Why average retention rates can lead to 50% error in CLV

And if you think that sucks, wait until you either a) use CLV to justify marketing spend or b) use CLV to justify your valuation with an investor. Oof, self-inflicted damage.<p>One particular solution is to stop reporting CLV as a single number. I mean, if you happen to know that there are two disjoint sets of Good Customers and Bad Customers than that is <i>very useful information</i> to anyone who needs to know the CLV number to do their job. "What can we afford to spend to acquire a new customer? I have a new channel I want to try." "It really depends on whether you're getting Good Customers or Bad Customers. We can only pay $50 for BCs, but for GCs we can go $200+."<p>You then get into issues like "How do I tell the difference between a Bad Customer and a Good Customer at an equivalent vintage where neither have churned yet?" It may be the case that there are behaviors which you can use as a proxy for which group someone is likely to fall in. Dharmesh Shah talks often about a Customer Happiness Index that Hubspot uses, which is essentially a regression that predicts churn rate based on measurable customer behavior. It's all sorts of win to find that something like that works for your business. (Hypothetical example: if Dropbox found that customers who used photo sharing are the best possible Dropbox customers, it would make sense to test things like a) biasing marketing to target photo sharers or b) bias product design to push photo sharing as a feature.)

A proposed solution, for the critique of any mathematicians here:<p>Always split each channel into ten 10-percentile bands, instead of just taking an overall average. Then model total expected revenue per channel based on these bands.<p>Perhaps as few as three 33-percentile or five 20-percentile bands would be enough if the number of samples is low when first testing a channel.

Also, for most SaaS apps, your first month churn is typically <i>WAY</i> higher than your second, third and fourth. You need to simulate in order to find the true LTV, also, be sure to include the fact that ARPU typically goes up over time as people upgrade their accounts, so account for that too.

For anyone interested in some further reading: <a href="http://marketing.wharton.upenn.edu/documents/research/Schweidel_Bradlow_Fader_Portfolio_MgmtSci_11.pdf" rel="nofollow">http://marketing.wharton.upenn.edu/documents/research/Schwei...</a><p>I included this technique in some work I did about a year ago, spoke a few times with the authors. Granted, this is geared towards operations that offer multiple services/varying levels of service as part of a "portfolio". Think telecom and finance. It addresses CLV, but includes a regular reassessment of value based on a customers pattern of behavior.<p>tldr; Instead of being service/no service it looks at a customer's propensity to change service (upgrade or downgrade), this includes downgrading to the point of termination.

I've always struggled with CLV calculations–both figuring it out and deciding if it's even valuable to know.<p>We have customers in the thousands and, while a handful leave every month, many have been paying monthly for 2, 3 or more <i>years</i>. So, doesn't the CLV for a business that has been around for 5+ years <i>change constantly</i>? What good is that?<p>Also, we have a setup fee for some products. So, once we keep a customer for 30 days (i.e. they're no longer eligible for a refund), they are worth <i>at least</i> the amount of the setup fee, right?<p>BUT, if my CLV is $1,500 for a product with a setup fee, does that mean it's okay for me to spend $1,200 to acquire 1 new customer? I have a hard time believing that ...

This article stops short of giving the one piece that we need to take action on this: The formula.<p>So, given a database full of customer records, all of which have a signup date, some of which have a cancel date, what is the formula one would use to determine the average expected "lifetime" of a customer.<p>I suspect that the author doesn't want to give us this formula, as it's part of the secret sauce that the company behind the blog post sells. Still, maybe if we ask nicely enough we can guilt it out of him?

I understand why CLV is an interesting number. Maybe it is only really useful to know that you can spend money attract profitable customers. I used to think my business is awesome because I largely get repeat and referral business. When I started thinking about CLV I started to understand that I had no real formula for acquiring new customers. It's important to show you can take active steps to bring in new customers.

It looks to me that those calculations are only correct if you ignore future customers. Only your current customers are used for the calculation.<p>CLV assumes that everything continues as it is today, including gain and loss rates of customers. If you stop gaining new customers, then yes, it's going to be WAY out.

Building a simple monte carlo or markov chain monte carlo simulation of attrition <i>and upgrades</i> to predict LTV, while their analysis of why average is not a great way to calculate, I'd be interested in reading about better alternatives.

I was baffled by the site for a moment giving me a blank page. Apparently css sets the opacity of the main area to 0 and javascript has to come in to override it.

The post title should really expand the acronym "CLV" since the title makes no sense unless you already know what CLV is.

If you're using CLV to determine the cap on the cost of acquisition then the average is important unless you know what kind of customer you're buying in advance.