How Long is a Customer LifeTime?

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.

There’s always two questions about the topic of Lifetime Value – how do you quanitify value, and how long is / how do you measure / decide what a Lifetime is? For now we’ll leave the value question unanswered, because a lot of that depends on company culture and what question you are trying to answer. Plus, it depends on how you measuree a Lifetime.

So let’s do the Lifetime thing first, shall we? To the Drillin’…

Q:  First of all thanks for an excellent web site – I often visit it to learn and / or get inspiration in my work.

A:  Thanks for the kind words!

Q:  Anyway, I work in a telco retention department and I’m trying to calculate a true and fair value for customer life time answering the question : “How long do we on average have a customer?”.

A:  A both noble and useful pursuit!

Q:  I have data on when customers signed up and when they left (or of course whether they are still here). My first problem is whether to include both lost and existing customers in the calculation.  If you only include the customers you lost you are only able to answer the question for those.  If you include existing customers you don’t know what life time to use for them.

A:  Well, yes, that’s correct.  But you’re really trying to accomplish several things at the same time, so you can break the analysis into different parts and then apply some business logic to get your answers. 

Let’s assume that for whatever reason, your company attracts two kinds of customers in terms of their profitability – really profitable ones and really unprofitable ones.  Whether the customer is profitable or not (as is often the case) is a direct function of how long they remain a customer.  So all your disconnected customers are “bad” and all your current customers are “good”. If you analyze the average life of disconnected customers, you will come up with the average life of a “bad” customer.  If you analyze the average life of all current customers, you will find out the average life of a “good” customer.

These lives could, and perhaps should, be different.  Or should they?

What you may find is the average life of a bad customers is actually longer than the average life of a good customer.  That’s an interesting idea, isn’t it?  What it means is the quality of your customers has been falling over time; that for some reason you are attracting more “short cycle” customers than you used to.

Conversely, if you find that the average life of current customers is longer than that of disconnected customers, the quality of customers has been rising over time. 

You should also look into the active customer base and do an analysis of customers by life, for example, % 1 year, % 2 years, % 3 years, etc.  Here is what this typically looks like:

1 year 20%
2 years 40%
3 years 20 %
4 years 15%
5 + years 5%

This is a “snapshot” of the customer base, what it looks like today.

What you will probably find (depending, of course, on how long you have been in business) is that, for example, only 5% of active customers have been customers for 5 years or more. Now, just because there are customers who last for over 5 years, that doesn’t mean the “average” life of active customers is 5 years, does it?

Of course not.  Somewhere in your analysis of active customers you will find a “bulge” (in this case above, at 2 years) where the highest % of active customers are, and this is probably a good number to use for the average life.  It may also surprise you that this active customer life is remarkably similar to the average life of a disconnected customer.  If that happens, I think you have the answer to your question, right?  Despite the fact you may have active customers with longer lives, the disconnected customers provide a good view into the “average life”.

To drive further down this path and provide more information on “expected” life with active customers, you can do a “Longitudinal” study. Instead of a “snapshot”, you get something more like a “movie“, which will give you deeper insight into the LifeCycle and thus LTV.

Start with customers who all became customers in the same quarter or year (say 5 years ago), and look at the % of these who are still  active over the years, for example:

Active at 1 year – 100%
Active at 2 years – 60%
Active at 3 years – 30 %
Active at 4 years – 20%
Active at 5 years – 5%

There’s a serious drop off between years 2 and 3.  So if you’re talking “average life”, it’s probably somewhere in there.

Now, the average life is not a particularly useful number, by itself.  Telco customers have different lives depending on how they were acquired (the offer), what services they take, and how old their hardware is.  If you have collected “source” information on customers, for example, which campaigns they responded to when they became customers, then you can look at the average life in a more actionable way. I n other words, the question is not just the life, but the life in relation to how the customer was acquired.

Here is the same Longitudinal study from above with Source information:

1 year 100% Source: 60% discount mailer
2 years 60% Source: 65% TV / Radio
3 years 30 % Source: 80% Newspaper
4 years 20% Source: 75% Internet
5 + years 5% Source: 70% “tell-a-friend”

So over the years, as customers cycle out, we find that the ones with the longest life came from the campaign “tell-a-friend”.  In other words, the “average life” of a customer differs depending on source, and the average life generated by the campaign “tell-a-friend” is longer than average, and much longer than the average life of a customer generated by campaign “discount mailer”.

Frankly, I’m not sure where people got the idea that LifeTime Value is a static number.  If that were true, why bother with retention programs?  If your retention program is a success, LTV should increase.  If you do something that causes decreased satisfaction, LTV will decrease.  Expected LTV is dynamic based on what your company is doing at any one time.  The only time you get “final LTV” is when the customer actually defects.  When you go through a cycle where you do new kinds of advertising / change product offerings the “expected LTV” will always change.

Managing customer value is not really about the absolute LifeTime Value of the customer, but the expected LifeTime Value based on what kinds of changes you are making in marketing, service, technology, etc.  You can track expected LifeTime Value and the relative LifeTime Value between customer segments in the present, so what difference does it make if you don’t really know the “end game” on value until the customer terminates?

Tracking changes in relative and expected LTV LTV when comparing customer segments is the most powerful tool a marketer can possess.  If you can predict changes in customer value before they happen, you can take action in a timely way if you need to.

Q:  Second, I think it is relevant to limit myself to look back a specified amount of time, say 2 years worth of data.

A:  I’m not sure why you would do this or what is driving this thought…is it because your contracts are typically 2 years long?  Then you’re looking at more like a “churn” type of thing, what % renew the contract…

Q:  As an example, consider a company that has been in business for 10 years and only ever had 3 customers, one leaving after 1 day (ie life time 0), the second left after 5 years and the third is still a customer.  What is the customer life time in this case ?  It is 2.5 years for the lost customers but that does not say much about the actual customer base today.  You could say it is 5 years as the average between the three.  Or you could say that it is 10 years (and counting) based on your existing customer base.

A:  Well, that’s not a very realistic example, but I think I get your point.  If you have been in business 25 years, the average life of a customer “today” in relative terms is of course more useful, since so much has changed in the telco business over the years.

If you want to try to find a good cut-off, do the same kind of analysis referred to above – analysis of customers by life, for example, % 1 year, % 2 years, % 3 years, etc. and just keep going.  You will see the percentage of customers in a given year approach zero.  Where you cut off (1%, 5%, 10%?) would be arbitrary, but remember, LTV analysis has to be actionable to be useful.  If you can’t see the company ever designing a campaign that would reach under 5% of customers, cut it off there.  I think 2 years is a very short window for an LTV analysis, though.

Q:  I hope you can give me a little guidance.

A:  Should be enough here to get you started!


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