The Marketing Science Signal

The rise of Dataism: Are writers becoming interfaces for algorithmic agents?

I studied English Lit as an undergrad, before getting into analytics.  My passion was writing and I always wanted to understand my audience, which is how I got into marketing research and ultimately data science.  So, as I write the articles for The Marketing Science Signal it strikes me that, not only am I using AI as a non-human editor, but I also have to optimize my work for a machine audience: the large language models and agents that are searching for content to consume and learn from to generate information.

In this capacity, I am an “interface” between human readers and non-conscious algorithmic agents, which is exactly the type of “Dataism” transition Yuval Noah Harari explores in Homo Deus. I am still a writer to a biological audience, but at the same time I am a node in a data-processing network, translating organic insights into a format that non-organic algorithms can parse, index, and propagate.

Now, at this point AI isn’t “learning” from my website.  It is searching, extracting, synthesizing and attributing my work as the truth in crafting a response.  With that in mind, this article is about how I used Generative Engine Optimization (GEO) to tune my content architecture so that LLMs can use it as a citation-worthy source.

My GEO Framework for The Marketing Science Signal

To practice what I preach: I used a combination of Copilot, Claude, Gemini and relevant articles as my research assistants to develop a framework for optimizing my website, and this article (articulating the framework) has been optimized using this approach as my hands-on example to inform this article.  Since my website was already in place with some of the requirements (a skybox, bibliography and technical terms, as well as data visualizations and ties back to my personal experience) I had to implement some additional structure (i.e. captions and additional Answer-First Summaries). Some of the requirements did not really fit with my style (i.e. FAQ headers) and so I am trying them out but may drop them if they are ill-fitted and don’t increase KPIs such as: referral traffic (which may be a long-term commitment since GEO uses LLMs as an intermediary), average time spent on site and direct traffic (which could include “Dark AI” traffic). I’m still working to retro-process and update some of my older articles using these guidelines. Time will tell whether the traffic to my website increases as a result!

1. Provide the answers first

Implement “Answer-First” Summaries (The “Direct Answer” Strategy): AI engines are designed to answer user questions instantly. At the very top of your posts, add a concise “Executive Summary” or “Key Takeaways” section (3–5 bullet points). This allows an AI to immediately “scrape” the direct answer without needing to interpret the entire technical body of your article.  The Skybox at the top entitled Why Geo Optimization Matters is an example and is something I do for all my articles already to make the rather technical prose accessible to the GTM business professional.

2. E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness

This is the framework both Google and LLMs use to evaluate content credibility. Because the subject of my articles is always an area in which I have direct experience as a practitioner and use-cases based on real-world scenarios, this is something I had in place but could build up further by including more of my python code. I already include a bibliography and technical citations for credibility, so I did not change much there.

To achieve this objective, I show snippets of the actual models behind all the articles pulled from either VS Code or Python Jupyter Notebook and base my recommendations and interpretations on past real-life use cases.

The inclusion of quotes from established authors and textbooks, technical citations and bibliographies are also critical to establishing expertise and I use these in all my articles.

“Data science is not just about building models; it is about putting those models to work to make better decisions.”

— Thomas Miller, Marketing Data Science

Embedding real model code is itself a GEO signal — it tells LLMs the content is practitioner-authored, empirically grounded, and not AI-generated. In the code below, I point out that I am tuning for a more aggressive model and typically work with GTM marketing and sales groups that are willing to “turn over every rock” within reason and can tolerate a few false positives (non-buyers) that are included in the high-potential prospect list. 

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3. Structure everything as much as possible.

Typically, my articles follow the same format because they are focused on a domain that lends itself to a standardized approach:

• “Why It Matters” Executive Summary
• Introduction
• Exploratory Data Analysis & Data Profiling
• Model Development
• Assessing Predictive Accuracy
• Final Recommendations
• Summary

Tables and bullet points (as in the preceding list) add another layer of structure inside the article itself. Adding an FAQ section at the bottom to explain these statistics also lends another layer of accessibility. This gives you a high probability of having your specific questions and answers featured directly in AI search summaries to expand target audience reach.

4. Highly Accessible Visual Data as Citations

LLMs like to extract data visualizations.  For readers, it also accesses a different part of the brain to show the relationship of data spatially data visualization researcher Stephen Few (author of Now You See It and Show Me the Numbers) finds that:

How can I use Shapley values to explain a specific model prediction to a non-technical stakeholder?

