IDAEO: Marketing in the Age of AI

After completing this course, you can assess a merchant's progress across S1–S10, its Marketing Audiences and Optimization Domains, and verify results with Performance Metrics.

First master the Seven Laws of IDAEO, the Five Dimensions of IDAEO Marketing, and the Nine Elements of the IDAEO Framework; then learn audiences, journey, measurement, verification, Service Tiers, and results; finally, look at the results and theoretical basis from IDAEO's own website and early real-world test merchants.

Chapter 0　IDAEO: AI-Era MarketingHow did TK Lin build IDAEO from real-world website measurement, and what makes up this marketing discipline for the AI era?
Chapter 1　Audience Strategy (Who to Target): Five Marketing Audiences and Six Optimization DomainsWhat do the different Marketing Audiences come to do, and which Optimization Domains do they map to?
Chapter 2　Journey Design (Which Steps to Take): AI Customer Journey: Four Phases, Ten StepsWhen customers find a merchant through AI, how do the four phases and ten steps connect?
Chapter 3　Measurement Sources and the AI Black Box: The Data Foundation of Performance MeasurementWho provides the numbers for each step, and how is the AI black box measured?
Chapter 4　Trust and Verification (Why Believe It): Credible Facts and External CorroborationHow does a merchant's claim become a credible fact that AI can cite?
Chapter 5　Service Tiers (How Far to Go): AEO Exposure, AEO+ Word of Mouth, IDA Brand PRWhich journey steps does each service tier cover, and how do you choose Performance Metrics?
Chapter 6　Performance Measurement (How to Count Results): AI Impressions and Full-Journey ValueWhat results do AI impressions, AI Content Use, and site visits each represent?
Chapter 7　IDAEO Field Results: Our Own Site and Early Field-Test MerchantsHow 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?
Chapter 8　AI Visibility and Theoretical FoundationsWhere in the journey does AI Visibility sit, and what is the theoretical basis of IDAEO?

Chapter 0

IDAEO: AI-Era Marketing

This chapter answersHow did TK Lin build IDAEO from real-world website measurement, and what makes up this marketing discipline for the AI era?

Why it comes hereRead the origin, the real-world measurement foundation, and the structural definitions first; only then can you understand how each chapter connects AI exposure to the marketing results that follow.

The Origin of IDAEO: How TK Lin Turned Real-World Measurement into a Marketing Discipline

From the Front Line of Digital Marketing to the AI Era

TK Lin is a serial entrepreneur. In 2008 he founded the social advertising agency ADPartner Asia-Pacific Digital Advertising Group and has spent eighteen years on the front line of digital marketing; in 2016 he founded the AI ChatBot company Botpartner in Paris, in 2020 he founded Washinmura in Chiba, Japan, and in 2026 he founded IDAEO. Customers have started asking AI first, so marketing must connect to AI too.

From 53 Visits on the First Day to 105.5 Million Cumulative Reads by AI and Search Engines

On 2026/3/3, TK Lin began measuring AI visits to his own website: 53 on the first day, and 635,000 cumulative requests in March. By 10/8, AI visit measurement had been running for 220 days. From 3/3 to 10/8, AI and search engines read the site 105.5 million times. Because the website is already heavily used by AI, marketing must make getting AI to understand and trust it a foundational task; he turned these real-world measurements into the foundation of a marketing discipline.

Marketing First Makes AI Understand and Trust, Then Lets AI Speak for You

Customers have shifted from searching and comparing ten links themselves to asking AI and getting one answer. TK Lin therefore extended Marketing Audiences to AI: first make AI understand and trust, then let AI speak for the brand. AI Agents are the main force of future AI traffic: AI is moving from “answering” to “acting for customers,” and site visitors are extending from crawlers that read content to AI Agents that carry out tasks. Real-world industry data shows the change in search and AI Content Use: among the data with year-earlier records, Google Search clicks in each month from June to September 2026 were only about half of the same month last year (September total: 52,926 → 25,928). Customers find answers through AI, so marketing must connect to this touchpoint too. The same batch of real-world industry data shows: from April to September, Copilot cited the content 37,793 times, while Bing Search clicks in the same period were 10,177; both numbers come from the same platform (Microsoft), and AI citations were 3.7 times the search clicks. Citations make content the basis of AI answers, and they are also a marketing result.

Seven Steps Linked into a Methodology Grounded in Real-World Measurement

TK Lin linked real-world measurement, verification, theory, process, diagnostics, solutions, and results into one system, IDAEO: starting from First-Party Data, supporting credible facts with a verification model built on big data from three regions, combining 17 academic theories and 78 further readings, and connecting to the four phases and ten steps, the ten Journey Diagnostics gates, and Service Tiers. Finally, nine Performance Metrics verify results, and eight measured figures together with First-Party Data show the results accumulated so far, which is the basis for judging journey progress.

Core, Framework, and Components Each Have Their Place

IDAEO's structural definition is: the seven laws are the core, the five dimensions are the framework, and the nine elements are the components. TK Lin connected real-world measurement and verification data to theory, then put process, diagnostics, methods, and solutions into the same marketing discipline. The laws provide principles for judgment, the dimensions arrange the work, and the elements connect the work to results, giving real-world measurement a clear framework for interpretation.

Now: Field Testing Together with Merchants and Website Owners

IDAEO has been field-tested and validated at Washinmura, with the first batch of merchants, and with the second batch of industries, and is about to expand into public field testing. Next, IDAEO will recruit more merchants and website owners to test together and study how IDAEO can deliver better results in each of their marketing scenarios. Because merchants' and website owners' First-Party Data keeps supplying practical evidence, joint field testing will push forward advanced research on theory, practice, methodology, and solutions.

This Is a Living Theory

IDAEO gains new data and field validation every month, and its theoretical and empirical foundations keep getting stronger. Interaction between customers and AI keeps changing, so the theory must test its existing judgments against new results so that its explanations of audiences, journeys, and performance keep pace with the changes.

How IDAEO Was Built: Seven Steps from Field Testing to Performance

Step	What we did	Where to see it
① Field Testing	Measure First-Party Data, accumulating 105.5 million requests, 97.46 million AI Impressions, and visitor types as the basis for marketing decisions.	See Figure 6-1, “Full-Journey Performance Dashboard,” and Chapter 1
② Verification	Build the IDAEO Verification Model on big data from Japan, Taiwan, and Hong Kong to support credible facts.	See Figure 4-1, “IDAEO Verification Model”
③ Theory	Combine 17 theories and 78 further readings so marketing design has a theoretical source.	See Figure 8-2, “IDAEO Theoretical Foundations Map,” and the references at the end of the book
④ Process	Build the four-phase, ten-step AI Customer Journey, connecting information preparation to customer return.	See Figure 2-1, “AI Customer Journey Map”
⑤ Diagnosis	Set up the ten Journey Diagnostics gates so every step has a clear acceptance check.	See Figure 2-2, “Journey Diagnostics Gate Map”
⑥ Solutions	Connect IDAEO Solutions and service tiers to the four phases and ten steps, arranging the scope of support by journey.	See Figure 5-2, “Performance Metrics Mapping Matrix,” and Figure 5-1, “Service Tiers Ladder”
⑦ Performance	Check the journey against nine Performance Metrics, paired with eight measured figures and First-Party Data, to measure marketing results.	See Figure 6-1, “Full-Journey Performance Dashboard”

Figure 0-1　Evolution of the Three Marketing Eras

Read left to right: how the way customers find businesses changes, then the Marketing Audiences, strategy, and Performance Measurement; the third column is IDAEO. The way of reaching customers changes with each era, but the core of marketing is always to let the right customers find you, trust you, and choose you.

How to readRead the three eras from left to right and compare the five characteristics from top to bottom; the rightmost column is IDAEO.

Figure 0-2　The Seven Laws of IDAEO: Overview Map

The seven laws are the core; the five dimensions are the framework. Read top to bottom: law name, one-line statement, why, and the matching dimension. The Law of Audience Adaptation says “expression varies with the audience,” because people and AI understand information differently; the Law of Identity Continuity says “the entity's identity is always consistent,” because AI's recognition of an entity builds up over the long term; the Law of Independent Verification says “verification has a cost, and conclusions follow the facts,” because credibility comes from judgment that is not swayed by the client; the Law of Fact Primacy says “credible content starts from verified facts,” because only once verified does AI use it with confidence; the Law of Claim Traceability says “every claim has a traceable basis,” because only when it can be traced does AI cite with confidence; the Law of External Corroboration says “a credible claim must have external corroboration,” because independent viewpoints complete a one-sided account; the Law of Full-Journey Outcomes says “AI Content Use at every stage counts as a result,” because customers complete their decision right in the AI answer. The bottom row maps in order to Audience Strategy (who to target), Service Tiers (how far to go), Trust and Verification (why believe it), and Performance Measurement (how to count results).

How to readRead from top to bottom: law name, one-line summary, why, and corresponding dimension; the bottom row connects to Figure 0-3, “Panorama of the IDAEO Theory Framework,” linking the core principles to the framework. All seven laws land on the ten steps—Journey Design is where the seven laws are put into practice together, so the bottom row of Figure 0-2 does not list it separately. Services stack up tier by tier on the same entity, so the Law of Identity Continuity corresponds to Service Tiers.

Figure 0-3　Panorama of the IDAEO Theory Framework: Five Dimensions and Nine Elements

The seven laws are the core, the five dimensions are the framework, and the nine elements are the components. Read from top to bottom: definition, dimensions, and elements; each cell belongs to the one above it, and the bottom row links to related content by figure number and figure name, tying principles to results.

How to readRead from top to bottom: the one-line definition, the five dimensions, and the nine elements; each cell corresponds to the dimension above it, and the bottom row marks, by figure number and figure name, where each element is expanded.

Key Points of This Chapter

Claim	Why
IDAEO is AI-Era Marketing: it links the complete journey through real-world website measurement, credible facts, and theory.	Customers have shifted from searching and comparing links to asking AI and getting an answer. Marketing must first make AI understand and trust, then let AI speak for the brand. TK Lin built this theory on real-world measurement of AI visits to his own website.
AI Impressions come first; the rest of the journey follows.	AI must read the content in S2–S4 before there is any basis, from S5 onward, for being mentioned, being cited, and customers showing up. This creates a Compounding Lead: the earlier content is read, the earlier it accumulates use and new data, and the next round is more accurate with a bigger lead. The cumulative volume in Figure 7-1 shows this growth: 635,000 at the end of March → 21.4 million at the end of June → 99.15 million at the end of September → 105.5 million cumulative by 10/8.
The seven laws are the core, the five dimensions are the framework, and the nine elements are the components.	The laws provide principles for judgment, the dimensions organize marketing work, and the elements connect the principles to results.
The Five Dimensions of IDAEO Marketing link the whole process: Audience Strategy (who it is for), Journey Design (which steps to take), Trust and Verification (why anyone should believe it), Service Tiers (which tier to deliver), and Performance Measurement (how results are counted).	Each dimension has its own task; together they cover marketing from information preparation to customer return.
The Nine Elements of the IDAEO Framework correspond to one another: Marketing Audiences, Optimization Domains, Customer Journey, Journey Diagnostics, IDAEO Solutions, Verification Principles, Service Tiers, Performance Metrics, and First-Party Data.	Real-world measurement, verification data, and theory connect through the journey, diagnostics, and solutions to results verification, forming a complete methodology.
IDAEO is a Living Theory.	Each month's new data and real-world verification test the existing claims and strengthen the theoretical and empirical foundation.

Terms

Marketing: Covers online and offline; it gets the right customers to find you, trust you, and choose you.
Online Marketing: Marketing includes online and offline; online marketing is the part that reaches customers, builds relationships, and drives their choice through online channels. IDAEO connects existing marketing work to the AI stage.
IDAEO: The AI-Era Marketing founded by TK Lin: based on real-world measurement, verification data, and theory, it links process, Journey Diagnostics, methods, solutions, and results verification. The name combines ID (Identity), IDA (Identity Data Authority) and AEO (Answer Engine Optimization); its full English name is Identity-Driven Answer Engine Optimization.
The Seven Laws of IDAEO: The core of IDAEO: the principles for judgment that integrate audience, identity, verification, facts, traceability, corroboration, and full-journey outcomes.
Five Dimensions of IDAEO Marketing: The framework of IDAEO: it organizes marketing work through Audience Strategy (who it is for), Journey Design (which steps to take), Trust and Verification (why anyone should believe it), Service Tiers (which tier to deliver), and Performance Measurement (how results are counted).
Nine Elements of the IDAEO Framework: The components of IDAEO: Marketing Audiences, Optimization Domains, Customer Journey, Journey Diagnostics, IDAEO Solutions, Verification Principles, Service Tiers, Performance Metrics, and First-Party Data.
First-Party Data: Data that IDAEO measures and accumulates directly, used to build the methodology and verify results.
Living Theory: IDAEO gains new data and new real-world verification every month, and its theoretical and empirical foundation keeps strengthening.

FAQ

Q: Why is IDAEO a marketing discipline?

A: TK Lin connected real-world website measurement, verification data, and theory into a framework that covers audiences, journey, diagnostics, solutions, and results. Customers ask AI before they choose, so marketing must understand both people and AI, and verify the results of every stage of the journey.

Q: How do the seven laws, five dimensions, and nine elements differ?

A: The seven laws are the core, the five dimensions are the framework, and the nine elements are the components. When reading, first use the laws to understand the principles for judgment, then use the dimensions to grasp the scope of work, and finally check the elements to see how each piece of work connects to results. All seven laws land on the ten steps—Journey Design is where the seven laws are put into practice together, so the bottom row of Figure 0-2 does not list it separately.

Q: Do you still need to keep doing online marketing?

A: Yes. Marketing includes online and offline, and online marketing is one part of it. IDAEO is AI-Era Marketing: it connects existing marketing work to the AI stage, because customers now ask AI before they choose.

Q: What marketing changes did TK Lin see in real-world website measurement?

A: From 3/3 to 10/8, AI and search engines read the site 105.5 million times. The website is already heavily used by AI, so marketing must make getting AI to understand and trust it a foundational task.

Q: Why must a merchant's identity information always stay consistent?

A: Consistent identity information lets AI link the content it reads each time back to the same merchant, continuously building its knowledge of and trust in the brand. This is what the Law of Identity Continuity means by “the entity's identity is always consistent.”

Self-CheckUsing the origin story, state TK Lin's real-world measurement starting point and the seven-step construction sequence; distinguish the seven laws, five dimensions, and nine elements; and explain how AI exposure supports the subsequent journey and how monthly real-world measurement keeps the theory growing.

Chapter 1

Audience Strategy (Who to Target): Five Marketing Audiences and Six Optimization Domains

This chapter answersWhat do the different Marketing Audiences come to do, and which Optimization Domains do they map to?

Why it comes hereIdentify audiences and functions first; only then can you read the journey numbers.

Figure 1-1　Panorama of the Five Marketing Audiences: Functions, Optimization Domains, and Measured Results

Read from top to bottom: Audience Strategy, Marketing Audiences, functions, Optimization Domains, number of visits, visitor types, and the top four; each name has its English below it. Optimization Domains list, in order, the abbreviation, the Chinese full name, and the English full name, with related terms for the same domain below, because the same function goes by different common names. The “makes AI Overviews” cell is led by AIO and linked to AEO: AIO focuses on search engine AI Overviews, and AEO gets AI answers to use you. For Humans, the functions list “Organic Traffic” and “Local Search” side by side; Automated Bots provide “Automated Information Services.” AEO also supports AI real-time reads, AI Agents, and Automated Information Services.

Terms from the same field are listed directly under the main name according to how they are used. AIO is linked to AEO; the industry uses AIO to mean “AI Overview Optimization” or as a general term for “AI optimization,” and both correspond to getting AI answers to use your content. In IDAEO, AIO refers to optimizing search engine AI Overviews. ASO, CRO, and SMO belong to traditional digital marketing and work alongside the Six Optimization Domains.

How to readRead top to bottom: Audience Strategy, Marketing Audiences, function, Optimization Domains, visit counts, visitor types, and the top four.

Figure 1-2　AI Traffic Classification Tree: How Do You Tell Which Type of AI Traffic an AI Request Belongs To?

How to readStart from the request at the top and read down through the classification basis and function; the end point is which of the Marketing Audiences it belongs to.

Figure 1-3　AI Platforms and Crawlers Family Tree: Which Companies and Which Crawlers Sit Under Each Function

Extended figures: Figure 1-2, “AI Traffic Classification Tree,” determines which type of AI traffic an AI request belongs to; Figure 1-3, “AI Platforms and Crawlers Family Tree,” lists the AI Platforms and crawlers under each function.

How to readRead from root to branch: function, AI Platforms, and crawlers; the same company on different branches means it takes part in different functions.

Figure 1-4　IDAEO Traffic Atlas: Five Traffic Categories, Each Family, and Every Crawler

How to readFrom left to right are the five traffic categories, with each category's total at the top of its column; within a column, rows are grouped by family, one crawler per row with its visit count, and a blank means no traffic. In the AI Crawlers column, the dot on the three-position track shows which journey step that crawler sits at. This figure follows each company's official documentation (checked 2026/10/14), which places Applebot, PetalBot, YouBot, and Kagibot under Search Engine Crawlers, the same classification used in the main text. Data period 2026/3/3–10/8; ＊ marks records counted separately.

