IDAEO Atlas | First Edition
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EO
Figure Relationship Map
The seven laws are the core, the five dimensions are the framework, and the nine elements are the components
The roof is the core and theory | The five pillars are the five dimensions | The foundation is measurement sources and field results
First Edition Textbook · Atlas
35 figures · Data as of 2026/10/8
Go to Chapter 0 of the textbook
Core
What IDAEO Is
How did TK Lin build
IDAEO from
real-world website
measurement, and
what makes up this
marketing discipline
for the AI era?
Figure 0-1 Evolution of the Three Marketing Eras
Figure 0-1
Evolution of the
Three Marketing Eras
The three eras of marketing,
showing the expansion of
Marketing Audiences
Figure 0-2 The Seven Laws of IDAEO: Overview Map
Figure 0-2
The Seven Laws of
IDAEO: Overview Map
The Seven Laws of IDAEO are
the core of the entire book
Figure 0-3 Panorama of the IDAEO Theory Framework: Five Dimensions and Nine Elements
Figure 0-3
Panorama of the
IDAEO Theory
Framework: Five
Dimensions and Nine
Elements
The Five Dimensions of IDAEO
Marketing are the framework
See also:
Figure 2-2 · Figure 5-2 · Figure 7-1
How to Read This Map
1
Start at the roof: Figures 0-1, 0-2, 0-3
2
Figure 0-3 expands down into the five pillars
3
Read each pillar from top to bottom
4
The foundation is measurement sources and field results
5
Drag to move; zoom for detail
6
Click any figure to see which figures it links to
Go to Chapter 8 of the textbook
AI Visibility
and Theory
Back into the full journey, matched to theory
Where in the journey
does AI Visibility
sit, and what is the
theoretical basis of
IDAEO?
Figure 8-1 AI Visibility Positioning Map: Where Visibility Sits in the Full Journey
Figure 8-1
AI Visibility
Positioning Map:
Where Visibility
Sits in the Full
Journey
AI Visibility metrics are set
against the full journey,
stressing that the
fundamentals are S1–S4
Figure 8-2 IDAEO Theoretical Foundations Map
Figure 8-2
IDAEO Theoretical
Foundations Map
17 existing theories are
arranged by the five
dimensions, and each cell
lists the source and the
application
See also:
Figure 2-4 · Figure 6-6
Go to Chapter 1 of the textbook
1
Audience Strategy
For whom
What do the different Marketing
Audiences come to do, and which
Optimization Domains do they map to?
Figure 1-1 Panorama of the Five Marketing Audiences: Functions, Optimization Domains, and Measured Results
Figure 1-1
Panorama of the Five
Marketing Audiences:
Functions,
Optimization
Domains, and
Measured Results
The functions, Optimization
Domains, and measured results
of the Five Marketing
Audiences, shown together
See also:
Figure 1-3 · Figure 1-4 · Figure 2-1 · Figure 5-1 · Figure 7-3
Figure 1-2 AI Traffic Classification Tree: How Do You Tell Which Type of AI Traffic an AI Request Belongs To?
Figure 1-2
AI Traffic
Classification Tree:
How Do You Tell
Which Type of AI
Traffic an AI
Request Belongs To?
How do you decide which
category of AI traffic an AI
request belongs to?
Figure 1-3 AI Platforms and Crawlers Family Tree: Which Companies and Which Crawlers Sit Under Each Function
Figure 1-3
AI Platforms and
Crawlers Family
Tree: Which
Companies and Which
Crawlers Sit Under
Each Function
The AI Platforms and crawlers
that correspond to each
function
See also:
Figure 1-1
Figure 1-4 Who Runs Which Crawlers: Major AI Platforms’ Crawlers and Their Journey Positions
Figure 1-4
Who Runs Which
Crawlers: Major AI
Platforms’ Crawlers
and Their Journey
Positions
Answers “What kinds of AI
Crawlers are there?”: which
crawlers each of the five
major AI Platforms runs, and
which journey step each sits
at
See also:
Figure 1-1
Go to Chapter 2 of the textbook
2
Journey Design
Which steps to take
When customers find a merchant through AI, how do
the four phases and ten steps connect?
