LEO Customer 360 — Data Journey Map

Business question

“Who is this customer, what are they doing now, what are they likely to do next, and what should LEO do about it?”

Stage	1. Capture	2. Assemble	3. Score	4. Segment	5. Syndicate	6. Engage
Purpose	Collect behavioral & transactional signals	Build unified Customer 360	Turn signals into intelligence	Decide who belongs to which audience	Push audience/profile to channels	Deliver personalized experience
Data	Web, App, POS, CRM, Ads, Social	Identity + Profile + Events	RFM, CLV, Lead, Churn, Propensity	Behavioral / Value / Intent	Audience + attributes + scores	Content, Offer, Product, Message
Core LEO capability	Event Tracking	Identity Resolution	Customer Intelligence	Segmentation	Activation	Personalization
Output	Raw Events	Unified Profile	Customer Scores	Audience	Activated Audience	Customer Interaction
Feedback	New event	Profile update	Score recalculation	Segment movement	Delivery / response	Conversion / behavior

1. The visual Data Journey

                         LEO CUSTOMER 360
                              DATA JOURNEY
 
 ┌──────────┐    ┌──────────┐    ┌──────────┐
 │ CAPTURE  │───▶│ ASSEMBLE │───▶│  SCORE   │
 └──────────┘    └──────────┘    └──────────┘
      │               │               │
      ▼               ▼               ▼
 Web / App        Identity         RFM
 POS              Resolution       CLV
 CRM              Customer 360     Lead Score
 GA4              Customer Graph   Churn Score
 Ads              Event History    Propensity
 Social
      │               │               │
      └───────────────┴───────────────┘
                              │
                              ▼
                       ┌───────────┐
                       │  SEGMENT  │
                       └───────────┘
                              │
                    ┌─────────┴─────────┐
                    ▼                   ▼
             Behavioral           Predictive
              Segment              Segment
                    │                   │
                    └─────────┬─────────┘
                              ▼
                       ┌───────────┐
                       │ SYNDICATE │
                       └───────────┘
                              │
             ┌────────────────┼────────────────┐
             ▼                ▼                ▼
           CRM/           Ads/Media         Website/
         Marketing                         App/API
                              │
                              ▼
                       ┌───────────┐
                       │  ENGAGE   │
                       └───────────┘
                              │
                              ▼
                     Customer Response
                              │
                              ▼
                         New Events
                              │
                              └───────────────▶ CAPTURE

This makes the important point:

LEO Customer 360 is a closed-loop system, not a database.

2. Detailed Data Journey

Stage	Steps in the Data Journey	Data Interaction / Platform	Data Flow
Capture	Customer visits website	Website + LEO Tracking SDK + GA4	`page_view`
	Customer searches	Website / App	`search`
	Customer views product	Website / App	`item_view`
	Customer adds product	Website / App	`add_to_cart`
	Customer purchases	POS / Ecommerce / CRM	`purchase`
	Customer responds to campaign	Email / SMS / Zalo / Ads	`campaign_*`
	Customer interacts with social/media	Meta / TikTok / Zalo / Google	`ad_`, `social_`
Assemble	Identify visitor	Identity Resolution	`visid → profile_id`
	Resolve known customer	Phone / Email / CRM ID	`identity_resolved`
	Merge anonymous + known identity	Identity Graph	`profile_merged`
	Build Customer 360	Profile + Events + Transactions	`profile_updated`
	Build Customer Graph	Customer ↔ Product ↔ Channel ↔ Campaign	`relationship_created`
Score	Calculate RFM	Customer transactions	`rfm_score_updated`
	Calculate CLV	Transactions + customer history	`clv_score_updated`
	Calculate Lead Score	Behavior + profile + intent	`lead_score_updated`
	Calculate Churn Score	Behavior + inactivity	`churn_score_updated`
	Calculate propensity	ML / AI	`propensity_score_updated`
Segment	Behavioral segmentation	Event stream	`segment_entered`
	Value segmentation	RFM / CLV	`segment_entered`
	Intent segmentation	Product behavior	`segment_entered`
	Predictive segmentation	ML score	`segment_entered`
Syndicate	Send audience to marketing	CRM / Marketing Automation	`audience_synced`
	Send audience to Ads	Google / Meta / TikTok	`audience_activated`
	Send profile to website	Personalization API	`profile_activated`
	Send profile to sales	CRM	`lead_activated`
Engage	Personalize content	Web / App	`content_impression`
	Recommend product	Recommendation API	`recommendation_shown`
	Send campaign	Email / SMS / Zalo	`message_sent`
	Customer clicks	Channel	`message_clicked`
	Customer converts	Ecommerce / POS	`purchase`
	Feedback returns to LEO	All channels	New event → Capture

3. LEO Event Catalog

I would organize the Event Catalog into 10 event families, rather than having one huge flat event list.

