The Simple Math Behind Every Profitable Business — Customer Lifetime Value
Note: Republishing this post I shared on the Medium paid subscription a few years ago. This was originally written in March 2015.
How much is a Customer Worth?
This is the question behind Customer Lifetime Value, a measure of the total expected gain to a business from an individual customer. At the core, it is a concept — just a Mental Model: “Consider how much money a business stands to make from one customer before they stop being a customer (for any number of reasons).”
As an idea, it’s helpful for a few reasons:
Taking a long-term view of customer relationships, rather than a transactional view.
Understanding the upper limit of spending to acquire customers before growth becomes unprofitable.
Considering profitability from a per-customer perspective, and ways to improve it.
Customer Lifetime Value (LTV) is useful as a crude heuristic, but there are ways to calculate it in detail, to account for more and more variables that will increase the accuracy of the measurement.
In this Edition, we’ll go through a crescendo of complexity, starting with simple examples and getting to fully-developed formulas at the end. We’ll end with caveats and the possible mistakes or misunderstandings in this model, so please learn this thoroughly so you don’t go off half-cocked. For this particular subject, half-educated may be more dangerous than no awareness at all.
Customer Lifetime Value 101: The Basics
To get a sense for how this model fits into actual strategies, let’s check out this article from Wharton, which gives us a few examples of companies employing strategies based on the LTV model: Amazon and Sprint.
Amazon will lose money on each $199 Kindle Fire it sells, but hopes to make back that money and more on tablet users who are expected to spend more than other customers. Sprint is not expected to turn a profit selling Apple’s iPhone for at least three years, but expects that gamble to pay off in happier users who will bring in more subscribers.
These businesses are looking at the long-term relationship with their customers — Amazon in particular. If a 30-year-old is a customer, they may very well be a customer for the next 30 years, and that’s incredibly valuable.
LTV in Context of CAC
There is no ‘correct answer’ for a good LTV — what matters is how it fits into your business, specifically your Customer Acquisition Cost (CAC). Together these form the Unit Economics of the business. Here’s a simple example of these calculations, from Ryan Allis of iContact:
Two critical questions determine whether we were ready to raise capital:
What is the lifetime value of an average customer?
How much do you spend to acquire an average customer?
Since iContact operated on a subscription model, I could estimate the lifetime value of an average customer by taking the monthly average revenue per user (ARPU) and multiplying it by the average number of months a customer stayed. I knew the average monthly revenue per customer was $45 at the time. I also knew our monthly average churn rate was about 3%, meaning an average customer stayed with us 1/0.03 or about 33 months. So to get an estimate of the lifetime value we simply multiplied $45 and 33 to get about $1500.
ARPU x Months of Life Before Cancelling = Lifetime Value
Then to calculate how much we spent to acquire an average customer, Jud told me to simply take what we spent per month on advertising and divide that figure by the number of new customers we acquired in a month. At the time, we were spending about $100,000 per month on advertising to acquire 330 customers per month. So our Customer Acquisition Cost was about $300.
Advertising Spend / Customers Acquired from Advertising = Customer Acquisition Cost
There it was. We knew we spent $300 up front to acquire a revenue stream of $1500 over about three years. This was very profitable transaction to make.
Thanks toMark Stansburyfor suggesting this resource — quoted section is 3/4 down the page under “Unit Economics.”
Segmenting Customers for Accurate Customer Lifetime Value
Allis’ example above is a great simplification, and it can hold if there is only one channel that customers are coming from. However, to get a more complete understanding, we have to match LTV to CAC by channel. Not all users are equally valuable because some stay longer, spend more, etc.
Averaging can be dangerous to a true understanding of the data. Just take a look at this simple example, where each of these data sets have the same average and linear fit:
You can’t trust averages of aggregate data. Dig deeper!
They all tell very different stories. In order to avoid making mistakes in interpreting your LTV data, look at it by segmenting it into channels and comparing to Channel CAC.
This post from VC Rob Moffat, a statistician formerly of Google and Bain does a fantastic job of showing just how deeply CAC can be understood. Without an accurate CAC, LTV won’t mean much. Here’s a visual of the CAC breakdown, which we should attempt to match with LTV:
Can’t be easy to collect this data, but it’s important insight!
Looking at this breakdown, an LTV of $25 could be excellent for some channels (SEO), but disastrous for others (Offline Ads, SEM).
Many thanks to the inimitable Clay Patterson for suggesting this post.
Improving LTV
Of course, LTV is not assigned and then constant for all time — there are myriad ways to improve customer lifetime value (or to allow it to languish and fall.) This graphic from David Skok shows the tactics that can be employed to increase LTV, either to increase profitability, or to make a channel profitable in context of CAC.
This is your playbook for increasing profitability in current channels, and for unlocking growth by creating more viable Customer Acquisition channels by increasing LTV.
Thanks to Scott Smith of CatchaFire and Jonno Elliott of Virgin for suggesting David Skok’s post.
