Actionable Customer Retention Measurement

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Topic Overview

Hi again folks, Jim Novo here.

Simple question below, not so simple answer. There’s a lot of conflicting ideas floating around on the subject of how to measure customer retention properly, and to be honest, it really does depend on the type of business we’re talking about. Further, in order to properly measure customer retention – in a way you can take action to improve retention / increase profits – you have to define it first, and that can be as much of a challenge as the actual measurement. Ready for a trip down into the depths of this area? Hang on, it’s quite a ride, you Driller you …

Q:  How do most companies measure customer retention?  Is there a formula?

A:  The short answer is not many companies outside of specific industries are very adept at customer retention – yet.  For traditional (not-online-born) companies, it is most commonly used in telecommunications, financial services (including insurance), direct marketing (catalogs / web sites, etc.), subscriptions / publishing, and the travel industry.

The reason for this concentration: these industries have traditionally collected detailed data on customer interactions as part of the offline business model.  Now that many other industries are collecting data on customer interactions online, the lessons learned in these “lead” industries are proving quite valuable for industries new to direct customer interaction.

A “standard” way to measure it, if you are looking to align your metrics with Wall Street and your financial statements for example, is “12 month active”.  Any customer you have had contact with in the past 12 months is still a customer, any customer with no contact in the past 12 months is a defected customer.

This is a retail / mail order oriented view, and if you sell products, then “contact” means “purchase”.  If you are in the services business, it could be any contact – phone call, e-mail, sales call, download.  Divide the number of 12 month active customers by the total number of customers and you have your retention rate.

There is no reason you can’t use “24 month active” or “36 month active” or “5 year active”.  The point is to define what retention is for your particular business and stick with it.  Get agreement on what makes sense for a measuring stick and try to improve.  Often your own data will tell you what the best “no activity cutoff” is for your business.

Retention is really a “continuum”, and retention rate is always “relative” to your perspective. If you use a very “tight” definition like “12 month active”, you will lower your retention rate. As you expand the time period, your retention rate rises. The problem with most companies is they expand this cutoff time period to infinity, meaning every customer is still a customer unless they notify you they are not. Is this a useful measurment? Doubt it…

It makes sense for retailers to use “12 month active” because seasonality and holidays drive many shopping opportunities over a year’s time; if the customer doesn’t purchase at least once across all these opportunities, they’re probably not a customer anymore.  If you sell cars, you probably go with something more like “5 year active”.  Computers, maybe “3 year active”.  Defining retention rate this way is certainly better than having no definition of retention at all, but it’s not optimal.

You might notice that using this type of definition, your retention rate will naturally fall over time as the base of “all customers” grows.  You can tighten this up a bit by defining a standard base, as in “of all customers who had at least 1 contact in the past 36 months (36 month active), what percent are 12 month active?  This way truly defected customers are not included in the “total customers” base used to calculate retention rate.  I mean, if they are not customers, they should not be included in the base of “all customers”, right?  Or you can track multiple retention rates – 12 month active, 24 month active, and 36 month active. 

If you’re more interested in driving increaed profitability rather than creating a bunch of fancy charts for management to fawn over, here is another way to look at retention:

Pick a month when your business is heaviest. Find all the New customers generated during that month and flag them in your database (for reporting convenience). Each month, run a report to see how many of those customers have had a transaction with you since the very first one; include all months since the first transaction for analysis. Over time, you will see a downward sloping curve like this:


Of course, if you have customer history, you can go back 2 or 3 years and run the same type of analysis from the perspective of “today”: of the New customers I generated 3 years ago, what percent have had a transaction with us in the past year? This percentage is your actual, hardcore retention rate for that group of new customers.

But here’s the thing.  As I tell many people, you can’t put a “retention rate” in the bank, it’s really not very actionable because you can define it however you like.  Ideally, you want to study your customer base for the most actionable retention measure, and use that measure to drive increased profitability.

Another, perhaps more tangible and actionable way to look at retention is to make customers “prove” they are still customers.  This may not work for every business, but it is worth mentioning because some cultures prefer it.

This is where the idea of retention as a “relative” measure really comes into play. The question is not whether the customer is “retained” or “defected”, but what is the likelihood the customer is retained, what is the economic potential of the customer to the business relative to other customers? If the potential is high, it is worth spending money to ensure the customer is retained.? If the potential is low, it’s not worth taking action.

This likelihood is like the “odds” in betting, and you can use these likelihoods to optimize marketing budgets.  If I have $1 to “bet” on a customer using a marketing program or contact, I want to place that bet where the likelihood of “winning” (making a profit) is highest.  I can rank customers by their potential to be winners, and place bets on all of them down to where my likelihood to win is low or negative.

