Skip to content

rahul-c1/Predict-Churn

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

To determine the percentage of customers that have churned, take all the customers you lose during a time frame, such as a month, and divide it by the total number of customers you had at the beginning of the month. Do not include any new sales from that month.

For example, if Company ADG had 500 customers at the beginning of the month and only 450 customers at the end of the month,  its customer churn rate would be:

Customer Churn Rate = (Customers beginning of month – Customers end of month) / Customers beginning of month

(500-450)/500 = 50/500 = 10%

If your organization prefers, you can use that same method on a different time frame such as quarterly or annually.

Dashboard

To determine the percentage of revenue that has churned, take all your monthly recurring revenue (MRR) at the beginning of the month and divide it by the monthly recurring revenue you lost that month minus any upgrades or additional revenue from existing customers. Just like for customer churn, new sales in the month don’t count toward revenue churn as you are looking for how much total revenue you lost. New revenue from existing customers is revenue you have gained.

For example, if Company ADG had $500,000 MRR at the beginning of the month, $450,000 MRR at the end of the month, and $65,000 MRR in upgrades that month from existing customers, its revenue churn rate would be:

Revenue Churn Rate = [(MRR beginning of month – MRR end of month) – MRR in upgrades during month] / MRR beginning of month

(($500,000 – $450,000) – $65,000)/$500,000 =

($50,000 – $65,000)/$500,000 =

(-$15,000)/$500,000 = -3%

Note the negative revenue churn rate means you actually gained revenue that month!

As before you can choose a different time frame, such as quarterly or annual. Just remember that if you do, you’ll need to look at quarterly or annual recurring revenue, not monthly. Also, as the example pointed out, a major benefit to calculating revenue churn is that it’s possible to include upgrade revenue.

Now that you understand the basics of calculating customer churn and revenue churn, let’s dig a little deeper.

Customer Churn ≠ Revenue Churn

The first thing to point out is that customer churn and revenue churn are not always the same.

EXAMPLE

Company ADG has 2 product lines:

Basic: 5,000 customers that pay $500/month per customer = $2,500,000 MRR

Premium: 1,000 customers that pay $1,250/month per customer = $1,250,000 MRR

This gives ADG a total of 6,000 customers and $3,750,000 MRR.

Let’s say that in one month, 180 basic customers and 20 premium customers churn.

Customer Churn

(180 + 20)/6,000 = 200/6,000 = 3.33%

Revenue Churn

((180 * $500) + (20 * $1250))/$3,750,000 =

($90,000 + $25,000)/$3,750,000 = $115,000/$3,750,000 = 3.07%

Note that, while similar, customer and revenue churn rate are not identical because the basic and premium packages are not worth the same revenue. This discrepancy will only grow as you gain more product lines or the price difference between product lines grows. Therefore it is very important to clearly communicate which method you use and be consistent in your regular reporting.

It’s also important to note that you may need to use both calculations as you manage your business. Revenue churn is a great way to report on performance and understand the financial health of your customer base. Customer churn is important for staffing reasons as an employee can only manage so many accounts at one time.

 

http://www.evergage.com/blog/how-calculate-customer-churn-and-revenue-churn/

 

http://info.evergage.com/in-app-messaging-guide

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 77.4%
  • Python 18.7%
  • R 3.9%