Churn Rate vs Retention Rate: CS Metrics Showdown

Customer Churn and Retention Rates are still very much relevant in post-sales circles. While they were primarily used by service teams a few years ago, these have now become key business customer-led growth metrics for businesses across all industries. Let’s understand what they help achieve, examine the main differences, and take a closer look at their inherited limitations.

While both of these post-sales metrics have a place in every CS playbook, they also have unique characteristics that serve different purposes. The Churn Rate measures the failures, while the Retention Rate measures the successes. The former helps investigate lost business causes, while the latter is used for business growth and detecting new growth opportunities. That said, let’s dive into the specifics.

What is Retention Rate?

Customer Retention Rate (CRR) is a metric that gives Post-sales teams the ability to determine how successful they have been in retaining customers. Keeping track of Retention Rates is important because maintaining and nurturing the existing customer base is much cheaper than acquiring new ones. Furthermore, the probability of upselling and making cross-sales is also much higher.

The numbers back up this claim. Over 80% of businesses today agree that retention is much cheaper than acquisition operations. Furthermore, just a mere 5% increase in your Retention Rate can boost profits by 25% and often more.

Retention Rate should not be confused with Net Renewal Rate (NRR). The latter shows revenue retention in a specific time frame. Customer Retention Rate, also known as Logo Retention Rate, calculates the number of customers. 

So how do you calculate your retention rate? It’s as easy as 1-2-3-4.

1. Determining the number of customers you have at the end of a specific period. This is usually done on a monthly basis.
2. Once you have a figure, you need to subtract the number of new customers you’ve acquired during this precise time period from it. 
3. Divide the number of newly acquired customers by the total number of customers you had at the beginning of that period. 
4. Multiply the number you have now by one hundred. That’s your CRR.

Related: All You Need to Know About NRR

Businesses that rely on heavy customer engagement and activation rates (think Slack, Monday, Asana, etc.) can also use the N-Day Retention calculation. This metric calculates how many users returned to use the service within the first few days. For example, if 20 out of 100 customers returned to the service within 3 days, your 3-day retention is only 20%. However, this doesn’t cover long-term trends.

What is Churn Rate?

Customer Retention Rate, not to be confused with Revenue Churn, essentially helps Post-Sales teams understand (and monitor) the number of customers that have churned. This basic yet insightful indicator can help all businesses that work with subscription models track their progress and understand if customers are opting out before covering the costs of acquisition (marketing, pre-sales, etc).

Constantly monitoring and minimizing the Churn Rate helps businesses retain customers and reduce investment in acquisition campaigns, while raising the chances of upselling or even cross-selling. This metric can also be used to conduct in-depth market research and competitor analysis. The insights from these actions can then be applied by CS teams to finetune their playbooks and strategy.

The calculation is pretty straightforward as well. While the basic formula is just dividing the number of churned customers by the number of customers you had at the beginning of a specific period, it’s recommended to use the adjusted method. 

Adjusted Churn Rate = Churn / Midpoint of the Customer Amount

First, you need to calculate the midpoint value by adding the number of customers on the first and last day, before dividing by two. This helps normalize the growth changes, making the results much more accurate than the traditional method. However, do note that even this method can be inconsistent. Every time frame (weekly, monthly, etc) will deliver different results. Pick one and stick with it.

Read More: Customer Churn Rate

How about some real-life examples? Streaming giant Netflix had a monthly Churn Rate of 2.4% in 2021, just like its rival Disney+. Hulu, a fierce competitor and a big player, almost touched the 5% mark during the same time period. 

Churn Rate vs Retention Rate

Customer Retention Rates and Churn Rates go hand in hand. For example, a business with a poor Retention Rate should automatically have a higher Churn Rate. This obviously applies to the opposite case, where a high Retention Rate means there is little churn in the business. That said, these two metrics are totally different from the practicality standpoint and they serve different purposes.

Customer Churn Rate is all about trying to measure the failures and drops in the customer base. This metric is commonly used for damage control and investigative purposes, where the underlying factor needs to be pinpointed in order to mitigate it. The Retention Rate is all about minimizing acquisition costs, accesses the health of the business, and identifying new growth opportunities.

It’s also important to mention that the linear correlation between the two won’t always exist. You can have a business that is very good at acquisition or suddenly have many users downgrading to lower tiers. The numbers won’t always add up.

Related: Top CS Voices to Follow on Social Media

Post-Sales Needs More

While both Retention Rates and Churn Rates have their place in all CS playbooks today, they are also becoming increasingly limited in their functionality. Modern businesses need to adopt a customer-first approach where they are conducting ongoing customer sentiment analysis. CS teams also need to understand relationship dynamics, especially in big accounts with multiple stakeholders.

Retention and Churn Rates are still good indicators, but finding the root causes of these issues requires the breaking down of data from communication channels and revealing human signals. That’s the only way to derive unbiased insights into customer behavior and uncover blind spots. Besides the lowered manual labor, CS teams can then cut through the noise to improve performance across the board.

Staircase AI leverages advanced machine learning (ML) models to analyze millions of customer interactions that can’t be spotted or processed with the naked eye. It then turns them into actionable human insights, user-centric analytics that are often the missing piece when it comes to understanding customers, boosting retention, eliminating churn, and recognizing new growth opportunities.