Monthly Archives: April 2007

Book: Managing Customers as Investments

Are You Spending More on Your Customers than They are Really Worth? authors Gupta and Lehmann ask.

Based on my experience, Why Should We Care? is the return question.  Lots of companies apparently do not care, at least not yet.  But someday these companies will “hit the wall” with the traditional focus on acquiring new customers, and then they will care.  Just a Case of History Repeating, don’t ya know. 

When I published the 3rd edition of my book, I was pretty sure the business world had finally made it past “Why Should We Care?” and would be on to “How Do We Do This?”.  Wrong, as Bryan and Ron constantly remind me.  Before the book reviewed below was published, I was using what I called the “portfolio approach to managing customers” idea to set up the Current Value / Potential Value model.  Spent an entire chapter on the idea.  Apparently, that was not quite enough to answer the “Why Should We Care” question.  My bad.  Turns out the same idea is worthy of an entire book.  Sigh…

So for those of you who are more interested in the “Why Should We Care?” (90% of You?) as opposed to the tactical “How Do We Do This?” presented in the Measuring Engagement Series, I give you the following book:


This is a 6 Chapter, no nonsense, 165 page book that is heavily annotated with the kinds of “proof” you need to potentially get your Boss to care about this topic.  I mean, the boss-person can just hit the EndNotes (another 33 pages with the Appendix) and find out how the theories, formulas, and examples in this book have all been well documented by a slew of hard core academics and Consultainers alike.  These references to various studies, books, papers, and so on probably have more impact than say, sending an e-mail to the boss titled “Interesting Stuff” with a link to my blog…

The book is easy to read and shoves all the “Math” into the Appendix so whether you’re using the right or left brain you can follow right along – ignore the math and just Grok the pictures, or get right down into full-blown proofs with your Calculus shoes on.  Have it your way, as they say.

Just look at these Chapter Titles:

1.  Customers are Assets – no argument there from me.  Want definitive proof?  Here it is.

2.  The Value of A Customer – do you know the value of yours?  Here is an easy – and I mean easy – way to estimate this value.  Call it Lifetime Value “Light” –  it’s way better than what you have now, I bet.

3.  Customer-Based Strategy – Oh, to develop Strategy and actually do something based on the Value of the Customer, as opposed to whining about how they are “in control”.  This is the best chapter in the book.  Customers can only take control if you give it to them, you know.  And that’s a Strategy problem.

4.  Customer-Based Valuation – they’re talking firm valuation here, for the purposes of acquiring companies or selling them.  As I said before, Wall Street uses the Current Value / Potential Value Model.

5.  Customer-Based Planning – as in building Customer Value right into the Business Plan, so the execution is rock solid.

6.  Customer-Based Organization – sure, the tough one.  How you make it all work in the org.  A bit of the Analytical Culture thing.

The Gupta and Lehmann book is great because it takes what I’ve learned through 20 years of “exposure” (Why You Should Care) and explains it in corporate speak, creating links to stock prices and all kind of other good stuff the CFO would really like to hear about, like projecting future sales, estimating the buyout value of the firm, evaluating acquisitions, and so forth.  All from the same kind of customer analysis we just worked through in the Measuring Engagement series.  Really.  Except they hide all the numbers from you – unless you go looking for them.

Oh, and did I mention this info is all well documented by a slew of hard core academics and Consultainers alike?  Seriously though, there are over 120 footnotes that at the very least provide you a library of solid references and case studies on the topic of using customer value data to drive increased profits.

So, if you’re a Marketer trying to create a bridge to Finance, get a copy for your friend over there.  If you’re an analyst trying to get a deeper understanding of why customer analysis matters to the business side, get a copy for yourself.  If all of a sudden you are in charge of “CRM” or “Customer Experience” or whatever they are calling running a business that doesn’t shred its own customers these days, get this book for the sake of your company.  The book really is a great read and might help you make sense of all the disparate and seemingly conflicting marketing and service ideas you read about today.

And while you’re at Amazon, get a guide for the people who will have to turn all this Customer Value Data into Profits for you – mine.

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Measuring Engagement Series

This post is an index for the Measuring Engagement series.  The following posts were written sequentially but appear on the blog in reverse chronological order which makes a hell of a mess of trying to understand a somewhat complicated topic.  So instead, try reading them sequentially using this index:

Recency Defines Engagement

Jonesin’ for Some ROI

Recency Defines Engagement: Campaigns

Recency Defines Engagement: Visitors

Recency Defines Engagement: Customers

Book: Managing Customers as Investments

This material is about the Tactical measurement of Engagement and dis-Engagement.  If you’re interested in a Strategic framework for applying these measurements, see A Framework for Engagement.

