Churn rate: how to calculate it, read it and reduce it

The basic customer churn formula is:

churn rate = customers lost during the period / customers at the start of the period

Say a subscription product starts March with 400 paying customers. During March, 30 new customers sign up and 12 existing customers cancel, so it ends the month with 418.

• Customers lost: 12
• Customers at the start: 400
• Monthly churn rate: 12 / 400 = 3%

Notice that the 30 new customers play no part. Churn measures what happened to the customers you already had. If you divide by the end-of-month figure (418) you get about 2.9%, and if a new customer signs up and cancels within the same month, some teams count them and some do not.

None of these choices is wrong, but mixing them is. Write down your definition, including what counts as "lost" (cancelled, lapsed after a failed payment, downgraded to free), and use it every month so the trend means something.

For products with a lot of mid-month movement, some teams divide by the average of the start and end counts instead. That is fine too, as long as you stick with it.

Logo churn counts customers. Revenue churn counts money. They can tell very different stories.

Losing ten customers on your smallest plan and losing one customer on your largest plan might cost the same revenue, but the first looks far worse on logo churn. The reverse is also true: a healthy logo churn can hide the loss of your biggest account.

Revenue churn usually comes in two forms. Here is a worked example for a product starting the month on £20,000 of monthly recurring revenue (MRR):

• Churned MRR (customers who cancelled): £600
• Contraction MRR (customers who downgraded): £200
• Expansion MRR (upgrades and extra seats): £1,000

Gross revenue churn = (600 + 200) / 20,000 = 4%. This is what you lost before any upsell, and it can never be negative.

Net revenue churn = (600 + 200 - 1,000) / 20,000 = -1%. A negative figure means expansion from existing customers more than covered what you lost. This is often called negative churn, and it is the reason seat-based products watch net figures closely.

Track both. Gross churn tells you how leaky the product is; net churn tells you whether the customers who stay are growing.

You cannot multiply monthly churn by 12. Each month you lose a percentage of a smaller base, so the losses compound rather than add up. The conversion is:

annual churn = 1 - (1 - monthly churn) ^ 12

With 3% monthly churn: 1 - 0.97 ^ 12 = 1 - 0.694 = about 30.6% a year, not 36%.

Going the other way, from an annual figure to its monthly equivalent:

monthly churn = 1 - (1 - annual churn) ^ (1 / 12)

Two cautions. First, this assumes churn is steady across the year, which it rarely is; new customers tend to churn faster than long-standing ones. Second, customers on annual contracts can only leave at renewal, so a monthly churn rate calculated across a mixed base will look low for eleven months and spike in the twelfth. Report monthly and annual plans separately if you sell both.

A single churn rate blends customers who joined last week with customers who have been around for three years. Cohort analysis separates them by grouping customers by the month they signed up and tracking each group over time.

An illustrative cohort table (made-up numbers, showing cumulative share of each cohort that has cancelled):

Reading down a column tells you whether newer customers are churning less at the same age. Here, March's first-month churn is lower, which might be the effect of an onboarding change shipped that month.

Reading along a row tells you when customers leave. If most of the churn happens in month one, the problem is activation and onboarding. If it builds steadily, the product is not holding value over time, and the fix is different.

You can also build behavioural cohorts, such as customers who did or did not complete a key action in their first week, to test which behaviours predict staying.

Numbers tell you how many people leave. Only customers can tell you why.

Exit surveys

Ask one required multiple-choice question at cancellation, with a free-text box after it. Keep the options concrete: too expensive, missing a feature, switched to another tool, no longer need it, too hard to use, temporary pause. Review the free text every month, not just the counts, because the most useful answers rarely fit a box.

Cancellation flows

A good cancellation flow offers a genuine alternative to the reason given: a pause for someone who is away, a cheaper plan for someone on a budget, a link to the missing feature if it exists. A bad one hides the cancel button or makes people phone in. That is a dark pattern, it damages trust, and regulators in the UK, EU and US have all taken an interest in how hard subscriptions are to cancel.

Involuntary churn

Some customers leave without deciding to, because a card expired or a payment failed. Retrying failed payments on a sensible schedule, emailing before a card expires and making it easy to update payment details can recover a meaningful share of these without any change to the product.

Much churn starts long before cancellation, when a new customer stalls in onboarding or hits the same problem twice. OpenHelm's journey analyst maps your product's key journeys from GA4 events and files a task with the evidence wherever users stall, and funnels and retention appear as charts in the same place. See user funnels.

On the support side, each product gets a real inbox where a triage agent answers from what the product actually does and hands defects to the engineering agent, so a frustrated customer's bug report becomes a fix rather than a cancellation reason. You choose how much it does alone: an inbox can hold agent replies for your approval, send and notify you, or send automatically. See customer support.