Did you know that it’s 50% easier to sell and retain a current paying customer than to acquire new customers? In fact, a 5% increase in customer retention increases profits by 25% to 95%.
In running a company effectively, it is very important to get why clients do or don’t buy more products or services.
Customer churn rate is the term used when a customer stops using a business’s services or products after a certain period.
Despite knowing the importance of reducing customer churn, many businesses struggle to uphold their customers. For one reason to another, Customers decide to reduce or even cancel the subscription & stop buying from the businesses.
In this post, we’re going to learn about the how to calculate customer churn rate along with different terms associated with customer churn, its analysis & prediction.
What is Customer Churn?
Customer churn is a measurement of how many customers, accounts, contracts, bookings, and other types of customers a company has lost over time.
It means when a customer stops being a customer—ie, they stop using your products or services.
It is also known as the rate of attrition or just plain ”churn”. Customer churn is the metric that is most widely tracked & heavily- discussed by subscription companies.
Churn is determined by actual consumption or failure to renew a contract (when the product is sold on a subscription model). Churn rate is frequently measured over a set period of time, such as monthly, semiannual, or yearly.
Each new user is the reason for the product’s growth pace. Customers will eventually stop using the product or cancel their membership because they no longer need it, are unhappy with their user experience, can no longer afford it, or switch to alternative alternatives or rivals. For that time period, the “Churn” refers to clients who quit paying or using the services.
What is Customer Churn Analysis?
**Customer churn refers to the rate at which consumers discontinue buying the company’s products or services. Customer Churn Analysis is the method for calculating this rate.**
Churn analysis explains the reasons why customers do not return or repeat their purchases. Customer Churn analysis helps businesses to understand the consumer’s behavior by using churn data :
- Which customers are leaving?
- Why are they leaving?
- Which customers are likely to churn shortly? And
- What can they do to reduce customer churn?
Simply said, customer churn analysis determines the percentage of customers that do not return vs the percentage who do. Companies can identify trends that can help them avoid failure or take successful products or services to the next level by digging further into these numbers.
Churn analysis tells not just the churn rates but beyond. It’s about understanding the underlying reasons and controlling churn in time using the data.
As we know, customer Churn is inevitable. But it’s an opportunity to learn, improve customer relations and close any leaks in your business revenue stream.
Higher churn rates than the industry average, on the other hand, suggest a problem with service, pricing, delivery, product quality, or other areas of the customer experience.
Only Churn analytics cannot help in managing Customer churn. After identifying trends and gaining insights, new methods and rules must be implemented to reduce churn and boost brand value with each new client.
How Does Customer Churn Analysis Work?
With each customer churns, there is usually an early indication that is uncovered with the churn analysis.
Customer Churn analysis requires a client database as well as a spreadsheet or some other application to look further into the data. Exporting customer churn data straight from Erp systems, such as churn rate and customer renewal rates, can reduce time and enhance accuracy.
To gain valuable insights, a high-level Churn analysis for all clients should be undertaken, with data broken down by product, area, client group, or other granular variables particular to the organization. It will give the team insight into why and where firms are losing clients.
For example, suppose you offer software on an annual subscription basis and have 1000 customers one year, but only 300 subscribers the following year, i.e. only 30% of your subscribers have renewed. It is clear that there is an issue with the application or the pricing. After seeing the results, you can decide whether the application needs to be improved or if it’s time to reconsider the price and packaging. When all of the issues are resolved, the churn rate will automatically decrease.
How to calculate customer churn rate in easy steps?
Two statistics are required to calculate customer churn: the number of customers at the start and the number of customers at the end of the time period.
Here are the steps to determine the churn rate :
- Choose a timeframe: monthly, annual, or quarterly.
- Calculate the total number of clients at the start of the time period.
- By the end, determine the number of churned consumers.
- Divide the number of lost customers by the number of customers before the churn.
- Multiply the obtained number by 100.
Let’s imagine your company has 5000 customers at the start of the year.
However, due to a few poor customer service contacts and expired yearly contracts, you lost 1000 consumers.