Key Points of This Chapter

Claim	Why
The Five Marketing Audiences are Humans, Search Engine Crawlers, AI Crawlers, AI Agents, and Automated Bots.	Each type visits for a different purpose; only by measuring them separately can you see the marketing value clearly.
Classify AI traffic by function first, then look at the AI Platforms.	The same AI platform may run training, indexing, and real-time retrieval at the same time; function says more about what the content is read for.
AI Agents are the future main force of AI traffic.	AI is moving from “answering” to “acting for customers,” and site visitors are extending from crawlers that read content to AI Agents that carry out tasks. Measured AI Agent Visits have already reached 18.09 million, of which automated browsing accounts for 17.95 million (counted from 8/26), classified by behavior, and named AI Agents account for 144,000, showing the scale of automated visits.
The Six Optimization Domains are SEO, AIO, AEO, GEO, LLMO, and MEO; AIO is linked to AEO. SEO builds the search index, AEO gets AI answers to use you, AIO focuses on search engine AI Overviews, GEO builds the AI index, LLMO corresponds to copying back for training, and MEO corresponds to Local Search.	AI Overviews are also one form of AI answers using content, so AIO is subordinate to AEO; the other domains map by audience and information function.
AI and search engines combined: 105.5 million (since 3/3); Organic Traffic from Humans: 310,000 (since 8/26).	The two figures cover different periods and each shows scale. Content must be acquired before it can support later use, so AI reading is the foundation of marketing.
AI Crawlers copied content back for training 52.2 million times, built the AI index 3.99 million times, and retrieved in real time 650,000 times.	Bulk reads and question-triggered reads serve different purposes, so the counts must be read together with function.
The Optimization Domains in Figure 1-1, “Panorama of the Five Marketing Audiences,” list the abbreviation, the Chinese full name, and the English full name, with terms for the same domain listed below.	Different common names are gathered in the same cell, so IDAEO Learners can map industry terms back to the same function.

Terms

Five Marketing Audiences: Humans, Search Engine Crawlers, AI Crawlers, AI Agents, and Automated Bots.
Functions: The work each of the Marketing Audiences performs, such as copying back for training, indexing, and real-time retrieval.
AI Agent: AI that acts for customers.
Automated Information Services: Services such as information organization, analysis, and web page previews provided by Automated Bots.
Organic Traffic: The number of times customers click through to the website from search or AI.
Local Search: Customers looking for merchants or services by location and nearby needs.
AIO: AIO optimizes for search engine AI Overviews and is linked to AEO: both get AI answers to use your content, and AIO specifically covers the AI Overviews of search engines such as Google.
Tracking in Progress: IDAEO keeps accumulating measurement results and will publish them later.

FAQ

Q: What kinds of AI Crawlers are there?

A: There are three types by function: copying back for training, building the AI index, and real-time retrieval. Copying back for training lets models accumulate information; building the AI index brings content into the information sources used later; real-time retrieval responds to a customer's question in the moment. Look at the function first, then at which company, because the same AI Platform may do several jobs. For how to tell them apart, see Figure 1-2, “AI Traffic Classification Tree”; for every company and crawler, see Figure 1-3, “AI Platforms and Crawlers Family Tree”; for which companies appear in each of the five traffic categories and how many visits every crawler made, see Figure 1-4, “IDAEO Traffic Atlas.” What acts for customers is the AI Agent, which is counted separately from AI Crawlers.

Q: What is the difference between AI Crawlers and AI Agents?

A: AI Crawlers mainly acquire information; AI Agents mainly act for customers. Performance is measured separately: the number of times content was acquired, and the results of acting on customers' behalf.

Q: Why are AI Agents the future main force of AI traffic?

A: AI is moving from providing answers to acting on customers' behalf. Customers hand searches, bookings, and other tasks to AI, so the AI that arrives at the site has to understand the information and complete the job; merchant marketing therefore has to support content use and acting on customers' behalf.

Q: Is the industry's AIO the same as IDAEO's AIO?

A: Yes, they line up. The industry uses AIO for “AI Overview Optimization” and also as a general term for “AI Optimization”; both usages sit under AEO, because both get AI answers to use your content. In this branch, IDAEO uses AIO to mean the optimization of search engine AI Overviews.

Q: How do you decide which category of AI traffic an AI request belongs to?

A: Following Figure 1-2, “AI Traffic Classification Tree,” look at what this visit does: copying back for training, indexing, real-time retrieval, or acting for customers. Function determines the category, because the same AI Platforms may perform several kinds of work.

Q: Why do the 17.95 million automated browsing visits count as AI Agents?

A: This book counts the 17.95 million automated browsing visits as AI Agents and labels them separately as automated browsing. The behavior of these programs operating a browser has the characteristics of automatically executing tasks, and classification follows the measured function; named AI Agents account for 144,000.

Q: Where are the Local Search numbers?

A: Local Search is still being tracked and will be published later; Figure 1-1, “Panorama of the Five Marketing Audiences,” currently lists Organic Traffic. Local Search and Organic Traffic use different statistical definitions, so the results are presented separately.

Q: How does AEO optimization differ from SEO?

A: SEO focuses on search engine indexing and search presentation; AEO gets AI answers to use your content, covers AIO's overview optimization, and supports AI real-time retrieval, AI Agents, and Automated Information Services. Search presentation and AI answers reach people differently, so results must be read together with function.

Q: What do GEO and LLMO each cover?

A: LLMO corresponds to “copying back for training,” and GEO corresponds to “building the AI index.” Model training and answer use are different functions, so Figure 1-1, “Panorama of the Five Marketing Audiences,” lists them separately.

Q: Where do conversion rate optimization and social media optimization go?

A: ASO, CRO, and SMO belong to traditional digital marketing and serve people. This work improves how people reach and choose, and can work alongside the Six Optimization Domains.

Q: Which kind of crawler reads each of the four ways AI mentions you?

A: ① is read by AI Crawlers for training, at S3 Train. ②③④ are read by Search Engine Crawlers and AI Crawlers, at S4 Index and S5 Ask. See Figure 2-5.

Self-CheckBased on one record of “someone asked AI, and AI immediately came to read the merchant page,” identify which of the Marketing Audiences it belongs to, its function, and which of the Optimization Domains it maps to, then explain what the merchant must support when AI acts for customers.

Chapter 2

Journey Design (Which Steps to Take): AI Customer Journey: Four Phases, Ten Steps

This chapter answersWhen customers find a merchant through AI, how do the four phases and ten steps connect?

Why it comes hereEach audience has its role; the Customer Journey links these roles into a path that can be diagnosed.

Figure 2-1　AI Customer Journey Map: Four Phases, Ten Steps

Read from top to bottom: journey phases, journey steps, the participation of the Marketing Audiences, and Journey Diagnostics; the same column is the same step, linking each role to its results.

How to readRead top to bottom: journey phase, journey step, who is acting, Journey Diagnostics. The five small cells in each step stand for the Five Marketing Audiences in order: dark green is the lead, medium green takes part, light green is slightly involved, and gray is the remaining roles.

Figure 2-2　Journey Diagnostics Gate Map: Ten Steps, Ten Gates

Below the ten steps, each has one gate and one diagnostic question; each later phase builds on the results of the one before. After S10, the next round starts again from S5. Training authorization at S3 is the merchant's choice, and real-time retrieval at S5 answers immediate questions, so each step is diagnosed by its own function.

How to readRead the journey steps left to right; below each are the gate name and diagnostic question.

Figure 2-3　Five Marketing Audiences Journey Swimlane Diagram

The steps each of the Five Marketing Audiences passes through, and the scope of measured records at each step.

How to readEach row is one of the Marketing Audiences, with S1–S10 running across; first see which steps it passes through, then the records at each step and the scope of measured tracking.

Figure 2-4　Journey Coverage Map of AI Platforms: The Furthest Step Each Has Reached

How to readRead the ten steps left to right and follow each company's arc to its end point; the column where it ends is the farthest step this figure shows for each AI platform.

Figure 2-5　4 Ways AI Mentions You + the Final Gate

How to readThe top shows the two situations and the four ways, each with its crawler type and step; the colors of the progress bar below match the top: S3 for ①, S4–S5 for ②③④, S6–S7 for ⑤.

Key Points of This Chapter

Claim	Why
AI Customer Journey: Four Phases, Ten Steps, in order: S1 Merchant Readiness, S2–S4 AI Information Acquisition, S5–S8 AI-Assisted Decision and Action, S9–S10 Customer Conversion and Re-engagement.	Preparation in the early phases supports use in the later ones, and the four phases align marketing responsibilities with results. AI Impressions come first; only then does the rest of the journey follow. S2–S4 get content read by AI, building the foundation for the mentions, citations, and customer visits from S5 onward.
Each step has a diagnostic question, verifying progress step by step from information preparation to customer re-engagement.	Each step maps to one question, so improvement work has a clear place.
In S1 the merchant prepares its information; S2–S4 are led by crawlers; S5–S7 are human–AI interaction; in S8 AI Agents act; S9–S10 are customer conversion and re-engagement.	Each step has a different lead actor and needs different support. S8 moves AI from answering to acting for customers, which is exactly why AI Agents will become the main driver of future AI traffic.
S10 loops back to S5, forming a continuous cycle.	When the customer raises a need again, the next round of AI-assisted decision-making begins.
Crawls in S2–S5, site arrivals in S8, and customer visits in S9 are measured first-party by IDAEO; indexing and search impressions come from search platforms; mentions and answers in S6–S7 are tracked by IDAEO live tests.	Each source represents a different touchpoint; the numbers only make sense when placed back in the journey. In the figure, S6 lists search result impressions (the number of times the search side lists you); the mention rate in AI answers will be published later.

Terms

S1–S10: The ten steps, in order, are S1 Prepare, S2 Crawl, S3 Train, S4 Index, S5 Ask, S6 Mention, S7 Answer, S8 Act, S9 Visit, and S10 Return.
Journey Phases: Merchant Readiness, AI Information Acquisition, AI-Assisted Decision and Action, and Customer Conversion and Re-engagement.
Ten Diagnostic Gates: Each of the ten steps has its own gate: Ready, Reachable, Learnable, Indexable, Readable, Selectable, Credible, Actionable, Visitable, Returnable.
Journey Diagnostics: Confirm progress and improvement priorities by journey step and performance metrics.

FAQ

Q: How are the four phases divided?

A: By task: Merchant Readiness, AI Information Acquisition, AI-Assisted Decision and Action, and Customer Conversion and Re-engagement. Each phase has a different lead actor and different results; dividing into phases lets you arrange support and verify progress.

Q: Who decides whether to allow S3 Train?

A: The merchant decides training permissions and configures information acquisition paths as needed. Training, indexing, and real-time retrieval each have their own function; the merchant can arrange them according to marketing goals.

Q: Is S5 led by people or by AI?

A: In S5, a person raises a need and AI then retrieves the information to answer. Both asking and content use happen at this touchpoint, so journey diagnostics look at demand and retrieval results.

Q: Why is S6 “AI mentions a few merchants”?

A: S6 is the step where a brand enters the customer's consideration set. AI picks relevant merchants within the limited space of an answer; being mentioned is the selection signal.

Q: Are the ten steps a merchant's to-do list?

A: The ten steps describe the full journey of a customer finding a merchant through AI; the merchant's focus is getting S1 ready and supporting later use. S2–S8 involve both AI and the customer, each with its own tasks.

Q: Why is the journey a cycle?

A: Customers still have new needs after making a choice; S10 Return loops back to S5 and starts the next round of AI-assisted decision-making. The customer journey theory in Figure 8-2 (IDAEO Theoretical Foundations Map) covers ongoing contact, and re-engagement is also the starting point of the next round of needs.

Q: What are the ways AI comes to mention you?

A: Roughly two situations. AI already remembers you: ① it read your public pages during training (S3). AI looks you up online right then: ② your website, ③ you on other platforms, ④ a third-party verification page (S4–S5). All four ways must finally pass S6–S7: AI names you and gets you right. See Figure 2-5.

Q: Which of the four ways should come first?

A: ② Your website is under your own control: first state clearly who you are, what you do, and why you can be trusted. ③ is written by others and ④ is confirmed by a Third-Party Source; these connect to Chapter 4, Trust and Verification.

Self-CheckA merchant has already been mentioned in S6 and next needs to confirm its business hours are correct; name the corresponding journey phase, journey step, and diagnostic question.

Chapter 3

Measurement Sources and the AI Black Box: The Data Foundation of Performance Measurement

This chapter answersWho provides the numbers for each step, and how is the AI black box measured?

Why it comes hereSort out source, scope, and unit first; only then can you make sense of the results.

Figure 3-1　IDAEO Measurement Service Blueprint

How to readRead from top to bottom, from website to person, and compare each layer's results with the scope of ongoing tracking.

Figure 3-2　Content Journey Map and the AI Black Box

How to readRead the journey phases horizontally, comparing the number cells with the live-test topics inside the black box.

Figure 3-3　Journey Outcome Matrix Across AI Platforms

How to readOne row per platform, S1 to S10 across; nodes mark published results, and the rest should be read as still being tracked. In the figure, S6 lists search result impressions (the number of times the search side lists you); the mention rate in AI answers will be published later.

Figure 3-4　Google: 7,410,298 search impressions｜The platform with the most complete numbers along the whole path

How to readRead each step from left to right; check source, period, and unit first, then compare numbers on the same basis.

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

How to readCompare with Figure 3-4 (Google): look first at retrievals and customer arrivals, then at the scope of ongoing tracking in the middle.

Figure 3-6　Microsoft: 12,050,061 search crawler reads｜The platform that currently provides AI answer citation numbers

How to readCompare with Figure 3-4 (Google), and note Microsoft's AI answer citation volume and its reporting period.

Figure 3-7　Anthropic: 24,479,305 training requests｜Highest training volume of any platform

How to readCompare with Figure 3-5 (OpenAI); read the requests arriving at the site and the platform's internal live-test topics separately.

Figure 3-8　Apple: 24,615,429 indexing requests｜Highest indexing volume of any platform

How to readLook at crawl volume first, then follow the journey to see the tracking scope of later use.

Figure 3-9　Meta: 11,583,411 training requests｜All reads go to training

How to readLook at AI crawls and customer arrivals separately, reading each result in its own unit.

Figure 3-10　How Do You Know AI Cites You? Measuring Both Ends of the Black Box

How to readOn the left are first-party records from the site and the edge: VPS access logs show site requests and paths, and Cloudflare shows AI crawler requests and AI referrals (AI real-time retrievals: 651,000). On the right are platform data and direct questions: generative AI impressions in Google Search Console (figures for this site still pending), 31,711 Copilot answer citations in Bing Webmaster Tools, and 84% from asking multiple AIs. Below, the same URL and the same period link reads, impressions, and citations into one checkable path; requests, impressions, citations, and question ratios use different units and cannot be added together. This is a method diagram; record-by-record cross-verification on the same page has not yet been completed.

Key Points of This Chapter

Claim	Why
The AI black box is the training, ranking, mentions, answers, and actions that happen inside AI platforms.	Mentions, answers, and actions in S6–S8, along with the training and ranking that follow crawls in S3–S4, are all completed inside the platform. The website records crawls and site arrivals directly; S5 real-time retrieval also has first-party numbers. In the figure, the S6 number is search-side impressions; the mention rate in AI answers will be published later.
IDAEO measures both ends of the black box, then uses platform data and periodic live tests to measure the results of content use.	Crawls before entering and site arrivals after leaving are both first-party measurements; search impressions (in the figure, S6 lists search-side impressions; the mention rate in AI answers will be published later) and Copilot's 31,700 citations come from platform data; periodically asking multiple AIs directly confirms whether you were mentioned and whether the answers were correct. Taken together, these sources connect content use inside the platform to verifiable results.
Periodic live tests of TK Lin's personal IDA show content use in answers inside the black box.	Live test of TK Lin's personal IDA: on 9/20, launch day, AI citations were 0; on 10/3, a live test of 100 questions across five AI models found that 84% of answers used this IDA as a source, ranking it first among all sources. Periodic live tests reveal content use inside the black box; this is a single-case citation result, and platform-wide numbers for AI Mention Rate and AI Answer Accuracy Rate will be published later.
Results from each source are read separately by unit.	Requests, pages, and clicks represent different events; only comparisons on the same basis are meaningful for marketing.
The platform-wide AI Citations figure published in this book is Copilot's 31,700 since 7/22.	This is the citation result provided by Copilot; the remaining platforms are still being tracked and will be published later. Label the source and period clearly so comparisons are made on the same basis.

Terms

Black Box: Mentions, answers, and actions in S6–S8, along with the training and ranking that follow crawls in S3–S4, all happen inside AI platforms; IDAEO measures results through both-end measurement and periodic live tests. In the figure, the S6 number is search-side impressions; the mention rate in AI answers will be published later.
Platform Data: Indexing, search impressions (the S6 number in the figure is the search-side figure; the mention rate in AI answers will be published later), and clicks provided by platforms such as Google and Bing, plus AI citation data provided by platforms such as Copilot.
Indexed: The result of a search platform adding pages to its index, counted in pages.
Search result impressions: The number of times a page appears in search results. The S6 cell in the figure lists this search-side figure; the mention rate in AI answers will be published later.