Approached this way, retention is not an arbitrary idea, it can be pinned down to profit.  At the point where the company can no longer make any profit from the customer, where the likelihood of “winning” my marketing bet is low, the customer is considered defected.


Sound complicated?  Here’s a simple way to get started with this general idea:

Take a small random sample of your customer base and organize it by monthly Recency buckets – last transaction < 1 month ago, last transaction 1 – 2 months ago, last transaction 2 – 3 months ago, last transaction 3 – 4 months ago, etc. At the tail end you can use “Last transaction greater than 36 months ago” or a similar idea.

Then create an irresistible offer or other reason to contact the customer in line with your business, with the objective of moving the customer to take some kind of action.  Deliver the offer or contact and look at response rate by Recency bucket.  You will see a downward sloping response curve as the Recency buckets get “older” which eventually approaches zero.

This is your “likelihood” curve, these are your “odds” at winning a marketing bet on a particular group of customers segmented by Recency.  Now, I would argue that in many businesses, if the customer is not generating transactions / contacts, they are no longer a customer.  They can’t just “sit there” with “potential”.  They have to be in the game generating activity.  The longer it has been since the last contact with the customer, the less likely it is they are still a customer, and you will see this in your response curve.  The “potential value” of the customer falls as the time since last contact rises.  At some point the potential value of the customer will  approach zero.

The question for you when looking at this curve is this: where along this downward sloping curve does it make the most sense to attack the defection?  In many businesses, this can be demonstrated in a purely economic sense.  At some point along this curve, the cost to reach out and try to slow the defection of a customer by reselling, upselling, or cross-selling exceeds the value generated by retaining the customer.  At some point along this curve, the cost to make an offer to the customers exceeds the profits generated by those customers who respond to it.  As the Recency buckets get “older”, you will get to the one bucket where your efforts to reach out generate economic losses.

That point is “economic defection”, not because the customer won’t respond anymore at all or even respond ever, but because you can’t make any money trying to get a response / retain them, there is no longer positive economic value in the customer, relative to customers in the more Recent buckets.  If the point this happens is at the 18 month Recency bucket, then a customer is defected when there has been no contact / purchase in 18 months.

Period, end of story. A hard, economic cutoff.

Salespeople are intimately familiar with this approach to managing resources.  In their language, the “lead goes cold”.  Doesn’t mean the lead will never buy, but it does mean it is not worth expending resources at this time to try and close the prospect.  The same applies to customers.  If it makes no economic sense to retain the customer, why waste the money?  Just mark the customer “defected” and spend where profit likelihood is higher.

Under this approach, your “retention rate” would then be the number of 18 month active customers divided by the total number of customers.  But now you have a much more powerful idea driving this metric, because it is defined by economic defection rather than some arbitrary cutoff.  Do you follow the logic?

There are some products and services where this “potential value curve” is distorted by external, often predictable cyclical forces that create “flatlines” in economic behavior.

Car purchase cycles come to mind.  If it is known by my dealer I trade cars every 35 months, then the downward sloping potential value curve doesn’t start to kick in until just past the point I become very likely to trade in my car, at about 35 months.  My potential value is a “flatline” in years 1 and 2.  

At some point near the 35 month mark, my potential value to the dealer who originally sold me the car starts to rise as the time to trade comes closer.  If my 35 month anniversary passes without this dealer seeing me, my downward sloping potential value curve kicks in with a vengeance, dropping very rapidly over the next few months to zero.  Why?  Because after this 35th month, it is highly likely I have already traded with another dealer.  This type of curve also has implications for marketing “bets” and when economic defection occurs.  Trying to “chase” me at this point has very low likelihood of economic reward for the dealer.

What you really need to do is get agreement on how to measure your retention rate, and then just stick with it. The way you define it is not really the issue; the point of defining retention is to create a measuring stick that drives a plan of action. You can attack customer defection anywhere along the curve, but there are always periods where it is most profitable to attack. To find these periods, you have to test your programs / offers / sales techniques along the entire curve to see where you can drive the highest profitability.

As you might expect, different customer segments have different slopes to their potential value curves, often depending on:

* Media source of the new customer
* Offer made to acquire the new customer
* First product / service / contact type

Once you define defection for all customers, you can then start segmenting and define defection curves for specific customer segments, generally of the 3 types above (source – offer – product). This in effect creates a series of new potential value curves that tilt the odds for winning your marketing bets even more highly in your favor by segment.

I hope the above helps rather than providing too much information.  Let me know if you have any questions!


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