Churn Rate

What is churn rate?

Churn rate measures how quickly customers leave. The basic customer churn rate is the number of customers lost during a period divided by the number you had at the start of it. Monthly and annual rates are both common; what matters is using one consistently.

Churn can be voluntary, when a customer cancels, or involuntary, when a card fails or a payment is missed and the subscription lapses. Involuntary churn is often a large and fixable part of the total, and it is worth tracking separately because the fix is operational rather than a product or service change.

It matters most in software as a service (SaaS), subscriptions and memberships, where revenue depends on renewals. Ecommerce brands track it too, but because there is no contract, they first have to define when a customer counts as lost, such as no purchase within a chosen window. Churn feeds directly into customer lifetime value: the faster customers leave, the less each one is worth.

What is the difference between customer churn and revenue churn?

Customer churn, sometimes called logo churn, counts accounts lost. Revenue churn measures recurring revenue lost, which includes both cancellations and customers moving to cheaper plans.

The two can tell different stories. Losing many small customers can look alarming in customer churn while barely moving revenue. Losing one large account can do the opposite.

Net revenue churn goes one step further by subtracting expansion revenue, meaning upgrades, extra seats and add-ons from existing customers. When expansion outweighs losses, net revenue churn turns negative, and the existing customer base grows in value even before new customers are added. Most subscription businesses track gross and net revenue churn alongside customer churn so that no single figure hides a problem.

What causes churn?

Churn is rarely about one moment of cancellation. It usually traces back to something earlier:

• No early value: customers never completed onboarding or reached the result they bought for.
• Poor fit: acquisition brought in customers outside your ideal customer profile, who were unlikely to stay.
• Product gaps: missing features, reliability problems or a better alternative.
• Budget or business change: cost cutting, a merger or a project ending.
• Lost champion: the person who chose you leaves, and nobody else uses the product much.
• Failed payments: expired cards and declined charges that nobody follows up on.

Cancellation surveys capture the stated reason, but it is often not the whole story. Compare what customers say with what their usage and support history show.

How do you predict churn before it happens?

Most customers show warning signs before they leave. Common ones are falling product usage, fewer active users on an account, no logins from the admin, a run of support tickets, unanswered renewal emails and late or failed payments.

Many teams combine these into a health score: a simple rating per account that flags who needs attention. Rule-based health scores are easy to start with and easy for account managers to understand.

With enough history, predictive analytics can estimate each customer's chance of leaving from patterns in past churned and retained accounts. The prediction is only useful if it triggers an action, such as a check-in call, a targeted help email or a review of the account's setup, while there is still time to change the outcome.

How do you reduce churn?

Start by finding out when and why customers leave. Break churn down by cohort, plan, acquisition channel and segment, and read cancellation reasons and exit survey answers. The pattern usually points to one or two causes worth fixing first.

Then work on the causes. Improve onboarding so new customers reach value sooner. Run lifecycle messages that teach, remind and check in at the moments people tend to drift. Set up payment recovery emails and retries for failed charges. Give account managers a short list of at-risk accounts each week.

Save offers at the point of cancellation can help, but use them carefully. They treat the symptom, and offering discounts too freely teaches customers to threaten to leave.

How does Vizio AI work with churn?

In our Growth Studio, churn is part of the funnel, not just a finance metric. Growth analytics break churn down by cohort, channel and segment so you can see where it starts.

We then build the fixes that sit in our scope: onboarding and follow-up sequences, lifecycle campaigns, and marketing automation for check-ins and payment recovery emails. Where churn traces back to poor fit, we revisit audience and positioning so acquisition brings in customers more likely to stay.

The starting point is agreeing on a churn definition and looking at when, and for whom, customers leave in the data you already have.