Marketing^AI

Enoch H. Kang

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

JAN 28

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
12/31/2025

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
12/04/2025

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
11/30/2025

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
09/24/2025

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
09/24/2025

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
08/25/2025

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
08/18/2025

The Agent Economy: From Bots to Monetized Markets

We explore the imminent shift in the digital economy from a defensive model, where websites block automated agents, to an open, transactional "agent economy." This transformation is driven by the realization that website content is a valuable capital good for AI models, leading to a move towards monetized access via APIs. We detail the unsustainable "arms race" between scrapers and blocking technologies, advocating for APIs as an economically rational solution to monetize information goods. It then introduces the rise of autonomous AI agents as new consumers, necessitating machine-centric API design, new platforms (marketplaces, A2A commerce), and evolving business models like Agent-as-a-Service and outcome-based pricing. Finally, We highlight critical challenges, including the need for a micropayment infrastructure, widespread standardization, and robust regulatory frameworks to address data privacy, intellectual property, and potential algorithmic collusion.

32 min

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

Creator

Enoch H. Kang
Years Active

2025 - 2026
Episodes

117
Rating

Clean
Copyright

© Enoch H. Kang
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Marketing^AI