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? →

When Acquisition Spoils Retention

Analytical Culture, Analytics Education, DataBase Marketing, 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.

OK, here’s a bit of a tough one – what if while investigating customer retention problems you find out that customer defection is highly correlated to specific salespeople or marketing programs? What if I told you this correlation is pretty common – but unrecognized, because hardly anybody goes looking for it? And if found, find trouble doing something about it?

Two issues – you can try to predict / save a customer in the process of defecting, and / or you can hunt down / fix the source of the defection – why is it happening in the first place?

Welcome to the politics of customer retention – and make sure to put your Drillin’ shoes on …

Please note: XXX is a major cell phone provider…

Q:  I’m an XXX customer – I saw an ad for a new phone I wanted for $230.  I went in to the XXX store and asked for the phone – the clerk rang it up at $580!! I showed him the ad.  He said that price is for new customers and he could not give it to me at that price.  So it made me feel that XXX did not value my business.  I then cancelled with XXX service and have told about 10 people about this situation.

A:  Right, this is a pretty common problem with companies that don’t understand
customer retention.  They’re so focused on acquisition that they cause defection and that’s where a lot of the churn in that particular business comes from.  I’d chalk it up to totally clueless marketing management.  

The irony of this situation:  XXX used to be one of the “gold standard” 1-to-1 marketers in the good ‘ol days.

In the first place, companies should not “broadcast” these kinds of offers, because you understand the impact, the leverage, the “costs 5x as much to acquire a customer as retain one” and so forth. If you want to make offers like that, you try to use discrete channels – direct mail and so on, as opposed to newspapers or radio / TV. The strategic issue is people are defecting at such a high rate the company thinks they need to really drive acquisition to make up for it instead of concentrating on retention, which would be less costly and more profitable overall. But even worse, these aggressive acquisition programs are actually increasing the likelihood of customer defection!

Continue reading When Acquisition Spoils Retention →