## Places Of Interest

### How to Calculate Churn Rate in SaaS

This calculation is not as straight forward as you may think. If you ever tried to calculate the churn rate of a SaaS company, you know it is not straight forward as far as modeling goes. This template is free and makes it a bit more clear the best way to calculate it with accuracy per the renewal rate assumption. The key is to think in terms of cohorts.

Example Sheet (have fun and hit File and Make a Copy for your own editable version): https://docs.google.com/spreadsheets/d/1iiSOrvRolsX-xEk5PZYM5P2ROEld9Mgz/edit#gid=2140676291

Check out full SaaS financial models here.

The idea is simple, of the customers I had at the end of a period, how many of those customers are still left now. You can apply that logic to any time frame and figure out your 'churn' that happened in the period.

As far as modeling goes, it is a bit complex. You have to know the number of customers added per month, the length of a contract (month-to-month or 6/12/18/etc... months) and the average customers that renew at the end of each contract period.

This model will spit out the monthly churn rate for 5 years and the annual churn rate from the end of year 1 to the end of year 2. As you play with the assumptions, you see some things you might not expect in the monthly churn rate. This is because the actual churn rate depends on the proportion of customers leaving relative to those being added and how that percentage is measured against the long-term stabilized customer count. We are thinking in terms of cohorts now.

Putting an average renewal rate of 95% and contracts that last 1 month (month-to-month cancel any time) will get you something different than you think. Intuitively, you may think the churn rate is 5% per month. That is not true though. It would only be true for the very first month, but as you start to add new customers, that percentage of the whole will change and it is actually a long-term stabilized rate of 9.5% and that will only be true if you add the exact same amount of customers every single month. When that number varies per month, the churn rate is going to be different and depend on the proportion added and when against how many are leaving.

The annual churn rate is just as difficult. If you have 12 month contracts and a renewal rate of 80%, you churn rate overall will be 20% for the first full period (customers left at the end of year 2 that existed at the end of year 1), but if you go further and say the total customers that exist at the end of year 3 that existed at the end of year 2 it will be 22%. This also assumes the customers added per month never changes.

It is not intuitive when you actually go to calculate the churn rate on an annual basis. You have to build a matrix and then manually target the right column length that makes sense for what was described in the paragraph above.

I went ahead and added a second churn rate calculation in the model to go from year 2 to 3 that was not in the video, but the same exact concept is applied.

Churn Based on MRR / ARR

Note, if you are trying to calculate the churn measured in dollars, the same concept applies, except you take the total recurring revenue at the end of a period against the total recurring revenue of those same customers at the end of the next period as your target figures instead of customer count.

For example, if I have 10 customers doing \$1,000 per month or \$12,000 per year and then in 12 month i am getting \$800 per month from that same group (some may left / downgraded) then the churn on MRR is 20% and the churn on ARR is also 20% (\$9,600 relative to \$12,000)

It is possible to have a negative churn and in that case the revenue you are getting from the customers that stayed is higher than the revenue from the initial group of customers (same customers paying more now and make up for lost customer revenues).

Other Types of Cohort Modeling

There are all kinds of industries that also use cohort modeling to come up with financial forecasts. They vary in industry as you will see this a lot with oil and gas models where you want to look at the performance of wells that are deployed over time. Certain logic gets applied to them over their implementation life and it effects revenues.

There are other business models I've done in different industries, such as ATM machines, 3D printing, franchisor licensing, laundromat, equipment rental, and all kinds of industry-based financial models that use the concepts of cohort modeling.