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Customer Analysis

Customer Analysis

The retention number moved. Weeks of report-building later, the team knows what happened, and still not why. This skill closes that gap: it reads outcomes, actions and the customer base against your Goals & Objectives memory and the definitions your team actually runs on, from Customer Health and Adoption through Value Realisation, Renewal, Expansion and Customer Risk, then explains the change and how strongly the evidence supports the explanation. Leaders and managers validate the causality, CS Ops and RevOps ship the agreed fix into the memory it belongs to as an experiment its owner approves, and the next quarter's outcomes judge it.

Used across

Customer Success

Referenced by

Customer Analysis

Churn Analysis

Customer Weekly Review

Applies

Goals & Objectives

Customer Health

Adoption

Value Realisation

Renewal

Expansion

Customer Risk

Customer Analysis
Customer Analysis·4 RETENTION THEORIES TESTED THIS WEEK
Analysing Farndale's retention…
TriggerWeekly review · Farndale tests the pricing theory
Live stateReading Customer State + outcomes · Farndale's segment
Applying skillRunning Customer Analysis v2
Tests retention theories against Farndale's record
A Health criterion changed on Farndale, unlogged
Pricing blamed, Farndale's expansion held steady
Farndale's onboarding ran twice the normal length
Reference memoryAgainst Goals/Objectives · Customer Health · Adoption · Renewal
ActionExplanation written to the account, cause flagged for the CSM
Skill
Customer Analysis
v2v3
Customer AnalysisFarndale's decay explains it
Portfolio ReviewFarndale is the exception
Voice of Customer AnalysisFarndale's ticket matches
See all 4 capabilities Run 24 times today
Upgrade proposed
Memory version recorded on every customer-state changev3
The dip traced to one Customer Health criterion changed unlogged.
Approve upgrade to Customer Analysis?
The current way > AI added on > AI-native

The why behind the number

The weekly review and the sudden dip both demand the same answer: why did the number move? Most answers arrive too late to change anything.

01 | The Current Way

02 | AI Added On

03 | AI-Native

The weekly customer review walks the same dashboards

Reports joined by hand each week

CS Ops joins CRM, support and usage extracts into a weekly pack. It shows movement clearly and explains almost none of it.

Movement reported, reasons guessed

The summary describes the trend, but it was never connected to the playbooks and definitions in use, so why it moved stays a debate.

The review answers why, with evidence

The review opens on why the number moved and what drove it, evidence shown. The meeting turns to choosing which changes ship.

Expansion stalls for a second straight quarter

An analysis project spun up from zero

Someone starts a project: weeks of exports and joins. The answer arrives after the quarter it was meant to save.

Charts that cannot name a cause

The charts come back sooner, but speed was never the problem: no chart can test which definition or playbook change caused the stall.

Traced to the logic that caused it

The team learns which definition or playbook change stalled expansion, and the fix ships as an experiment its owner approves.

Referenced by

Capabilities that run this skill

Customer Success

Customer Analysis

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SEE HOW IT WORKS

Customer Success

Churn Analysis

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SEE HOW IT WORKS

Customer Success

Customer Weekly Review

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SEE HOW IT WORKS
The Platform

One system that understands, decides, acts and learns.

Every GTM signal flows through an AI-native operating layer into a system that runs on the surfaces your team already uses.

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GTM Data & Knowledge
CRM · Emails · Calls · Marketing · Product · Support · Documents · Research
AI-Native Operating Layer
Context · Memory · Skills · Agents · Decision Traces
AI-Native Customer Success System
Understand
Account Health
Churn Drivers
Expansion Signals
Product Usage
Decide
Renewal Risk
Expansion Opportunity
Account Plan
Intervention Priority
Act
Meeting Prep
QBR Prep
Sales Handover
Save Plays
Learn
Upgrade Health Model
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Upgrade Playbook
Upgrade Forecast
Surfaces
CRM · Slack · Teams · ChatGPT · Claude · MCP · API
Output
Briefings · Artifacts · Alerts · Recommendations · Approvals · Actions

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FAQ

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How can AI analyse customer success?

By joining what happened to what was done. Retention, adoption and expansion movements are judged next to your Goals & Objectives memory and the named definitions behind them, Customer Health, Renewal and the rest. The output says why the number moved and what to improve; leaders validate the causality.

How does CS analysis drive system upgrades?

Directly. A finding is a proposed change to a named memory, Renewal or Adoption for instance, shipped as an experiment once its owner approves. The next quarter's outcomes then judge the change, so each cycle either improves the rule it touched or gets rolled back.

Is this a health check on one account?

No. Customer Health Assessment judges one account's condition; this skill diagnoses the whole book. It asks why retention, adoption or expansion moved across every account and which definition or playbook to change; health judgements are one strand of its evidence.

What stops a plausible finding becoming a bad change?

Three things: people validate the causality before anything ships, the owning approver signs off the memory change, and the change runs as an experiment whose outcomes are tracked. A finding that fails against the next quarter's outcomes is reversed, and the reversal is recorded too.

When does this skill actually run?

On a rhythm and on demand. The Customer Weekly Review opens with its read, RevOps and leaders put one-off questions through the Customer Analysis capability, and Churn Analysis consumes it when losses need explaining. A finding made in Monday's review can be an approved change by Friday.