Modeling Defections – When is a Customer No Longer a Customer?

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.

Metrics are not usually also Models; the metrics have to be fine-tuned / combined and built up into models. And executing this process usually depends alot on what type of business is being analyzed, and what kind of problem is targeted for a solution. So while it’s pretty simple to define a metric, creating a version of the metric that specifically addresses the challenge at hand can be a bit more difficult. Not hard, mind you; it mostly just takes a decent understanding of how the business works. Want some examples? Read on, O Fellow Driller …

Q:  Is Latency, as a metric, out of the question when the spread of the number of days in a latency period is so wide that to average them out and call the resultant figure “Acceptable days to date of predicted purchase” would seem meaningless?  I am thinking about the disparity in latency between customers who are Heavy, Moderate and Low users.

A:  I’m not sure I have enough context to understand the question (what are you trying to accomplish by using the metric?) but Latency is what it is.  In other words, you take your clue from the existing behavior itself.  If the average Latency for a certain segment is 2 years, well, it is, and that’s not too long or too short, it just is.  Whether you can act on that information is another story; it depends on what you are trying to accomplish.

For example, average Latency on major home appliances, depending on brand, is anywhere from 5 to 10 years.  Is that too long of a “spread” to make the metric useful?  No.  It just is what it is, and you deal with it. Typically these ideas are used to reallocate marketing spend away from waste on unresponsive segments towards segments that will generate incremental profits.

Now, it could be what you are really getting at has more to do with failing to identify the defection.  In other words, you are trying to average customer Latencies that are “open-ended” or infinite and you’re coming up with a useless number.  If that’s what you mean by “latency period is so wide that to average them out would be meaningless” it sounds to me like you need to call the defection so there is some endpoint you can use to measure with.

For example, let’s say you have 10 years of data and it includes people who bought only one time 10 years ago.  Clearly, those people are no longer customers.  To include them in a Latency analysis would create a meaningless number, both arithmetically and behaviorally.  You have to put a stake in the ground and declare “no activity for over 2 years, they are a defected customer and will not be included in the analysis”.  This takes the “infinite Latency” problem out of your hands.

Another approach (and I’m just guessing you are talking about retail) is to look at 2nd purchase Latency – average number of days between 1st and 2nd purchase.  Let’s say it’s 90 days.  So from now on, any new buyer who doesn’t make a 2nd purchase within 90 days of the first is considered a defected customer and excluded from any analysis, because literally, they are likely to have infinite Latency and the “spread” is meaningless.

A third approach taken by people who have been in this kind of business a long time and trust these metrics is to set a threshold for being a customer at all.  At HSN (after many years of testing), we came to the point where we didn’t even consider you a “customer” until you made a 2nd purchase.  We kept track of the number of 1x buyers, but that’s about it.  We discovered there was no profitable way to create a “relationship” with these buyers, so they were not considered customers from a marketing investment perspective.

Q:  In this case (Latency period is so wide), is Recency the way to go?

A:  Well, again, I lack enough context to answer that question specifically because I don’t know what you are trying to accomplish and with what kind of business.  But in general, Recency is a better bet when the behavior is not predictable, and Latency is a better bet when the behavior tends to be cyclical or repeat at regular intervals.  So for general retailing, Recency is a better predictor of likelihood to purchase.  If you are looking at the oil change business, Latency would be a better predictor of likelihood to get another oil change at a certain point in time.

Latency and Recency can be used at the same time on different segments, it’s not an “either or” situation.  For example, you could use Latency for Heavy and Moderate, Recency for Low, if that makes sense in your situation.

For example, let’s say that in your case “Low” means there really isn’t enough transactional activity to determine Latency.  So you use Recency for that segment.   This approach is often used in remote retail, I call it a “one and done”.  

After first purchase, you allow Recency to expand, so that multi-buyers “tip their hand” and you don’t end up making offers you did not have to make to get the 2nd and 3rd purchase.   At some point (typically 45 – 90 days out), the majority of multi-buyers have shown themselves, and you make one offer to the remaining 1-time buyers.  Those that respond become multi’s, the majority of the rest will never buy a second time and it’s a waste of money to promote to them – this is what I mean by “one and done”.

In other words, you give them some time to prove whether they are customers or not, then you provide them one last shot by giving them a nudge.  Anybody who does not respond is now a defected customer and that’s it.  If they come back and prove themselves to be a customer, fine, we’ll include them in all the customer marketing.  But if they don’t, it’s  relationship game over.

Q:  My purchase of your book has generated a second purchase!  By a colleague of mine.

A:  Thanks for that!  If you’d like to elaborate on the specifics of what you are trying to accomplish, perhaps I can be more helpful!


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