Figure 2-1 AI Customer Journey Map: Four Phases, Ten Steps
Figure 2-1
AI Customer Journey
Map: Four Phases,
Ten Steps
AI Customer Journey: Four
Phases, Ten Steps, mapped
against Marketing Audiences
and Journey Diagnostics
See also:
Figure 1-1 · Figure 5-1
Figure 2-3 Five Marketing Audiences Journey Swimlane Diagram
Figure 2-3
Five Marketing
Audiences Journey
Swimlane Diagram
How the Five Marketing
Audiences move through S1 to
S10
Figure 2-2 Journey Diagnostics Gate Map: Ten Steps, Ten Gates
Figure 2-2
Journey Diagnostics
Gate Map: Ten Steps,
Ten Gates
Ten steps and Ten Diagnostic
Gates: one gate per step,
each with a diagnostic
question
See also:
Figure 0-3 · Figure 5-2
Figure 2-4 Journey Coverage Map of AI Platforms: The Furthest Step Each Has Reached
Figure 2-4
Journey Coverage Map
of AI Platforms: The
Furthest Step Each
Has Reached
Using S1–S10 as a common
scale, compare how far each
company goes and its farthest
step
See also:
Figure 8-2
Go to Chapter 4 of the textbook
3
Trust and Verification
Why trust it
How does a
merchant's claim
become a
credible fact
that AI can
cite?
Figure 4-1 IDAEO Verification Model: Three-Party Verification and Credible Facts
Figure 4-1
IDAEO Verification
Model: Three-Party
Verification and
Credible Facts
The IDAEO Verification Model
combines Official Source,
First-Party Source, and
Third-Party Source evidence
to support credible facts
Go to Chapter 5 of the textbook
4
Service Tiers
To which tier
Which journey steps
does each service
tier cover, and how
do you choose
Performance Metrics?
Figure 5-1 Service Tier Ladder: AEO Exposure, AEO+ Word of Mouth, IDA Brand PR
Figure 5-1
Service Tier Ladder:
AEO Exposure, AEO+
Word of Mouth, IDA
Brand PR
Service tiers, strategy, and
verification mapped to the
ten steps
See also:
Figure 1-1 · Figure 2-1
Figure 5-2 Performance Metrics Mapping Matrix
Figure 5-2
Performance Metrics
Mapping Matrix
Nine Performance Metrics
mapped to journey steps,
diagnostic questions, IDAEO
Solutions, and data
See also:
Figure 0-3 · Figure 2-2
Go to Chapter 6 of the textbook
5
Performance Measurement
How results are counted
What results do AI impressions, AI Content Use, and site visits each represent?
Figure 6-1 Full-Journey Performance Dashboard: Eight Measured Figures and First-Party Data
Figure 6-1
Full-Journey
Performance
Dashboard: Eight
Measured Figures and
First-Party Data
Three categories of
performance metrics, each
matched to what it measures
See also:
Figure 3-1 · Figure 6-6
Figure 6-4 Full-Journey Outcomes Flow Map: From AI Exposure to Customer Visits
Figure 6-4
Full-Journey
Outcomes Flow Map:
From AI Exposure to
Customer Visits
Site-wide traffic split by AI
Platforms and purpose, ending
in Organic Traffic
Figure 6-6 Cold Start Timeline: AI Platforms Arriving in Sequence and the Early-Mover Dividend
Figure 6-6
Cold Start Timeline:
AI Platforms
Arriving in Sequence
and the Early-Mover
Dividend
Cold start and early
dividends: the rhythm in
which AI platforms arrive one
after another
See also:
Figure 6-1 · Figure 8-2
Figure 6-2 Ten-Step Results Overview
Figure 6-2
Ten-Step Results
Overview
Overview of the ten steps,
showing the measured results
of each step
Figure 6-5 Journey Results Comparison of Nine AI Platforms
Figure 6-5
Journey Results
Comparison of Nine
AI Platforms
Same-scale comparison of each
platform's crawl volume and
referral results
Figure 6-3 AI Visit Structure Map: Who Comes and What They Come to Do
Figure 6-3
AI Visit Structure
Map: Who Comes and
What They Come to Do
Traffic distributed by
Marketing Audiences and
function
See also:
Figure 3-2
What the Lines Mean
Expand
One cell of the previous figure, detailed here
Extension
Extended figure named in the textbook
Next / Compare
The figure or text says to read them together
Sequence
Order within a chapter, from basic to advanced
Go to Chapter 3 of the textbook
Measurement Sources and the AI Black Box
The Data Foundation of Performance Measurement
Who provides the numbers for each step, and how is the AI black box measured?
