Segment to Best Determine LifeTime Value (LTV)

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.

LTV has to be actionable.  If  you can’t take action on the information, it’s not relevant anyway.

There you go, the most universally true rule when attempting calculation of LTV.

And the best / easiest way to accomplish this is to identify similar customer behaviors and segment the customers by these behaviors – THEN figure out LTV by segment.

If you can’t actually take action on the information, then why spend countless $$ and hours fussing over all the reasons the number you come up with might be wrong and trying to solve unsolveable data or corporate issues? The best idea to implement when developing / using LTV is consistency – let’s get the team to agree on what LTV is and how to measure it, stick with those ideas for at least several years, test and take action on the results to uncover value, THEN (perhaps) discuss improvements!

Q:  I have just been reading your series on Comparing the Potential Value of Customer Groups. I am having trouble calculating the lifetime value of our customers.

A:  Yes, well, everybody does for some reason!  Often the problem is too much
focus on trying to look at the “average customer” as opposed to segmenting
customers.  By segmenting first, it’s both easier to get to LTV *and* more useful since it’s easier to take action on  a segment than the “average customer”.

Q:  Our company provide accounting software solutions to small to medium sized owner operated  businesses.  Because of what we sell and who we sell to, a lot of our customers are most likely to just buy one or two of our software products and unless they sign up for support (only around 15% do), we may never here from them again.  It is therefore very difficult to determine an average / standard lifetime that customers use our product.

A:  Sure.  First, the 15% segment that does sign up for support sound like good customers to me.  So that’s one segment.  How long do they typically stay signed up?  That’s the average life for this segment.

Then there are probably people who upgrade over time, right?  I can’t imagine an accounting product that people would not upgrade – perhaps not every cycle, but every 2nd or 3rd cycle.  That’s another segment.  Then there are probably some who both follow the upgrade cycle and pay for support.  These are probably the “best customers” and they are a unique segment as well.

And finally, you have the buyer who makes one purchase and you never see again.  These people are also a segment.

Q:  What should I base it on, how long our customers use our products (which would be almost impossible to determine), or how long they spend money with us?  So I measure on average the time between the first and last transaction of customers who have the highest Recency???

A:  LTV is based on profit, not how long they use the product.  But here we get to the other common problem with determining LTV – what is the “life”?  The answer can be found in your data, or you use your knowledge of the business to approximate.

For example, using your data and looking at best customers, how long have the best ones stayed with you?  That’s the maximum life, but you can use it as the “standard” life, if you wish.  A more critical look would be to look at defected best customers – how long did they stay before they left you?

Which begs the question – how do you know when they have left?  Well, if you don’t feel comfortable / can’t look to the data for this answer, use your knowledge of the business.

For example, does it make sense to you that someone who bought one of these packages 5 years ago and has not upgraded is likely to ever purchase from you again?  They must be on a different platform by now, right?  And if they are not, they are continuing to run the package on an old machine, is this the kind of person / company that is likely to be a future buyer?  I think not.  So call the LifeTime 5 years and work from there.  Is it 3 years?  Perhaps; simply get agreement internally on what the “right” length is and use that.

Or, go to the customers for the answer.

Survey customers who have not purchased in 2 years, 3 years, 4 years, 5 years etc. and ask them how likely they are to buy from you again.  If you try to contact people who bought 5 years ago and you can’t – phone number incorrect, e-mail bounces, etc. – well, you have your answer, don’t you?  

If you can’t contact the customer, you can’t market to them.  

If you can’t market to them, there is no opportunity to increase their value.  

If you can’t increase their value, their LifeTime (at least for this cycle) has ended.

If this same person was to come back and order again, from a marketing perspective, they would really be a new customer, wouldn’t they?  With a new company and in a new situation?  Probably, and that is probably how you would treat them anyway, right?

So, a lot of the LTV issue comes down to this: LTV has to be actionable.  If  you can’t take action on the information, it’s not relevant anyway.  So find the easiest way you can justify one way or the other to do the calculations, and move into action on the information.  Some segments will be easier to figure out, some harder. Repeat and optimize as you learn.

And remember, what you are really after in the end is to find some way to rank these segments by source and value.  The actionable part of LTV analysis is this:  what is the value of customers we acquire through different marketing efforts?  Which ads, salespeople, products, service packages, etc. generate the most valuable customers? Once you line up cost to acquire with customer value, then you are on the way to optimizing marketing to drive the highest profitability possible.

Hope that helps!


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