FAQ

Q: How do you know AI cites you?

A: Look at three directions together. First, citation counts published by platforms: the one this book has published is Copilot's 31,700 since 7/22. Second, periodically ask multiple AIs to check mentions and answer accuracy. Third, a before-and-after case comparison: on 9/20, the day TK Lin's personal IDA went live, AI citations were 0; on 10/3, a live test of 100 questions across five AI models found that 84% of answers used this IDA as a source. There is one more signal: AI real-time retrievals of 651,000, visible in first-party records, show that AI is reading the page while answering, but reading is not citing. Citation happens inside AI Platforms, so the results of content use are measured from these three directions; they are summarized in one diagram in Figure 3-10, “How do you know AI cites you?”; see also Figure 3-2, “Content Journey Map and the AI Black Box,” and Figure 3-6, “Microsoft.”

Q: Where do Google's and OpenAI's numbers come from?

A: Google's indexing, search impressions (the S6 number in the figure is the search-side figure; the mention rate in AI answers will be published later), and clicks come from the search platform; OpenAI's retrievals and customer arrivals come from first-party measurement. The data scopes differ, so check source and step first when placing them side by side.

Q: Can IDAEO open the AI black box?

A: Yes. First, locate the black box: mentions, answers, actions, and the training and ranking that follow crawls happen inside AI platforms. Next, measure crawls and site arrivals from both ends, combined with search impressions (in the figure, S6 lists search-side impressions), Copilot citations, and periodic questions to multiple AIs, to confirm mentions and answer accuracy. Finally, check results against a case: live test of TK Lin's personal IDA: on 9/20, launch day, AI citations were 0; on 10/3, a live test of 100 questions across five AI models found that 84% of answers used this IDA as a source, ranking it first among all sources. This evidence measures AI content use results; platform-wide numbers for AI Mention Rate and AI Answer Accuracy Rate will be published later.

Q: Who is counted in this chapter's numbers?

A: Platform results are shown as IDAEO platform-wide totals, while TK Lin's personal IDA is an answer live test of a single piece of content. The subjects and measurement methods differ: the single-case citation ratio describes that one test, while platform-wide metrics are read from published data.

Q: Bing has indexed 375,000 pages and Google 282,000. How should you compare them?

A: Look at the two separately; each represents that platform's indexing result. The same page gets indexed by both, so adding them together double-counts.

Self-CheckIdentify the Black Box: S6–S8, and what happens after the S3–S4 crawls; explain what evidence first-party measurement at both ends, platform data, and periodic live tests each provide, then use TK Lin's personal IDA to explain the difference between an individual citation result and all-platform metrics. When reading S6 on the figure, make clear that the number is what the search side displays; the mention rate in AI answers will be published later.

Chapter 4

Trust and Verification (Why Believe It): Credible Facts and External Corroboration

This chapter answersHow does a merchant's claim become a credible fact that AI can cite?

Why it comes hereAnswer accuracy in S7 is judged by credible facts, and verification also underpins the service tiers.

Figure 4-1　IDAEO Verification Model: Three-Party Verification and Credible Facts

The IDAEO Verification Model is built on big data from Japan, Taiwan, and Hong Kong. Data is accumulated over the long term and continuously checked, serving as the basis for identifying credible facts.

How to readStart with the IDAEO Verification Model at the top, then the evidence roles of the Official Source, First-Party Source, and Third-Party Source, and finally the verification result for each fact.

See the appendix “Claim Basis Table” at the end of the book: it lists the basis type and sources for each of the book’s 57 claims.

Key Points of This Chapter

Claim	Why
IDAEO uses credible facts and external corroboration to turn merchant claims into a basis AI can cite.	The IDAEO Verification Model is built on big data from Japan, Taiwan, and Hong Kong, accumulating data over the long term and checking continuously to confirm the credibility of each individual fact.
Law of Fact Primacy: credible content starts from verified facts.	Every claim rests on verified facts; only once verified does AI use it with confidence.
Law of Claim Traceability: every claim has a traceable basis.	Only when it is traceable can AI cite it with confidence.
Law of External Corroboration: a credible claim must have external corroboration.	Independent viewpoints complete a one-sided account.
Verify facts one by one, and keep confirming they are still current.	AI cites specific content, so when information is updated it needs a matching credible basis.

Terms

Triangulated Verification: The principle of combining Official Source, First-Party Source, and Third-Party Source evidence to form a credible judgment.
Official Source: Information provided by the public sector, such as government public registrations.
First-Party Source: A statement by a merchant or its owner about their own information.
Third-Party Source: Public information provided by independent external sources.
Verification Principles: Build credible claims on verified facts, a traceable basis, and external corroboration.
Re-verification: The professional work of re-confirming that a fact is still valid, based on the information's timeliness.

FAQ

Q: What do the Official Source, First-Party Source, and Third-Party Source each provide?

A: The Official Source provides public registrations, the First-Party Source states its own information, and the Third-Party Source provides external corroboration. The sources have different viewpoints; read together, the facts are more complete.

Q: Why does verification have a barrier to entry?

A: Verification is a massive, resource-intensive job with real scale and technical barriers. That is because the IDAEO Verification Model is built on big data from Japan, Taiwan, and Hong Kong accumulated over the long term, and it also requires continuous checking and re-verification on expiry so that every conclusion has a reliable basis.

Q: Why must verification go down to individual facts?

A: Each fact has its own verification result. AI cites individual details such as phone numbers, addresses, and business hours; each has a different basis and timeliness, so each must be verified separately.

Q: How do verification results stay valid?

A: Through continuous checking and re-verification on expiry. Brand and service information changes, so credible judgments must keep up with the facts.

Q: How does verification relate to S7?

A: Verification provides the benchmark for judging answer accuracy in S7. The details in AI answers must match verified facts to build credible brand perception.

Q: Where does a third-party verification page sit among the four ways AI mentions you?

A: It is way ④: when AI looks you up online, it reads facts someone has verified for you. Your website is the First-Party Source; a verification page from a Third-Party Source adds outside corroboration to the facts. See Figure 2-5.

Self-CheckPick one merchant claim and explain how First-Party Source information, external corroboration, and information timeliness together support a credible judgment.

Chapter 5

Service Tiers (How Far to Go): AEO Exposure, AEO+ Word of Mouth, IDA Brand PR

This chapter answersWhich journey steps does each service tier cover, and how do you choose Performance Metrics?

Why it comes hereUnderstand the journey and verification first, then choose the service scope by marketing goal.

Figure 5-1　Service Tier Ladder: AEO Exposure, AEO+ Word of Mouth, IDA Brand PR

AEO Exposure covers S1–S4, AEO+ Word of Mouth covers S5–S6, and IDA Brand PR covers S7–S10; each higher service tier includes the previous tier, because later results build on the earlier foundation. For the steps it covers, each tier lists the number of Journey Diagnostics and IDAEO Solutions items (the Ten Diagnostic Gates are one per step; diagnostic items are the checks under each gate). IDA Brand PR covers the full journey (S1–S10), and the bottom row shows, in order, First-Party Source + Official Source, IDA verification, and IDAEO verification. AEO+ Word of Mouth gets AI to mention and recommend you in its answers, and IDA Brand PR includes regularly published AI Visibility results. The AEO optimization in Figure 1-1, “Panorama of the Five Marketing Audiences,” is divided by function; the service tiers in this figure are divided by journey scope.

How to readStart with the step ranges of AEO Exposure, AEO+ Word of Mouth, and IDA Brand PR; IDA Brand PR fully covers S1–S10. Then read down to the results and verification names for each segment.

Figure 5-2　Performance Metrics Mapping Matrix

Each of the nine Performance Metrics lists its journey steps, diagnostic question, IDAEO Solutions, and data. AEO Exposure maps to S1–S4 and builds an information base AI can use; AEO+ Word of Mouth maps to S5–S6 and drives AI mentions; IDA Brand PR maps to S7–S10 and supports credible answers, acting for customers, customer visits, and returns. Five metrics already have published all-platform figures, and the AI Readiness Score is scored merchant by merchant; the measurement methods for AI Mention Rate, AI Answer Accuracy Rate, and AI Return Visits are set, and the data will be published later.

How to readOne metric per column; read down for the step, question, IDAEO Solutions, and data. Read each result according to its publication status.

Key Points of This Chapter

Claim	Why
Service tiers build on the earlier foundations: AEO Exposure maps to S1–S4, AEO+ Word of Mouth to S5–S6, and IDA Brand PR focuses on S7–S10 and includes the first two tiers, so the full journey S1–S10 is completely covered.	AI Impressions come first; the rest of the journey follows. AEO Exposure turns the crawls in S2–S4 into accumulated AI Impressions; later mentions build word of mouth, and accurate answers and actions support brand choice.
Each higher service tier includes the tier below it.	Word of mouth builds on exposure, brand PR builds on word of mouth, and the service extends step by step across the whole journey.
The nine Performance Metrics are AI Readiness Score, AI Crawl Volume, AI Real-Time Retrievals, AI Mention Rate, AI Answer Accuracy Rate, AI Citations, AI Agent Visits, Search & AI Referral Visits, and AI Return Visits.	Each metric maps to a different result, linking service goals to acceptance criteria.
AEO+ Word of Mouth helps merchants stay on top of AI mentions; IDA Brand PR includes the AI Visibility results IDAEO publishes periodically.	The service expands with brand goals, and everything from first contact to choice can be measured.

Terms

AEO Exposure: The service tier covering S1–S4 that builds exposure through information readiness and AI crawling.
AEO+ Word of Mouth: The service tier covering S5–S6 that builds word of mouth by having AI mention and recommend you in its answers. Word of mouth in the AI era = AI speaking for you (AI Word of Mouth).
IDA Brand PR: Focuses on S7–S10 and includes the first two tiers, so the full journey S1–S10 is completely covered. The service tier that supports brand choice through credible answers, acting for customers, and customer return.
AI Readiness Score: How well prepared a merchant is at S1, scored 0–100.
AI Mention Rate: The share of the same set of questions in which AI mentions the merchant.
Performance Metrics: The nine measures that correspond to the results of each journey step.
AI Citations: The number of times AI answers list your content as a source.

FAQ

Q: Is AEO (the optimization domain) in Chapter 1 the same as the service tiers here?

A: “AEO” as an optimization domain is one of the Six Optimization Domains; “AEO Exposure” is the first service tier and, together with “AEO+ Word of Mouth” and “IDA Brand PR,” makes up the three tiers. Optimization Domains are divided by function; service tiers are divided by journey scope.

Q: Do all nine metrics have data yet?

A: Five metrics already have published all-platform figures, and AI Readiness Score is scored store by store; the measurement methods for AI Mention Rate, AI Answer Accuracy Rate, and AI Return Visits are set, and their data will be published later. The statistical scopes differ, so read per-store scores and all-platform results each on their own basis.

Q: Which tier should you start planning from to cover the journey most completely?

A: Start from IDA Brand PR: it focuses on S7–S10, includes the first two tiers, and fully covers the whole journey S1–S10; later results build on the earlier foundations. When planning, link AEO Exposure, AEO+ Word of Mouth, and IDA Brand PR, and use Figure 2-2, “Journey Diagnostics Gate Map,” to confirm how each step connects.

Q: How do service tiers extend with brand goals?

A: Arrange them by the brand's current goal: build AI exposure, build word of mouth from AI mentions, or extend to credible answers, acting for customers, and customer return. The goal sets the journey scope, and service tiers and Performance Metrics must be planned together.

Q: Which of the nine metrics should you look at first?

A: Start with the metric that matches the journey step you are evaluating now. Information preparation, AI mentions, and customer visits each have their own results, so choosing the right metric is what lets you judge progress.

Self-CheckA store wants AI to mention its brand in answers. Name the matching service tier, Performance Metrics, and journey step.

Chapter 6

Performance Measurement (How to Count Results): AI Impressions and Full-Journey Value

This chapter answersWhat results do AI impressions, AI Content Use, and site visits each represent?

Why it comes hereOnly by matching each step's value with its statistical basis can you read the numbers as marketing results.

Figure 6-1　Full-Journey Performance Dashboard: Eight Measured Figures and First-Party Data

Each cell lists AI results by step; the bottom row is IDAEO First-Party Data (as of 2026/10/8; indexed page counts are for September). The marketing meaning of AI Crawl Volume is AI Impressions. “Reads for AI Answers,” AI Crawl Volume, and AI Real-Time Retrievals cover overlapping ranges; each cell is listed separately by source and unit (requests, times, pages, clicks), and is suited to comparison on the same basis.

How to readRead down: Performance Metrics, the three categories, measurement targets, and figures; each cell is one kind of result.

Figure 6-2　Ten-Step Results Overview

How to readRead the ten steps from left to right; the thicker the bar, the larger the volume. The gray area is inside the AI Platforms, and measured results keep accumulating. S6 on the figure is search result display (the number of times the search side lists you); the mention rate in AI answers will be published later.

Figure 6-3　AI Visit Structure Map: Who Comes and What They Come to Do

Extended figures: Figure 6-3, “AI Visit Structure Map,” shows who comes and what they come to do; Figure 6-4, “Full-Journey Outcomes Flow Map,” lays out arrivals, indexing, display, citations, and visits along S1–S10.

How to readFollow the bands: Marketing Audiences on the left, functions on the right; the thicker the band, the larger the volume.

Figure 6-4　Full-Journey Outcomes Flow Map: From AI Exposure to Customer Visits

How to readRead from left to right: total site traffic → each AI platform → purpose → Organic Traffic; the thicker the band, the larger the volume, and purposes are ordered by journey step.

Figure 6-5　Journey Results Comparison of Nine AI Platforms

How to readOne platform per panel; read each step's results from left to right. Compare across panels using the same column and the same unit.

Figure 6-6　Cold Start Timeline: AI Platforms Arriving in Sequence and the Early-Mover Dividend

How to readEach row is one AI platform, with time on the horizontal axis; 30–35 days is the gap between the first crawler and the next from the same AI platform. Compare with the cumulative volumes in Figure 7-1, “IDAEO Growth Milestones,” to see how a Cold Start accumulates AI Impressions. AI traffic has an early-mover dividend: the earlier you are ready, the earlier you accumulate.

Key Points of This Chapter

Claim	Why
AI Content Use at every stage counts as a result.	Customers make their decisions inside AI answers, so S1–S8 and website visits each carry marketing value. Measured industry data backs this up: from April to September, Copilot cited the content 37,793 times, while Bing search clicks over the same period were 10,177. Both numbers come from the same platform (Microsoft); AI citations were 3.7 times search clicks.
In marketing terms, an AI Crawl Volume of 97.46 million means that many AI Impressions.	Each time AI or a search engine reads your content is one AI impression; AI Impressions are the marketing meaning of AI Crawl Volume. AI Impressions come first; the rest of the journey follows. Being read is the basis for later mentions and citations. This is exposure to AI—each time AI reads you counts as one, not how many people saw it.
Reads for AI Answers, 46.05 million, are the reads that serve search and AI answers.	These reads all serve search results or AI answers, so presenting them together better matches their use.
AI Real-Time Retrievals, 650,000, are content use triggered by immediate demand.	AI fetches information on the spot in response to a question; this metric reflects customers' immediate needs.
AI Citations, 31,700, mean the content became the basis of answers.	This is Copilot's citation result since 7/22; once content is listed as a source, brand information enters the customer's basis for judgment.
AI Agent Visits, 18.09 million, represent the scale of traffic moving from reading content to acting on customers' behalf.	AI Agents are the main force of future AI traffic, because AI is moving from answering to acting for customers. Of this, automated browsing accounts for 17.95 million (counted from 8/26), classified by behavioral signature, and named AI Agents for 144,000; visit volume shows the scale of contact, while task outcomes are read from subsequent actions.
Search & AI Referral Visits, 310,000 (search and AI combined; Google 268,000), represent customers arriving at the site via search or AI.	This metric corresponds to S9, linking upstream AI Content Use with customer action.
Cold Start: AI traffic accumulates from 0; the industry rule of thumb is about 90 days.	A new website has to wait for AI platforms to find it one by one; only with S1 ready can it start accumulating AI Impressions when crawlers arrive. Figure 7-1 “IDAEO Growth Milestones” shows the monthly volume growing all the way: 53 requests on the first day, 3/3 → 635,000 in March → 16.07 million in June → 35.99 million requests in August.
Warm Start: content meets S1 and goes live on an AI Traffic Hub. Rule of thumb: about 30 days; measured, the next crawler from the same AI platform arrived 30–35 days later (Figure 6-6); TK Lin's personal IDA case reached S7 (Answer) in 14 days.	Content plugs into the hub's existing AI traffic the moment it goes live, so it gets used sooner. TK Lin's personal IDA, measured: on launch day, 9/20, AI citations were 0; on 10/3, across 100 questions and five AI models, 84% of answers used this IDA as a source, and it ranked first among all sources.
The necessary condition for an AI Traffic Hub is accumulating ten million or more AI requests per month.	A large base of existing requests lets new content connect to AI traffic as soon as it goes live. The IDAEO platform (idaeo.ai) is an AI Traffic Hub. The IDAEO platform: 25.97 million in July, 35.99 million in August, 15.79 million in September. The monthly totals show the traffic base that can support a Warm Start.
AI traffic has an early-mover dividend: the sooner you are ready, the sooner you accumulate.	When a merchant has its information ready in advance, AI platforms can start reading as soon as they arrive; Figure 6-6 “Cold Start Timeline” records that for five of the six AI platforms, the gap between the first crawler and the next was 30–35 days (a lower bound for three of them). The earlier you go live, the earlier you connect to this accumulation of platforms arriving one after another.
Accumulate AI impressions now, and the lead widens with every round of use—this is the Compounding Lead.	Content that has been read enters AI training and indexes, making it easier to use later; each use leaves new data, which makes the next round of optimization more accurate and brings further accumulation. The cumulative volume in Figure 7-1 “IDAEO Growth Milestones”: 635,000 by the end of March → 21.4 million by the end of June → 99.15 million by the end of September → 105.5 million cumulative as of 10/8, showing results that build over time: content that started early keeps accumulating, and the volume latecomers must catch up on grows with it.

