Document Type: Framework
Status: Canon
Version: v1.0
Authority: Customer Brain
Applies To: All MWMS environments where customer behaviour reflects relationship strength or lifecycle progression
Parent: Customer Brain Canon
Last Reviewed: 2026-04-15
Purpose
Customer State Framework defines how MWMS classifies customers according to behavioural, relational, and lifecycle position.
Customers do not exist as a single static condition.
Customers move through states.
Each state influences:
engagement behaviour
trust stability
retention probability
support sensitivity
loyalty development
churn exposure
relationship value
Customer State Framework ensures MWMS maintains a consistent method for identifying where a customer sits in the relationship lifecycle.
Structured state visibility improves decision accuracy across retention, communication, support, and growth systems.
Scope
This framework governs classification of:
customer lifecycle position
relationship maturity level
engagement depth
retention strength indicators
loyalty development signals
churn exposure signals
post-conversion behavioural patterns
Customer State Framework applies across:
email lifecycle environments
membership environments
product or service usage environments
repeat purchase environments
support interaction environments
reactivation environments
relationship-driven conversion environments
Customer State Framework does not govern:
traffic acquisition logic
landing page decision structure
persuasion design
statistical experiment validation
compliance enforcement
capital allocation decisions
Those remain governed by:
Ads Brain
Conversion Brain
Creative Brain
Experimentation Brain
Compliance Brain
Finance Brain
Customer State Framework governs customer lifecycle classification logic.
Core Principle
Customers exist in identifiable states.
States influence behaviour.
Behaviour indicates relationship stability.
Relationship stability influences long-term value durability.
Clear state classification improves retention decision quality.
Undefined customer states produce inconsistent lifecycle decisions.
Customer State Framework ensures customer lifecycle position remains visible.
Customer State Categories
Prospect State
User has not yet converted but has demonstrated identifiable interest.
Signals may include:
content engagement
email sign-up
lead form interaction
repeat visits
Prospect state informs nurturing logic.
Prospect state supports conversion readiness development.
New Customer State
Customer has recently completed first meaningful action.
Examples:
first purchase
first subscription
first application completion
first booking
first onboarding completion
New customers require clarity and reassurance.
Early-stage experience strongly influences long-term relationship stability.
Active Customer State
Customer demonstrates ongoing interaction or usage behaviour.
Signals may include:
continued engagement
product or service use
repeated interaction
Active state indicates relationship continuity.
Continuity supports retention stability.
Repeat Customer State
Customer has demonstrated multiple meaningful actions across time.
Examples:
repeat purchases
repeated bookings
continued usage
Repeat behaviour strengthens loyalty potential.
Repeat behaviour improves long-term value stability.
Engaged Relationship State
Customer demonstrates stable ongoing interaction patterns.
Signals may include:
consistent usage
consistent engagement
positive interaction continuity
relationship familiarity
Stable engagement improves predictability of future behaviour.
Loyal Customer State
Customer demonstrates strong relationship preference and stability.
Signals may include:
consistent repeat behaviour
preference continuity
positive relationship signals
strong expectation stability
Loyal customers strengthen long-term system stability.
Loyalty increases lifetime value durability.
At-Risk Customer State
Customer behaviour indicates weakening relationship strength.
Signals may include:
declining engagement
increasing inactivity
reduced response frequency
reduced usage patterns
weakened behavioural continuity
At-risk signals enable early intervention.
Early intervention improves recovery probability.
Inactive Customer State
Customer has ceased meaningful interaction for a defined period.
Signals may include:
no engagement activity
no repeat interaction
extended inactivity period
Inactive state requires reactivation evaluation.
Inactive customers may still retain recoverable value.
Reactivated Customer State
Customer returns to meaningful interaction after inactivity.
Signals may include:
renewed engagement
new purchase behaviour
renewed response patterns
Reactivated customers may require adjusted lifecycle support.
Reactivation insight improves recovery modelling.
State Transition Principle
Customers may move between states.
Transitions provide intelligence.
Examples:
prospect to new customer
new customer to active relationship
active to repeat behaviour
repeat to loyal state
loyal to inactive state
inactive to reactivated state
Transitions reveal relationship dynamics.
Transition visibility improves lifecycle decision quality.
Behavioural Signal Interpretation
Customer state classification should consider:
interaction frequency
behavioural continuity
engagement depth
repeat action patterns
responsiveness patterns
support signals
Behavioural interpretation must remain consistent.
Consistent interpretation improves learning reliability.
State Clarity Rule
Customer state definitions must remain interpretable across systems.
Unclear state definitions reduce lifecycle coordination.
Clear definitions improve communication alignment.
Aligned understanding improves decision stability.
Relationship to Other Frameworks
Customer Brain Architecture
defines structural lifecycle model
Retention Signal Framework
interprets ongoing engagement patterns
Churn Risk Framework
identifies weakening relationship signals
Loyalty Framework
supports strengthening relationship continuity
Conversion Brain
creates initial relationship transition point
Research Brain
supports behavioural insight interpretation
Customer State Framework ensures lifecycle classification consistency across MWMS.
Failure Modes Prevented
customer lifecycle position remaining undefined
retention signals lacking interpretation
churn signals appearing too late
loyalty signals not being recognised
relationship progression lacking visibility
inconsistent lifecycle communication
Customer state clarity improves lifecycle decision stability.
Drift Protection
The system must prevent:
customer states being defined differently across systems
lifecycle categories becoming inconsistent
behavioural interpretation changing without structure
relationship states remaining informal
state transitions becoming unclear
Customer state definitions must remain stable.
Architectural Intent
Customer State Framework ensures MWMS maintains consistent lifecycle classification across environments so customer relationships remain visible, interpretable, and optimisable across time.
Structured state classification improves retention reliability.
Improved retention reliability strengthens long-term value durability.
Customer-state intelligence becomes reusable system capability.
Final Rule
If customer state is unclear, lifecycle decisions weaken.
Weakened lifecycle decisions reduce retention stability.
Reduced retention stability weakens long-term system growth durability.
Customer-state clarity must remain visible across the lifecycle.
Change Log
Version: v1.0
Date: 2026-04-15
Author: MWMS HeadOffice
Change:
Initial creation of Customer Brain Customer State Framework defining structured lifecycle classification model across MWMS.
END CUSTOMER BRAIN CUSTOMER STATE FRAMEWORK v1.0