Category Archives: Driller Q & A

How Long is a Customer LifeTime?

Analytical Culture, DataBase Marketing, Driller Q & A, Marketing Research, Marketing thru Operations, Measuring Engagement, ,

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. 

Continue reading How Long is a Customer LifeTime? →

Segment to Best Determine LifeTime Value (LTV)

Analytics Education, Customer Experience, DataBase Marketing, Digital Analytics, Driller Q & A, Marketing thru Operations, Measuring Engagement, ,

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???

Continue reading Segment to Best Determine LifeTime Value (LTV) →

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

DataBase Marketing, Driller Q & A, Marketing / Tech Interface, Marketing thru Operations, Measuring Engagement,

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.

Continue reading Modeling Defections – When is a Customer No Longer a Customer? →