Cold Start and Warm Start

	Cold Start	Warm Start
Starting point	AI traffic accumulates from 0.	Content plugs into the existing traffic of an AI Traffic Hub.
Prerequisites	S1 data is complete, so AI that arrives can get the content.	Content meets S1; the hub accumulates tens of millions of AI requests every month.
How long	Industry rule of thumb: about 90 days.	Rule of thumb: about 30 days; TK Lin’s personal case reached S7 in 14 days.
Measured results	3/3, day one: 53 requests → 635,000 cumulative by end of March → 21.4 million by end of June → 99.15 million by end of September.	TK Lin’s personal IDA: launched 9/20; measured on 10/3, 84% of AI answers used it as a source.
What merchants do	Get S1 ready and go live as early as possible; keep accumulating AI impressions.	Publish content that meets S1 on an AI Traffic Hub.
Effect of time	The later you start, the larger the accumulated volume you have to catch up on.	Stands on the existing accumulation from the moment it goes live.

Terms

AI Content Use: Use of content in AI reading, answer citations, and acting on customers' behalf.
AI Crawl Volume: The number of times AI and search engines obtain content in S2–S4: crawled for training, AI indexing, and search indexing, totaling 97.46 million. Add real-time retrievals of 650,000, named AI Agents of 144,000, and Automated Bots of 7.23 million, and you get the 105.5 million reads by AI and search engines.
AI Impressions: Each time AI or a search engine reads your content is one AI impression; AI Impressions are the marketing meaning of AI Crawl Volume. This is exposure to AI—each time AI reads you counts as one, not how many people saw it.
Reads for AI Answers: The number of reads that serve search results and AI answers.
Cold Start: AI traffic accumulates from 0; the industry rule of thumb is about 90 days.
Warm Start: Content meets S1 and goes live on an AI Traffic Hub. Rule of thumb: about 30 days; TK Lin's personal IDA case reached S7 (Answer) in 14 days.
AI Traffic Hub: A platform that already has a large volume of AI requests; content connects to existing AI traffic as soon as it goes live. Necessary condition: ten million or more AI requests accumulated per month. The IDAEO platform (idaeo.ai) is an AI Traffic Hub.
Search & AI Referral Visits: This book measures it as the number of times customers click through to the website via search or AI.
Compounding Lead: Compounding Lead. Accumulated AI impressions bring subsequent use; use leaves new data, which makes the next round of optimization more accurate and generates further accumulation, so an early start's lead widens over time.

FAQ

Q: What is the difference between the 52.2 million reads crawled for training and real-time retrievals?

A: Training reads let the model accumulate information; real-time retrievals respond to a customer's question at that moment. Their triggers and uses differ, so reading them separately is what makes the value of long-term accumulation and immediate demand clear.

Q: Does 97.46 million mean more than ninety million people saw it?

A: The 97.46 million is AI Impressions; the unit is reads, counting the number of times AI and search engines read the content. Each read puts the content into AI's information sources for later mentions and citations. This is exposure to AI—each time AI reads you counts as one, not how many people saw it.

Q: Why are reads for search and for AI answers counted together?

A: “Reads for AI Answers” totals the reads that serve search results and AI answers, showing the scale at which content is used. Compare it with answer citations and customer visits to see the downstream results.

Q: What do AI Citations of 31,700 mean?

A: It is the citation result since 7/22 from Copilot, the platform that has published its figures; figures from other platforms will be published later. Because each platform discloses by its own scope and period, citation volume has to be read together with its source.

Q: How long does a new website wait for AI?

A: A Warm Start takes about 30 days as a rule of thumb (measured: the next crawler from the same AI platform arrived 30–35 days later); a Cold Start takes about 90 days by industry rule of thumb; TK Lin's personal IDA case reached S7 in 14 days (live on 9/20; measured on 10/3 across 100 questions and five AI models, 84% of answers used it as a source). A Cold Start accumulates from 0, waiting for AI platforms to arrive one after another; a Warm Start connects to an AI Traffic Hub's existing reads, and the time depends on the traffic base at launch.

Q: What kind of platform counts as an AI Traffic Hub?

A: The necessary condition is ten million or more AI requests accumulated per month; the IDAEO platform (idaeo.ai) is an AI Traffic Hub. The IDAEO platform: 25.97 million in July, 35.99 million in August, 15.79 million in September. Because a large existing volume of reads lets new content connect to AI traffic, judge by the monthly totals.

Q: How do you keep performance assessment close to business results?

A: Look at impressions, mentions, trusted answers, and S9 Visit together, and watch for returns. Goodhart's Law in Figure 8-2 “IDAEO Theoretical Foundations Map” states that once a single metric becomes the target, it gets distorted; only by cross-referencing multiple results do you get a complete judgment.

Q: What can a merchant do first during a Cold Start?

A: Get S1 ready first and go live as early as possible, so every AI visit builds knowledge of the brand. Content that has been read is easier to use again, and the new data each use leaves makes the next round more accurate—this is the Compounding Lead; the earlier you start, the earlier you build the base for the next round of growth. You can also put the prepared content live on an AI Traffic Hub to tap into its existing accumulation. The cumulative volume in Figure 7-1: 635,000 by the end of March → 21.4 million by the end of June → 99.15 million by the end of September → 105.5 million cumulative as of 10/8, showing the base that an early start keeps building.

Self-CheckWhich journey steps do the eight measured figures in Figure 6-1 “Full-Journey Performance Dashboard” correspond to? Explain the marketing meaning of AI Impressions, AI Citations, and Search & AI Referral Visits; compare the starting point, prerequisites, and time required for a Cold Start and a Warm Start, and explain why AI Agent Visits are tracked.

Chapter 7

IDAEO Field Results: Our Own Site and Early Field-Test Merchants

This chapter answersHow 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?

Why it comes hereOur own site and early field-test merchants validate IDAEO together. Start with the cumulative results in Figure 7-1 “IDAEO Growth Milestones”, then look at the field results for platform merchant pages and merchant websites. First-party data from different settings shows the AI impressions, search contact, and customer visits of small and medium-sized businesses.

Figure 7-1　IDAEO Growth Milestones: From Zero to One Hundred Million Requests

How to readRead the title, approach, and result of each milestone in order from 1–8, then compare against the cumulative curve. 5/20–8/23 is the stage in which IDAEO methodology validation took shape; from 8/24 on, counting uses platform-verified AI identities, which identifies them more precisely. The corresponding theory is noted under each card.

Figure 7-2　Monthly Ranking Shifts of AI Platforms

How to readEach row is one rank; follow the same-colored line to see each platform’s monthly rank, then read it together with the crawl volume for the same period.

Figure 7-3　Early Field-Test Merchant Results: The Growth IDAEO Delivered

How to readRead from top to bottom: first the Google search clicks for IDAEO platform merchant pages, then the cumulative number of times AI brought customers into merchant pages, to see the growth IDAEO delivered; next, the growth results of the first batch of merchant websites after joining the IDAEO field test; then the search clicks, AI crawls, and bookings brought by AI for our own Washinmura website (hotel and tourism industry); and finally the AI impressions, the platforms that came to read, and their purposes for the IDA pages of the second batch of field-test industries. Read each figure according to the labeled subject, period, and purpose; the figures for the first batch of merchant websites are site-wide opens.

Key Points of This Chapter

Claim	Why
IDAEO is field-tested not only on the IDAEO site itself and Washinmura; early field-test merchants validate it too.	Platform merchant pages, merchant websites, and the second batch of field-test industries provide first-party data from different settings, so the actual results of the same theory can be compared across industries. Washinmura is the first field-test site for the IDAEO methodology. Our own Washinmura website (www.washinmura.jp, hotel and tourism industry) received 109,140 Google search clicks (3/3–10/8); of the 95,333 crawls by AI and search engines (8/20–10/8), 11,333 were real-time retrievals, with AI reading the site on the spot when someone asked it, about 1 in every 8 crawls (11.9%). Washinmura has already received bookings brought by AI; booking data will be published later. Because questions trigger AI to read the website, and AI has already brought bookings, these results map to the journey: S2–S4 AI crawling → S5 Ask → S9 Visit (bookings brought by AI).
The IDAEO platform can also help every small and medium-sized business, so everyone rises together.	The platform connects merchant information to AI crawling, search, and customer visits. Google search clicks on merchant pages rose from 297 in March to 15,128 in September, up for seven consecutive months, with September at 5.4 times April; the first customer brought by an AI answer came on 4/15, reaching 774 cumulative by 10/8; AI impressions from 5/19 to 10/9 were about 50.67 million. Merchants can use first-party data to verify the results of each stage.
① Measurement officially launched → Law of Full-Journey Outcomes (Performance Measurement); ② Search exposure rolled out in parallel → Law of Audience Adaptation (Audience Strategy); ③ First visitor arrived via an AI answer → Journey Design: S9 Visit.	Measurement gives the results of every stage a basis, and search exposure connects content to search audiences; when an AI answer brought a customer for the first time, upstream content use connected to an actual arrival in the journey. These records give the laws and the journey results that can be checked against each other.
④ Brand knowledge presentation fully upgraded → Law of Identity Continuity (S1 Merchant Readiness); ⑤ Research-Evidence-Driven Optimization → Theoretical foundation: 17 existing theories; ⑥ New IDAEO site built → Five Dimensions and Nine Elements.	A consistent brand identity lets AI link new information back to the same entity; research supports the design, and the new site implements each dimension and element, so later content use builds on the earlier foundation.
⑦ Crawl performance hit a new peak → AI Impressions (S2–S4 AI Information Acquisition); ⑧ Cumulative requests passed one hundred million → Compounding Lead and Living Theory.	The crawl peak shows the scale at which AI acquires information; continued content use leaves new data, making the next round of optimization more precise. The cumulative results echo the Compounding Lead, and each month’s new field data also grows the theory. The cumulative curve in Figure 7-1 records this growth: 635,000 by end of March → 21.4 million by end of June → 99.15 million by end of September → 105.5 million cumulative by 10/8. A Warm Start takes about 30 days as a rule of thumb (measured: the next crawler from the same AI platform arrived 30–35 days later); a Cold Start takes about 90 days by industry rule of thumb; TK Lin’s personal IDA case reached S7 in 14 days. Warm Start content meets S1 and goes live on an AI Traffic Hub, taking over the existing accumulation.
Among the first batch of merchant websites that joined the field test and have complete records for both May and September, some have already grown after joining the IDAEO field test. Site-wide opens from May to September: a parenting content site rose from 52,817 to 55,286, a food-service equipment manufacturer from 842 to 1,275, an event staging design firm from 442 to 1,741, a leather goods brand from 555 to 908, a hair salon from 74 to 128, and a studio from 3 to 329.	Site-wide opens record the change in visits for each industry after joining the field test. With consistent subjects and periods, this first-party data is available for joint research and cross-industry comparison.
The IDA pages of the second batch of field-test industries received 373,776 AI impressions from 7/18–10/8 (the number of times they were read by AI and search engine crawlers).	10 platforms read the site; the top six were Meta 170,137, OpenAI 60,869, Anthropic 59,178, Amazon 37,218, Apple 36,744, and Microsoft 9,407. By purpose, copying for training led with 327,365, building AI indexes and search indexes 46,399, and real-time retrievals 12. The records show that the content of the second batch of field-test industries has entered AI’s information sources; search clicks and customer arrivals are still being tracked and will be published later.

Terms

Milestone: A growth node marked by methodology implementation and its corresponding results.
Research-evidence-driven optimization: Combining relevant papers with methods proven effective in field tests to improve marketing results.
Full-Methodology Site Build: Integrating audience, journey, trust and verification, and performance measurement from the very start of building the site.
Cumulative curve: A curve showing the continuous accumulation of AI and search engine requests over time.
Early field-test merchants: The earliest batch of merchants and site owners who put measurement on their own websites and ran field tests together with IDAEO.
Joint field test: Collaborative research in which merchants, site owners, and IDAEO validate the methodology together using first-party data.

FAQ

Q: What main growth line do the eight milestones show?

A: Theory says what to look at; field results provide outcomes to check against it: measurement and search exposure map to Performance Measurement and Audience Strategy, and AI bringing customers maps to customer visits; the brand knowledge upgrade, research evidence, and the new site build map to identity consistency, theoretical grounding, and a complete framework; the crawl peak and continued accumulation map to AI Information Acquisition, Compounding Lead, and Living Theory. The cumulative volume in Figure 7-1: 635,000 by end of March → 21.4 million by end of June → 99.15 million by end of September → 105.5 million cumulative by 10/8. Each month’s new data is used to test these relationships and strengthen the theoretical and empirical foundation.

Q: What does Research-Evidence-Driven Optimization mean?

A: IDAEO compiles relevant papers from the web and optimizes its own site with methods proven effective in field tests. Research provides the basis; field testing links optimization choices to real content-use results.

Q: What is distinctive about the new IDAEO site build?

A: The new site applies the full IDAEO methodology directly, bringing the Marketing Audiences and Optimization Domains from Figure 1-1, “Panorama of the Five Marketing Audiences,” into the plan. Information, trust, and results are linked from the moment the site is built, so later growth builds on the earlier results.

Q: What does “from 8/24 on” on the curve mean?

A: From 8/24 on, counting uses platform-verified AI identities, which identifies visitors more precisely. 5/20–8/23 is the stage in which IDAEO methodology validation took shape. When comparing monthly volumes across 8/24, use periods on the same basis.

Q: How are changes in platform rankings used in marketing decisions?

A: Read crawl volume and ranking for the same period together to observe how contact with each platform changes. Ranking reflects relative position; crawl volume reflects actual contact. Reading both together gives a better grasp of audience distribution.

Q: What do the early field-test merchants’ data show?

A: The data show the growth IDAEO delivered: search clicks on platform merchant pages keep rising, and AI answers bring customers to the site; the first batch of merchant websites show growth after joining the IDAEO field test. Search clicks, AI bringing customers, and site-wide opens on merchant websites each measure a different kind of contact; looking at them separately by subject and period makes the marketing meaning of each result clear and provides a basis for joint research. The IDA pages of the second batch of field-test industries received 373,776 AI impressions from 7/18 to 10/8, read by 10 platforms including Meta, OpenAI, Anthropic, Amazon, Apple, and Microsoft, showing that the content of the second batch of field-test industries has already been acquired by AI; search clicks and customer arrivals are still being tracked and will be published later.

Q: Why recruit more merchants and website owners to field-test together?

A: To study how IDAEO can work better for merchants and website owners, and to keep deepening the theory, practice, methodology, and solutions. Because First-Party Data from more merchants and website owners shows actual results across different settings, joint field testing gives these advanced studies a more complete basis.

Self-CheckUsing the different settings of the IDAEO site itself, Washinmura, and the early field-test merchants, explain what joint validation means; using the milestones and monthly rankings, describe the path from zero to one hundred million requests. Then read Figure 7-3, “Early Field-Test Merchant Results,” and interpret the numbers, periods, and marketing value for platform merchant pages, the websites of each industry, and the second batch of field-test industries, explaining how the platform helps small and medium-sized businesses improve together.

Chapter 8

AI Visibility and Theoretical Foundations

This chapter answersWhere in the journey does AI Visibility sit, and what is the theoretical basis of IDAEO?

Why it comes hereOnly by grasping the full journey and the theoretical sources can you clearly explain how the metrics relate to the methodology.

Figure 8-1　AI Visibility Positioning Map: Where Visibility Sits in the Full Journey

AI Visibility tools mainly measure S5–S7, observing how the brand appears in answers. The fundamentals of AI Visibility are S1–S4: if S1–S4 are not done well, AI Visibility will fall short of what you want. Information readiness and AI Information Acquisition are the basis for being mentioned; IDAEO links the upstream preparation with S8–S10 (acting for the customer, conversion, and return) into one full journey.

How to readRead from top to bottom: the ten steps, industry topics, industry metrics, IDAEO Journey Diagnostics, and Service Tiers; each column is the same step.

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; “Theoretical Foundations and References” at the end of the book also lists the further readings in eight categories.

How to readIn each cell, look at the author, year, and theory first, then follow the arrow to the corresponding IDAEO design.

See the appendix “Claim Basis Table” at the end of the book: it lists the basis type and sources for each of the book’s 57 claims.