A. Identity & Profile

Event	Meaning	Primary Source
`identity_created`	New identity detected	LEO
`identity_resolved`	Anonymous identity matched to customer	LEO CIR
`identity_merged`	Multiple identities merged	LEO CIR
`profile_created`	Customer 360 profile created	LEO
`profile_updated`	Customer attributes changed	CRM / POS / LEO
`profile_deleted`	Profile removed	CRM / Privacy
`consent_updated`	Consent preference changed	CRM / Website

B. Web & App Behavior

Event	Meaning
`session_started`	Customer starts session
`page_view`	Page viewed
`screen_view`	Mobile screen viewed
`search`	Customer performs search
`item_view`	Product viewed
`category_view`	Product category viewed
`content_view`	Content viewed
`video_start`	Video started
`video_complete`	Video completed
`click`	CTA / link clicked
`form_start`	Form started
`form_submit`	Form submitted
`login`	Customer logs in
`logout`	Customer logs out

C. Ecommerce

Event	Meaning
`product_view`	Product viewed
`add_to_cart`	Product added
`remove_from_cart`	Product removed
`cart_view`	Cart viewed
`checkout_start`	Checkout started
`checkout_step`	Checkout step completed
`purchase`	Transaction completed
`refund`	Transaction refunded
`cancel`	Order cancelled
`wishlist_add`	Product added to wishlist
`wishlist_remove`	Product removed

D. Retail / POS

Event	Meaning
`store_visit`	Customer visits store
`pos_transaction`	POS transaction
`product_purchased`	Product purchased
`product_returned`	Product returned
`coupon_redeemed`	Coupon redeemed
`loyalty_point_earned`	Points earned
`loyalty_point_redeemed`	Points redeemed

E. Marketing

Event	Meaning
`campaign_exposed`	Customer entered campaign exposure
`message_sent`	Message sent
`message_delivered`	Message delivered
`message_opened`	Message opened
`message_clicked`	Message clicked
`campaign_converted`	Campaign conversion
`campaign_unsubscribed`	Customer unsubscribed
`ad_impression`	Ad impression
`ad_clicked`	Ad clicked

F. Lead & Sales

Event	Meaning
`lead_created`	New lead
`lead_updated`	Lead information changed
`lead_qualified`	Lead qualified
`lead_assigned`	Lead assigned to salesperson
`sales_contacted`	Sales interaction
`sales_opportunity_created`	Opportunity created
`sales_opportunity_won`	Opportunity won
`sales_opportunity_lost`	Opportunity lost

G. Customer Service

Event	Meaning
`ticket_created`	Customer service case
`ticket_updated`	Case updated
`ticket_resolved`	Case resolved
`chat_started`	Customer starts chat
`chat_completed`	Chat completed
`complaint_created`	Complaint registered
`feedback_submitted`	Feedback submitted
`rating_submitted`	Customer rating

H. Product & Recommendation

Event	Meaning
`recommendation_requested`	Recommendation API called
`recommendation_shown`	Recommendation displayed
`recommendation_clicked`	Recommendation clicked
`recommendation_added_to_cart`	Recommended product added
`recommendation_purchased`	Recommended product purchased
`content_recommended`	Personalized content shown

I. Intelligence Events

These are especially important for AI-first Customer 360.

Event	Meaning
`rfm_score_updated`	RFM recalculated
`clv_score_updated`	CLV recalculated
`lead_score_updated`	Lead score changed
`churn_score_updated`	Churn probability changed
`propensity_score_updated`	Purchase propensity changed
`intent_detected`	Customer intent detected
`persona_updated`	Persona representation changed
`next_best_action_generated`	NBA generated
`next_best_product_generated`	NBP generated

J. Segment & Activation

Event	Meaning
`segment_entered`	Customer enters segment
`segment_exited`	Customer leaves segment
`audience_created`	Audience created
`audience_updated`	Audience changed
`audience_synced`	Audience sent to destination
`activation_started`	Activation started
`activation_completed`	Activation completed
`activation_failed`	Activation failed

4. The most important part: Event → Customer 360 transformation

The reference image shows the data moving and transforming. For LEO, I would explicitly show this layer:

RAW EVENT
   │
   ▼
┌─────────────────────┐
│ Event Normalization │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│ Identity Resolution │
│ visid / phone/email │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│ Customer 360 Profile│
│ Profile + Events    │
│ Transactions        │
│ Relationships       │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│ Feature Engineering │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────────────┐
│ Customer Intelligence       │
│ RFM | CLV | Lead | Churn    │
│ Intent | Propensity | AI    │
└──────────┬──────────────────┘
           │
           ▼
┌─────────────────────┐
│ Dynamic Segmentation│
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│ Activation / CDP    │
│ CRM | Ads | Web | App│
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│ Customer Interaction │
└──────────┬──────────┘
           │
           └───────────────► NEW EVENT

5. Canonical LEO Event Schema

The Event Catalog should ultimately map to one canonical event contract.

