Customer Defection Rejection Part 2 : Proving the Business Case – Simply!

This is a multi-part series, see: Part 1 – Part 2 – Part 3Part 4Part 5

In Part 1, we looked at two different ways you can use to start the conversation about marketing productivity. Both the attrition approach and the retention approach should be eye-openers for those who have not considered the question of marketing productivity before. If a small basket of customers generates the majority of current revenues or value, why would you spend the marketing budget equally on all customers?

Yet, there can still be skeptics in the crowd. “Well, the company offering has changed over the years so looking at past data is not really fair” is a common complaint. Another is the “Well, we provide much better service now than we did back then” is another. I’ve heard them all; a parade of reasons not to believe the actual data you put forth.

So what’s a marketer / analyst to do? How about looking at some current data that will point to the productivity trend in “real time”?

You have some kind of recent campaign data, don’t you? You can probably use that data to prove the point. It doesn’t matter if you are generating leads or selling products or just trying to generate e-mail opens or visits; your campaign data holds productivity secrets that will be revealed – with the proper analysis.

Let’s say you do a lot of e-mail drops and you drop to all available names, no matter how long it has been since you have had any response from the customer. Response can be anything – purchases, opens, visits – whatever it is you track for success.

Let’s say you typically get a response rate of 10%. What if I told you your 10% response rate is probably distributed like this:

Now, it may be over a longer time period (say 5 years) or it may have a different “slope” to it, but the fact is that the longer it has been since a person responded, the less likely it is they will respond again. In B2B, this effect often shows up as the “cold lead”. All salespeople have dealt with this effect at some time in their career. The longer it has been since the lead was generated, the less likely it is they can close the lead. In fact, the effect is so pronounced that many salespeople learn from experience to sort their leads by date of generation and pursue the most Recent first. All else equal, this approach consistently generates a higher close rate and salespeople naturally gravitate to this method over time.

How do you see what this “response curve” looks like for your company? Well, here are two possible ways to go about it:

Next Campaign: Before your next drop, set up tracking and reporting so that you capture the previous last response date of all new responders. There are several ways to do this depending on your systems. You could tag each e-mail itself with the last date of response or contact, perhaps embedding a code in the link structure. Or you could do a lookup as responses come in and write previous contact data to the promotion file. Be aware that some systems keep a “last contact date” that is overwritten with each new contact, so you will have to plan accordingly if that is the case.

Previous Campaign: This may be easier or impossible depending on your set up. Keeping backups of databases in pretty common practice these days. You may be able to locate a backup of the customer or contact database from just prior to your previous campaign. Take the responders from this campaign and match back to the backup of the database prior to campaign drop to identify the last response date of each responder to the previous campaign.

Either way, you will undoubtedly see a pattern similar to the one above. Response by Recency may drop off much more quickly or more gradually, but the pattern is there.

Now, this data is a “snapshot” in time rather than a “movie” like the attrition method or the retention method. But think about this; every time you do a campaign, you get a “snapshot” that looks like this one. It’s a single frame or scene for the whole retention movie, isn’t it?

A series of these campaign snapshots stitched together is what creates the 3, 5, or 10 year movie that results in most of your current revenue coming from newer customers. It’s why only 20% of your customers who were new 5 years ago are still customers (attrition), or 80% of your revenue is coming from customers who were new in the past couple of years. These monthly “campaign snapshots” create the 5 year attrition / retention “movie”.


To say the least, this data proves that marketing to “all customers” is not a very effective and certainly not the most productive way to spend a marketing budget. There is tremendous waste in this model. Forget about whether your boss believes customers can be “retained” or not; or that you can design High ROI customer retention programs that will drive serious increases to the bottom line. Forget about LifeTime Value.

What you have right in front of your eyes is indisputable evidence of the Customer LifeCycle, and the fact is that after a certain point in the LifeCycle, most customers no longer respond to you or your marketing.

Are these non-responsive customers “defected”? I don’t know, but does it really make a difference what you call them? What their LifeTime Value is and all that?

From a marketing productivity perspective, these customers are simply unresponsive; and to continue to spend budget on them is a lot of wasted time and money. So the question you have to ask yourself is this: what else could I do with the budget I have been using to contact these people who will not respond? How could I use that budget more effectively to drive increases in bottom line profits?

The answer is you want to reallocate that budget towards slowing down the process that creates unresponsive customers in the first place. You have to act earlier in the LifeCycle we have just seen, and you have to act more forcefully than what it is you are doing now.

Go to Customer Defection Rejection Part 3 : Spreading the Average (Recency)


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