Key Points of This Chapter

Claim	Why
External AI Visibility mainly corresponds to the answer phase, S5–S7.	The tools ask questions to observe whether the brand is mentioned; the focus is on AI’s answers.
The fundamentals of AI Visibility are S1–S4	Only when S1 data is complete does AI have full content to understand; the crawls in S2–S4 bring the content into the information sources for search, training, and answers, supporting later mentions and citations. If S1–S4 are not done well, AI Visibility will certainly fall short of what you want.
IDAEO connects the foundation of S1–S4 with the action, conversion, and return of S8–S10 in a single journey.	The front end supports the brand being used; the back end carries the customer’s choice. Only full-journey measurement shows the results clearly.
IDAEO’s theoretical foundations fall into eight categories: marketing fundamentals, generative engine and AI search research, AI customers and AI Agents, information retrieval and credibility, performance measurement, start-up and accumulation, official documentation of AI Platforms and agent protocols, and the theory behind the seven laws and the journey.	The eight categories of literature support audiences, journey, credible information, judging results, start-up timing, and how AI Platforms actually operate, making the basis of the framework traceable.
The figure lists 17 existing theories; see “Theoretical Foundations and References” at the end of the book for the full references.	The figure lists the design correspondences; the full references give the author, year, and source.
Each month’s new data and field verification keep strengthening IDAEO’s theoretical and empirical foundation.	New results test the relationships among audiences, journey, and outcomes, letting the Living Theory keep absorbing real-world changes.

Terms

AI Visibility: How a brand appears in AI answers; IDAEO looks at both being mentioned and answer accuracy.
Mention Rate: The share of AI answers in a set of observation questions that mention the brand.
Theoretical Foundations: The research that supports the marketing framework and measurement design.
IDAEO Learners: The general public who want to understand and apply AI-Era Marketing.

FAQ

Q: If you already have an AI Visibility tool, do you still need IDAEO?

A: Yes. AI Visibility tools observe how the brand appears in answers; IDAEO then links the front end—information preparation and AI crawling—with the back end—acting for customers and customer visits. Answers build on the content foundation and also shape later choices; only by putting them back into the full journey can you grasp the results of each stage.

Q: How deeply do IDAEO Learners need to study the theory?

A: First understand which design each theory supports, then read the original literature as needed. Theory helps with judgment and application; once you grasp the correspondences, you can go deeper.

Q: How do you compare numbers from different AI Visibility tools?

A: First align the question scope, observation period, and metric definitions, then compare on the same basis. Each tool has a different design focus, so only comparisons on the same basis have clear meaning.

Q: What are the theoretical foundations of IDAEO?

A: IDAEO combines marketing fundamentals, generative engine and AI search research, AI customers and AI Agents, information retrieval and credibility, and performance measurement. The corresponding research includes Lemon & Verhoef (2016) on the Customer Journey, Aggarwal et al. (2024) on generative engine optimization, Gal & Elkin-Koren (2017) on algorithmic consumers, Brin & Page (1998) on search engine architecture, and Dong et al. (2015) on source credibility.

Q: Which part of being mentioned by AI does AI Visibility correspond to?

A: It corresponds to the final gate, S6–S7: whether AI names you and gets you right. The four ways before it are about letting AI read you; see Figure 2-5.

Self-CheckUsing Figure 8-1, “AI Visibility Positioning Map,” explain where the answer phase sits and why S1–S4 are the fundamentals; then take one theory and explain how it supports IDAEO’s design, and explain how each month’s new data and field results grow the theory.

Standard Glossary

The whole book uses only the terms in this table. Marketing originated in the United States, so each term is fixed first in English and then in Traditional Chinese; Simplified Chinese stays close to Traditional Chinese, and Japanese follows the same line as English. The last two columns show which marketing concept each term corresponds to, and its definition.

English	Traditional Chinese	Simplified Chinese	Japanese	Marketing Concept	Definition
Marketing	行銷	营销	マーケティング	Marketing	Covers online and offline; it gets the right customers to find you, trust you, and choose you.
AI-Era Marketing	AI 時代行銷學	AI 时代营销学	AI時代のマーケティング学	Marketing	Marketing for people and AI together, in an era when customers ask AI directly.
The Seven Laws of IDAEO	IDAEO 七大定律	IDAEO 七大定律	IDAEOの七大法則	Marketing Principles	The core of IDAEO: the principles for judgment that integrate audience, identity, verification, facts, traceability, corroboration, and full-journey outcomes.
Marketing Audiences	行銷受眾	营销受众	マーケティング・オーディエンス	Target Audiences	The people and AI that marketing must reach and whose needs it must understand.
Humans	人類	人类	人（生活者）	Consumers	People who look for merchants through search, AI, or local search.
Search Engine Crawlers	搜尋引擎爬蟲	搜索引擎爬虫	検索エンジンクローラー	Search Channels	AI that builds the index and produces AI Overviews for search engines.
AI Crawlers	AI 爬蟲	AI 爬虫	AIクローラー	AI Channels	AI that copies content back for training, builds AI indexes, and retrieves in real time for AI companies.
AI Agents	AI Agent	AI Agent	AIエージェント	Proxy Decision-Makers	AI that acts for customers.
Automated Bots	機械爬蟲	机械爬虫	自動サービスボット	Information Touchpoints	AI that provides Automated Information Services such as information analysis and web page previews.
Optimization Domains	優化領域	优化领域	最適化領域	Channel Optimization	The scope of optimization, divided by Marketing Audiences and information function.
Search Engine Optimization	SEO	搜索引擎优化	検索エンジン最適化	Search Marketing	Get search engines to index you and rank you.
AI Overview Optimization	AIO	AI 概览优化	AIによる概要の最適化	Search Marketing	AIO optimizes for search engine AI Overviews and is linked to AEO: both get AI answers to use your content, and AIO specifically covers the AI Overviews of search engines such as Google.
Large Language Model Optimization	LLMO	大语言模型优化	LLM最適化	Brand Awareness	Get models to remember you during training.
Generative Engine Optimization	GEO	生成式引擎优化	生成エンジン最適化	Content Marketing	Get AI indexes to include you and AI answers to cite you.
Answer Engine Optimization	AEO	答案引擎优化	アンサーエンジン最適化	Real-Time Services	Get AI answers to use your content; covers AIO's AI Overview optimization and supports AI real-time retrieval, AI Agents, and Automated Information Services.
Map Engine Optimization (Local SEO)	MEO	地图引擎优化	MEO（マップエンジン最適化）	Local Marketing	Get customers to find you through local search.
Customer Journey	顧客旅程	顾客旅程	カスタマージャーニー	Customer Experience	The path a customer takes from forming a need and getting information to choosing and returning.
S1 Prepare	S1 店家準備好	S1 店家准备好	S1 準備	Information Preparation	The merchant's information is complete, ready for people and AI to understand the brand.
S2 Crawl	S2 爬蟲來敲門	S2 爬虫来敲门	S2 クロール	Reach	AI visits the site to get brand information.
S3 Train	S3 抄回去訓練	S3 抄回去训练	S3 学習	Brand Memory	AI takes content for model training.
S4 Index	S4 建目錄	S4 建目录	S4 インデックス	Information Indexing	Search engines and AI add the content to their indexes.
S5 Ask	S5 有人問 AI	S5 有人问 AI	S5 質問	Need Formation	A customer states a need, and AI goes on to get the information to answer it.
S6 Mention	S6 AI 點名幾家	S6 AI 点名几家	S6 言及	Consideration Set	AI mentions relevant merchants in its answer.
S7 Answer	S7 AI 回答細節	S7 AI 回答细节	S7 回答	Information Evaluation	AI answers with specific information about the merchant.
S8 Act	S8 替客人辦事	S8 替客人办事	S8 代行	Acting for Customers	AI Agents search, book, and carry out tasks for customers.
S9 Visit	S9 客人上門	S9 客人上门	S9 来訪	Customer Conversion	The customer takes action by clicking through to the website or visiting the store.
S10 Return	S10 回來再問	S10 回来再问	S10 再訪	Customer Loyalty	The customer raises a need again and returns.
Journey Diagnostics	旅程診斷	旅程诊断	ジャーニー診断	Marketing Audit	Confirm progress and improvement priorities by journey step and performance metrics.
Ten Diagnostic Gates	十道診斷關卡	十道诊断关卡	十の診断ゲート	Journey Management	Each of the ten steps has its own gate: Ready, Reachable, Learnable, Indexable, Readable, Selectable, Credible, Actionable, Visitable, Returnable.
Triangulated Verification	三角驗證	三角验证	三角検証（トライアンギュレーション）	Source Credibility	The principle of combining Official Source, First-Party Source, and Third-Party Source evidence to form a credible judgment.
Official Source	官方	官方	公的情報源	Source Credibility	Information provided by the public sector, such as government public registrations.
First-Party Source	本方	本方	当事者情報源	Brand Claims	A statement by a merchant or its owner about their own information.
Third-Party Source	第三方	第三方	第三者情報源	external corroboration	Public information provided by independent external sources.
IDAEO Service Tiers	服務分級（三級）	服务分级（三级）	サービス階層	Service Design	How far to go: choose AEO Exposure, AEO+ Word of Mouth, or IDA Brand PR by goal; each covers a different span of the journey.
AEO Exposure	AEO 曝光	AEO 曝光	AEO露出	Brand Exposure	The service tier covering S1–S4 that builds exposure through information readiness and AI crawling.
AEO+ Word of Mouth	AEO+ 口碑	AEO+ 口碑	AEO+ クチコミ	Word-of-Mouth Marketing	The service tier covering S5–S6 that builds word of mouth by having AI mention and recommend you in its answers. Word of mouth in the AI era = AI speaking for you (AI Word of Mouth).
IDA Brand PR	IDA 品牌公關	IDA 品牌公关	IDAブランドPR	Brand PR	Focuses on S7–S10 and includes the first two tiers, covering S1–S10 in full; the service tier that supports brand choice through credible answers, acting for customers, and customer return.
Performance Metrics	成效指標	成效指标	成果指標	Marketing Performance Measurement	The nine measures that correspond to the results of each journey step.
AI Readiness Score	AI 就緒分	AI 就绪分	AIレディネス・スコア	Readiness Assessment	How well prepared a merchant is at S1, scored 0–100.
AI Crawl Volume	AI 讀取量	AI 读取量	AIクロール数	AI Exposure	The number of times AI and search engines obtain content in S2–S4: crawled for training, AI indexing, and search indexing, totaling 97.46 million.
AI Real-Time Retrievals	AI 當場來讀量	AI 当场来读量	AIリアルタイム取得数	Real-Time Demand	The number of times AI retrieves content in real time in response to a customer's question.
AI Mention Rate	AI 點名率	AI 点名率	AI言及率	Brand Mentions	The share of the same set of questions in which AI mentions the merchant.
AI Answer Accuracy Rate	AI 正確率	AI 正确率	AI回答正確率	Information Quality	The share of AI answers about the merchant that match verified facts.
AI Citations	AI 引用量	AI 引用量	AI引用数	Citation Outcomes	The number of times AI answers list your content as a source.
AI Agent Visits	AI Agent 到站量	AI Agent 到站量	AIエージェント訪問数	Agent Touchpoints	The total number of website visits by automated browsing classified by behavioral signature and by named AI Agents; named AI Agents can be identified directly as visiting on customers' behalf.
Search & AI Referral Visits	AI 導客量	AI 导客量	検索・AI経由流入数	Referral Traffic	This book measures it as the number of times customers click through to the website via search or AI.
AI Return Visits	AI 回訪量	AI 回访量	AI再訪数	Customer Re-engagement	The result of the same customer asking again and visiting again.
AI Content Use	AI 取用	AI 取用	AIによるコンテンツ利用	Content Performance	Use of content in AI reading, answer citations, and acting on customers' behalf.
AI Visibility	AI 能見度	AI 能见度	AI可視性	Brand Visibility	How a brand appears in AI answers; IDAEO looks at both being mentioned and answer accuracy.
First-Party Data	第一方數據	第一方数据	ファーストパーティデータ	First-Party Data	Data that IDAEO measures and accumulates directly, used to build the methodology and verify results.
Organic Traffic	自然流量	自然流量	自然流入	Organic Reach	The number of times customers click through to the website from search or AI.
Tracking in Progress	實測追蹤中	实测跟踪中	計測中	Performance Measurement	IDAEO keeps accumulating measurement results and will publish them later.
Black Box	黑盒子	黑盒子	ブラックボックス	Decision Process	Mentions, answers, and actions in S6–S8, along with the training and ranking that follow crawls in S3–S4, all happen inside AI Platforms; IDAEO measures them through both-end measurement and periodic live tests.
Five Marketing Audiences	五類行銷受眾	五类营销受众	五つのマーケティング・オーディエンス	Target Audiences	Humans, Search Engine Crawlers, AI Crawlers, AI Agents, and Automated Bots.
Four AI Audiences	四類 AI 受眾	四类 AI 受众	四つのAIオーディエンス	AI Customers	Search Engine Crawlers, AI Crawlers, AI Agents, and Automated Bots.
Six Optimization Domains	六大優化領域	六大优化领域	六つの最適化領域	Channel Optimization	The scope of optimization that SEO, AIO, AEO, GEO, LLMO, and MEO cover together; AIO handles search engine AI Overviews, and AEO handles AI answers and real-time retrieval.
Five Dimensions of IDAEO Marketing	IDAEO 行銷五大構面	IDAEO 营销五大构面	IDAEOマーケティングの五つの構成軸	Marketing Framework	The framework of IDAEO: it organizes marketing work through Audience Strategy (who it is for), Journey Design (which steps to take), Trust and Verification (why anyone should believe it), Service Tiers (which tier to deliver), and Performance Measurement (how results are counted).
Audience Strategy	受眾策略	受众策略	オーディエンス戦略	Target Market	Who it is for: arrange information and contact according to the needs of Marketing Audiences.
Journey Design	旅程設計	旅程设计	ジャーニー設計	Customer Experience	Which steps to take: link brand contact, AI Content Use, and customer action into a journey.
Trust and Verification	信任驗證	信任验证	信頼と検証	Brand Trust	Why anyone should believe it: support brand claims with credible facts and external corroboration.
Performance Measurement	成效衡量	成效衡量	成果測定	Marketing Results	How results are counted: use the metrics matched to each step to read the value of content use and conversion.
Nine Elements of the IDAEO Framework	IDAEO 架構九大要素	IDAEO 架构九大要素	IDAEOフレームワークの九要素	Marketing Framework	The components of IDAEO: Marketing Audiences, Optimization Domains, Customer Journey, Journey Diagnostics, IDAEO Solutions, Verification Principles, Service Tiers, Performance Metrics, and First-Party Data.
IDAEO Solutions	IDAEO 解決方案	IDAEO 解决方案	IDAEOソリューション	Marketing Support	Professional services matched to the Customer Journey and marketing goals.
Verification Principles	驗證原則	验证原则	検証原則	Credibility	Build credible claims on verified facts, a traceable basis, and external corroboration.
AI Customer Journey: Four Phases, Ten Steps	AI 顧客旅程：四階段十步驟	AI 顾客旅程：四阶段十步骤	AIカスタマージャーニー：四つのフェーズと十のステップ	Customer Journey	Links S1–S10 through Merchant Readiness, AI Information Acquisition, AI-Assisted Decision and Action, and Customer Conversion and Re-engagement.
Merchant Readiness	店家就緒	店家就绪	店舗レディネス	Information Preparation	At S1, brand information is ready for people and AI to understand.
AI Information Acquisition	AI 資訊取得	AI 信息获取	AIによる情報取得	Information Touchpoints	At S2–S4, AI acquires information for search, training, and answering.
AI-Assisted Decision and Action	AI 輔助決策與行動	AI 辅助决策与行动	AIによる意思決定・行動支援	Decision Support	At S5–S8, AI responds to the need, mentions brands, answers details, and acts for customers.
Customer Conversion and Re-engagement	顧客轉換與回流	顾客转化与回流	顧客のコンバージョンと再エンゲージメント	Conversion and Relationship Management	At S9–S10, the customer takes action and engages the brand again.
AI Impressions	AI 曝光數	AI 曝光数	AIインプレッション数	Exposure Measurement	Each time AI or a search engine reads your content is one AI impression. This is exposure to AI—each time AI reads you counts as one, not how many people saw it; it is the marketing meaning of AI Crawl Volume.
Automated Information Services	自動化資訊服務	自动化信息服务	自動化情報サービス	Information Services	Services such as information organization, analysis, and web page previews provided by Automated Bots.
Local Search	在地搜尋	本地搜索	ローカル検索	Local Marketing	Customers looking for merchants or services by location and nearby needs.
IDAEO Learners	IDAEO 學習者	IDAEO 学习者	IDAEO学習者	Marketing Education	The general public who want to understand and apply AI-Era Marketing.
Law of Audience Adaptation	受眾適配定律	受众适配定律	オーディエンス適応の法則	Audience Strategy	Expression varies with the audience, because people and AI understand information differently.
Law of Identity Continuity	身分連續定律	身份连续定律	アイデンティティ連続性の法則	Brand Awareness	The entity's identity is always consistent; AI's recognition of an entity builds up over the long term.
Law of Independent Verification	驗證獨立定律	验证独立定律	独立検証の法則	Brand Trust	Verification has a cost, and conclusions follow the facts, because credibility comes from judgment that is not swayed by the client.
Law of Fact Primacy	事實先行定律	事实先行定律	事実優先の法則	Credibility	Credible content starts from verified facts; only once verified does AI use it with confidence.
Law of Claim Traceability	主張溯源定律	主张溯源定律	主張の追跡可能性の法則	Source Credibility	Every claim has a traceable basis; only when it can be traced does AI cite it with confidence.
Law of External Corroboration	外部印證定律	外部印证定律	外部裏付けの法則	external corroboration	A credible claim must have external corroboration, because independent viewpoints complete a one-sided account.
Law of Full-Journey Outcomes	全程成效定律	全程成效定律	ジャーニー全体の成果の法則	Marketing Performance	AI Content Use at every stage counts as a result, because customers complete their decision right in the AI answer.
IDAEO Verification Model	IDAEO 驗證模型	IDAEO 验证模型	IDAEO 検証モデル	Brand Trust	Built on big data from Japan, Taiwan, and Hong Kong, it combines Official Source, First-Party Source, and Third-Party Source evidence, accumulates data over the long term, keeps checking, and re-verifies on expiry, to support credible judgments on individual facts.
Cold Start	冷啟動	冷启动	コールドスタート	Traffic Accumulation	AI traffic accumulates from 0; the industry rule of thumb is about 90 days.
Warm Start	熱啟動	热启动	ウォームスタート	Traffic Accumulation	Content meets S1 and goes live on an AI Traffic Hub. Rule of thumb: about 30 days; TK Lin's personal IDA case reached S7 (Answer) in 14 days.
AI Traffic Hub	AI 流量樞紐	AI 流量枢纽	AIトラフィック・ハブ	Channels and Platforms	A platform that accumulates ten million or more AI requests per month; content connects to existing AI traffic as soon as it goes live. The IDAEO platform (idaeo.ai) is an AI Traffic Hub.
Compounding Lead	複利領先	复利领先	複利的リード	Traffic Accumulation	AI impressions accumulate and bring more content use and data; the earlier you start, the more your lead widens over time.
AI Platforms	AI 平台	AI 平台	AIプラットフォーム	Channels and Platforms	Companies that operate search engines, AI services, and crawlers, such as Google, OpenAI, Microsoft, Anthropic, Apple, and Meta.
Living Theory	活的學說	活的学说	生きている理論	Marketing	IDAEO gains new data and new real-world verification every month, and its theoretical and empirical foundation keeps strengthening.