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Recency Defines Engagement: Customers

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

The good thing about doing customer analysis as opposed to visitor analysis is that you don’t need a fancy-dancy web analytics set-up to do it.  Most folks will be able to take advantage of the following ideas using some simple queries on the customer database or an export to a spreadsheet.

Last time we addressed the topic of measuring Engagement – and attributing actual Value to it – we were looking at how to predict the effect of Content changes on Revenues using the Current Value / Potential Value visitor segmentation model.  This time, we tackle the same kind of modeling at the commerce customer (online or offline) level.

Recall that with visitors, we looked at a segmentation using under or over 50 visits for Current Value and Last Visit within 2 months or over 2 months to define Potential Value (Engagement).  With e-commerce customers, the value of a single action (purchase) is generally much greater than the value of the average visit, so it’s worth it to create a finer segmentation because the data is more actionable – and the profit potential much greater.

Here we have the entire customer base of an online retailer in the 4 square Current Value / Potential Value grid we have used previously for Campaigns and Visitors – click on the image to enlarge:

As you can see, the vertical Current Value axis is Frequency of Purchase, with finer divisions for lower Purchase Frequency.  This is because as you move up towards greater Frequency, customer behavior becomes more similar, and you don’t want to have “infinite” segments.  On the lower end, the response behavior is different enough between say, a 2x buyer and a 3x buyer, that the segmentation is useful because the different segments respond differently to the same promotion.  The horizontal Potential Value / Engagement axis is made up of Days since Last Purchase (Recency) blocks of 30 days each out to 120 day Recency where, as with Frequency, the behavior starts to become similar so it’s not worth looking at finer detail.

Of course, you can break the customer base into as many segments as you want on either axis, you just want them to be large enough to be worth taking action on.  Smaller customer base, probably fewer segments is better.  Larger customer base, more segments.  And you can certainly define your actions and divide your Quadrants in any way that makes sense to you – usually based on some kind of testing.  Consider this CV / PV customer map a “default” place to start.

The four colors represent the same Four Quadrants we have been working with throughout this series:

Q1 (Green) is the Rocket fuel customer set – highest Current Value and highest Potential Value – they are best customers (Current Value) who are also the most engaged (Potential Value).  Q2 (Yellow) are newer customers with Low Current Value but are still Engaged and so have high Potential Value; the Blue square within the Yellow region contains brand new customers – a very Recent first purchase.  Q3 (Orange) contains former, dis-Engaged Best customers.  Q4 (Violet) contains the dreck of the customer base – 1x or light buyers that never bought again.  These folks are often created by inappropriate or mis-targeted acquisition campaigns.

Why would you want to do this segmentation?  Well, asked another way, does it make any sense to you that the optimal communication and offer stream would be the same for each of these 4 segments?  Think about it.

Q1 folks love your company and are eager buyers, with high likelihood to purchase again.  Do you want to use a heavy discount approach with these folks, giving up margin you would likely capture anyway?  Instead, how about trying to enagage them across multiple product lines or inviting them to participate in feedback panels or other high engagement activities?  At least you know who you are talking to – as opposed to “random surveys” where you have absolutely no idea who you are getting feedback from.

The Q2 area (Yellow) contains up-and-coming best customers, brand new customers, and customer dreck headed for Q4.  You can tell which is which by just looking at the chart – up and comers are top right of Q2, new customers bottom right of Q2, pre-dreck on the left side of Q2 on the border of Q4.  Do you want to send all these groups the same communication stream and offers?  Really?  Is that approach “optimized”, from a marketing perspective?

The Q3 (Orange) folks are former best customers.  ‘Nuff said there.  This group requires special communications handling and depending on their Current Value, are worthy of further research.  This is where a lot of your service problems, over-promising on Brand, and unfulfilled customer expectations lie.  Again, since you know exactly who they are, a survey here might be helpful, don’t you think?

The Q4 (Violet) area needs to be turned inside out and viewed by campaign source, product purchased, and so forth.  Why are you creating dreck customers?  Are your offers too strong?  Your featured products creating negative experiences?  Your list sources not really what they claim to be?

