Have you ever considered how calculating Customer Lifetime Value incorrectly could affect your business?
Customer Lifetime Value (aka CLV or LTV) is one of the key performance metrics for every SaaS company. It lets you know much you can spend to acquire customers and still:
- Generate profit from the user acquisition, and/or
- Stay within budget for generating user base growth.
While there are a few ways to calculate CLV, they all start with the following formula:
Calculating CLV might look easy, but it’s often not that straightforward: reality doesn’t always align easily with formulas.
Working with a particular company, we recently calculated four different values for CLV, depending on the data source(s) we used:
That’s quite a range. Why so much discrepancy?
The first value we found was $1333. The reaction we got to this CLV was: it’s too good to be true! (Insider tip: a reaction like this is usually a sign you should investigate further.)
The company’s dashboards reported a churn rate of 3% and an ARPA of $40. So, calculating CLV should be a simple three-minute task, right?
Not so fast.
Validating Customer Lifetime Value
One unwritten law of science says that for something to be true, it needs to be true from every angle. You can’t just zoom in on black and white stripes to determine if an animal is a zebra or a white tiger, for example.
The company we worked with has been in business for over seven years, so we wanted to see how the customer lifetime value of $1333 matched the actual average revenue generated by users who were no longer customers.
The lifetime of a user is calculated as 1 / Churn Rate, so in this case the lifetime is 33 months, or a little less than three years. This means we have enough users who’ve already churned to calculate how much each of them spent on the company’s products.
We exported ecommerce data on all of the ex-customers, including how much they spent with the company, to calculate both an average and a distribution for this group. There were more than 20,000 users in this churn group, giving us enough data to consider our results relevant.
The average amount each customer spent with the company was under $180. Here’s what the distribution looked like:
How do we explain this to the company? We just found out that all the users who left the company over the last seven years spent an average of $180. But earlier, we estimated that the company’s current users would end up spending more than $1300.
In the words of our famed mathematician: something doesn’t seem right.
When we calculated the $1333 CLV, we were using aggregate data provided by a range of different tools. To validate this figure, we needed to try and re-calculate churn and ARPA from scratch.
The company we were working with sells a FinTech product designed for small businesses or solopreneurs with a monthly subscription. Customers can choose to buy a yearly subscription at a discount, or buy different add-ons with a one-time payment.
The churn reported in the company’s dashboards wasn’t separated between monthly and yearly users or add-on purchases. The average revenue per account was calculated as revenue divided by number of customers, but customers with annual subscriptions had a significant impact on this metric.
Armed with this background information, we understood that we had one thing left to do: calculate churn and ARPA just for monthly customers.
Calculating Monthly Churn
To calculate monthly churn, we need to know how many customers we have on the first day of a given month. We get that by counting the number of unique customers who paid for a monthly subscription – new acquisitions plus renewals – in the previous month.
To calculate the churn rate, we count the number of monthly subscriptions that expired during the current month and divide it by the number of customers at the beginning of the month.
We wanted to be sure that this churn rate was correct, so we verified that users whose subscriptions had expired didn’t purchase a different subscription later. It turned out that a considerable proportion of users (roughly 10%) exhibited this behavior.
Excluding these users, we calculated the CLV to be $162. This was far less than the initial calculation and seemed too bad to be true.
We then recalculated churn rate by removing the users who bought something later, since they didn’t churn after all.
The final value for churn rate was between 10% and 12%.
Because we calculated the customer lifetime value only for users with monthly subscriptions, ARPA needs to represent how much a customer spends on average during a given month.
Calculating ARPA is easy as long as you filter your data correctly. For monthly ARPA, divide the revenue generated from monthly subscriptions by the number of monthly customers.
We discovered that the true ARPA value was between $23 and $26, not $40 as reported initially. The $40 figure included one-time purchases and yearly subscriptions, which skewed the data by almost 100%.
So, with the new numbers in place, here’s the CLV for monthly users:
That figure closely matches our analysis of users who’ve already left the company. The company’s price increase over the last year is also clearly reflected in this CLV.
What about those yearly subscriptions?
To calculate CLV for annual subscriptions, we needed to answer this question: How many yearly subscribers renew? It turned out the annual renewal rate was less than 10%, giving us a 90% churn rate.
The second question is how much people spend on average on their yearly subscriptions. The answer: $240.
So, CLV for the yearly subscriptions:
One step further
The final step is to calculate 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.
Customer Lifetime Value reports are offered out of the box with InnerTrends. We integrate automatically with your payments platform and make sure that all your data is filtered and computed correctly. So you can always count on getting your true CLV, no matter where your customers come from.
Schedule a demo if you are interested in learning more.