What drives what, and by how muchB2B SaaS and subscriptionsUse case, not a client result
What actually drives customer churn, and how fast?
The decision it informs
Where to put the next unit of customer success effort, and which accounts to act on this month.
A dashboard showsWho left, and when.
An investigation tells youWhich factors raised the odds of leaving, by how much, and how early the signs showed.
Onboarding completed
Support response time
Product usage in month one
Price change at renewal
Contract length
Number of active users
01
A ranked driver tableEach factor, the direction, the size of effect per unit change and a confidence range.
02
A risk windowHow early the signs appear, so the team acts while there is still time.
03
A what-if workbookChange support response time or onboarding completion and see the effect on retention.
- What if first-response time halves?
- What if every new account completes onboarding?
- What if renewal prices rise on one segment only?
Method 1Survival analysisHow long customers stay, and how each factor changes the chance of leaving over time.
Method 2Driver modelThe effect of each factor with the others held fixed, with a range, not a single number.
Method 3Cohort comparisonCustomers who were alike except for one thing, to separate cause from coincidence as far as the data allows.
Data we would look at
Subscriptions and renewalsProduct usage eventsSupport tickets and response timesInvoices and price changesAccount notes from customer success
First week: the questions we ask
How do you define a lost customer, and when does the clock start?Which actions does the team control: onboarding, support, pricing, account reviews?What would you do differently if one factor mattered twice as much as another?What have you already tried, and what did you conclude from it?