LTV 201: Formula Calculations
The businesses for which LTV is most obvious and easy to calculate are subscription businesses: Phone, Cable, Spotify, Netflix, etc. Let’s take a look at an example Phone Subscriber Calculation from Database Marketing, the originators of the formula for customer lifetime value:
This 6-page PDF that goes through the definitions, formulas, and relationships for all of these measures is an incredible resource. By reverse-engineering this, you should be able to put your own LTV analysis together.
Thanks to Bo Fishback for the pointer that phone/cable companies are fantastic models for LTV calculations.
LTV 301: Required Reading
If you only read two things on LTV, it should be the 2 posts in this section. David Skok’s post, Saas Metrics 2.0 is unbelievably thorough and thoughtful. It takes us through a careful blend of math, mental models, and strategic insight that is truly enlightening.
He touches on all of the tangential concepts as well, like Churn:
In the early days of a SaaS business, churn really doesn’t matter that much. Let’s say that you lose 3% of your customers every month. When you only have a hundred customers, losing 3 of them is not that terrible. You can easily go and find another 3 to replace them. However as your business grows in size, the problem becomes different. Imagine that you have become really big, and now have a million customers. 3% churn means that you are losing 30,000 customers every month! That turns out to be a much harder number to replace.
Pricing:
Turns out that pricing your product right can have a huge impact on the unit economics. Not simply by getting the average MRR right, or by providing upsell opportunities — but also by signaling what pieces of the product are most valuable.
and the tension between good Unit Economics and Growth:
One of the biggest challenges we face is the trade-off between growth and unit economics (specifically churn). Many of the things that we have done to reduce churn have (potentially) come at the expense of lowering our growth rate. Those have been some of our hardest decisions.
David Skok’s post, Saas Metrics 2.0 is a crown jewel of LTV knowledge, for anyone in any kind of business, not just saas — read it well.
Thanks to Scott Smith of CatchaFire and Jonno Elliott of Virgin for suggesting David Skok’s post.
Bill Gurley on Dangers of LTV
As mentioned in the intro, there are dangers and downsides of LTV thinking, and it’s crucial to understand the potential pitfalls of models when using them. Gurley’s post, The Dangerous Seduction of the Lifetime Value Formula, does a fantastic job at this, and is broadly-cited as one of the best collections of practical wisdom on LTV anywhere online.
Here’s Gurley’s warning:
Seduced by the model, its practitioners often lose sight of the more important elements of corporate strategy, and become narrowly fixated on the dogmatic execution of the formula. In these cases, the formula can be confused, misused, and abused, much to the detriment of the business, and in many cases the customer as well.
One of the most curious ideas in the post:
This may be the single most important issue and it lies at the heart of why the LTV model eventually breaks down and fails to scale ad infinitum. Tren Griffin, a close friend that has worked for both Craig McCaw and Bill Gates refers to the five variables of the LTV formula as the five horsemen.
What he envisions is that a rope connects them all, and they are all facing different directions. When one horse pulls one way, it makes it more difficult for the other horse to go his direction. Tren’s view is that the variables of the LTV formula are interdependent not independent, and are an overly simplified abstraction of reality.
Tren Griffin expands on this in A Dozen Things I learned from Eric Ries:
I like to say that ARPU, COGS, CPGA, churn, and WAAC are the Five Horsemen of the Business Apocalypse. Each Horseman is deadly and can kill you on their own. Understanding each Horseman is worthy of a book or two, or at least an article.
Gurley’s post is a must-read for anyone in a business employing the LTV model, and a fantastic complement to Skok’s post. If nothing else, read these two posts together and you’ll know enough to be effective!
Thanks to Bo Fishback for suggesting this post and inspiring the topic choice.
LTV 401: Homework
For those who want to become black-belts in practicing LTV, here are some additional resources. If you’re feeling like you know enough for now, skip to the part where you share this and tell your friends to sign up for Evergreen.
HBS Case Study to Calculate Customer Lifetime Value
A Harvard Business School Case Study takes a look at the Rosewood Hotel chain, and debates whether investing more in a corporate brand would increase Customer Lifetime Value by increasing the number of stays per year per guest.
Here are the learning objectives that came with the Case:
To understand the concept of customer lifetime value (CLTV) and the importance of maximizing a customer’s lifetime value for the firm; learn the components of customer lifetime value and how each component can be estimated; calculate customer lifetime value based on a combination of financial and non-financial data; and explore risks and opportunities associated with corporate branding vs. the branding of individual products.
If that sounds like interesting Homework to you, you can go here and purchase the Case, and work through the problem to test yourself!
INSEAD Paper on Customer Lifetime Value
This paper is an academic approach to the Models and Analysis of LTV. It’s 50 pages and has big scary formulas that look like this:
Nope, I have no idea.
To go deep (Mariana Trench deep) on Lifetime Value Calculations, this will be a good place to start, and read down the citations from there.
Open Questions
Creating this collection led me into all kinds of curious mind-wanderings. This is a powerful model to approach business, and can be useful in starting companies, managing them and as an investment thesis. Here are some of the things I found myself wondering. If you know about them or have any helpful resources, please comment or send me a note!
Which businesses have Dead-ends to consumption time that decrease LTV ? (Diapers?)
Which businesses have the highest LTV?
Which businesses have greatest increase to LTV?
What is the lowest-LTV business that is still sustainable?
Be curious.