Churn rate — Why size of your SaaS company matters

Churn rate is a metrics so widely used in the SaaS market that it is easy to naturally think it is a well understood metrics.

Churn rate, defined for example as “the ratio of customers leaving a company over a certain period of time divided by total number of customers at the beginning of that period”, sounds like a straightforward and easy to calculate Key Performance Indication (or KPI).

However in reality understanding churn rate and driving useful insights can quickly become tricky. Worst, if misunderstood, it can lead to bad judgment and potentially wrong investment decisions!

There is much to say about the different approaches to analyse churn rate and the context around the company can have a huge impact on the numbers you are analysing.

In a previous post, I have shown how churn rate in high growth companies can be deceiving. In this post I will focus on why comparing the churn rate of a $500k ARR and $5m ARR can be misleading.

How to calculate churn

That simple questions can bring a lot of different responses. Let’s say you want to know your average churn rate over last year. For each months, there are at minimum two ways to calculate churn:

1 — Take total number of customers who left over the month and divide it by total customers at the beginning of the month.

2 — Take total number of customers who left over the month and divide it by average number of active customers over the month.

Right. Now we said we wanted to look at average monthly churn over the year. There are also different ways to calculate that.

1 — Take the average monthly churn % over twelve months.

2 — Take total number of customers lost over the year divided by average number of active customers over the year divided by twelve.

“Ok. We get it, there are several approaches to calculate churn. Each of these do not seem so complicated right ?”

Yes, the examples above are still fairly easy to understand. But now, instead of calculating your churn rate based on number of customers, what if you’d like to calculate it based on revenue ?

The first step here will be to ask yourself how do you define churn revenue?

1 — As the revenue generated last month by customers who have not renewed?

2 — As the total of the decrease in revenue for each customer, including downgrade?

3 — As the total revenue generated last month by customers who have not renewed plus the net between total downgrade and upgrade?

Also, what about customers that were simply late to renew and did so a week later? How do you care for these ones to make sure that the churn that you calculate is accurate?

Churn can get quickly confusing. If you still don’t believe me at this point, you can also trust Patrick Campbell from Profitwell who shows 4 different ways to calculate churn.

Which methodology to choose?

As far as I am concerned, and keeping in mind that I will adapt my approach to the specificity of the SaaS business I am reviewing, as a rule of thumb I generally look at churn rate on a monthly basis and divide lost customers by number of customers at the beginning of the period (what Profitwell call “the simple way”).

It’s simpler to implement, and in most cases it will depict a fair picture of how the business is trending in terms of churn rate. And at least when I have to explain the calculation to other people, be it to a colleague, a client, a student or a partner, I am not using up all their mental power and they still got some in the tank when we move to the nitty gritty of the analysis

Understanding the SaaS subscription model

It is important to understand for the continuation of this post how SaaS in particular and subscription models in general work.

A simple representation of how number of active customers change every month is shown below.

We see in the example above that the company is gaining a fixed number of new customers every month, but that its churn rate is constant at 8.3% (which is equivalent to say that on average customers remain customers for 12 months).

Number of new customers will generally be function of the marketing spend or of the sales team size, while in the example above, with churn rate staying constant, the number of lost customers each month will be a function the number of active customers. In this scenario, if the company does not increase marketing spend or / and sales team, here what should happen.

The company is bound to reach a flat growth. After month 36, to increase its top line the company will either have to invest in customer acquisition, decrease churn, or upsell existing customer / optimize pricing.

On a side note, PriceIntelligently wrote an interesting report on pricing strategy in which they point out that the most efficient way to increase growth is actually monetization. Churn optimization comes second, and counter-intuitively customer acquisition comes last!

Cohort analyses provide a better understanding of a SaaS company’s churn

Just looking at monthly churn rates won’t be enough to really understand the churn pattern of a SaaS company. Instead, I highly recommend to have a look at what is called a cohort analysis.

In short, a cohort analysis allows you to segment your customers into different categories and look at the churn pattern through time for each category. Below is presented an example of a cohort analysis where customers are segmented by vintage date (ie. the month in which they first signed up) and the excel model to do this analysis on your own company is available here.

If you’d like to know more about cohort analyses, i’d recommend to have a look at this blog post on Chartmogul from Ed Shelley.

Different SaaS companies have different churn patterns

Now, if we were looking at the cohort analysis of a company with fixed monthly churn rate, ie. losing customers uniformly, the cohort analysis would look similar to the one below:

Of course this is only a theoretical example and numbers never give such a clean picture in real life.

What is also true in real life is that the business and customer pattern may not be best described by a constant average churn rate. Let’s take an example of a company with a very polarizing product, meaning that either customer will hate the product and churn quickly or love it and stay until the end of time. Such company’s cohort analysis could look like this:

The paradigm has now changed. Instead of having a constant churn rate, we are now looking at a SaaS company with a constant number of churn customers. The monthly change in total active customers would now be looking like below:

You can already clearly notice how this impact the churn rate. Now let’s have a look over a longer period of time.

And assuming a monthly revenue per customer (or ARPU) of $200, let’s have a look at what metrics look like over time.

The above clearly shows that with constant monthly number of customers acquires and constant number of customers lost, mathematically the bigger the ARR of the company, the lower the churn rate (and the lower the growth).

Analysis tips to go further

Let me point out one more thing. The average customer lifetime, ie. the number of months a customer remains a customer on average, which is used to calculate LTV (Lifetime Value) or CLTV (Customer Lifetime Value) is generally calculated as 1/churn rate.

It feels like in the case of the company we just looked at where a few customers where 70% of new customers drop within 3 months and 30% of customers remains for eternity, looking at LTV that way would not make a lot of sense. So how should you analyze a SaaS company which a similar customer pattern?

The answer is actually straightforward : Segmentation! As a general rule, segmenting your analyses will go a long way. In this case, simply split the customers in two groups: Customers that churn within 3 months and customers that did not. Based on that you’ll be able to get helpful insight on how these customers.

Also, if you are managing or currently looking at a company with such a pattern in customer acquisition and churn, the next question you should ask yourself is : Can we find a way to target only the customers that stay? That would sound like a great way to optimize your marketing campaign and increase your CAC (customer acquisition cost) / LTV. Trying different segmentations could help : Segment by size, by country, by acquisition channel, by industry… And find out whether customers that stay have common characteristics!

Conclusion

Before comparing metrics of two SaaS companies, make sure you understand their growth pattern and don’t let a difference in size fool you! The same churn rate can hide different realities. And looking back at the two examples above, the SaaS company with constant churn rate vs. the SaaS company with constant monthly net customer acquisition, ask yourself : which one would you pick if you had to invest?

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