{
  "schema_version": "1.0",
  "event_id": "uuid",
  "tenant_id": "PNJ",
  "datetime": "2026-08-22T13:00:00+07:00",
  "unix_timestamp": 1787378400000,
 
  "event_name": "add_to_cart",
  "metric": "add-to-cart",
 
  "visid": "anonymous-or-device-id",
  "profile_id": "customer-123",
  "session_id": "session-456",
 
  "source": {
    "channel": "website",
    "platform": "ecommerce",
    "integration": "leo_tracking_sdk"
  },
 
  "page": {
    "url": "https://shop.example.com/product/ring-001",
    "title": "Diamond Ring",
    "referrer": "https://google.com"
  },
 
  "campaign": {
    "utm_source": "google",
    "utm_medium": "cpc",
    "utm_campaign": "summer_sale",
    "utm_content": "ring_banner"
  },
 
  "customer": {
    "phone_hash": "...",
    "email_hash": "..."
  },
 
  "product": {
    "product_id": "RING-001",
    "category": "Jewelry",
    "brand": "Example",
    "price": 25000000,
    "quantity": 1
  },
 
  "custom_data": {},
 
  "profile_traits": {}
}

The important architectural decision is:

event_name describes what happened. profile_id describes who did it. source describes where it happened. datetime describes when it happened. custom_data describes the business context.

6. Event Catalog → Customer 360

This is where the Event Catalog becomes useful rather than just documentation.

Event	Customer 360 Impact	Intelligence
`page_view`	Update behavioral history	Interest
`search`	Update intent	Search intent
`item_view`	Add product interest	Product affinity
`add_to_cart`	Increase buying intent	Conversion probability
`checkout_start`	Strong purchase signal	Purchase propensity
`purchase`	Update transaction history	RFM / CLV
`refund`	Update value / behavior	Risk
`message_opened`	Update channel affinity	Engagement score
`message_clicked`	Increase campaign intent	Lead score
`store_visit`	Update offline behavior	Omnichannel affinity
`feedback_submitted`	Update CX profile	CX score
`identity_resolved`	Unify customer identity	Customer 360
`lead_score_updated`	Update intelligence	Lead prioritization
`churn_score_updated`	Update risk	Retention
`segment_entered`	Update audience membership	Activation
`recommendation_clicked`	Update preference	Personalization
`purchase` after recommendation	Attribute conversion	Recommendation ROI

7. The LEO version of the original six-stage picture

I would actually use these labels in your final architecture/slide:

CAPTURE
Collect every meaningful customer signal
        ↓
ASSEMBLE
Resolve identity + build Customer 360
        ↓
UNDERSTAND
Calculate scores + intent + AI signals
        ↓
SEGMENT
Create dynamic audiences
        ↓
ACTIVATE
Syndicate profiles & audiences
        ↓
ENGAGE
Personalize the customer experience
        ↓
LEARN
Capture response and feed intelligence back
        ↺

I recommend UNDERSTAND instead of simply SCORE for LEO, because your architecture is going beyond traditional lead scoring into RFM + CLV + Lead + Churn + Propensity + Intent + Persona + Next Best Action.

So the final LEO loop becomes:

Capture → Assemble → Understand → Segment → Activate → Engage → Learn

That is a much stronger framing for AI-first Customer 360 than the original Lead Scoring & Nurturing diagram.

LEO Customer 360 — Documentation

Explorer

LEO Customer 360 — Data Journey Map

LEO Customer 360 — Data Journey Map

Business question

1. The visual Data Journey

2. Detailed Data Journey

3. LEO Event Catalog

A. Identity & Profile

B. Web & App Behavior

C. Ecommerce

D. Retail / POS

E. Marketing

F. Lead & Sales

G. Customer Service

H. Product & Recommendation

I. Intelligence Events

J. Segment & Activation

4. The most important part: Event → Customer 360 transformation

5. Canonical LEO Event Schema

6. Event Catalog → Customer 360

7. The LEO version of the original six-stage picture

Graph View

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