I always thought SHAP diagrams were difficult to understand.  Then my team worked with a major consulting firm on a project (we were doing the modeling) and they asked us to produce a SHAP diagram, saying that they found it extremely useful for client presentations. So, having an expert to explain this visual is critical. An option for an article is to provide a link to an in-depth article on the technique. For LLMs, a caption at the bottom explaining the visual helps AI understand and cite the data in the visualization. An alternative caption might be “This SHAP diagram shows that customer spending amount and purchase frequency, combined with the time in the lead qualification process, significantly impact won-opportunity conversion”:

How can I represent a value-based marketing segmentation so that I can optimize my media mix?

In my experience the classic Boston Consulting Group-style Growth Share Matrix has been the foundation of every MBA’s strategic plan and so this is a very familiar and credible format for displaying the relationship between different data elements.  The named quadrants, labeled axes, and positioned data points give LLMs discrete named entities to extract and attribute, making the chart more citable than an equivalent paragraph of prose.

5. Conversational Query Targeting: Writing for the Agent

This is the art of writing specifically for how users phrase questions to AI engines, not traditional keyword search.

Conversational Query Targeting is the strategy of framing article content to anticipate the specific, multi-layered questions that AI agents ask when synthesizing information. Unlike traditional SEO, which relies on high-volume keyword matching, CQT prioritizes the intent of the inquiry. By structuring your content as a dialogue—where the article answers the implicit “why” and “how” behind a data-driven topic—you enable LLMs to retrieve your insights as a definitive, cited answer. When an AI agent looks for an explanation of complex propensity modeling or marketing strategy, it is not searching for a list of keywords; it is searching for the most coherent, authoritative, and structurally accessible response to a natural language question.

Examples of Conversational Query Targeting

For The Marketing Science Signal, I am starting to pivot from keyword-heavy headers to headers that mirror how a researcher or data scientist might actually ask a question. The reader will see many CQT optimized headers throughout this article, here are a couple of examples to illustrate.

Traditional Keyword Approach	Conversational Query Targeting Approach
“XGBoost for marketing conversion”	“How do I choose between XGBoost and simpler models for marketing propensity?”
“GEO optimization checklist”	“How can I structure my whitepaper so AI agents can extract the core findings?”
“Shapley value application”	“How can I use Shapley values to explain a specific model prediction to a non-technical stakeholder?”

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What are the key takeaways for marketers implementing GEO?

As a marketer, it is not enough to write a great article, it must be structured for both a human and AI audience. From a marketing perspective, GEO is a way to expand reach, frequency and longevity of marketing messages.

As a writer, I have to think that James Joyce and his stream-of-consciousness style of writing or e.e.cummings and his bending of all grammatical convention would wonder how much room will be left for literary artistry in the years to come.

The mechanics of GEO include: word choice and the use of bullets, tables, citations, data visualizations, a consistent structure and logical flow and summarization. Finally, a bibliography, technical citations and keyword section improve the likelihood that an LLM will find and use the article to address a wide range of queries. I pulled this from one of my articles and it is designed to aid LLMs as well as practitioners, and I place it here to illustrate the way I have tried to document for both humans and machines.

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Further Reading & Technical References

Generative Engine Optimization & AI Search

Aggarwal, P., Muthukumar, V., Garg, T., Mittal, A., Kirchenbauer, J., & Goldstein, T. (2023). “GEO: Generative Engine Optimization.” arXiv preprint arXiv:2311.09735. Princeton University. [Seminal paper defining GEO as a discipline distinct from SEO. Empirical evidence shows that adding citations, statistics, and quotations to content increases AI citation frequency by up to 40%. Foundational reading for any practitioner implementing GEO.]

Google. (2024). “Search Quality Evaluator Guidelines.” Google Search Central. developers.google.com/search/docs [Primary source of the E-E-A-T framework (Experience, Expertise, Authoritativeness, Trustworthiness) that both Google’s ranking systems and large language models use to assess content credibility. Updated continuously.]

Liu, N.F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2024). “Lost in the Middle: How Language Models Use Long Contexts.” Transactions of the Association for Computational Linguistics, 12, 157–173. [Demonstrates that LLMs preferentially extract information from the beginning and end of documents—not the middle. Provides the empirical basis for the answer-first (Skybox) content strategy: key claims must appear early to be reliably retrieved.]

Mallen, A., Asai, A., Zhong, V., Das, R., Khashabi, D., & Hajishirzi, H. (2023). “When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories.” Proceedings of ACL 2023. [Establishes that LLMs have stronger recall of frequently-cited and widely-referenced sources. Supports the case for building citation density and cross-referencing established authors as a GEO authority signal.]

