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Appendix: Claim Basis Table · IDAEO: Marketing in the Age of AI

Appendix: Claim Basis Table

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.

ClaimBasis TypeReferencesNotes
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 provide a foundation for the rest of the journey; impressions do not guarantee that the later steps will happen.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 (see Chapter 8).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?
Visits by AI Agents have been observed, and their scale is still being tracked.First-Party Data, Academic Literature, Theory Definition[40] Gal, M. S., & Elkin-Koren, N. (2017)From the 7th revision, the forecast that AI Agents will be the "main force" is dropped; the book only states they have appeared and are being tracked. 18.09 million = 17.95 million automated browsing + 144,000 named agents, from First-Party Data.
The Six Optimization Domains are SEO, AIO, AEO, GEO, LLMO, and MEO. SEO builds the search index, AIO focuses on search engine AI Overviews, and AEO gets AI answers to use your content. GEO improves visibility in generative answers, LLMO corresponds to copying back for training, and MEO corresponds to Local Search. AEO and GEO both operate at the answer stage, and AIO is linked to AEO. Building the AI index is the name of an S4 function, not GEO.Academic Literature, Theory Definition[31] Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). GEO: Generative Engine Optimization
AI, search engines, and Automated Bots combined: 105.5 million (since Mar 3; these are platform-scale figures and cannot be compared directly with a single merchant's website). Organic Traffic from Humans: 310,000 (since Aug 26). Requests before Aug 24 may include duplicates.First-Party Data
AI Crawlers read content 52.2 million times for training and 3.99 million times for building the AI index (including 2.82 million by the site's own Cloudflare AI Search), and made 650,000 real-time reads.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 and its 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—S1 Merchant Readiness, S2–S4 AI Information Acquisition, S5–S8 AI-Assisted Decision and Action, S9–S10 Customer Conversion and Re-engagement. S1–S10 are diagnostic checkpoints, not a sequence every customer must follow.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 to check where the merchant is getting stuck.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 (not AI mentions) 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 record whether it appears in AI search source lists.First-Party DataOf 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 AI Citations figure published in this book comes from Copilot: the IDAEO platform has been cited 31,700 times since Jul 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 — Read first; AEO+ Word of Mouth — Named in the answer; IDA Brand PR — Answered right, visit, return
Service tiers build on the earlier foundations: AEO Exposure — Get Read First (S1–S4), AEO+ Word of Mouth — Get Mentioned (S5–S6), and IDA Brand PR — Accurate Answers, Visits, and Returns (S7–S10, including 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. Results at each stage must be assessed separately; an earlier stage cannot stand in for a later one.Platform or Standards Documents, Academic Literature, Theory Definition[82] Li, J., Huffman, S., & Tokuda, A. (2009)
An AI Crawl Volume of 97.46 million is what this book calls AI Impressions. This includes reads by search engines for indexing.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, include reads for search indexing 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)
The IDAEO platform's 31,700 AI Citations mean its content was listed as a source in answers.Platform or Standards Documents, Theory Definition
AI Agent Visits, 18.09 million, represent the volume of site arrivals. Whether actions were completed on customers' behalf still depends on subsequent results.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 ThumbAbout 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. The rule of thumb is about 30 days, not a deadline for crawler arrival. A separate Cold Start observation from a single site found a gap of 30–35 days before the next crawler from the same platform (Figure 6-6). For three platforms, this is a lower bound and cannot be used to estimate Warm Start timing. 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
The early-mover dividend in AI traffic means preparing information early so arriving crawlers can read it.First-Party Data, Academic Literature, Theory Definition[58] Lieberman, M. B., & Montgomery, D. B. (1988)
Compounding Lead is currently a hypothesis: whether accumulating AI impressions can widen a lead remains to be verified.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 also contribute field data.First-Party Data, Platform or Standards Documents
The small and medium-sized businesses in the field test saw the following changes during their time on the IDAEO platform.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, most grew and some did not. For the websites that grew, site-wide opens from May to September were as follows: 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 Jul 18–Oct 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–S4Academic 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. The period is Mar 3, 2026–Oct 8, old site and new site combined. AI, search engines, and Automated Bots total 105.5 million requests (105,487,618). This is consistent with IDAEO's approved basis. These are platform-site totals and cannot be compared directly with a single business's official website. Records before Aug 24 may include duplicate requests. AI Crawl Volume is 97.46 million, listed by the purpose of the requests. Copied back for training: 52.2 million. This means requests made for training. Building the AI index: 3.99 million (including 2.82 million from our own Cloudflare AI Search, to be deducted in next month's update). Building the search index: 41.26 million. The indexing figures only indicate requests made for indexing. These requests do not mean the content has been used in training or included in an index. Applebot, PetalBot, YouBot, and Kagibot are classified as Search Engine Crawlers. The classification follows each company's official documentation (checked Oct 14, 2026).
  • AI Agents: 17.95 million is automated browsing from Aug 26, 2026–Oct 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 Aug 26, 2026 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 (Aug 24, 2026–Oct 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 (Oct 3, 2026): 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, for 500 answers in total. 84% means this IDA appeared in the source list the AI retrieved (four of the five models used the same search interface; the questions named the author himself; a single case), corresponding to 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. Oct 11 follow-up: of 16 Perplexity questions in four languages, 13 had this IDA among their sources. A single case cannot be generalized directly to businesses.
  • 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 (Washin Co., Ltd.; 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 Oct 8, 2026, 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 Mar 3–Oct 8; whole site Feb 12–Oct 8; automated browsing Aug 26–Oct 8; per-site breakdown Aug 20–Oct 8; Copilot citations Jul 22–Oct 8. Before Aug 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 the record of AI Platforms arriving in sequence. For five of the six AI Platforms, the first crawler and the next were 30–35 days apart. This is the gap between different crawlers from the same AI platform appearing in sequence. Crawlers whose first day is Mar 3, 2026 were already present when measurement began. 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. The industry rule of thumb is about 90 days. A Warm Start has content that meets S1 and goes live on an AI Traffic Hub. This means 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. Records before Aug 24 may include duplicate requests, so the counting basis differs before and after that date. The figure for September was lower than for August; keep the counting basis in mind when comparing months. The rule of thumb for a Warm Start is about 30 days. TK Lin's personal IDA case reached S7 in 14 days: live on Sep 20, tested on Oct 3 with 100 questions and five AI models. 84% means this IDA appeared in the source list the AI retrieved (four of the five models used the same search interface; the questions named the author himself; a single case). See the data notes for “TK Lin's Personal IDA Field Test” for limitations. The findings cannot be generalized to businesses.
  • 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 Jul 18–Oct 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 (Mar 3–Oct 8) are taken from search platform data; its 95,333 AI and search engine reads and 11,333 AI real-time retrievals (Aug 20–Oct 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 Aug 13.
Data Sources
  • IDAEO product feature registry (2026-10-09 edition)
  • IDAEO methodology document (2026-10 edition)
  • IDAEO first-party measurement records (data through 2026-10-08)
  • “Website Traffic in the AI Era” introductory course (2026-09-28)
  • Theory and terminology verification log (2026-10-11)
Generated: 2026-10-20 22:53 JST

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IDAEO: Marketing in the Age of AI · 林毅祥(TK Lin) · 1st edition, 9th revision · · https://km.idaeo.ai/idaeo/textbook?lang=en · CC BY 4.0