This means your year’s customer churn is 500 churned customers divided by the 5000 former customers, ie 1000/5000=0.2
When multiplied by 100, you get a 20% client churn rate.
Customer Churn Rate = (total number of Lost Customers ÷ Total no of Customers at the Start of Time Period) x 100
Customer Churn Rate = (1000÷ 5000) x 100
Customer Churn Rate = (0.20) x 100
Customer Churn Rate = 20%
How To Predict Customer Churn?
Churn prediction is probably one of the most important factors in the overall profit-making of any business. Therefore, it is very important to calculate and evaluate customer churn metrics regularly so that it is easy for businesses to spot and eliminate the problems.
Churn Prediction is essentially detecting which clients are most likely to cancel a subscription to a service or leave a service based on their usage.
From a company point of view, It is very necessary to gain customer churn information because acquiring a new customer is 15 times costlier than retaining old ones. Hence, churn prediction analysis gives leverage to identify at-risk customers and prevent them from leaving the service.
While the ability to predict which customer is at high risk of churning, and proactively addressing the circumstances- represents that you care for your customer and helps to avoid loss of revenue.
Churn Prediction model:
Churn prediction modeling techniques help companies to understand the customer behaviors and attributes which indicates the risk and timing of churn.
To identify consumers’ likelihood to churn, this model combines historical customer data with machine learning techniques and logistics regression. Several methods are incompatible with predicting client attrition. The decision tree model is a machine learning model associated with this discipline that includes pre-processing of numerous data sources, as well as training and evaluation.
Customer journey analytics:
Customer Journey analytics is another factor that helps the prediction model. It combines data across multiple interactions to understand how customers navigate through brand interaction over time. This helps organizations to analyze customer journeys overall and recognize behavior that indicates whether the customer is happy or dissatisfied with the service they received.
Customer Journey analytics assess both past and present journeys to predict the churn. When a customer cancels their subscription, the reason behind the cancellation may not be found at that touchpoint at which churn occurred. But it might be hidden in historical journeys from the previous interactions. With full visibility into the overall journey, there might be some errors or dissatisfaction expressed by the customers that the organization forgets to reconsider or communicate. Most times it might be a complaint or trigger or even churn.
Journey analytics, which is backed by a single source of consumer journey data, improves your capacity to assess customer journeys and journey performance. Businesses can rapidly discover whether trips successfully assist consumers to achieve their goals by monitoring journeys rather than isolated customer behavior in a limited number of channels.
Similarly, journey analytics helps you to pinpoint the instances when clients are unable to achieve their goals. Monitoring omnichannel behavior on a regular basis helps you to see issues that have a negative impact on both the customer experience and business consequences. You can use customer journey analytics to:
- Real-time diagnosis of new problems
- Determine the most effective solution to these problems.
- Improvements should be prioritized depending on their potential impact on the customer experience and company goals.
Finally, customer journey analytics are required for optimal journey planning. Organizing activities within specific trips is a hit-or-miss approach until journeys are examined to see what works and what doesn’t. Taking initiatives to strengthen customer journeys is most effective when it is based on each customer’s complete brand experience.
Hence, Companies can use data-driven consumer insights to predict possible customer desires and issues, build appropriate strategies and solutions to address them, meet their expectations, and keep their business. Based on predictive analysis and modeling, businesses can segment clients and give customized solutions. Analyzing how and when churn happens during the client lifecycle with the services will enable the company to develop more effective preventative strategies.
Why is customer churn the most effective metrics for any business?
Churn rate is among the important business metrics that tracks business performance over time. The churn rate shows how a business’s growth is slowed down.
What is a good churn rate?
Churn rate below 2% is often considered good according to the various past research on churn rate studies.
How to reduce churn rate?
The best ways to reduce customer churn rate are calculating and analyzing customer attrition, creating and encouraging yearly subscriptions, collecting feedback, enabling customers to request a feature, and creating and maintaining FAQs.
Does churn rate affect retention?
Churn is the opposite of retention, which is the number of customers who stop using your product or service after a certain date. So yes, churn rate directly affects retention because it is the result of not retaining your customers.