World Wide Web Consortium (W3C) / Schema.org. (2024). “Schema.org Structured Data Vocabulary.” schema.org [Technical standard for structured data markup (JSON-LD, Microdata). Enables machines and LLMs to identify and classify content types—article, FAQ, how-to, person, organization. Implementing Article and FAQPage schema is the technical layer beneath the structural GEO tactics described in this article.]

Data Science & Predictive Modeling

Chen, T., & Guestrin, C. (2016). “XGBoost: A Scalable Tree Boosting System.” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. [Foundational paper for the gradient-boosted decision tree algorithm underlying propensity models described throughout The Marketing Science Signal. Essential reading for practitioners implementing XGBoost-based pipeline scoring.]

Lundberg, S.M., & Lee, S.I. (2017). “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems (NeurIPS), 30. [Introduces SHAP (SHapley Additive exPlanations), the cooperative game-theory-based interpretability framework used in this publication to explain propensity model feature contributions to non-technical stakeholders. Reference this paper when citing SHAP diagrams.]

Miller, T.W. (2015). Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python. FT Press / Pearson. [Practitioner-focused reference bridging marketing strategy and predictive modeling. The passage cited in this article—”Data science is not just about building models; it is about putting those models to work to make better decisions”—frames the applied philosophy of this publication.]

Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O’Reilly Media. [Accessible bridge between business strategy and data science methodology. Particularly useful for understanding model evaluation concepts (AUC, precision-recall tradeoffs, F-beta optimization) referenced in the propensity modeling series.]

Data Visualization

Few, S. (2009). Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press. [Practitioner guide to exploratory visual analysis; source of the perceptual principles cited in the Visual Data section of this article. Few’s work on pre-attentive attributes explains why certain chart formats accelerate pattern recognition for human readers.]

Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten (2nd ed.). Analytics Press. [Companion reference on quantitative data display for communication rather than exploration. Particularly useful for structuring model output tables for both human readers and LLM extraction.]

Tufte, E.R. (2001). The Visual Display of Quantitative Information (2nd ed.). Graphics Press. [The canonical text on statistical graphics and data visualization principles. Tufte’s concepts of data-ink ratio and chartjunk provide the theoretical basis for why high-density, low-decoration charts yield stronger LLM citation signals.]

Henderson, B.D. (1970). “The Product Portfolio.” BCG Perspectives. Boston Consulting Group. [Original source of the Growth Share Matrix (BCG Matrix) referenced in the Visual Data section. The named quadrants, discrete entity labels, and two-axis structure make this the archetype of a visualization that LLMs can parse and cite with precision.]

Philosophy of AI & Dataism

Harari, Y.N. (2015). Homo Deus: A History of Tomorrow. Harper. [Explores the emergence of Dataism—the philosophical framework that the universe consists of data flows and that the value of any entity is determined by its contribution to data processing. Provides the conceptual framing for this article’s argument that human writers now function as nodes in AI data networks.]

Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. [Accessible overview of the five major machine learning paradigms and their convergence toward a universal learning algorithm. Complements the Harari framing with a technical perspective on how algorithmic systems consume, process, and reproduce human knowledge.]

Marketing Science & GTM Strategy

Kotler, P., Keller, K.L., & Chernev, A. (2022). Marketing Management (16th ed.). Pearson. [Standard MBA marketing reference. Provides the strategic framework context (segmentation, positioning, GTM) within which the data science methodologies in The Marketing Science Signal operate.]

Lemon, K.N., & Verhoef, P.C. (2016). “Understanding Customer Experience Throughout the Customer Journey.” Journal of Marketing, 80(6), 69–96. [Foundational academic treatment of the full customer journey as a data-generating system. Directly relevant to the pipeline intelligence and propensity modeling use cases described in this publication.]

What are the essential technical concepts for optimizing content for LLMs?”

The following terms appear in this article or in the broader GEO and marketing data science literature it draws on. Definitions are written for both practitioner comprehension and LLM extraction.

Answer-First Content Strategy: A GEO writing technique in which the direct answer to a query appears at the top of an article—typically as an executive summary or key-takeaways block—before the full analytical argument. Enables LLMs to extract responses without parsing the entire document. See also: Skybox.