I don’t really want to use the word Persona here to describe the differences between customers and the appropriate messages in these four Quadrants, but the idea is similar.  If you can empathize with the customer based on their demonstrated behavior, you are simply going to be a more effective marketer.  This is the edge of the “right message, to the right customer, at the right time” tactical approach.

Further, your response rate for a particular promotion to any one “cell” on this customer map is going to remain fairly consistent over time.  Why?  Because the population in that cell is replaced by customers with the same behavioral profile each month.

Here’s how it works.  If you think about it, there is a non-stop process of customer migration across the map from right to left through the columns each month.  If a customer makes a purchase, they immediately move back to the right-most column and may move up a row.  Then, customers start to move across to the left again each month.  This pattern is highly visual and represents the LifeCycle of the customer.

Your job as a marketer is to make sure customers don’t march too far to the left, losing Potential Value as they move.  You try to re-engage them with each promotion and if they respond, the customer jumps back to the right and possibly up a row – increasing both their Current and Potential Value.  The most profitable campaign for each customer is defined by which cell the customer resides in at the time the campaign is dropped.  So you can still do a “monthly” newsletter, for example, but to maximize profits, the content / offers for each customer would be defined by what cell the customer is in at the drop point.

While this might sound complex, the good news is that the customers in any cell as a group generally respond at the same level for the same offer every time.  So once you figure out what the optimal campaign is for a cell, it doesn’t really change much over time, unless you further sub-segment (example below).  As customers move through the cells, they are generally exposed to a lot of different campaigns (whatever is highest ROI for the cell) which maximizes the chance of response and reduces promotional burn-out.

For those of you with a programming eye on this, I think you can see how this campaign process could be easily automated because the cells are well defined numerically – if customer has 3 purchases and no purchase in past 2 months, send “Campaign X”, if customer has 3 purchases and no purchase in past 3 months send Campaign “Y”, etc.  This creates a automated stream of “right message, to the right customer, at the right time” communications that are tailored to the actual behavior of the customer.

So how do you act on this info?  Let’s say I have a group of customers who have just passed into Q3 from Q1 – these are best customers who are dis-Engaging.  I know exactly who and how many there are – they are under the column “91 – 120 days” in the Orange Q3 Quadrant.  There are 844 of them (97 + 312 + 435).  What am I going to say to them, based on what I know of their value and current behavior?  How much am I willing to invest to keep them Engaged?  That’s the “drive more sales” angle.

The “drive more profits” angle would be to create control groups and test your messaging to this segment as well as the one preceding it (10+ units, 60 – 91 Days) and the one after it (10+ Units, 121-150 Days) and find out where the highest ROI is.  This type of bevahioral targeting is the fundamental driving force behind the Discount Ladder profit optimization technique.

But that’s just the beginning of using this kind of segmentation.  Consider these ideas:

1.  When you kick off a large scale acquisition campaign, you are going to see the Blue square in Q2 “bulge” with all the new customers.  Then, if you run this chart every month, you will see this bulge “pass through” the chart like a rat through a snake.  Will the bulge head up towards Q1, meaning the campaign is creating Best customers?  Will the bulge move to the left towards Q4, meaning you created a lot of dreck customers?  Will the bulge “fork” and parts of it head to different Quadrants, depending on product of purchase or offer taken?

As a marketer or analyst, is it valuable to be able to predict the long-term results of a campaign before it is over?

2.  You say, “Jim, that’s very cool and all, but the powers that be want all our segmentation by product affinity, you know, we customize communications and offers by the previous products purchased.  So we can’t really use this.”

Hmmm.  Let’s put aside whether this product-based segmentation decision makes any sense at all for the time being (the only sale you are willing to accept from the customer is for a specific product or category?), and take a look at how mapping the customer base using Current and Potential Value can help you put some facts behind these kinds of segmentation questions.

Let’s say for simplicity you have two product lines, hardware and software.  Further, let’s say your customer base is the one in the CV / PV model above, which I will show again below for clarity:

All Customers

OK, so let’s say you take this customer base, and run your product affinity segmentation.  Then you map each product segment by customer using the Current Value / Potential Value model, and this is what you get:

Software Segment

Hardware Segment

Note the label on the first map is “Software” and on the second in “Hardware”.  What do these customer maps tell you?

Well, you have about the same number of customers in each segment – 18,500 in Software and 17,534 in Hardware.  But you knew this.  Take a look at the totals along the bottom of the grid, representing the total number of customers in each Recency / Engagement column.  What do you see?

