Analyze Churn Data from SaaS Export

SaaS platforms export churn data with one row per cancellation event, often including partial months, duplicate customer IDs across different plans, and free-text cancellation reasons that need grouping. You need cohort retention rates, revenue impact by reason, and time-to-churn averages, but the export gives you raw event logs. Coefficient connects directly to your SaaS platform or imports the CSV, then cleans the data and builds pivot tables in Google Sheets or Excel—no formulas required.

Import your SaaS churn export and get cohort retention tables, revenue-by-reason pivots, and churn rate calculations in minutes instead of hours of formula work.

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Or start with

What is our monthly churn rate by cohort?Which cancellation reasons cause the most revenue loss?How does churn rate differ between annual and monthly plans?What is the average customer lifetime before churn?Do customers with more support tickets churn faster?

What this export contains

customer_id

subscription_id

cancellation_date

signup_date

plan_name

mrr

cancellation_reason

churned_arr

account_age_days

last_login_date

payment_failures

support_tickets

Common questions

How do I analyze churn data from SaaS export?

Import the CSV into Google Sheets or Excel, then deduplicate customers by keeping only their final cancellation_date. Group free-text cancellation reasons into 5-8 categories using a lookup table. Calculate cohort retention by grouping customers by signup month and measuring how many remain active after 1, 3, 6, and 12 months. Coefficient automates the import, deduplication, and reason grouping so you can focus on the analysis.

How do I calculate churn rate when customers have multiple subscription rows?

Filter to each customer's last cancellation_date and count unique customer_id values. Divide by the number of active customers at the start of the period. Counting all rows treats a downgrade as a churn, which inflates the rate.

How do I group 60 different cancellation reasons into categories?

Create a lookup table mapping each unique reason to a category like "Price", "Product Fit", "Competitor", "Payment Issue", or "Other". Use VLOOKUP or XLOOKUP to tag each row, then pivot on the category. Coefficient can apply this mapping automatically across all rows.

Why does my churned revenue not match the finance report?

The churned_arr column usually shows the full monthly or annual value, not prorated for partial periods. Multiply by the fraction of the billing period remaining (days from cancellation to renewal date, divided by total billing days) to get actual lost revenue.

How do I separate voluntary churn from payment failures?

If payment_failures is greater than zero and cancellation_reason contains "payment", "card", "declined", or "billing", tag it involuntary. Everything else is voluntary. This is not perfect but catches most cases when the export lacks an explicit flag.

What is a cohort retention analysis and how do I build it from this export?

Group customers by their signup_date month, then calculate what percentage are still active (no cancellation_date) after 1 month, 3 months, 6 months, and 12 months. This shows whether newer cohorts retain better than older ones and where drop-off happens in the lifecycle.

Try it with your own file

DataMimi reads the file you actually have — merged cells, headers below row one, totals pasted at the bottom — and shows which rows and columns every number came from.

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