Marketing^AI

Enoch H. Kang
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AI breaks down top marketing research papers into clear, quick insights.

  1. 2 MAR

    Semrush and the Agentic Economy Strategic Memo

    We explore how the agentic economy is transforming digital marketing from a competition for human attention to a struggle for algorithmic preference. As AI assistants increasingly handle search and transactions, companies like Semrush are pivoting to provide a measurement layer that tracks brand visibility within AI-generated responses. This strategic shift involves the creation of new metrics, such as AI share of voice, and the adoption of the Model Context Protocol to allow agents to query marketing data directly. The sources emphasize that in a world of agent-mediated decisions, success depends on maintaining a high-quality reputation and appearing as a cited authority in AI answers. Ultimately, the text outlines how traditional SEO is evolving into Generative Engine Optimization to ensure brands remain discoverable in both open and restricted AI ecosystems.

    45 min

  2. 28 GEN

    Richard Hamming: The Art of Doing Great Research

    We discuss a 1986 lecture by Richard Hamming, a renowned scientist who explores why certain researchers achieve extraordinary breakthroughs while others are eventually forgotten. Hamming argues that significant contributions are not merely products of luck or high intelligence, but result from deliberate habits such as working on the most important problems in one's field. He emphasizes the necessity of unwavering commitment, the courage to challenge established norms, and the ability to effectively communicate and sell one's ideas to the broader community. The text also highlights how personal traits, including a tolerance for ambiguity and a willingness to adapt to organizational systems, play a vital role in sustaining a productive career. Ultimately, the source serves as a strategic guide for individuals seeking to maximize their professional impact and leave a lasting legacy in science or engineering.

    38 min

  3. 31/12/2025

    Preference Engineering: Marketing in the Agentic Economy

    We describes a fundamental transition from the Attention Economy to an Agentic Economy, where autonomous AI agents increasingly handle market transactions. In this new landscape, **marketing** is reimagined as a technical discipline called **Data Source Engineering**, focusing on the rigorous collection of human preference data rather than simple brand persuasion. This evolution prioritizes **Preference Alignment** and **Active Learning**, using consumer interactions to refine the AI models that drive decision-making. The report highlights emerging **infrastructure protocols** and **elicitation frameworks** that allow brands to communicate directly with digital assistants. Ultimately, the role of the marketer shifts toward managing a **"Digital Twin"** of the market to ensure business agents accurately reflect and serve user intent. These sources argue that firms must master **technical signaling** and data integrity to remain visible to the algorithmic buyers of the future.

    39 min
  4. Generative Brand Choice

    04/12/2025

    Generative Brand Choice

    This paper proposes a novel approach to the challenging problem of forecasting consumer demand for new brands where preference is largely driven by intangibles. The central framework integrates a structural demand model, which initially estimates existing brand utilities, with a fine-tuned large language model (LLM). By training the LLM on textual descriptions of products and markets, the resulting model can successfully predict consumer preferences ($\delta_{jt}$) for items it has never encountered, significantly outperforming conventional models based on text embeddings. The author analyzes the internal mechanics of the LLM using techniques like sparse autoencoders to identify interpretable features influencing choices, thus offering guidance on brand positioning. Furthermore, the methodology allows researchers to perform advanced market simulations, such as solving for optimal pricing by combining LLM predictions with causal estimates derived from instrumental variable methods.

    34 min
  5. E-GEO: A Testbed for Generative Engine Optimization in E-commerce

    30/11/2025

    E-GEO: A Testbed for Generative Engine Optimization in E-commerce

    This paper presents a systematic study of **Generative Engine Optimization (GEO)** in the e-commerce sector, a practice now vital as LLMs deploy conversational shopping agents that rerank products. To address the lack of data and systematic methods in this emerging field, the authors introduce **E-GEO**, a novel benchmark dataset comprising over 7,000 realistic, intent-rich consumer queries paired with product listings. The research evaluates 15 existing content rewriting heuristics but finds that framing the process as an **optimization problem** yields much greater results. Specifically, an iterative prompt-optimization algorithm consistently delivers **superior ranking improvements** for products within the generative engine's output compared to relying on ad hoc rules. This successful systematic approach indicates the existence of a **stable, generally effective GEO strategy** that could be applied across various product domains.

    32 min

  6. 24/09/2025

    Improving Historical Census Transcriptions: A Machine Learning Approach

    This paper describes an effort to improve the accuracy of historical U.S. Census transcriptions using a machine learning model. The authors focused on correcting errors in name transcriptions from the 1940 census for Rhode Island, specifically targeting records where independent human transcriptions from Ancestry.com and FamilySearch.org disagreed. The improved transcriptions significantly increased the rate of linking individuals across census records, particularly benefiting records with low original legibility where human transcribers typically struggle. This approach promises to enhance the utility of historical census data for economic and social research by creating a higher quality, linked dataset across multiple periods.

    18 min

  7. 24/09/2025

    Regulation, Investment, and Misallocation in Natural Gas Pipelines

    This is from a working paper that analyzes the regulatory distortion in investment incentives within the United States natural gas pipeline network. The authors develop and estimate a structural model to compare the marginal social value of pipeline capacity—tied to regional gas price differences—against the financial incentives of firms operating under fixed rate-of-return regulation. The paper finds that firms' incentives to invest frequently exceed the social value of capital, emphasizing the critical role of the costly regulatory approval process as a secondary tool to control investment and prevent overcapitalization. Ultimately, the authors suggest a welfare-improving reallocation of regulatory costs, recommending streamlining approval in the Northeast while tightening scrutiny in the Southeast and Mountain West regions to address a persistent spatial misallocation of capital.

    15 min

  8. 25/08/2025

    On the Structural Basis of Conditional Ignorability

    This paper examines the challenges of conditional ignorability, a key assumption in causal inference used to identify causal effects from observational data. It argues that assessing this assumption is more complex than often perceived, as it implicitly requires evaluating numerous structural configurations within covariate sets. To address this, the authors propose a new framework using Cluster Causal Diagrams (CG(3)), which abstracts the internal structure of covariates into three blocks: treatment (X), outcome (Y), and adjustment covariates (Z). This approach introduces structural ignorability, a concept evaluated using a modified back-door criterion on CG(3) diagrams, offering a more transparent and practical method for assessing causal assumptions. The paper highlights that while conditional ignorability cannot be reliably assessed at this level of abstraction, structural ignorability provides a principled middle ground between the traditional potential outcomes (PO) framework and comprehensive structural causal models (SCMs).

    19 min
Elenco completo (118)

Descrizione

AI breaks down top marketing research papers into clear, quick insights.

Informazioni

  • Creatore
    Enoch H. Kang
  • Anni di attività
    2025 - 2026
  • Puntate
    118
  • Classificazione
    Contenuti adatti a tutti
  • Copyright
    © Enoch H. Kang
  • Sito web del podcast
    Marketing^AI