Theoretical Foundations and References

Every design in IDAEO has a theoretical source. The top half is the full bibliography for the 17 theories listed in Figure 8-2 (19 sources); the bottom half is 78 further readings in eight categories: marketing fundamentals, generative engine and AI search research, AI customers and AI Agents, information retrieval and credibility, performance measurement, start-up and accumulation, official documentation of AI Platforms and agent protocols, and the theory behind the Seven Laws and the journey.

Theory	Source	How IDAEO Uses It
The 17 theories listed in Figure 8-2 of this book (19 sources)
Retrieval-Augmented Generation	Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems 33 (NeurIPS 2020). arXiv:2005.11401.	AI retrieves before answering → real-time retrieval
Principal–Agent	Jensen, M. C., & Meckling, W. H.（1976）。Theory of the firm: Managerial behavior, agency costs and ownership structure。Journal of Financial Economics, 3(4), 305–360。	AI Agents are the customer's agents
AI-readable standards (robots protocol)	Koster, M., Illyes, G., Zeller, H., & Sassman, L. (2022). Robots Exclusion Protocol. RFC 9309, IETF (Proposed Standard), September 2022.	AI follows rules when reading websites → let AI through
AI-readable standards (structured data vocabulary)	Schema.org. About Schema.org. Schema.org community (founding companies: Google, Microsoft, Yahoo, Yandex).	AI follows rules when reading websites → let AI through
Customer Journey (consumer decision journey)	Court, D., Elzinga, D., Mulder, S., & Vetvik, O. J. (2009). The consumer decision journey. McKinsey Quarterly, June 2009.	Consumer decisions are a loop that circles back → Ten Steps
Customer Journey (Zero Moment of Truth, ZMOT)	Lecinski, J. (2011). Winning the Zero Moment of Truth. Google (free e-book).	Customers search online before buying, and the path can move forward or turn back → Ten Steps
Attention Scarcity	Simon, H. A. (1971). Designing Organizations for an Information-Rich World. In M. Greenberger (Ed.), Computers, Communications, and the Public Interest (pp. 37–72). Baltimore: The Johns Hopkins Press.	More information makes attention scarcer → S6 AI mentions only a few at a time
Information Asymmetry	Akerlof, G. A.（1970）。The Market for "Lemons": Quality Uncertainty and the Market Mechanism。The Quarterly Journal of Economics, 84(3), 488–500。	Asymmetric quality information affects transaction decisions → verification fills in the credible evidence
Source Credibility	Hovland, C. I., & Weiss, W.（1951）。The Influence of Source Credibility on Communication Effectiveness。Public Opinion Quarterly, 15(4), 635–650。	Who says it affects whether people believe it → Law of Claim Traceability: every claim comes with its source
Three Elements of Trust	Mayer, R. C., Davis, J. H., & Schoorman, F. D.（1995）。An Integrative Model of Organizational Trust。The Academy of Management Review, 20(3), 709–734。	Trust rests on ability, benevolence, and integrity, and updates with behavior → trust updates with time
Entity Resolution	Fellegi, I. P., & Sunter, A. B.（1969）。A Theory for Record Linkage。Journal of the American Statistical Association, 64(328), 1183–1210。	The same business must match on multiple fields → Law of Identity Continuity
Data Provenance	Moreau, L., & Missier, P. (Eds.) (2013). PROV-DM: The PROV Data Model. W3C Recommendation, April 30, 2013.	Where and when each piece of data came from → Law of Claim Traceability
Third-Party Certification Intermediaries	Lizzeri, A.（1999）。Information Revelation and Certification Intermediaries。The RAND Journal of Economics, 30(2), 214–231。	The certification intermediary decides how much information to disclose → Law of Independent Verification
Signaling Theory	Spence, M.（1973）。Job Market Signaling。The Quarterly Journal of Economics, 87(3), 355–374。	Signal costs differ by type, and the high-quality party can better afford them → credible signals for service tiers
Capability Maturity Model	Paulk, M. C., Curtis, B., Chrissis, M. B., & Weber, C. V.（1993）。Capability maturity model, version 1.1。IEEE Software, 10(4), 18–27。	Capabilities are graded and climb level by level → service tiers
Versioned Pricing	Shapiro, C., & Varian, H. R.（1998）。Versioning: The Smart Way to Sell Information。Harvard Business Review, 76(6), 106–114。	The same product is offered in versions → service tiers differ in content
Hierarchy of Effects	Lavidge, R. J., & Steiner, G. A.（1961）。A Model for Predictive Measurements of Advertising Effectiveness。Journal of Marketing, 25(6), 59–62。	Awareness, liking, and action come in sequence → Exposure, Word of Mouth, Brand PR
Share of Voice	Jones, J. P.（1990）。Ad Spending: Maintaining Market Share。Harvard Business Review, 68(1), 38–42。	Advertising share of voice is read alongside market share → the concept is borrowed to measure AI Mention Rate
Goodhart's Law	Goodhart, C. A. E. (1976). Problems of Monetary Management: The U.K. Experience. In Papers in Monetary Economics, Vol. I. Sydney: Reserve Bank of Australia. (Presented at a conference in Sydney in July 1975; commonly cited as Goodhart 1975)	When a measure becomes a target, it loses its power to measure → look at clicks and AI content use at every step together
Further Reading: Marketing Fundamentals
The official definition of marketing	American Marketing Association. Definition of Marketing. AMA official website (accessed 2026-10-11).	Marketing is the activity of creating and exchanging value for customers and society → the audience extends to AI
AIDA (attention, interest, desire, action)	Strong, E. K., Jr.（1925）。Theories of selling。Journal of Applied Psychology, 9(1), 75–86。	Strong organized the attention, interest, desire, action account of sales stages → an early reference for dividing the Customer Journey into stages
Customer Journey and customer experience	Lemon, K. N., & Verhoef, P. C.（2016）。Understanding Customer Experience Throughout the Customer Journey。Journal of Marketing, 80(6), 69–96。	The journey is divided into before, during, and after the purchase decision, with multiple touchpoints → the Ten Steps add AI touchpoints
Customer-based brand equity	Keller, K. L.（1993）。Conceptualizing, Measuring, and Managing Customer-Based Brand Equity。Journal of Marketing, 57(1), 1–22。	Brand equity comes from the brand knowledge in customers' minds → extended to the knowledge in AI's mind
Service-dominant logic	Vargo, S. L., & Lusch, R. F.（2004）。Evolving to a New Dominant Logic for Marketing。Journal of Marketing, 68(1), 1–17。	Value is co-created with customers in use → AI acting on their behalf is part of that co-creation
The effect of word of mouth on new product diffusion	Arndt, J.（1967）。Role of Product-Related Conversations in the Diffusion of a New Product。Journal of Marketing Research, 4(3), 291–295。	Positive word of mouth drives consumer adoption of new products → word of mouth is the core of “AEO+ Word of Mouth”
Two-step flow and opinion leaders	Katz, E., & Lazarsfeld, P. F.（1955）。Personal Influence: The Part Played by People in the Flow of Mass Communications。Glencoe, IL: Free Press。	Opinion leaders interpret and relay messages for the public → AI plays the same intermediary role
The sales effect of traditional and social earned media	Stephen, A. T., & Galak, J.（2012）。The Effects of Traditional and Social Earned Media on Sales: A Study of a Microlending Marketplace。Journal of Marketing Research, 49(5), 624–639。	Earned media drives sales → evidence from a third-party source carries more weight than self-claims
Service blueprint	Shostack, G. L.（1984）。Designing Services That Deliver。Harvard Business Review, 62(1), 133–139。	A service blueprint marks the potential failure points in a process → the Ten Steps confirm what to improve step by step
Commitment and trust (relationship marketing)	Morgan, R. M., & Hunt, S. D.（1994）。The Commitment-Trust Theory of Relationship Marketing。Journal of Marketing, 58(3), 20–38。	Trust and commitment are the keys to successful relationship marketing → Trust and Verification connects to S10 Return
Customer value	Gupta, S., Lehmann, D. R., & Stuart, J. A.（2004）。Valuing Customers。Journal of Marketing Research, 41(1), 7–18。	Customers' long-term value can be used to estimate firm value → outcomes are tracked all the way to return visits
Further Reading: Generative Engine and AI Search Research
Generative Engine Optimization (GEO)	Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). GEO: Generative Engine Optimization. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '24), pp. 5–16.	First defined Generative Engine Optimization (GEO) and measured visibility → the academic basis for GEO as a field
Verifiability of generative search engines	Liu, N. F., Zhang, T., & Liang, P. (2023). Evaluating Verifiability in Generative Search Engines. Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 7001–7025.	How well citations support generative search answers varies → accuracy must be measured separately
Ranking manipulation in conversational search engines	Pfrommer, S., Bai, Y., Gautam, T., & Sojoudi, S. (2024). Ranking Manipulation for Conversational Search Engines. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024), pp. 9523–9552. arXiv:2406.03589.	Web page text can manipulate AI recommendation rankings → hence the need for three-party verification
Manipulating language models to increase product visibility	Kumar, A., & Lakkaraju, H. (2024). Manipulating Large Language Models to Increase Product Visibility. arXiv preprint arXiv:2404.07981.	Adding one passage to a product page can push it to the top of the recommendations → mention rate can be manipulated
Adversarial SEO against language models	Nestaas, F., Debenedetti, E., & Tramèr, F.（2025）。Adversarial Search Engine Optimization for Large Language Models。ICLR 2025。arXiv:2406.18382。	When everyone races to manipulate AI, everyone's results get worse → IDAEO advocates building on verifiable facts for the long term
AI search favors earned media	Chen, M., Wang, X., Chen, K., & Koudas, N. (2025). Generative Engine Optimization: How to Dominate AI Search. arXiv preprint arXiv:2509.08919.	AI search cites third-party sources more than brand-owned ones → third-party evidence matters most
A benchmark of whether conversational SEO works	Puerto, H., Gubri, M., Green, T., Oh, S. J., & Yun, S. (2025). C-SEO Bench: Does Conversational SEO Work? Advances in Neural Information Processing Systems 38 (NeurIPS 2025 Datasets & Benchmarks), pp. 31145–31169. arXiv:2506.11097.	Most rewriting tricks have limited effect → judge optimization results by measured performance
The “cited, therefore credible” gap in AI search	Narayanan Venkit, P., Laban, P., Zhou, Y., Mao, Y., & Wu, C.-S. (2025). Search Engines in the AI Era: A Qualitative Understanding to the False Promise of Factual and Verifiable Source-Cited Responses in LLM-based Search. Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT '25), pp. 1325–1340. arXiv:2410.22349.	AI answers with sources attached still often get things wrong → verify every fact, one by one
AI summaries reduce clicks	Chapekis, A., & Lieb, A. (2025). Google users are less likely to click on links when an AI summary appears in the results. Pew Research Center, July 22, 2025.	Users click fewer links when an AI summary appears → measure both clicks and AI Content Use
Further Reading: AI Customers and AI Agents
Algorithmic consumers	Gal, M. S., & Elkin-Koren, N.（2017）。Algorithmic Consumers。Harvard Journal of Law & Technology, 30(2), 309–353。	Algorithms choose and place orders on people's behalf → marketing targets extend to AI customers
Adoption barriers for automated shopping systems	de Bellis, E., & Johar, G. V.（2020）。Autonomous Shopping Systems: Identifying and Overcoming Barriers to Consumer Adoption。Journal of Retailing, 96(1), 74–87。	Consumers face psychological barriers when they hand purchase decisions to a system → S8 needs a foundation of trust
How AI shopping agents pick products	Allouah, A., Besbes, O., Figueroa, J. D., Kanoria, Y., & Kumar, A. (2025). What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce. arXiv:2508.02630; also in Proceedings of the ACM Web Conference 2026, pp. 8697–8700.	AI agents show position and model biases when picking products → merchants must optimize for agents
An experimental environment for agent-to-agent markets	Bansal, G., Hua, W., Huang, Z., Fourney, A., Swearngin, A., Epperson, W., Payne, T., Hofman, J. M., et al. (2025). Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets. arXiv preprint arXiv:2510.25779.	Uses a simulated market to study how agents on both sides of a transaction interact → the experimental basis for S8
Further Reading: Information Retrieval and Credibility
PageRank and large-scale search engine architecture	Brin, S., & Page, L.（1998）。The anatomy of a large-scale hypertextual Web search engine。Computer Networks and ISDN Systems, 30(1–7), 107–117。	Crawl, index, and compute authority from links → the technical prototype for S2 and S4
Authorities and hubs (HITS)	Kleinberg, J. M.（1999）。Authoritative sources in a hyperlinked environment。Journal of the ACM, 46(5), 604–632。	Authority is established by who points to you → citations from third-party sources are where authority comes from
Assessing source trustworthiness by factual correctness (KBT)	Dong, X. L., Gabrilovich, E., Murphy, K., Dang, V., Horn, W., Lugaresi, C., Sun, S., & Zhang, W.（2015）。Knowledge-based trust: Estimating the trustworthiness of web sources。Proceedings of the VLDB Endowment, 8(9), 938–949。	Rates trustworthiness by whether a page's facts are right → the basis for fact-by-fact verification
Knowledge Graph (entities, not strings)	Singhal, A. (2012). Introducing the Knowledge Graph: things, not strings. Google official blog, May 16, 2012.	Search moved from matching strings to recognizing entities → merchants must become recognizable entities
A survey of knowledge graphs	Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., de Melo, G., Gutierrez, C., Kirrane, S., Labra Gayo, J. E., et al. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1–37.	How knowledge graphs are built, verified, and updated → the academic overview for S4
E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness)	Google Search Central (2022). Our latest update to the quality rater guidelines: E-A-T gets an extra E for Experience. Google Search Central Blog, December 15, 2022. See also Google Search Central. Creating helpful, reliable, people-first content (accessed 2026-10-11).	Google's quality rater guidelines look at four dimensions, with trust mattering most → a reference point for verification
