Theoretical Foundations and References · IDAEO: Marketing in the Age of AI
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 proposed the GEO optimization framework and measured visibility → this book follows its core direction: raising content's visibility in generative answers. |
| 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.
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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