Churn rate
The churn rate is a metric giving the share of customers or users leaving a company, a subscription or a service within a defined period. It is the counterpart to retention and one of the most important early indicators of a business model's health, particularly with recurring revenue as in SaaS or subscriptions. A high churn rate means customers won with effort are quickly lost again, which slows growth and makes acquisition costs inefficient. Measuring and lowering churn systematically is therefore a core aim of data-driven steering in online marketing and closely linked to customer lifetime value and retention.
Also known as: attrition rate, customer churn
How is churn rate calculated?
The simplest formula is: churn rate = customers lost in the period divided by customers at the start of the period, multiplied by 100. If a company with 1,000 customers at the start of the month loses 30 of them, the monthly churn rate is three per cent. A consistent definition of churn matters: cancellation, expiry without renewal or inactivity beyond a threshold for instance.
Alongside the customer churn rate, which counts customers, the revenue churn rate is central. It measures the revenue lost and can differ considerably from customer churn if mainly large or small customers leave. Net revenue churn additionally accounts for upgrades among existing customers and can even become negative if growth within the base exceeds the losses, a strong sign of a healthy business model.
Some pitfalls lurk in the calculation. The period chosen influences the result greatly: a monthly rate cannot simply be multiplied by twelve, because the shrinking base dampens the effect. How new customers within the period are handled has to be clearly defined too. Cleaner is to look at a fixed cohort at the start and measure how many of them are still there at the end, rather than mixing arrivals and departures.
Churn and customer lifetime value
The churn rate is directly connected to the Customer lifetime value. Simplified, the average customer lifetime is the reciprocal of the churn rate: at five per cent monthly churn a customer stays twenty months on average. If the churn rate falls to two and a half per cent, the lifetime doubles and with it roughly the value of every customer won, without anything having to change in acquisition.
This leverage often makes reducing churn more profitable than winning new customers. While acquisition is bought with rising costs and falling efficiency, a lower churn rate improves revenue, marketingROI and predictability. Understand the link and you deliberately shift budget from short-term acquisition to onboarding, customer success and retention.
Typical benchmarks by industry
What counts as a good churn rate depends heavily on business model and industry. In B2B SaaS, monthly figures of one to two per cent count as solid, while established enterprise providers are often below one per cent. B2C subscription services naturally record higher figures, because the barriers to switching are lower; five per cent a month and more is no rarity there.
What is decisive is less the absolute figure than the trend and the comparison with your own acquisition. As long as growth in new customers exceeds churn, the company grows net. An apparently low rate can nevertheless be problematic if, projected over twelve months, it means a considerable loss to the base: three per cent a month corresponds to around a third of the customer base over the year.
Measuring and predicting churn
To understand attrition, the churn rate is combined with further analysis. The Cohort analysis shows at which point in the lifecycle customers typically leave, making the critical phases visible. A Segmentation reveals which customer groups are most at risk of leaving. The data comes from Web analytics, CRM and billing systems whose metrics can be Marketing dashboards can be bundled.
More advanced teams use churn prediction models estimating each customer's likelihood of leaving from behavioural signals such as falling usage, absent logins or support requests. Such early warnings allow at-risk customers to be approached proactively before they cancel. A regular Performance audit of the underlying data makes sure the signals are reliable.
Reducing churn: levers and data protection
The most effective levers against churn start early: good onboarding that makes the value tangible quickly reduces early churn considerably. Later in the life cycle, targeted email sequences, reactivation campaigns and responsive support help. Lost customers can in part be won back through win-back campaigns.
Because churn analysis processes usage and contract data, it is bound by the GDPR. Behaviour-based forecasts require a clear legal basis and transparency towards customers. Anyone using tools such as Matomo in a privacy-friendly way and relies on first-party data with valid consent creates a solid, legally sound basis for churn analysis. It also matters that the results do not get lost in reporting but land in a fixed process reliably handing customers at risk to the responsible teams and then measuring the effect of the countermeasures again.
Frequently asked questions
How do you calculate churn rate?
The churn rate is the number of customers lost in a period divided by the number of customers at the start of that period, multiplied by 100. With 1,000 customers at the start and 30 leaving, the monthly churn rate is three per cent. A consistent definition of what counts as churn matters.
What is a good churn rate?
That depends heavily on the industry. In B2B SaaS, one to two per cent a month counts as good; enterprise providers are often below that. B2C subscription services naturally have higher rates. What matters is the trend and whether new customers won exceed those lost. Note too that even low monthly rates can mean considerable losses to the base over a year.
What is the difference between customer churn and revenue churn?
Customer churn counts customers lost, revenue churn measures revenue lost. The two can differ sharply when it is mainly very large or very small customers who leave. Net revenue churn additionally includes upgrades from existing customers and can turn negative when the base grows more than it loses.
How can I predict churn?
Churn prediction models use behavioural signals such as falling usage, absent logins or a cluster of support requests to estimate each customer's likelihood of leaving. The data needed comes from web analytics, CRM and billing. At-risk customers can thus be approached before they cancel.
Which measures reduce churn most effectively?
Strong onboarding that makes the benefit tangible early has the greatest effect and so reduces early churn. Later on, targeted email sequences, reactivation campaigns and fast support help. Win-back campaigns for customers already lost can pay off too.
Related terms
The expected total value of a customer across the whole relationship — the basis for budgeting and steering.
An analysis method that compares groups of users who started at the same time, making behaviour and retention visible over time.
Dividing a heterogeneous market or customer base into homogeneous groups so that messages and measures can be targeted.
Direct marketing by email and newsletter to win customers, keep them and grow revenue.
Core marketing metrics such as CAC, ROAS, conversion rate and CLV for measuring marketing success.
A marketing approach that bases strategy, campaigns and optimisation systematically on data and metrics rather than intuition.
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