View profile

DataDiary by InnerTrends - Issue #5

January 4 · Issue #5 · View online
DataDiary by InnerTrends
Avoid disaster by correctly calculating the Customer Lifetime Value (CLV)
Some say that Customer Lifetime Value (aka CLV) is the only metric that matters. (Forbes: CLV: The Only Metric That Matters)
Whether it is the most important one or simply one of the key performance metrics of SaaS companies, one thing is for sure: a big fat CLV will take your business to the next level.
A word of caution though: However easy the CLV formula might seem, calculating it could turn into a headache.
Get it right – and it will help you:
  • Reach the maximum performance possible in user acquisition and thus generate more profit, and
  • Stay within budget for generating user base growth.
Get it wrong – and you might:
  • Overestimate your CLV and end up with negative growth, or
  • Underestimate your CLV, so you won’t make use of all the available marketing channels, and thus lose potential clients and precious revenue
So… how can you get it wrong?
While there are a few ways to calculate CLV, they all start with the following formula:
CLV: Customer Lifetime Value // Churn Rate: The rate at which customers cancel their subscription // ARPA: Average revenue per account (customer) for a defined period of time (eg, monthly)
Easy as it might seem, this formula can generate confusing results.
Why is it so challenging?
Let me give you a real-life example: 
We’ve recently calculated the CLV for a company and… surprise surprise – we got 4 different values:
                     $162              $222              $814               $1333
That’s quite a range. Why such a huge discrepancy?
In our case, it turns out that the initial result we got, $1333, was unrealistic. Nobody believed it.
So we decided to clean the data out. We started with test orders because the company considered them negligible. We discovered they weren’t negligible at all. The second estimate was of $814. Still, this seemed far-fetched.
We also noticed they put yearly and monthly customers together. What we did next was to calculate churn and ARPA just for monthly customers.
Our third result was $162. We came to this result after excluding all the users whose subscriptions had expired that month. This result was far less than the initial calculation and seemed too bad to be true.
It turned out that a considerable proportion of users (roughly 10%) purchased another subscription after the first one had expired. So we included them in our fourth attempt. With the final numbers in place, the CLV for monthly users was $222.
We took our analysis one step further and calculated the CLV based on acquisition channels. Clearly, not all users are the same.
For the company in question, organic channels generated more than 60% of customers, who had a lifetime value of $255, while paid channels generated 40% of the customers, who had a lifetime value of $172.50.
This breakdown gave the company a much clearer view of what their cost of acquisition should be per channel.
So then… how can you get it right?
The answer lies in the data sources you use and how deep you dig for info to get the right results.
The secret is to uncover the various layers of data and make sure that all the relevant info is filtered and computed correctly. If you get a result that seems unlikely, make certain you double-check it and look at the numbers from all angles to see what you’ve missed.
One way to validate the CLV is to compare it with the average revenue received from all the users who have ever churned. That is not an estimate, it is an actual number which refers to the past. If it is too far from the value you’ve just discovered from your present data, it means something is probably wrong.
So, if you have friends who are struggling with client retention forecasts or spending too much money to attract new clients because they are having a hard time calculating their CLV, just forward them this email.
And if you are interested in learning more, or have any questions on the matter, we are happy to share our expertise.
Did you enjoy this issue?
If you don't want these updates anymore, please unsubscribe here
If you were forwarded this newsletter and you like it, you can subscribe here
Powered by Revue