Figure 3-1 IDAEO Measurement Service Blueprint
Figure 3-1
IDAEO Measurement
Service Blueprint
Division of measurement among
websites, crawlers, the back
end and front end of AI
platforms, and humans
See also:
Figure 6-1
Figure 3-2 Content Journey Map and the AI Black Box
Figure 3-2
Content Journey Map
and the AI Black Box
Measurement results for each
journey phase and the AI
black box
See also:
Figure 6-3
Figure 3-3 Journey Outcome Matrix Across AI Platforms
Figure 3-3
Journey Outcome
Matrix Across AI
Platforms
Summary table of records
across S1–S10 for all AI
platforms
Figure 3-10 How Do You Know AI Cites You? Measuring Both Ends of the Black Box
Figure 3-10
How Do You Know AI
Cites You? Measuring
Both Ends of the
Black Box
Answers “How do you know AI
cites you?”: citation happens
inside the AI Black Box
See also:
Figure 3-6
Figure 3-4 Google: 7,410,298 search impressions｜The platform with the most complete numbers along the whole path
Figure 3-4
Google: 7,410,298
search impressions｜
The platform with
the most complete
numbers along the
whole path
Google's crawls, search
impressions, and customer
arrivals can be compared in
sequence along the journey
See also:
Figure 3-5 · Figure 3-6
Figure 3-5 OpenAI: 10,602,782 training reads｜Nearly nine in ten reads go to training; also the platform with the most real-time retrievals
Figure 3-5
OpenAI: 10,602,782
training reads｜
Nearly nine in ten
reads go to
training; also the
platform with the
most real-time
retrievals
OpenAI accumulates
information through training
and also answers live
questions through real-time
retrieval
See also:
Figure 3-4 · Figure 3-7
Figure 3-6 Microsoft: 12,050,061 search crawler reads｜The platform that currently provides AI answer citation numbers
Figure 3-6
Microsoft:
12,050,061 search
crawler reads｜The
platform that
currently provides
AI answer citation
numbers
Microsoft's data connects
search indexing, answer
citations, and customer
arrivals
See also:
Figure 3-4 · Figure 3-10
Figure 3-7 Anthropic: 24,479,305 training requests｜Highest training volume of any platform
Figure 3-7
Anthropic:
24,479,305 training
requests｜Highest
training volume of
any platform
Anthropic's crawling focuses
on model training, showing
the scale at which content
enters training use
See also:
Figure 3-5
Figure 3-8 Apple: 24,615,429 indexing requests｜Highest indexing volume of any platform
Figure 3-8
Apple: 24,615,429
indexing requests｜
Highest indexing
volume of any
platform
Apple's crawling is
concentrated on indexing,
bringing content into the
information sources for later
use
Figure 3-9 Meta: 11,583,411 training requests｜All reads go to training
Figure 3-9
Meta: 11,583,411
training requests｜
All reads go to
training
Meta's crawl records
correspond to training use,
while customer arrivals are
read as organic traffic
Go to Chapter 7 of the textbook
IDAEO measurements
Our Own Site and Early Field-Test Merchants
How did IDAEO’s own site go from zero to one hundred million requests? What
results did the first batch of joint field-test merchants achieve?
Figure 7-1 IDAEO Growth Milestones: From Zero to One Hundred Million Requests
Figure 7-1
IDAEO Growth
Milestones: From
Zero to One Hundred
Million Requests
Eight milestones and the
cumulative curve show the
site’s track record from zero
to one hundred million
requests
See also:
Figure 0-3 · Figure 7-3
Figure 7-2 Monthly Ranking Shifts of AI Platforms
Figure 7-2
Monthly Ranking
Shifts of AI
Platforms
Monthly ranking changes in
crawl volume by platform
Figure 7-3 Early Field-Test Merchant Results: The Growth IDAEO Delivered
Figure 7-3
Early Field-Test
Merchant Results:
The Growth IDAEO
Delivered
Early field-test merchants
verify it too
See also:
Figure 1-1 · Figure 7-1
IDAEO: AI-Era Marketing, First Edition | Author: TK Lin | Publisher: 株式会社和心 (IDAEO)
Next
Expands into the five dimensions
Extension
Extension
Next
Next
Extension
Next
Links to Figure 5-1
See Figure 2-1
Extension
Data foundation
Compare cumulative volume
One per platform
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