The FEVER fact-checking dataset	Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018). FEVER: a Large-scale Dataset for Fact Extraction and VERification. NAACL-HLT 2018 (Long Papers), pp. 809–819.	Each claim is judged against the evidence as supported, refuted, or insufficient → the prototype for the three verification states
The FActScore atomic-fact accuracy rate	Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W., Koh, P. W., Iyyer, M., Zettlemoyer, L., et al. (2023). FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation. EMNLP 2023, pp. 12076–12100.	Split an answer into single facts and score each one for correctness → how the accuracy rate is calculated
Survey of hallucination in language generation	Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., et al. (2023). Survey of Hallucination in Natural Language Generation. ACM Computing Surveys, 55(12), 1–38.	Language models generate unsupported content → why the accuracy rate must be measured
Further Reading: Performance Measurement
Net Promoter Score (NPS)	Reichheld, F. F.（2003）。The One Number You Need to Grow。Harvard Business Review, 81(12), 46–54。	Willingness to recommend correlates with company growth → a reference for measuring word of mouth; return visits are tracked in addition
Multi-channel conversion attribution	Li, H., & Kannan, P. K.（2014）。Attributing Conversions in a Multichannel Online Marketing Environment: An Empirical Model and a Field Experiment。Journal of Marketing Research, 51(1), 40–56。	Conversions must be apportioned across every channel touched → AI channels count in attribution too
Data-driven multi-touch attribution	Shao, X., & Li, L. (2011). Data-driven multi-touch attribution models. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '11), pp. 258–264.	Use data to estimate each touchpoint's contribution → links crawl volume to referral visits
Campbell's law (a metric used for decisions gets distorted)	Campbell, D. T.（1979）。Assessing the impact of planned social change。Evaluation and Program Planning, 2(1), 67–90。	The more a quantitative metric drives decisions, the more it is distorted → the nine Performance Metrics must check one another
Experimental versus observational comparison of advertising effectiveness	Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D.（2019）。A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook。Marketing Science, 38(2), 193–225。	Effectiveness calculated from observational data often diverges from experimental results → always compare performance on the same basis
Further Reading: Start-Up and Accumulation
First-mover advantage	Lieberman, M. B., & Montgomery, D. B.（1988）。First-mover advantages。Strategic Management Journal, 9(S1), 41–58。	Entering first brings an advantage and also a cost → the early-mover dividend is held through sustained investment
Increasing returns and path dependence	Arthur, W. B.（1989）。Competing Technologies, Increasing Returns, and Lock-In by Historical Events。The Economic Journal, 99(394), 116–131。	Under increasing returns, an early lead can lock in the market → the theoretical reference for Compounding Lead
The Cold Start problem	Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '02), pp. 253–260.	A recommender system struggles to recommend new items that have no accumulated data → the theoretical reference for Cold Start
Further Reading: Official Documentation of AI Platforms and Agent Protocols
OpenAI crawler documentation	OpenAI. Overview of OpenAI Crawlers. OpenAI developer documentation (accessed 2026-10-11).	GPTBot, OAI-SearchBot, and ChatGPT-User each have their own purpose → three kinds of reading: training, indexing, and real-time retrieval
Anthropic crawler documentation	Anthropic. Does Anthropic crawl data from the web, and how can site owners block the crawler? Claude Help Center (accessed 2026-10-11).	ClaudeBot, Claude-SearchBot, and Claude-User each have their own purpose → one platform also splits into three kinds of reading
Perplexity crawler documentation	Perplexity. Perplexity Crawlers. Perplexity official documentation (accessed 2026-10-11).	PerplexityBot builds search results; Perplexity-User reads when a user asks → indexing and real-time retrieval
Amazon crawler documentation	Amazon. About Amazonbot. Amazon Developer (accessed 2026-10-11).	Amazonbot improves products and services and may be used for training; Amzn-User reads on user request → training and real-time retrieval
Google crawler overview	Google. Google's common crawlers. Google Crawling Infrastructure documentation (accessed 2026-10-11).	Googlebot builds the search index; Google-Extended controls AI use → treat Search Engine Crawlers and AI use separately
External measurement of AI crawling and referrals	Tomé, J. (2025). The crawl-to-click gap: Cloudflare data on AI bots, training, and referrals. Cloudflare Blog, August 29, 2025.	External measurement counts AI crawling and referrals separately → measure AI Impressions and customers brought by AI separately
Model Context Protocol (MCP)	Model Context Protocol. Specification (2025-06-18 version). modelcontextprotocol.io.	AI Agents access data and tools through a standard interface → S8 Act
Agent-to-Agent Protocol (A2A)	Google (2025). Announcing the Agent2Agent Protocol (A2A). Google Developers Blog, April 9, 2025.	Different AI Agents hand off tasks through a shared standard → AI Agents are new Marketing Audiences
Agent Payments Protocol (AP2)	Google Cloud (2025). Announcing Agent Payments Protocol (AP2). Google Cloud Blog, September 2025.	Agent transactions need user authorization and traceable credentials → Trust and Verification from S8 to S9
The llms.txt proposal	Howard, J. (2024). The /llms.txt file (currently v2). llmstxt.org, first published September 3, 2024.	Prepare a content guide for models → the AI-readable layer of S1 Prepare
Further Reading: Theory Behind the Seven Laws and the Journey
Law of Audience Adaptation｜Semantic Web	Berners-Lee, T., Hendler, J., & Lassila, O.（2001）。The Semantic Web。Scientific American, 284(5), 34–43。	Software agents can act for people only when web pages carry machine-readable structure → address humans and AI each in a form they can read
Law of Audience Adaptation｜Media Richness	Daft, R. L., & Lengel, R. H.（1986）。Organizational Information Requirements, Media Richness and Structural Design。Management Science, 32(5), 554–571。	Different information needs suit different forms of expression → arrange content formats by audience
Law of Identity Continuity｜Brand as Signal	Erdem, T., & Swait, J.（1998）。Brand Equity as a Signaling Phenomenon。Journal of Consumer Psychology, 7(2), 131–157。	Consistent, credible brand signals reduce customer uncertainty → trust accumulates only when identity stays consistent over time
Law of Identity Continuity｜Entity Linking	Shen, W., Wang, J., & Han, J.（2015）。Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions。IEEE Transactions on Knowledge and Data Engineering, 27(2), 443–460。	Systems link text back to the same entity by name and context → only a consistent name lets AI link back to the same merchant
Law of Independent Verification｜Audit Independence	DeAngelo, L. E.（1981）。Auditor size and audit quality。Journal of Accounting and Economics, 3(3), 183–199。	Verification quality depends on the ability to find problems and the willingness to report them truthfully → verification conclusions do not change with the client relationship
Law of Independent Verification｜Quality Disclosure and Certification	Dranove, D., & Jin, G. Z.（2010）。Quality Disclosure and Certification: Theory and Practice。Journal of Economic Literature, 48(4), 935–963。	The effect of third-party certification depends on the certifier's incentives and practices → the overall theoretical reference for IDA verification
Law of Fact Primacy｜Data Quality	Wang, R. Y., & Strong, D. M.（1996）。Beyond Accuracy: What Data Quality Means to Data Consumers。Journal of Management Information Systems, 12(4), 5–33。	Data quality covers not only accuracy but also completeness, timeliness, and interpretability → verify each merchant fact item by item and mark its validity period
Law of Claim Traceability｜Attribution of Generated Content	Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G. S., et al. (2023). Measuring Attribution in Natural Language Generation Models. Computational Linguistics, 49(4), 777–840.	Factual sentences in AI-generated content must point back to an identified source → every claim comes with traceable support
Law of Claim Traceability｜Data provenance	Buneman, P., Khanna, S., & Tan, W.-C. (2001). Why and Where: A Characterization of Data Provenance. Database Theory — ICDT 2001, Lecture Notes in Computer Science 1973, pp. 316–330.	Data must be able to answer “why it was produced and where it came from” → link every claim back to concrete evidence
Law of External Corroboration｜Communicator bias	Eagly, A. H., Wood, W., & Chaiken, S.（1978）。Causal inferences about communicators and their effect on opinion change。Journal of Personality and Social Psychology, 36(4), 424–435。	Listeners infer why a speaker says what they say → a First-Party Source's claim is stronger when paired with independent corroboration
Law of External Corroboration｜Triangulated Verification	Jick, T. D.（1979）。Mixing Qualitative and Quantitative Methods: Triangulation in Action。Administrative Science Quarterly, 24(4), 602–611。	Evidence from different sources can be checked against each other → read the Official Source, First-Party Source, and Third-Party Source together
Law of Full-Journey Outcomes｜Needs met without a click	Li, J., Huffman, S., & Tokuda, A. (2009). Good abandonment in mobile and PC internet search. Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '09), pp. 43–50.	When users leave without clicking, part of the reason is that the results page already met their need → content use at the answer stage is itself an outcome
Law of Full-Journey Outcomes｜Customer equity and return on marketing	Rust, R. T., Lemon, K. N., & Zeithaml, V. A.（2004）。Return on Marketing: Using Customer Equity to Focus Marketing Strategy。Journal of Marketing, 68(1), 109–127。	Marketing investment must connect to customers' long-term value → nine metrics run from the front end all the way to return visits
S6 Mention｜Consideration set	Hauser, J. R., & Wernerfelt, B.（1990）。An Evaluation Cost Model of Consideration Sets。Journal of Consumer Research, 16(4), 393–408。	Evaluation has a cost, so customers put only some brands into their consideration set → AI Mention Rate is the chance of making the shortlist
S6 Mention｜Recall drives choice	Nedungadi, P.（1990）。Recall and Consumer Consideration Sets: Influencing Choice without Altering Brand Evaluations。Journal of Consumer Research, 17(3), 263–276。	A brand that is recalled and enters the consideration set can change the choice → being mentioned by AI is itself an outcome
S8 Act｜Levels of automation	Parasuraman, R., Sheridan, T. B., & Wickens, C. D.（2000）。A model for types and levels of human interaction with automation。IEEE Transactions on Systems, Man, and Cybernetics – Part A, 30(3), 286–297。	Automation comes in four types: information acquisition, analysis, decision, and action → tell whether AI Agents are looking up information or getting things done for the customer
S10 Return｜Expectation confirmation	Bhattacherjee, A.（2001）。Understanding Information Systems Continuance: An Expectation-Confirmation Model。MIS Quarterly, 25(3), 351–370。	When use meets expectations, people are more willing to keep using → return visits show whether customers are willing to ask again and come back
AI Impressions｜Times read and how much the model remembers	Kandpal, N., Deng, H., Roberts, A., Wallace, E., & Raffel, C. (2023). Large Language Models Struggle to Learn Long-Tail Knowledge. Proceedings of the 40th International Conference on Machine Learning (ICML 2023), PMLR 202, pp. 15696–15707. arXiv:2211.08411.	A model's chance of getting a fact right depends on how many relevant documents are in its training data → the number of times content is copied back for training is worth measuring
AI Impressions｜Repetition and model memory	Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramèr, F., & Zhang, C.（2023）。Quantifying Memorization Across Neural Language Models。ICLR 2023。arXiv:2202.07646。	The more often content repeats in the training data, the more easily the model memorizes it verbatim → the value of S3 Train
AI Visibility｜Brand salience	Romaniuk, J., & Sharp, B.（2004）。Conceptualizing and measuring brand salience。Marketing Theory, 4(4), 327–342。	Brand salience is how easily a brand is noticed and recalled in a buying situation → AI Mention Rate measures the chance a brand is recalled in AI answers
Performance Metrics｜Construct validity	Cronbach, L. J., & Meehl, P. E.（1955）。Construct validity in psychological tests。Psychological Bulletin, 52(4), 281–302。	A metric must prove it measures the concept it is named for → each of the Performance Metrics states clearly what it measures
IDA Third-Party Verification｜Credence goods	Darby, M. R., & Karni, E.（1973）。Free Competition and the Optimal Amount of Fraud。The Journal of Law and Economics, 16(1), 67–88。	Some qualities are hard for customers to judge even after use → leave facts like licenses and professional credentials to third-party verification
Warm Start｜Crawler revisit scheduling	Cho, J., & Garcia-Molina, H.（2003）。Effective page refresh policies for Web crawlers。ACM Transactions on Database Systems, 28(4), 390–426。	Crawler resources are limited, so revisit frequency is allocated by weighing how much pages change → the mechanism reference for Warm Start
Compounding Lead｜Preferential attachment	Barabási, A.-L., & Albert, R.（1999）。Emergence of Scaling in Random Networks。Science, 286(5439), 509–512。	As a network grows, new links favor nodes that already have more links → the network-science reference for Compounding Lead
Compounding Lead｜Cumulative advantage	Merton, R. K.（1968）。The Matthew Effect in Science。Science, 159(3810), 56–63。	Those who already have a reputation receive more recognition and attention → content used early has a better chance of being used again
Living Theory｜Research programme	Lakatos, I. (1970). Falsification and the Methodology of Scientific Research Programmes. In I. Lakatos & A. Musgrave (Eds.), Criticism and the Growth of Knowledge (pp. 91–196). Cambridge: Cambridge University Press.	A research programme has core claims and progresses by continually predicting new facts → the Seven Laws are the core, and new data each month keeps testing them
Living Theory｜Design science	Hevner, A. R., March, S. T., Park, J., & Ram, S.（2004）。Design Science in Information Systems Research。MIS Quarterly, 28(1), 75–106。	A designed method must be built, evaluated, and refined repeatedly in real settings → monthly live tests are used to revise the diagnostics and metrics