The Software segment has much higher Potential Value / Engagement than the Hardware segment.

If you look at the 61 – 90 day column, you see both the Software and Hardware segment have an equal number of customers.  But the Software segment is clearly much more Engaged than the Hardware segment, as evidenced by higher totals in the columns to the right of the 61-90 day column for Software than Hardware.  Conversely, in the columns to the left of the 61 – 90 day column, the totals for Hardware are higher than Software – these customers are less Engaged.

In other words, even though the gross customer numbers in these segments are close, the composition of the segments is quite different.  Software has a higher number of very engaged Best customers and potential up-and-comers (Q1 and Q2), where Hardware has a higher number of dis-Engaged Best customers and dreck customers (Q3 and Q4).  Further, you can say with certainty that relative to the Hardware segment, the average customer in the Software segment is going to create more value for the company in the Future.

This ought to tell you something about the way you optimize marketing to each segment, and the way you should market within each segment, not to mention something about the products and / or service satisfaction in each segment.

Just by looking at these maps, I can tell you several things:

1.  The response rate for Software campaigns will be consistently higher, over and over, than the response rate to Hardware campaigns – pretty much regardless of what kind of offer you make, as long as the offers are similar.

2.  For the same dollar spent, the Software segment is driving your business, the Hardware segment is dragging it down.  You can either roll with that situation and reinforce it in your communications, or you can try to fix Hardware.  For example, when you choose to feature an item, all else equal, I’d feature Software, because it has the longest customer legs and drives higher repeat purchase.

3.  For the same dollar spent, I would focus more heavily on Software in new customer acquisition Campaigns because this segment generates better, higher value customers for the business.

Now, at this point, I hope something has occurred to you.  That’s right, you could do this same mapping using any customer segmentation scheme you think is meaningful and compare the value of the customer maps.  Compare the results of Campaigns using these customer maps.  Compare organic search versus paid search.  Compare Geography, if you think that is meaningful.  Compare average price points, order sizes, shipping choices, coupon usage, e-mail opens, whatever customer variable you want – and find out which variables drive the highest customer value.

Further, you can use this model across any kind of “action” you want to map – purchases, visits, downloads, blog posts, phone calls, whatever you want.  You can use it to compare customer value across channels, and start building the knowledge you will need to optimize the business in an omni-channel world.

My final point is somewhat abstract but I want you to consider this: What is the value to the business of having a customer value model you can use to:

1.  Objectively measure the value of content or products to a customer segment without inside-company bias

2.  Predict the value of a customer segment to the company in the Future

3.  Drive the allocation of content, design, or marketing spend towards highest ROI

4.  Provide consistent, repeatable campaign targeting results, so you can actually predict response and ROI

5.  Analyze any customer “action” variable, in any channel, across any segmentation scheme

6.  Present customer valuation concepts to Execs and fresh-faced MBA’s alike in a simple to understand format

The same basic model, over and over, to make highly actionable customer decisions with.

Do you think using this model might streamline the marketing decision making process, result in more accurate decisions being made, reduce campaign turn-around time, and result in higher profitability for your company?

As always, comments and questions on the above are appreciated.

The last post in the Measuring Engagement series is here.

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The Analyst’s Enigma

Some of you might know that I produced the Web Analytics Association’s continuing education courses on Web Analytics (with Co-Chair Raquel Collins and a ton of great contributors).  One part of this project I am very proud of is the 4th Course, Creating and Managing the Analytical Business Culture.  This course was a real “stretch” goal because initially, the Education Committee thought there simply would not be enough material to create an entire course on this subject.  But we did it, and more importantly, the students really love it, because it addresses the “politics of analytics” they face every day.

One of the exercises in this 4th course is called The Analyst’s Enigma.  It always generates a lot of intense discussion and without giving away too much of the actual exercise, I thought you might like to either comment on how you would handle this situation or share your own similar war stories and how you handled them. 

By the way, it’s based on an actual situation you may well have experience with (or will in the future) as web analytics folks spread their wings and bring their business optimization skills to the rest of the company.  Here goes:

The Analyst’s Enigma

You are the manager of web analytics at a large public company.  The “web stuff” in the company started out in IT, so due to legacy reasons you are part of IT, but your internal client is really the marketing department, who is now in charge of the web site.  About 95% of your time and effort is spent working with marketing to optimize the web site, a project that has been very successful.

