Category Archives: DataBase Marketing

Recency Defines Engagement: Campaigns

DataBase Marketing, Measuring Engagement, Web Analytics

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

DataBase Marketing, Measuring Engagement

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.

“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 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.

Recency Defines Engagement

DataBase Marketing, Measuring Engagement

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

Ron ends this post on customer Engagement / Retention with:

“While the ROI may not be immediate, an investment in engagement is better than an investment in retention. The key to future profitability isn’t in simply keeping customers – it’s from deepening their relationships. And engagement is a necessary pre-condition for that to happen.”

I don’t disagree with the thrust of this idea, but I also don’t see any real difference between retention and engagement, so I can’t do one instead of the other. To me, a retained customer is one that is actively engaged, and that is how I measure retention – through engagement. In other words, Retention describes a Customer “status”, and Engagement is how one actively measures and manages that status – this is what I always thought CRM was supposed to be about.

Now, if you have never defined a defection or have a really meaningless, non-actionable definition of retention, then I can understand why you might swap one for the other. For example (and I’m just thinking out loud here), if a bank’s definition of a retained customer includes a customer that has a small amount of money in the bank and just leaves it there, with no transactional activity, for 10 years – they are probably counting dead customers as “retained” and certainly need to take a look at their definition. The dead are not very actionable, don’t you know. I can see why this might cause a problem, and the bank might be looking for a little “engagement” to really define a retained customer. If that’s the case, the bank should change the definition of retention to something that really represents a retained customer (which includes active engagement), rather than “swap” retention for engagement.

But let’s not get caught up in Buzzword Bingo. Ron’s work (and attempted hijack of retention!) bring up the need for a stable framework addressing this whole customer value management area that can be used by the all the different factions – the Brand / ARF hijackers, the web analytics folks, and even bankers! Funny, I have one that has worked very well for many years across many different industries, both online and offline. I’ll go through the short explanation here; if you want the full development of the idea, check out the book sample PDF.

One of the things we learned at Home Shopping Network was that the more interactive and dynamic the customer environment, the more a typical LifeTime Value approach to managing customer value tended to break down. One thing that happens when you go interactive with the customer is you create many more opportunities to screw things up and sometimes in a bigger way than ever before possible. This means the Customer LifeCycle becomes unstable in new ways we had not seen before. The most significant effect was rapid changes in behavior; in terms of value, customers might either go wild on spending or drop off the face of the earth without warning. So we came up with a “proxy” for Lifetime Value that was much more effective to use when customers are more interactive. It looks like this:

Customers have both a Current Value and a Future or Potential Value; these Values sum to LifeTime Value. Current Value can be anything from the current bank balance / number of relationshipsto how many purchases to some measure of visit activity and length of visit; the point is, the activity happened in the past so there is really nothing you can do about it. This value is “sunk” and both revenues and costs associated with this value cannot be changed.

However, Potential Value can be measured and acted upon. At any one point in the Customer LifeCycle, the Potential Value is in flux – it largely depends on the relationship the company has with the customer, and can increase or decrease. These changes in Potential Value typically take place when the customer has direct interaction with the company, the so-called “touchpoints”. Success or failure from the perspective of the customer and the customer experience at these touchpoints determines whether Potential Value rises or falls.

So what you really want to focus on is measuring the Potential Value of a customer and changes in it, because if you take care of Potential Value, LifeTime Value will take care of itself.

Once you determine the Current Value and Potential Value of each customer or customer segment, you can literally “map” the customer base into quadrants, and take the appropriate marketing action based on which quadrant the customer resides in, as indicated in the chart.? It’s a budget or resource allocation model. Note that this model is similar to Ron’s chart in several ways; his “Breadth of Relationship” is what I would consider Current Value, and “Customer Engagement” is an indicator of Potential Value.

You say, “OK, fine Jim, I get it, I can even measure Current Value – it’s just the number of times a customer did something like visit or post, or the amount of spend. But how do I measure Potential Value?”

Well, Ron’s banking example and Eric’s web analytics example both used Recency – the time since last event – to help define “engagement”. For example, Ron used “in the past six months” to qualify the Current Value components “How often did you move money between different accounts”and “How often did you check your savings rate”. The more Recently the activity occurred, the more “engaged” the customer. As long as you believe that engaged customers have higher Potential Value (and everybody seems to think so!), then Recency is your Potential Value metric. A different but similar metric to measure Potential Value that often makes more sense in B2B and supply chain is Latency, the time between events. We’ll skip Latency here, but if you are interested in that side, check out The B2B Software Example. Same idea as below, different metric.

Now, I imagine some folks are having trouble visualizing what this Recency / Potential Value thing is all about, so let’s go to the pictures. Let’s take a look at the Recency metric to describe the Potential Value of visitors to a web site:

Here we have about 6 million visitors to a web site over a 90 day period. This is a program that requires log-in to view content, so these are all authenticated visitors. Note that the vast majority of visitors have visited in the past few days, and then you have a bunch whose last visit was more than a few days ago, all the way out to last visit 90 days ago. If you believe that visitor engagement has value to the web site, and you believe that Recent activity is indicative of engagement, then all you need is some proof that the more Recently a visitor has visited, the more Potential Value they have relative to another visitor. Right?

