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Customer Churn Analysis Tool
See where your customers leave: by how long they have been with you, by plan or group, and month by month, with a diagnosis of what it means.
Coming back?
Choose the latest progress file (Excel) you saved from any of our tools. It reopens where you left off.
Analyze your own data
Your full customer list as a CSV: every customer's start date, plus a cancellation date for those who have left. Leave it blank for customers still with you.
or drop the file here
See an example first
A chain of four gyms with about 2,000 members, losing 4.5% of them a month.
Your customer records never leave your browser
The file is analyzed on your computer. To write your diagnosis, only summary figures (rates, shares and counts) are sent to our server; no names, customer IDs, dates or group names. How it works
Only need your churn rate? Use the quick calculator
Check the data
The tool has guessed which columns to use. Change them if a guess is wrong, and check the summary below before you go on.
Data check
See the first five rows of your file
Where your churn is happening
Overview
By how long people had been customers
Against the average: a group's monthly rate divided by your average. 2.0× means it cancels twice as often. The highlighted row has the highest rate.
Has it changed? Earlier months against recent months
Monthly churn rate for each tenure group, averaged over the first three and the last three months of the period. A group whose rate has risen while the others held steady is the one behind a rise.
Early customer churn
By customer group
Month by month
Save your progress
Download your progress file to continue from this point any time, on any computer: choose it under "Coming back?" at the start of the tool. Each new file includes everything so far, so keep the latest one; stage 2 adds to it. It also holds all the figures, ready to use in Excel. Your results stay in this browser only until you close it.
Not saved yet.
Take your results with you
A formatted PDF report of this analysis, ready to share with your team. To keep working on these results later, use the progress file above.
About the tool
Why it exists
Your overall churn rate hides where churn comes from
Most businesses know their churn rate. Fewer know that it usually bunches: in the first few months, or among one plan, region or sales channel. This tool finds that bunching from a plain customer list, then explains what the pattern often means and whom to look at next.
Unlike most churn software, it doesn't ask you to connect your billing account or sign up. It is the first stage of our customer churn playbook, which covers the four stages that follow.
Before you start
What your customer list needs
One row for every customer, including those still with you. Without the active customers there is nothing to divide by, so a list of cancellations alone can't give a rate.
| Column | Needed? | What it holds |
|---|---|---|
| Start date | Yes | When the customer signed up or first paid. Often called created, signup or start date. |
| Cancellation date | Yes | When they canceled, blank for customers still active. Often called canceled, ended or churn date. |
| Group | Optional | Anything you want churn split by: plan, price tier, region, sign-up channel, salesperson. |
Most billing systems and CRMs can export this. Other columns are ignored, so a full export works; spreadsheets need saving as CSV first. Dates can be written as 2025-03-04, 04/03/2025 or 03/04/2025, and the tool asks when it can't tell which. Files up to about 60 MB work, which is several hundred thousand customers. A year or more of history gives the clearest picture: the tool analyzes the last twelve full months.
The method
How the tool works it out
Monthly churn rate. Customers who canceled during a month, out of the customers you had at its start. Only customers present at the start of a month can count as that month's cancellations. The average across the period weights each month by its number of customers.
By tenure. At the start of each month every customer falls into one of three groups: first three months, months 4 to 12, or more than a year, and each group's rate is worked out the same way.
Early leavers. Separately, of the customers who joined during the period, the share gone within 30, 60 and 90 days of joining. This counts customers who joined and left within a single month, who never appear in a monthly rate.
By group. The same calculations for each value in the column you choose; the ten largest groups are shown and the rest combined.
The diagnosis. The group that cancels most often against your average is the hot spot, provided it has at least ten cancellations, since a handful can look high by chance. The diagnosis describes what that pattern often points to. Your data shows where churn happens, not why, which is what the later stages of the playbook find out.
What leaves your computer. Your file is analyzed in your browser. To write the diagnosis, the page sends our server the rates, shares and counts only, without names, IDs, dates or group names, and the server keeps nothing. Your latest result is saved in your browser, and you can clear it at any time.
Knowing when this is enough
When to bring in more help
For most small and mid-sized businesses this stage is enough to aim the next steps: it shows where churn is concentrated, which decides whom to survey and interview.
It stops being enough when you need to predict which individual customers will leave, weigh churn by revenue across many products and contracts, or join data held in several systems. Those are jobs for a data analyst or a customer success platform, and these results make a useful brief for either.