Over time, your success with the web site optimization process and as an analyst has been recognized in the company, and you occasionally get requests to do “problem solving” sessions with other parts of the company.  Typically, you run these projects through the same analytical process you used with the web site: Define objectives, create / get buy off on KPIs, measure baselines, develop ideas for testing, test and measure the results of those tests.

Recently, you were working on a project for customer service, trying to develop / improve KPI’s for the measurement of performance in the call center, which has a very rich data set.  This data is surprisingly similar in many ways to the traffic data set from the web site.  A phone call is very much like a visit; it has a duration, it typically has a number of steps like a web site funnel, and the steps end with accomplishing or not accomplishing a goal.  The call center is trying to evolve from relying on simple metrics that score only “efficiency” to a KPI that better balances efficiency and a good customer experience.

Your web site work has lately focused on a new campaign that the marketing folks are very proud of.  It’s blowing the doors off anything they have ever done in terms of response, thanks in large part to your analytical work on promotions.  The success of this program is widely known throughout the company, as is your role in the success.

On a Friday afternoon, you find yourself with some free time to devote to the customer service KPI project.  While examining the call center data set, a remarkable possibility presents itself.

Using a new, experimental KPI you have developed that balances efficiency and customer experience in the call center, it appears that every time marketing drops this new, highly successful campaign, there is a dramatic negative spike in this new call center KPI.  The correlation between the marketing campaign drop and negative spike in the call center KPI is extremely high, leaving no doubt in your mind that that there is a causal relationship between the two events.  There is always some negative impact on the call center when a campaign drops, but nothing like the magnitude of the impact caused by this most successful campaign.  Nothing about the campaign execution – for example, the volume of the drop – would lead one to conclude it should cause problems in the call center.

What would your next steps be?  What should you do with this knowledge that (as far as you know) only you possess?   The topics below might be worth touching on:

Topic 1.  You work for IT, and your main internal client is marketing.  The customer service analysis is a side project.  What responsibility do you have for resolving the apparent conflict between optimizing marketing and optimizing the call center?  Is it your responsibility to try and “optimize the company” across all the business units by providing this kind of information?

Topic 2.  One alternative would be to try and alter the new KPI (which you feel is very, very good) so that it masks the effect of the marketing campaign on the call center.  This would reduce potential internal conflict, for sure, but would result in a weaker, less trustworthy KPI for the call center.  Would you consider this route?  What if your main client (Marketing) suggests you “tweak the customer service KPI a little bit to help us out on this”, what would you say?

Topic 3.  What kind of action plan can you imagine for trying to resolve the apparent conflict between the success of the campaign and the performance of the call center?  Would you call a meeting first or speak privately to some folks and discuss a potential meeting?  Who would you speak to 1st, 2nd, and 3rd given your ties to IT, marketing, and customer service?  Who would be invited to the first meeting?

Looking forward to hearing your ideas on this or similar situations you have faced.  If you’re relating a real analytical culture war story, you might think about changing the names to protect the innocent!

And / Or, if you’d like to share your story interactively with the Course 4 students in their Cafe’ (chat), we’d love to have you as a guest.  FYI, the students are primarily adults who are already working with web analytics as part of their job who now are faced with a need to upgrade their skills.  Let me know if you are interested in sharing your story with them – you can use the “Email Jim” link below to contact me.

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Banners versus Search

Alan quotes a Fred Wilson post on the “return of the banner” as a significant force due to Google’s DoubleClick purchase. 

I had pretty much the opposite reaction – this is a chance for Google to prove what banners are really worth and replace a lot of that banner inventory with more targeted avails, aka Adsense or some variant based on DoubleClick tracking data.

For example, I think the much touted “view-through” metric that really helped out the banner business is up for grabs here.  The unresolved problem (to my knowledge) with tracking view-through is the lack of cross-cookie tracking.

Let’s say you are in search mode, you search and arrive at a site that has banners.  Even though you really were scanning the text on the page and ignoring the banners, you are counted as being “exposed” to the banners.  You continue searching and land at the site the same banners are linked to, and complete an action.

The banners will get credit for the “view through” on this action, even though you were searching and / or clicking on PPC ads.  To make matters worse, you will probably also credit SEO or PPC for the conversion – so you’re double-counting.

If you are Google with DoubleClick, you can reconcile and sequence all this activity if Google is the search engine being used, and figure out what the real value of a banner is.  Branding value aside, of course..;)

Would you be surprised if the true value of a banner ad is a lot closer to an AdSense avail than an AdWords avail?  I wouldn’t be; in fact, I bet banners are worth less that AdSense avails – at least for generating conversions. 

