performance of campaigns like 1, 3, 8, and 9, eventually moving them from borderline to solidly into Quadrant 1.

Are you with me?

For those of you looking to integrate online data with offline, let me suggest that a simple “Quadrant Tag” of 1 – 4 for a customer would contain a ton of actionable data about the web behavior of that customer in a very small space.  You could create a “Master Tag” for the most important web KPI or export a series of Quadrant Tags for a variety of KPI’s.  There really is no need to send a lot of detail to CRM if you can send only the most actionable keys, which can be used for trigger-based responses in either campaign automation or call center scripts.  There is no better high-level summary of a customer’s web activity than a single digit that represents the Current and Potential Value of the customer on the web.

By the way, this model generally tracks with the valuation model folks on Wall Street use to value direct and database marketing companies.  Wall Street wants to know two things in this area: how much have customers spent, and how Recently did they spend it? 

This is why you so often hear metrics like “12 month Active Customers” used to describe Amazon or eBay.  Investors want to know not only the Current Value of the customer base, but also the Potential Value.  The 12-month model is a bit “slow” for my tastes, especially when you are talking about the web.  It’s much better to use the raw Recency stats to build out the Value Model above and find out exactly what the heck is going on. 

You know anybody that makes online campaign decisions based on a 12-month test window?

Recency is in fact a very simple, single variable, predictive model.  I’ll bet you any money if you go out and create a complex, multi-variate regression model for predicting “likelihood to repeat action” that Recency will be in there right at the top.  Not to say those more complex models are not valuable, you know; they are – as long as you have the capability to execute on them.  But Recency by itself is very powerful and you can use it right now, both online and offline, to measure engagement and optimize the Potential Value of your customer base.

As always. your comments and questions on the above are appreciated!  In the near future, we’ll look at more applications beyond Campaigns for using the very same mapping of Current and Potential Value for Visitors and Customers.

The next post in this series on Measuring Engagement is here.

Jonesin’ for Some ROI

The Measuring Engagement series starts here.  For a clickable index of the 5 part Measuring Engagement series, look here.

If your head is kind of splitting over the last post on Measuring Retention / Engagement and you’re looking for a bit lighter explanation of the concept, I offer it to you here, from the first chapter of my book:


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.

“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 has an MBA.  Wait a minute; I have? an MBA (though I got it 20 years prior to  his, no doubt).  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 who need 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.


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?”


“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 my existing customers?

So what do you say, fellow Driller?  Ready to cut that line at the Clinic?

The next post in this series on Measuring Engagement is here.