All entries were checked one by one on 2026-10-11 against publication records and official pages for author, year, title, and source; entries marked “preprint” have not been peer-reviewed. The Goodhart's Law paper was presented in 1975 and published in a collected volume in 1976. Official documentation keeps being updated; the content on the date of access applies. In the table, what comes before “→” is the literature's original statement, and what comes after “→” is how IDAEO applies it; IDAEO's conclusions come from real-world measurement, based on the measured data in each chapter, and the literature is their theoretical reference.

Appendix: Claim Basis Table

The claims in each chapter’s “Key Points of This Chapter” total 57, and each one is marked with what it rests on: First-Party Data (IDAEO’s own measurement records), Platform or Standards Documents (official documents from AI platforms and standards bodies), Academic Literature (the theoretical foundations and the numbered entries in the reference list), Rule of Thumb (industry or author rules of thumb, not measured on this site), and Theory Definition (IDAEO’s own definitions and forecasts). One claim can rest on several types at once. The classification was made independently by three reviewers, arbitrated against the data ledger, and then confirmed by the author. These are the basis types of the claims, which is a different matter from the Official Source, First-Party Source, and Third-Party Source of “Triangulated Verification” in Chapter 4.

Claim	Basis Type	References	Notes
Chapter 0 — IDAEO: AI-Era Marketing
IDAEO is AI-Era Marketing: it links the complete journey through real-world website measurement, credible facts, and theory.	Theory Definition
AI Impressions come first; the rest of the journey follows.	First-Party Data, Academic Literature, Theory Definition	[59] Arthur, W. B. (1989); [95] Merton, R. K. (1968)
The seven laws are the core, the five dimensions are the framework, and the nine elements are the components.	Theory Definition
The Five Dimensions of IDAEO Marketing link the whole process: Audience Strategy (who it is for), Journey Design (which steps to take), Trust and Verification (why anyone should believe it), Service Tiers (which tier to deliver), and Performance Measurement (how results are counted).	Theory Definition
The Nine Elements of the IDAEO Framework correspond to one another: Marketing Audiences, Optimization Domains, Customer Journey, Journey Diagnostics, IDAEO Solutions, Verification Principles, Service Tiers, Performance Metrics, and First-Party Data.	Theory Definition
IDAEO is a Living Theory.	Academic Literature, Theory Definition	[96] Lakatos, I. (1970)
Chapter 1 — Audience Strategy (Who to Target): Five Marketing Audiences and Six Optimization Domains
The Five Marketing Audiences are Humans, Search Engine Crawlers, AI Crawlers, AI Agents, and Automated Bots.	Platform or Standards Documents, Academic Literature, Theory Definition	[40] Gal, M. S., & Elkin-Koren, N. (2017); [68] Google (2025)
Classify AI traffic by function first, then look at the AI Platforms.	Platform or Standards Documents, Theory Definition	[61] OpenAI. Overview of OpenAI Crawlers. OpenAI developer documentation (accessed 2026-10-11); [62] Anthropic. Does Anthropic crawl data from the web, and how can site owners block the crawler?
AI Agents are the future main force of AI traffic.	First-Party Data, Academic Literature, Theory Definition	[40] Gal, M. S., & Elkin-Koren, N. (2017)	“Main force” is the author’s forecast; 18.09 million = 17.95 million automated browsing + 144,000 named AI Agents, which is first-party data.
The Six Optimization Domains are SEO, AIO, AEO, GEO, LLMO, and MEO; AIO is linked to AEO. SEO builds the search index, AEO gets AI answers to use you, AIO focuses on search engine AI Overviews, GEO builds the AI index, LLMO corresponds to copying back for training, and MEO corresponds to Local Search.	Academic Literature, Theory Definition	[31] Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). GEO: Generative Engine Optimization
AI and search engines combined: 105.5 million (since 3/3); Organic Traffic from Humans: 310,000 (since 8/26).	First-Party Data
AI Crawlers copied content back for training 52.2 million times, built the AI index 3.99 million times, and retrieved in real time 650,000 times.	First-Party Data, Platform or Standards Documents	[61] OpenAI. Overview of OpenAI Crawlers. OpenAI developer documentation (accessed 2026-10-11)
The Optimization Domains in Figure 1-1, “Panorama of the Five Marketing Audiences,” list the abbreviation, the Chinese full name, and the English full name, with terms for the same domain listed below.	Theory Definition
Chapter 2 — Journey Design (Which Steps to Take): AI Customer Journey: Four Phases, Ten Steps
AI Customer Journey: Four Phases, Ten Steps, in order: S1 Merchant Readiness, S2–S4 AI Information Acquisition, S5–S8 AI-Assisted Decision and Action, S9–S10 Customer Conversion and Re-engagement.	Academic Literature, Theory Definition	[5] Court, D., Elzinga, D., Mulder, S., & Vetvik, O. J. (2009); [22] Lemon, K. N., & Verhoef, P. C. (2016)
Each step has a diagnostic question, verifying progress step by step from information preparation to customer re-engagement.	Academic Literature, Theory Definition	[28] Shostack, G. L. (1984)
In S1 the merchant prepares its information; S2–S4 are led by crawlers; S5–S7 are human–AI interaction; in S8 AI Agents act; S9–S10 are customer conversion and re-engagement.	Platform or Standards Documents, Academic Literature, Theory Definition	[86] Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000)
S10 loops back to S5, forming a continuous cycle.	Academic Literature, Theory Definition	[5] Court, D., Elzinga, D., Mulder, S., & Vetvik, O. J. (2009)
Crawls in S2–S5, site arrivals in S8, and customer visits in S9 are measured first-party by IDAEO; indexing and search impressions come from search platforms; mentions and answers in S6–S7 are tracked by IDAEO live tests.	First-Party Data, Platform or Standards Documents
Chapter 3 — Measurement Sources and the AI Black Box: The Data Foundation of Performance Measurement
The AI black box is the training, ranking, mentions, answers, and actions that happen inside AI platforms.	Theory Definition
IDAEO measures both ends of the black box, then uses platform data and periodic live tests to measure the results of content use.	First-Party Data, Platform or Standards Documents, Academic Literature, Theory Definition	[81] Jick, T. D. (1979)
Periodic live tests of TK Lin's personal IDA show content use in answers inside the black box.	First-Party Data		Of the five AI models, four were queried through Perplexity’s search interface.
Results from each source are read separately by unit.	Academic Literature, Theory Definition	[57] Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019)
The platform-wide AI Citations figure published in this book is Copilot's 31,700 since 7/22.	Platform or Standards Documents
Chapter 4 — Trust and Verification (Why Believe It): Credible Facts and External Corroboration
IDAEO uses credible facts and external corroboration to turn merchant claims into a basis AI can cite.	Academic Literature, Theory Definition	[76] Dranove, D., & Jin, G. Z. (2010)
Law of Fact Primacy: credible content starts from verified facts.	Academic Literature, Theory Definition	[77] Wang, R. Y., & Strong, D. M. (1996)
Law of Claim Traceability: every claim has a traceable basis.	Academic Literature, Theory Definition	[9] Hovland, C. I., & Weiss, W. (1951); [78] Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G. S., et al. (2023). Measuring Attribution in Natural Language Generation Models
Law of External Corroboration: a credible claim must have external corroboration.	Academic Literature, Theory Definition	[80] Eagly, A. H., Wood, W., & Chaiken, S. (1978); [81] Jick, T. D. (1979)
Verify facts one by one, and keep confirming they are still current.	Academic Literature, Theory Definition	[77] Wang, R. Y., & Strong, D. M. (1996)
Chapter 5 — Service Tiers (How Far to Go): AEO Exposure, AEO+ Word of Mouth, IDA Brand PR
Service tiers build on the earlier foundations: AEO Exposure maps to S1–S4, AEO+ Word of Mouth to S5–S6, and IDA Brand PR focuses on S7–S10 and includes the first two tiers, so the full journey S1–S10 is completely covered.	Academic Literature, Theory Definition	[15] Paulk, M. C., Curtis, B., Chrissis, M. B., & Weber, C. V. (1993); [17] Lavidge, R. J., & Steiner, G. A. (1961)
Each higher service tier includes the tier below it.	Academic Literature, Theory Definition	[15] Paulk, M. C., Curtis, B., Chrissis, M. B., & Weber, C. V. (1993)
The nine Performance Metrics are AI Readiness Score, AI Crawl Volume, AI Real-Time Retrievals, AI Mention Rate, AI Answer Accuracy Rate, AI Citations, AI Agent Visits, Search & AI Referral Visits, and AI Return Visits.	Academic Literature, Theory Definition	[56] Campbell, D. T. (1979); [83] Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004)
AEO+ Word of Mouth helps merchants stay on top of AI mentions; IDA Brand PR includes the AI Visibility results IDAEO publishes periodically.	Theory Definition
Chapter 6 — Performance Measurement (How to Count Results): AI Impressions and Full-Journey Value
AI Content Use at every stage counts as a result.	Platform or Standards Documents, Academic Literature, Theory Definition	[82] Li, J., Huffman, S., & Tokuda, A. (2009)
In marketing terms, an AI Crawl Volume of 97.46 million means that many AI Impressions.	First-Party Data, Academic Literature, Theory Definition	[88] Kandpal, N., Deng, H., Roberts, A., Wallace, E., & Raffel, C. (2023)
Reads for AI Answers, 46.05 million, are the reads that serve search and AI answers.	First-Party Data, Theory Definition
AI Real-Time Retrievals, 650,000, are content use triggered by immediate demand.	First-Party Data, Platform or Standards Documents, Academic Literature	[1] Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks; [61] OpenAI. Overview of OpenAI Crawlers. OpenAI developer documentation (accessed 2026-10-11); [63] Perplexity. Perplexity Crawlers. Perplexity official documentation (accessed 2026-10-11)
AI Citations, 31,700, mean the content became the basis of answers.	Platform or Standards Documents, Theory Definition
AI Agent Visits, 18.09 million, represent the scale of traffic moving from reading content to acting on customers' behalf.	First-Party Data, Academic Literature, Theory Definition	[86] Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000)
Search & AI Referral Visits, 310,000 (search and AI combined; Google 268,000), represent customers arriving at the site via search or AI.	First-Party Data
Cold Start: AI traffic accumulates from 0; the industry rule of thumb is about 90 days.	First-Party Data, Rule of Thumb		About 90 days is an industry rule of thumb, not measured on this site (confirmed by the author on 2026-10-13); monthly reads from 53 → 35.99 million are first-party data.
Warm Start: content meets S1 and goes live on an AI Traffic Hub. Rule of thumb: about 30 days; measured, the next crawler from the same AI platform arrived 30–35 days later (Figure 6-6); TK Lin's personal IDA case reached S7 (Answer) in 14 days.	First-Party Data, Academic Literature, Rule of Thumb	[93] Cho, J., & Garcia-Molina, H. (2003)	About 30 days is a rule of thumb; the 30–35-day gap before the next crawler from the same AI platform is measured in Figure 6-6; 14 days and 84% come from TK Lin’s personal IDA field test (confirmed by the author on 2026-10-13).
The necessary condition for an AI Traffic Hub is accumulating ten million or more AI requests per month.	First-Party Data, Theory Definition
AI traffic has an early-mover dividend: the sooner you are ready, the sooner you accumulate.	First-Party Data, Academic Literature, Theory Definition	[58] Lieberman, M. B., & Montgomery, D. B. (1988)
Accumulate AI impressions now, and the lead widens with every round of use—this is the Compounding Lead.	First-Party Data, Academic Literature, Theory Definition	[59] Arthur, W. B. (1989); [94] Barabási, A.-L., & Albert, R. (1999)
Chapter 7 — IDAEO Field Results: Our Own Site and Early Field-Test Merchants
IDAEO is field-tested not only on the IDAEO site itself and Washinmura; early field-test merchants validate it too.	First-Party Data, Platform or Standards Documents
The IDAEO platform can also help every small and medium-sized business, so everyone rises together.	First-Party Data, Platform or Standards Documents, Theory Definition
① Measurement officially launched → Law of Full-Journey Outcomes (Performance Measurement); ② Search exposure rolled out in parallel → Law of Audience Adaptation (Audience Strategy); ③ First visitor arrived via an AI answer → Journey Design: S9 Visit.	First-Party Data, Theory Definition
④ Brand knowledge presentation fully upgraded → Law of Identity Continuity (S1 Merchant Readiness); ⑤ Research-Evidence-Driven Optimization → Theoretical foundation: 17 existing theories; ⑥ New IDAEO site built → Five Dimensions and Nine Elements.	Theory Definition
⑦ Crawl performance hit a new peak → AI Impressions (S2–S4 AI Information Acquisition); ⑧ Cumulative requests passed one hundred million → Compounding Lead and Living Theory.	First-Party Data, Theory Definition
Among the first batch of merchant websites that joined the field test and have complete records for both May and September, some have already grown after joining the IDAEO field test. Site-wide opens from May to September: a parenting content site rose from 52,817 to 55,286, a food-service equipment manufacturer from 842 to 1,275, an event staging design firm from 442 to 1,741, a leather goods brand from 555 to 908, a hair salon from 74 to 128, and a studio from 3 to 329.	First-Party Data
The IDA pages of the second batch of field-test industries received 373,776 AI impressions from 7/18–10/8 (the number of times they were read by AI and search engine crawlers).	First-Party Data
Chapter 8 — AI Visibility and Theoretical Foundations
External AI Visibility mainly corresponds to the answer phase, S5–S7.	Academic Literature, Theory Definition	[31] Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). GEO: Generative Engine Optimization
The fundamentals of AI Visibility are S1–S4	Academic Literature, Theory Definition	[88] Kandpal, N., Deng, H., Roberts, A., Wallace, E., & Raffel, C. (2023)
IDAEO connects the foundation of S1–S4 with the action, conversion, and return of S8–S10 in a single journey.	Academic Literature, Theory Definition	[83] Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004)
IDAEO’s theoretical foundations fall into eight categories: marketing fundamentals, generative engine and AI search research, AI customers and AI Agents, information retrieval and credibility, performance measurement, start-up and accumulation, official documentation of AI Platforms and agent protocols, and the theory behind the seven laws and the journey.	Theory Definition
The figure lists 17 existing theories; see “Theoretical Foundations and References” at the end of the book for the full references.	Theory Definition
Each month’s new data and field verification keep strengthening IDAEO’s theoretical and empirical foundation.	Academic Literature, Theory Definition	[96] Lakatos, I. (1970); [97] Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004)

Data Notes

First-Party Data: taken from IDAEO's first-party measurement records for the period 2026/3/3–10/8, old site and new site combined; AI and search engines together total 105.5 million requests (105,487,618), consistent with IDAEO's approved basis. AI Crawl Volume is 97.46 million, with component values of 52.2 million copied back for training, 3.99 million building the AI index (including 2.82 million from our own Cloudflare AI Search, to be deducted in next month's update), and 41.26 million building the search index; Applebot, PetalBot, YouBot, and Kagibot are classified as Search Engine Crawlers per each company's official documentation (checked 2026/10/14).
AI Agents: 17.95 million is automated browsing from 2026/8/26–10/8, listed separately by the function of programs operating a browser; named AI Agents are 144,000, for a total of 18.09 million. Hermes, OpenClaw, Devin, Grok, and Muse are listed as still being tracked, and named-visit results will be published later; Manus has 14 visits.
Humans: Organic Traffic of 310,000 is the number of click-throughs to the website from search or AI (mainly from 2026/8/26 onward), of which Google accounts for 268,000; Local Search is still being tracked and will be published later.
Automated Bots provide Automated Information Services: “AI tools” refers to requests labeled AI by name and classified by source under other automated services. SEO tool crawlers (Semrush, Majestic, Moz, Ahrefs, etc.) and web page previews (Facebook, LinkedIn, X, etc.) are listed by platform-verified identity (2026/8/24–10/8), counted separately, and fall outside the totals above. The identity and function of the 150,000 requests marked “Tracking in Progress” are being confirmed by IDAEO on an ongoing basis, and classification results will be published later.
Search result impressions: the breakdowns in each figure are the search platforms' original values (Google 7.41 million, Bing 910,000, totaling 8.32 million); the overview's approximately 8.22 million follows IDAEO's approved basis, a difference of about 100,000. Indexed pages are the September 2026 value; Bing is the sum of its subfolders.
TK Lin's personal IDA field test (2026/10/3): 100 questions in five groups of 20 (identity, company, expertise, Washinmura, IDA); the five AI models were Perplexity Sonar, OpenAI GPT-5.4, Anthropic Claude Sonnet 5, Google Gemini 3.5 Flash, and xAI Grok 4.5, all queried through Perplexity's search interface, for 500 answers in total. 84% means this IDA appeared in the source list the AI retrieved (419 answers); it appeared 439 times in the source lists, ranking first among all sources. For the 40 questions that name him directly (the identity and company groups), the figure was 91.5% (183 answers), with this IDA appearing 190 times. 10/11 follow-up: of 16 Perplexity questions in four languages, 13 had this IDA among their sources.
Start-up time: about 30 days for a Warm Start is a rule of thumb; Figure 6-6, “Cold Start Timeline,” adds a measurement: crawlers from the same AI platform arrive in sequence, 30–35 days apart. About 90 days for a Cold Start is an industry rule of thumb; 14 days is the measured case of TK Lin's personal IDA.
Field-test merchants: the first batch of field-test merchants and the second batch of field-test industries are all real operating businesses, and IDAEO has verified each one's identity; by agreement, this book lists only the industry, and merchant names stay in IDAEO's internal records.
Publisher: IDAEO is operated by 株式会社和心 (corporate number 8011401020677; Minato-ku, Tokyo, Japan); author TK Lin.
Theoretical foundations: all 17 existing sources have been verified one by one, with authors and years listed per the reference list; for six of them, verification covered the publisher's catalog entry and abstract. Goodhart was presented in 1975 and published in 1976. The theoretical basis is grouped into marketing fundamentals, generative engine and AI search research, AI customers and AI Agents, information retrieval and credibility, performance measurement, start-up and accumulation, official documentation of AI Platforms and agent protocols, and the theory behind the Seven Laws and the journey; see “Theoretical Foundations and References” at the end of the book for the full references.
External AI Visibility metrics: compiled from vendor documentation and each company's public definitions verified on 2026-10-11; comparisons use the same measurement scope and metric definitions.
All logos are trademarks of their respective owners and are used in this book to identify the AI Platforms.
The nine Performance Metrics and the IDAEO Solutions use this book's naming; validation data are counted across all platforms, and single-merchant results are measured from that merchant's own data. The measurement methods for AI Mention Rate, AI Answer Accuracy Rate, and AI Return Visits are set; IDAEO continues to accumulate them and will publish them later.
Same-domain terms within the Optimization Domains are grouped by this book's functional classification, following each term's context of use; AIO is linked to AEO, and both correspond to AI answers using content. AAO, AXO, AAIO, as well as GAIO and AISO, are listed per the AI Platforms' glossary pages verified on 2026-10-11; SXO, VSO, ASO, CRO, SMO, SGE, and LLM SEO follow common industry usage, and this round's item-by-item verification covered the AI Platforms' glossary terms above.
The extended figures follow the visual framework of the introductory course “Website Traffic in the AI Era”; the numbers are compiled from the same first-party measurement records through 2026/10/8, and journey steps are always written as S1–S10. The statistical scope of this edition is defined by the items listed in each figure.
Read each extended figure by its period: AI requests 3/3–10/8; whole site 2/12–10/8; automated browsing 8/26–10/8; per-site breakdown 8/20–10/8; Copilot citations 7/22–10/8. Before 8/24, first-party measurement records from multiple sources were merged and may include duplicate requests; when citing, note this scope as well.
Copilot citations of 31,700 follow IDAEO's approved basis and cover the formal content scope; the raw aggregate is 52,600.
“Reads for AI Answers,” 46.05 million, follows IDAEO's approved basis and includes search indexing and AI Overview generation, AI indexing, real-time retrieval, and named AI Agents. Of this, 41.26 million search-indexing reads are shared by search and AI answers; the rest are summed by function, presenting the scope of content use as a whole.
Figure 6-6, “Cold Start Timeline,” shows AI Platforms arriving in sequence and the early-mover dividend: for five of the six AI Platforms, the first crawler and the next were 30–35 days apart, the interval at which the same AI platform scales up in sequence. Crawlers whose first day is 2026/3/3 were already present when measurement began, so the listed intervals are calculated from the observation start and represent the lower bound of the actual interval. A Cold Start is a new website's AI traffic accumulating from 0, about 90 days by industry rule of thumb; a Warm Start has content that meets S1 and goes live on an AI Traffic Hub (a platform accumulating ten million or more AI requests per month; the IDAEO platform had 25.97 million in July, 35.99 million in August, and 15.79 million in September), about 30 days as a rule of thumb; TK Lin's personal IDA case reached S7 in 14 days: live on 9/20, tested on 10/3 with 100 questions and five AI models, and 84% of the answers used this IDA as a source. AI traffic has an early-mover dividend: the sooner you are ready, the sooner you accumulate.
AI Agent Visits of 18.09 million = automated browsing 17.95 million + named AI Agents 144,000. A further 5,102 AI Agent visits came through ordinary browsers; these are counted separately and are not included in any total.
Early test merchants, the second batch of test industries, and our own Washinmura official site: merchant-page search clicks, AI-referred customers, AI reads of merchant pages, and merchant official-site opens are taken from IDAEO first-party measurement records (queried 2026-10-10); AI exposure of the second-batch test industries' IDA pages is taken from IDAEO first-party measurement records (queried 2026-10-11, period 7/18–10/8; summed across the 10 platforms or across the three purposes, each totals 373,776); search clicks and Copilot citations for industry official sites are taken from authorized search platform data (queried 2026-10-11), totals only. The Washinmura official site's 109,140 Google search clicks (3/3–10/8) are taken from search platform data; its 95,333 AI and search engine reads and 11,333 AI real-time retrievals (8/20–10/8) are taken from IDAEO first-party measurement records. The Google comparison with the same month last year includes only official sites with last year's records; in the Bing total, data for some official sites starts from 8/13.