Each day of the 90 days represented on this chart contains a number of visitors whose last visit was on that day – last visit 90 days ago, last visit 89 days ago, and so forth. What if we tracked those groups for another 90 days to see if they visited the site again or not, to see if Recency of visit predicted likelihood to visit again? That’s just what we did, and for each day of last visit in the chart above, created a ratio of those who visited again to those who did not. The red line in the chart below = 1, meaning the number of people who visited again in the next 90 days equals the number who did not visit again:

Example: at the point labeled “45 days ago” in the first chart, there were 50,000 visitors whose last visit date was 45 days ago. When we looked at those same visitors 90 days later, 25,000 have visited again and 25,000 had not. 25K / 25K = 1, so visitors whose last visit was 45 days ago are just as likely as not to visit again. Bars below the red line indicate fewer visitors visited again than did not visit again; bars above the line, more visitors visited again than did not visit again.

Scary, huh? Like someone wrote a mathematical equation to describe the function. But it’s not math, it’s simply Frictionless Behavior (for more on Friction, see the book PDF). In this example, someone who visited yesterday is 484 times more likely to visit again than someone who visited 90 days ago. In other words, the more Recently the visitor came to the web site, the higher the Potential Value of the visitor, since they are more likely to come back and generate revenue for the web site in the future, relative to other visitors. Visit Recency is predictive of Potential Value.

Ironically, Recency is probably the single most powerful Branding engagement metric you could possibly think of – just don’t tell the Branding folks it comes from Database Marketing, please. We’d like them to use the metric, because it would really help build a bridge across all this “marketing confusion” that is out there on the web. It certainly would help if we could standardize on some kind of model that provides common ways to measure the success of both Brand and Direct.

And just to be clear, you can rank the Recency of any activity your customers engage in, online or offline. It might be certain types of bank transactions, for example. Or purchases. Or downloads. Whatever is an indicator of value to your company can me measured as both Current Value and Potential Value to create the model and customer mapping.

Customers with high Current Value and Low Potential Value are of the most concern; they are Best Customers in the process of defecting from your business, they are sliding down the slope in the graph above from right to left, they are becoming less and less likely to engage, they are losing Potential Value. In the model, it means they are falling from the “Keep these Customers” box to the “Should You Spend Money Here?” box.

Another way to say this is Frequency or Sales (Current Value) by itself is not equal to engagement. A customer that has purchased 10 times and the last purchase was 5 years ago has much less Potential Value / engagement going on than a customer who purchased 10 times and the last purchase was a month ago. Recency matters, it predicts Potential Value. Those of you who are counting Frequency to determine how well you are doing (we have 600 customers who have purchased over 20 times!), particularly if you are using this metric to project future financial success, really need to take a look at what percentage of those Best Customers have purchased Recently. Wall Street knows about Recency and they can punish you severely for not understanding it.

You say, “OK Jim, makes sense.  But as a marketer, what do I do with this, how do I use it to increase profits or better manage the business?”

Well, the first thing that comes to mind is this: you don’t want customers sliding past “Equilibrium” or 45 days in the chart above, where they start to become less and less likely to engage. You want to try and engage them before they get there and drive them up into a more Recent interaction. How specifically you do this is a creative exercise and will depend on the business; “thanking customers” in some way is usually appropriate, especially if it drives Surprise and Delight. In terms of budget though, this model narrows your targeting and so allows you to spend more on specific people.

For example, if you are limited (to stick with banking) statement inserts, instead of sending the same lame “retention” insert to every customer, why not come up with something that is high impact (engagement insert?) and send it only to high value customers getting ready to pass through the Equilibrium point on the Recency chart? Not only will you be delivering the “right message, at the right time, to the right person” but it will probably cost less overall and be more effective than the generic insert.

So, you need to:

1.Identify the most relevant value generating interactions with customers; this allows you to map Current Value and Potential Value. Current Value is simply the Monetary Value or Frequency of these transactions to Date or in the past several years; Potential Value is the Recency of the last interaction. If you have multiple contributing interactions, say “move money between different accounts” and “check your savings rate”, you can map each separately and see which is most predictive of customer engagement / retention.

2.Segment your messaging by Recency and measure performance; you are looking for the “sweet spot”, the highest response or profit related to re-engagement. For example, you can segment customers by number of months since last action, and then determine which of these buckets generates the highest ROI or re-engagement relative to the cost of your campaign. Here is what this looks like with e-mail:

This campaign gets a 10% response rate. But if you look inside it by monthly Recency buckets, the more Recent the last open or click was, the more likely you are to get another open or click. So while your overall “response” was 10%, it you look at it by Recency of Previous Interaction, it was anywhere from 28% to .6%. You get the same “waterfall” effect by Recency seen in the Visitor stats above, and where the big drops in interaction are – in this case at 3 months and 6 months – are most likely the highest ROI or best re-engagement opportunities you have. At some point in this waterfall, you start to lose money, so you should kill those segments; this is “economic defection” of the customer – the point at which you can no longer increase the value of the customer.

If you would like more info on this tactical track and find out how to turn Recency and Latency data into increased profits, check out the Marketing Productivity Series. If you want more details on the Current Value / Potential Value model, see the book sample PDF.

Your thoughts? Got any questions on the model or the approach?

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