I guess there will always be Branding folks who buy impressions and perceive value in them, without any further measurement.  You could measure the success of this tactic using Engagement with the Brand site – overall Engagement should rise, no banner click required.  Failing any improvement in Engagement, you could always say “the benefits all accrue offline” and be done with it.

Those interested in a more technical discussion of this view-through tracking issue, try here.

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*** ROI of BPM

An article in Optimize Magazine points to a study by TowerGroup that claims:

Customer retention and satisfaction, as well as better competitive advantage among financial-services firms, are directly linked to use of business-process management practices. 

There’s even a neat little graph under a section called The ROI of BPM that shows how the value of chasing a BPM project changes over time and eventually creates competitive value.

This is all pretty intuitive – the less you screw up the better your customer retention should be, right?

Anybody seen the TowerGroup report?  How many cases did they study?  Ron?  This seems right up your alley…

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Recency Defines Engagement: Visitors

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

Last time we addressed the topic of measuring Engagement – and attributing actual Value to it – we were looking at visitors generated by various campaigns.  Here is what the Frequency (average number of visits) and Recency (average days since last visit) look like in a web analytics interface:

Initial Campaign

And here is what the Campaigns, numbered 1 – 16, look like in the Current Value / Potential Value Map:

Quadrant 1 contains campaigns generating visitors with both high Current Value and high Potential Value – these are the campaigns deserving more investment because the visitors created generate highest value to the company now, and have the highest likelihood to generate more value in the future (are the most Engaged).  If you’d like to know more about what metrics drive the Map and how it was created, see here.

Beyond Campaigns, how else can we use the Current Value / Potential Value Map?

Search Phrases

One of the more interesting uses is looking at search phrases as the “campaigns”.  Search marketers, especially PPC folks, are often victims of initial conversion rate-itis, where campaigns are managed and funded based on a short-term conversion rate.  To be fair, often this is a systems integration problem more than anything else – there simply is not enough “visibility” in the out weeks to determine if longer-term conversion to final goal is occurring.  This is common where there is not a clean integration between web analytics and the back-end commerce system, for example.

Using the Customer Value Map with search phrases provides you with a way to imply a future conversion and balance out some of the decision making on short-term conversion.  If you know a certain search phrase is generating visitors who visit Frequently and are still Recent in their visit behavior (Quadrant 1), you can imply this phrase is going to be more profitable than a phrase generating visitors who end up in Quadrant 4.  For an example of this idea in action, see here

Likewise, let’s say you’ve optimized the heck out of all PPC campaigns as far as copy, landing page navigation, etc. and still have a number of phrases that are “breaking even” on an ROI basis.  But some of these break-even campaigns consistently deliver visitors who end up in Quadrant 1.  The last campaigns I would kill are the ones delivering visitors who end up in Quadrant 1, since these visitors have the highest Potential Value.  Kill Quadrant 4’s first, then 3’s, then 2’s to see if you can get where you need to go in the overall ROMI mix.  Then do anything you can (including fishing through databases / logs manually, if need be) to find out if those Quadrant 1’s are really not paying out – I’d bet something is missing, there is a break in the logic / code somewhere that is not giving credit where credit is due.

Navigation / Functionality

Before we get into this area, let’s step back a minute for a global thought. 

This Retention / Engagement analysis stuff may seem oddly strange to you, and if it does, this is probably the reason: what is most important to measure in this area is what does not happen

Think about it.  This is not what you are used to in web analytics (or most other transactional analysis) – you are always focusing on what did happen.  How many visitors, clicks, conversions, etc. happened?  But I ask you this: in terms of Objective / Action, where would you want to take action in the Engagement area, where would the highest payout be?  Right.  Not with the Visitors who are already Engaged, but with those who are becoming less Engaged – where something is not happening.

Keep that in mind as we go through the next section…

Has this ever happened to you?  Your revenue KPI’s start sinking, gradually at first, and then at an increasing rate.  You run around trying to figure out what the problem is – campaigns, changes in natural ranking, competitor activity, whatever.  You’re pulling your hair out because it doesn’t make any sense – everything is tracking “normal”, right?  No changes in the past few days, or even weeks?  Right.  So, what the heck is going on?

