Document Type: Framework
Status: Active
Version: v1.0
Authority: MWMS HeadOffice
Parent: Research Brain Canon
Last Reviewed: 2026-04-12
Purpose
The Customer Intelligence Data Capture Framework defines how MWMS systematically collects structured behavioural and declarative signals from customers across touchpoints in order to improve decision accuracy across the ecosystem.
Customer data is not collected for storage.
Customer data is collected to improve:
segmentation precision
message relevance
acquisition efficiency
CRO prioritisation
lifecycle optimisation
forecasting accuracy
merchandising intelligence
Improved signal quality improves decision quality.
Decision quality determines growth stability.
Scope
This framework governs:
what customer data is collected
where customer data is collected
when customer data is collected
how customer data is structured
how signal quality is prioritised
how progressive profiling is implemented
This framework applies to:
onsite forms
popups
checkout data capture
survey flows
quiz flows
review forms
post purchase surveys
lifecycle data enrichment
behavioural tracking layers
This framework does not govern:
data warehouse architecture
privacy compliance policy
platform implementation specifics
These are governed separately within Tech Stack Canon.
Core Principle
Customer understanding improves system performance.
Signal clarity improves:
personalisation accuracy
segmentation clarity
predictive modelling stability
Higher signal quality reduces:
CAC volatility
messaging inefficiency
targeting drift
Customer intelligence compounds over time.
Capture Layer Model
MWMS structures data capture across layered signal categories.
Identity Signals
identify the customer entity.
examples:
email address
phone number
account ID
hashed identity markers
identity signals enable cross-session continuity.
Behavioural Signals
capture observable actions.
examples:
page visits
product views
cart additions
checkout initiation
purchase completion
email opens
email clicks
SMS clicks
behavioural signals reveal:
intent strength
decision stage
friction points
behavioural signals are continuously generated.
Transaction Signals
capture economic relationship patterns.
examples:
purchase frequency
average order value
product mix
discount sensitivity
repeat purchase interval
transaction signals inform:
LTV estimation
segmentation quality
retention strategy
Preference Signals
capture declared interests.
examples:
category preference
product style preference
price sensitivity band
problem type relevance
preference signals improve:
content relevance
product recommendation accuracy
campaign segmentation precision
Context Signals
capture situational information.
examples:
device type
geography
time of interaction
seasonality timing
acquisition source
context signals influence:
message timing
channel prioritisation
campaign structure
Zero Party Signals
capture voluntarily declared intent.
examples:
self-declared preferences
survey responses
quiz outcomes
onboarding responses
fit preferences
lifestyle indicators
zero-party signals improve:
personalisation precision
creative targeting accuracy
lifecycle relevance
zero-party signals improve signal clarity beyond inference-based modelling.
Progressive Profiling Logic
MWMS avoids collecting excessive data in a single interaction.
Data capture should occur progressively across interactions.
progressive profiling improves:
completion rates
data accuracy
user experience quality
example structure:
step 1 identity signal
step 2 preference signal
step 3 behavioural classification
step 4 contextual enrichment
multi-step forms improve completion probability versus single-step forms.
Capture Timing Model
Data capture must align with behavioural readiness.
early stage interactions capture low friction signals.
later stage interactions capture higher complexity signals.
example:
email capture occurs early
preference capture occurs after trust signals
deeper profiling occurs after purchase
timing improves data accuracy.
Incentive Alignment Principle
users exchange data for perceived value.
value exchange examples:
discount incentive
early access incentive
personalization benefit
product recommendation accuracy
community participation
irrelevant data requests reduce completion probability.
relevance improves participation rates.
Signal Use Requirement
data should not be collected without planned use.
unused data creates:
complexity cost
storage overhead
segmentation confusion
data capture must map to decision logic.
example:
fit preference → product recommendation logic
use case preference → segmentation logic
purchase intent signal → lifecycle timing logic
Relationship to Segmentation Framework
captured signals enable meaningful segmentation.
segmentation quality depends on signal relevance.
high quality signals improve:
message timing accuracy
offer relevance
conversion probability
Relationship to Lifecycle Framework
captured signals enable stage-specific messaging.
customer journey stage identification improves:
content alignment
friction reduction
onboarding performance
Relationship to CRO Framework
behavioural signals reveal friction patterns.
example:
device-specific drop-off patterns indicate UX issues.
behavioural signals inform CRO prioritisation.
Drift Protection
the system must prevent:
collecting data without defined use case
over-collecting low-value signals
under-collecting high-value signals
collecting signals that cannot influence decisions
collecting signals that cannot be structured
data must improve decisions.
Architectural Intent
Customer Intelligence Data Capture Framework exists to ensure MWMS captures signals that improve decision quality across the ecosystem.
structured intelligence enables:
adaptive marketing systems
responsive lifecycle systems
predictive growth systems
signal quality determines system intelligence ceiling.
Future Expansion
predictive profiling models
behavioural clustering automation
dynamic preference updating
cross-channel identity resolution
probabilistic identity stitching
real-time segmentation engines
Final Rule
Data is only valuable when it improves decisions.
MWMS captures signals that increase decision precision.
Change Log
Version: v1.0
Date: 2026-04-12
Author: MWMS HeadOffice
Change: Initial creation of Customer Intelligence Data Capture Framework defining structured signal capture layers, progressive profiling logic, segmentation enablement structure, and decision-aligned signal prioritisation model.
CHANGE IMPACT
Pages Created:
Customer Intelligence Data Capture Framework
Pages Updated:
None
Pages Deprecated:
None
Registries Requiring Update:
MWMS Architecture Registry
MWMS Brain Registry
MWMS Canon Hierarchy Map
MWMS Intelligence Layer Map
Canon Version Update Required: No
Change Log Entry Required: Yes