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Churn Analysis

Churn Analysis

By the time a customer cancels, the reason on file is rarely where the loss began. This skill reads each churn against the account's whole history, applying your ICP, Sales Process, Customer Outcomes, Onboarding, Adoption, Value Realisation, Customer Risk and Renewal memories to the promise the deal was won on, how onboarding ran, what adoption and value followed and every intervention along the way. CS and sales leaders see where the account was really lost, and a cause that keeps recurring becomes an owner-approved change to the logic behind it, on either side of the handover.

Used across

Customer Success

Sales

Referenced by

Churn Analysis

Win-Loss Analysis

Sales Handover

Applies

ICP

Sales Process

Customer Outcomes

Onboarding

Adoption

Value Realisation

Customer Risk

Renewal

Churn Analysis
Churn Analysis·11 CHURNED ACCOUNTS READ THIS QUARTER
Analysing Thistlewood's churn…
TriggerChurned · Thistlewood Freight · $168K
Live stateReading Deal State + Customer State · history, outcome
Applying skillRunning Churn Analysis v2
Reads the account from the deal promise to the exit
Adoption never reached the promised second use case
Onboarding stalled twice, never escalated to sales
Renewal risk flagged 60 days out, no intervention logged
Reference memoryAgainst ICP · Sales Process · Onboarding · Adoption · Customer Risk
ActionChurn read written to the account, cause flagged for CS leadership
Skill
Churn Analysis
v2v3
Churn AnalysisThistlewood traced to onboard
Customer AnalysisThistlewood joins the read
Voice of Customer AnalysisThistlewood matches the theme
See all 5 capabilities Run 24 times today
Upgrade proposed
Onboarding stalls unresolved 30+ days escalate automaticallyv3
6 of 11 churns this quarter trace to a stalled onboarding step.
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The current way > AI added on > AI-native

Reading the loss all the way back

A churn's paper trail starts long before the cancellation. What the loss can teach depends on how far back anyone reads.

01 | The Current Way

02 | AI Added On

03 | AI-Native

The cancellation arrives and the post-mortem begins

A reason code and a meeting

The exit survey gives one reason, the post-mortem adds opinions, and the promise the account was sold on two years ago is in nobody's notes.

The stated reason, taken at its word

The summary stops where the customer's stated reason stops, because the promises, onboarding history and interventions were never in view.

The loss reads back to where it started

Leaders trace the path from sales promise through onboarding and adoption to the exit, and see which earlier failure the reason was hiding.

The quarterly review asks why a cohort churned

Anecdotes stitched into a theory

Someone builds a deck from the quarter's churns. Each loss has its own story, and the pattern across them is whatever the room agrees on.

Patterns in the exit data only

Clustering reason codes finds patterns in how customers leave, while how they were sold and onboarded never enters the read.

Findings that change how the next cohort is sold

Reviews end with changes to qualification or onboarding logic, approved by RevOps or the CS lead, so the next cohort is sold differently.

Referenced by

Capabilities that run this skill

Customer Success

Churn Analysis

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SEE HOW IT WORKS

Sales

Win-Loss Analysis

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SEE HOW IT WORKS

Sales

Sales Handover

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SEE HOW IT WORKS
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How can AI analyse churn causes?

By putting the churn in context: the deal that won the account, onboarding, adoption, value delivered and every intervention, judged with your ICP, Sales Process, Onboarding and Customer Risk memories. Causes come back with evidence and confidence, for CS, sales and product leaders to validate.

How can churn data improve Sales and CS?

When a cause traces to a rule the company wrote down, the finding can change the rule. Losses clustering in one segment challenge the ICP; onboarding stalls challenge the Onboarding memory. RevOps approves the sales-side changes, the CS lead the customer-side ones.

What evidence does a churn analysis use?

The account's whole commercial history: what the deal promised, how onboarding ran, adoption and usage over time, value delivered against what was agreed, risk flags and the interventions that followed, through to the churn itself. The stated cancellation reason is one input among many.

Where do churn findings end up?

In three places: the Churn Analysis capability presents them, Win-Loss Analysis reads churn alongside lost deals for the full picture of why revenue leaves, and Sales Handover uses them to fix the handover moments churn keeps tracing back to.

Where does Win-Loss Analysis end and this begin?

Win-Loss Analysis explains why deals close won or lost; Churn Analysis explains why customers leave after being won. They meet at the handover: a churn traced back to a sales promise sharpens the next win-loss read, and both can propose changes to the same memories.