Understanding the Volume of traffic by segment to your site is a given.  But what happens to visitor Value segments after their first visit cycle is important as well.  I can’t tell you how many times I have seen people screw themselves over the longer run because they are tracking / optimizing for Current Value rather than both Current and Potential Value.  This is a particularly important idea when you are testing new navigation / functionality and content or products, because it’s not only Campaigns that determine the long-term quality of visitors, but also the site itself.

Here’s an example.  Let’s say you have a simple visitor value segmentation of visitors during the past 12 months that divides the Current Value of Visitors into 2 groups – Frequency over 50 Visits and under 50 Visits.  Further, you divide Potential Value (Engagement) into 2 groups – Recency of Visit within 2 months and over 2 Months ago.  You end up with a 2 x 2 Visitor Value Map that looks something like this, with percentage of the 12 month visitor base listed in each Quadrant:

(Analysts: This simple data set, the first time you present it, may cause some rapid heart beats,  Trust me, most every site looks about like this – the majority of Visitors are in Quadrant 4 – have only visited a few times and have not been back lately.  What’s a few rapid heartbeats among friends anyway??  Gulp…  Hey, you’re an analyst, you’re used to this kind of thing!)

In the chart above, we see 10% of your Visitors are in Q1 (Quadrant 1) – at least 50 visits, Last Visit within 2 Months.  These are the 10% of your Visitors who probably drive the majority of your revenue, the “rocket fuel” visitors.  Q3 is where former best Visitors end up – they have high Frequency / Current Value but have abandoned visiting the site.  If you’re not clear how time since Last Visit date correlates to site abandonment, see here.

Now, let’s say you make a major change in navigation on the site.  Traffic flow to the site remains the same; all the same campaigns are running and everything seems normal.  Hopefully, conversion even goes up (that’s why you redesigned the nav, right?) 

A couple of months later, all of a sudden your revenue per visitor or visit metrics start to slip. 

Thankfully, you have been keeping track of the Percentage of Visitors in each Quadrant of your Customer Value Map over time (phew!) – I wonder what that looks like?  Here is what you find:

The Quadrant 1 Visitor segment (Top Graph, dark line) is shrinking; it has dropped from 10% of the visitor base to 6% or so over a 7 month period.  Doesn’t sound like much, right?  That is, until you remember that these Quad 1 rocket fuel visitors are responsible for a very significant portion of your revenue.  This means, of course, that your revenue per visitor follows the shrinking Quad 1 population right down the curve, as shown in the Bottom graph above.

Think about it.  In terms of gross numbers on the site, you would hardly notice a change like this in any of the “did happen” metrics.  Traffic and conversion, traffic and conversion, all just chugging along, right?  But this change in a small yet powerful group of Visitors significantly affects your Revenue KPI’s – because something did not happen.

Where are these Quad 1 visitors going?  Well, they are becoming dormant – they are moving into Quad 3 – high Frequency but poor Recency (Engagement).  It’s really the only place they can go; most can’t move to Q2 or Q4 because they have high Current Value as they start to move.  So as the population of Q1 shrinks, the population of Q3 rises, as seen in the Top chart.

What you are seeing in the chart above is a tangible visual representation of Best Visitor defection – visits not happening among most Valuable Visitors – that is hard to dispute.  Can you say Engagement Dashboard?

Then why is this happening?  I’d bet on the navigation change.  The problem is, of course, that unless you have a chart like the one above, it will be difficult to prove this idea to anybody, since the drop in the revenue KPI’s lagged the navigation change by such a long time, and all else remains consistent.

The fact is, you changed your “product” – the web site.  For some reason, the site simply does not generate or retain high value Quad 1 visitors like it used to.  Perhaps you pissed off the current Quad 1 Visitors with your changes.  Maybe the parts of the site that create new Quad 1 visitors are now buried in the new navigation, so up-and-coming Best Visitors (Quadrant 2) never find these high value creation areas. 

Did you bury sections of the site considered “low volume” in the navigation?  Better check that idea, because the low volume areas (uniquely targeted areas?) often create the highest value visitors.  You can check on this by running a Current Value / Potential Value Visitor Map for each Content Group – hopefully, before you make any changes to the web site!

Next time we visit this topic, we will look at Customers – those good folks who actually pay money to support a web operation.  If your web analytics tool does not support Visitor Frequency and Recency, you can still use the same Current Value / Potential Value model to manage Engagement through your customer database.

As always, your comments and questions appreciated…

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

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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…


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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.

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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.

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