Churn Analysis – A key to customer or revenue retention

Churn Analysis – A key to customer or revenue retention

Studies have proven that customer retention is the key for higher revenue, in another way acquiring new customers is quite expensive than customer retention. However, there are certain efficient ways to retain customers.  One powerful method is churn analysis, which ideally helps in identifying the reasons why customers leave and fix the issue. 

Churn analysis can be powered by a number of factors, and even small and gradual rise in churn percentage can lead to huge disruption to planning, so an in-depth understanding of what is churn and how to identify the ways to prevent it is of paramount significance.

In this article, we will have a brief on what is churn analysis, its significance, methods to reduce churn, and real-time analysis. 

What is Churn Analysis?

Churn analysis is a method to evaluate the journey of your customers and why they stop using your product or service. It will also evaluate which factors caused issues to customers and triggered them to stop using the brand. In marketing, retention and churn rates have an inverse relationship, which means whenever there is a drop in the churn rate there is a spike in retention and vice versa. Retention Analysis is a process to determine the ways to fix a specific problem and identify ways to offer value to your existing customers. 

How to do Churn Analysis? 

Calculate the churn rate with a simple formula: 

Number of customers lost in a period of time / No. of customers in the beginning period 

Determine the ways to find why and where your customers are leaving your brand. There are certain analytics tools available to give you ideas on why the churn is happening. 

Churn Analysis

A brief on customer churn and revenue churn 

When we talk about the churn, there are two types of churn which are customer churn and revenue churn. 

1. Customer Churn: It is a measure of customers that brand losses. 

Churn Rate = Customers at the beginning of a period – customers at the end of period) / Customers at the end of the period

2. Revenue Churn: It is a measure of revenue that business losses. 

Revenue churn = (MRR beginning of  a period – end of a period/ MRR beginning of the month) * 100 

Significance of doing Churn Analysis: 

For any business, it is important to keep an eye on the churn rate to keep flourishing, which will also depict the overall performance of a brand in terms of products and customers.  

Here are some of the important reasons for doing a customer churn analysis 

1. Determining the underlying issues your customers have with your brand that will help you provide more value and effectively prioritize improvements

2. The decrease in the customer churn will increase the overall retention rate and will give a boost in customer experience

3. Helps you in finding ways to prioritize the strategies to reduce churn rate and boost retention 

Here are some of the important reasons for doing a revenue churn analysis:

1. Helps you in finding the retention tactics that prioritize your potential clients or high-paying customers

2. Reducing the revenue churn rate will directly impact on your brand’s bottom line and profitability.

The Importance of Predicting Customer Churn 

The capacity to foresee that a specific customer is at a high chance of churning, while there is still an ideal opportunity to take some measures, speaks to an immense extra potential income source for each online business. Other than the immediate loss of income that comes from a client churning the business, the expenses of acquiring the client might not have just been covered by the client’s spending to date. Acquiring a new customer at the end of the day might turn out as a loss of investment. Furthermore, it is in every case very troublesome and costly to acquire another client than it is to hold an existing paying client. 

Reduce Customer Churn rate with focused Proactive Retention 

So as to prevail at retaining clients who might somehow relinquish the business, advertisers and marketing experts must have the option to anticipate in advance which clients are going to leave. The next step is to realize which promotional activities will have the best impact on every specific client. With the help of this information, a massive churn percentage can be reduced. 

Real-time Churn Analysis – a music streaming app

Churn analysis demonstrates to you where in the user journey they are disappointed and users are leaving. Usually, a customer stops using an app when it gradually disengages or finds some roadblock. If a brand could forecast this roadblock and are able to solve the issue, churning of that customer can be avoided. 

For instance, we can consider the mobile music streaming app. The churn rate of the music streaming app is over 12% for every month. When we look at the number for one month it is not huge, but when it comes as a consolidated rate, the number would be huge. The churn rate is 80% when users do not share a song in their first week after signing up. Hence, it is important for the app to explore methods to get a lot of users to share a song in their first few days.

Guidelines for effective Churn Analysis:

1. As a first step, break down the user journey into various phases, then start breaking down each phase. This will help you find the problem in each step that causes customers to drop. 

2. Reducing more steps in the onboarding process may potentially reduce the churn rate during onboarding.  

3. Doing churn analysis using analytical tools will reveal challenges specific to various types of customers.

4. If you can convince users to download and sign up for your app but can’t engage them, odds are that you are not meeting their needs. The earlier you figure out the reason, the earlier you can incorporate the required changes.


Without doing a churn analysis, any efforts to boost customer retention rates may not be fulfilled. If you want to see good customer retention rates, doing churn analysis is an efficient method. It is very helpful in identifying the predictors of churn because you can be very proactive about users that have a high risk to churn.



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