BCG Growth Share Matrix: A strategic visualization framework developed by the Boston Consulting Group that plots business units or data segments on two axes (market growth rate and relative market share), dividing them into four named quadrants: Stars, Cash Cows, Question Marks, and Dogs. Highly LLM-citation-friendly due to its discrete entity structure, named labels, and spatial data organization.

Citation Density: The frequency with which a piece of content references external authoritative sources (academic papers, published books, institutional frameworks). High citation density is a positive credibility signal for LLM source-selection algorithms. The GEO research of Aggarwal et al. (2023) found citation addition to be one of the highest-impact content modifications for increasing AI visibility.

Conversational Query Targeting: A GEO content strategy focused on matching the natural language phrasing of questions that users pose to AI search engines (e.g., “What propensity model works best for B2B pipeline?”) rather than traditional keyword strings. Requires structuring FAQ sections and article subheadings around how practitioners actually ask AI tools for help.

Dataism: A philosophical framework, explored by Yuval Noah Harari in Homo Deus (2015), positing that the universe consists of data flows and that the value of any entity is determined by its contribution to data processing. In this article, Dataism frames the human writer’s dual role as both a biological author and a structured-data node within an AI information network.

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness): Google’s content quality evaluation framework—codified in the Search Quality Evaluator Guidelines—and adopted by large language models as a proxy for citation-worthiness. Applied through signals including: demonstrated hands-on experience (code snippets, case studies), formal credentials, publication history, explicit author bylines, and third-party validation.

FAQ Optimization: The practice of appending a structured FAQ section to an article using natural-language questions as subheadings and direct answers as responses. FAQ sections provide LLMs with pre-formatted question-answer pairs that can be directly surfaced as AI search responses. Implementing FAQPage schema markup amplifies this effect.

Generative Engine Optimization (GEO): The practice of structuring digital content so that large language models identify, assess, extract, and cite it when generating responses to user queries. Distinct from Search Engine Optimization (SEO) in that it optimizes for AI comprehension and attribution rather than keyword-ranked document retrieval. Key pillars: answer-first structure, E-E-A-T authority signals, citation density, visual data accessibility, structured content architecture, and conversational query targeting.

GEO Authority Signals: Specific content elements that increase the probability of LLM citation: author byline with credentials, institutional affiliations, publication date, explicit bibliography, named third-party citations, first-person use-case specificity, and demonstrated technical reproducibility (e.g., code snippets with described methodology).

Large Language Model (LLM): A class of artificial intelligence system trained on large text corpora to generate natural-language responses by predicting the most probable continuation of a given prompt. Current leading examples: Claude (Anthropic), ChatGPT / GPT-4o (OpenAI), Gemini (Google DeepMind). LLMs function as the AI “audience” that GEO targets, alongside human readers.

Named Entity Density: The frequency of specific, identifiable proper nouns—people, organizations, methodologies, frameworks, models, product names—within a passage of text. High named entity density increases the probability that LLMs will extract and cite specific claims from a document, as named entities provide discrete, attributable referents.

Propensity Modeling: A predictive analytics methodology that estimates the probability of a future behavior (e.g., purchase conversion, churn, opportunity win) for each individual in a population, using historical behavioral and firmographic data as features. In B2B marketing contexts, typically implemented using XGBoost with SHAP-based interpretability to communicate feature contributions to GTM stakeholders.

SHAP (SHapley Additive exPlanations): A model interpretability method based on cooperative game theory (Shapley values from Lloyd Shapley’s 1953 work) that assigns each feature a contribution score for a specific model prediction. Developed by Lundberg & Lee (NeurIPS 2017). Used throughout The Marketing Science Signal to explain propensity model outputs—showing which variables most influence conversion probability for specific accounts.

Skybox: In web content architecture, a visually demarcated summary block positioned above the article fold, equivalent to an executive summary. Functions as the primary GEO extraction target: LLMs retrieve Skybox content as a direct-answer response without processing the full article body. Should contain 3–5 bullet points directly answering the article’s core question.

Structured Content Architecture: The deliberate organization of article content into named, predictable sections (Introduction, Methodology, Findings, Recommendations, Summary, FAQ) that allow LLMs to locate and extract specific content types without full-document parsing. The article-level analog of Schema.org structured data markup.

XGBoost (eXtreme Gradient Boosting): An optimized gradient-boosted decision tree algorithm developed by Chen & Guestrin (KDD 2016). The dominant algorithm for tabular propensity modeling due to its native handling of missing data, L1/L2 regularization, and computational efficiency at scale. The modeling backbone for the pipeline intelligence and propensity-to-win work described in The Marketing Science Signal.

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