Churn from an expired card
Some churn is a payment that failed, with nobody deciding to go. Screening leavers on whether they meant to go keeps involuntary churn out of the cause ranking, and billing gets its own short list of fixes.
Use caseReduce churn
Churn prevention software
Compare the customers who left with those at risk and those who stayed, rank the causes of churn by the evidence behind them, and fund the fixes that would have kept people.
Leavers, set against those who stayed
An exit reason on its own proves little, since customers who stay can report the same annoyances, so the study builds in the comparison from the start.
Last quarter's leavers, invited from a list
Customers who canceled or declined to renew last quarter reach the study through an uploaded list or a connection to the team's existing tools, and each is invited to talk about the decision to leave.
At-risk and retained, as the control
Customers showing warning signs, and customers who considered leaving but stayed, are interviewed on the same topics, so a cause counts only when it shows up more among leavers than among those who kept buying.
The relationship from month one
Each conversation walks forward from the first month, through onboarding, the first renewal and any change of contact, so the account of leaving is told as a sequence of events with dates attached.
The world's most ambitious teams research with Pulse.
GrowwCauses ranked, dated and matched to a fix
Retention spending is decided cause by cause, so each one reaches the review with its evidence, its timing and a proposed fix.
Causes ranked by weight of evidence
The reasons leavers gave are grouped by what went wrong, such as a lost champion, a missing feature or slow support, and each group is ranked by how many leavers raised it and how directly they tied it to leaving.
The month the doubt started
Leavers name when they first began to question the product, and that point is often months before the cancellation, which is where the cause began and where an intervention would have had a chance.
Fixes that would have kept them
Each leaver is asked what would have made them stay at that point, whether a feature, a call or a concession, and the answers become a short list of fixes attached to the causes they address.
How Pulse does it
Pull last quarter's leavers
Interview leavers, at-risk, retained
Rank causes and their origin
Fund the fixes
The method behind it
Understanding churn combines customer data with conversations. Analysis of who leaves and when shows where churn concentrates, by tenure, plan, channel or usage pattern, and survival analysis reveals the points in the relationship where risk is highest. Exit surveys give a first view of stated reasons, but the first reason offered is often the most socially acceptable one, such as price. Interviews with recent leavers reconstruct the history of the relationship: when doubts began, what triggered the decision and what might have kept them. Speaking with at-risk and retained customers adds the contrast needed to separate real causes from coincidences. Sizing each cause, by linking it back to customer data, turns a list of reasons into a prioritized set of fixes. Churn research works best as a continuing program, so new causes are spotted early and the effect of each fix can be measured.
Related
Retention spend aimed at the right cause
Downgrades as early warnings
Customers who downgraded instead of leaving are churn in slow motion. Drawn from the subscription list, downgraders describe what they gave up and why, which the product team reads as the early chapter of the story leavers tell.
The successor who inherited the tool
When the person who bought the product leaves the customer's company, a successor inherits a tool they did not choose. The brief can recruit successors as their own group, with their own profile and screener, so customer success learns what one needs in the first month.