Marketing^AI

This academic paper explores the design of fair and effective points-based rewards programs , which incentivize customer loyalty. The authors investigate two key challenges: individual fairness due to customer heterogeneity, as personalizing redemption thresholds can be perceived as unfair, and temporal fairness , as changes in thresholds, especially increases, can lead to customer dissatisfaction...

This paper presents a two-stage framework utilizing Large Language Models (LLMs) for generating statistically supported scientific hypotheses from a body of literature, effectively automating a process akin to meta-analysis . The first stage focuses on data extraction from scientific articles and tables using sophisticated prompting techniques to obtain relevant information, including numerical yi...

This paper describes HypoGeniC , a new method that uses large language models (LLMs) to generate and refine data-driven hypotheses . The process begins by creating initial hypotheses from a small sample, then iteratively updating them based on a reward function inspired by multi-armed bandits, which helps balance exploring new ideas and leveraging effective ones. The generated hypotheses can then...

This paper examine Large Language Models (LLMs) used as evaluators , a concept known as "LLM-as-a-Judge," comparing two primary methods: direct scoring and pairwise comparison . The analysis indicates that pairwise comparison generally yields more reliable results and better agreement with human preferences , especially for moderately sized LLMs, due to its simpler relative judgment task...

We discuss methods for obtaining phrase and clause-level attention from BERT-based models , which primarily operate at the token level. They explain how standard BERT attention works and highlight the challenge of granularity when trying to understand relationships between larger semantic units. Various approaches are outlined, including aggregating existing token attention , adapting hierarchical...

The paper examines the path for  AI wrappers , which are applications built on underlying AI models, to become  sustainable global SaaS  companies. It highlights the challenges faced by these wrappers, including  market saturation, ease of replication, and dependence on foundational AI models , which can lead to commoditization. The report argues that sustainable growth requires evolving into a &q...

We discuss how Artificial Intelligence (AI) is fundamentally transforming business software by becoming a new interface layer , changing how users interact with systems and how businesses operate. It highlights the distinction between Generative AI for content creation and Agentic AI for autonomous action, emphasizing the latter's role in driving enterprise automation. The text examines the im...

This research presents a novel training method for Vision Language Models (VLMs) focused on improving their ability to both assign scores to images and provide natural language explanations for those scores. By leveraging an existing image scoring dataset and an instruction-tuned VLM , the approach utilizes self-training without requiring additional external data or models. A key innovation is the...

This academic paper presents MANTIS , a new approach to training large multimodal models (LMMs) to handle interleaved text and images . Instead of relying on massive, potentially noisy pre-training datasets, the researchers developed MANTIS-INSTRUCT , a focused dataset of 721K instances designed to improve multi-image understanding. The paper evaluates MANTIS on several multi-image and single-imag...

We explore how Vision-Language Models (VLMs) are revolutionizing ad click prediction by processing both ad images and detailed user personas . It explains the architecture of VLMs , highlighting the dual-encoder structure and the importance of a shared embedding space and attention mechanisms in understanding the interplay between visual and textual information. The text discusses key VLM models l...

The research introduces LLaVA-Critic , a new open-source large multimodal model specifically designed to evaluate the performance of other multimodal models. Trained on a specialized dataset, it functions effectively in two primary ways: first, as an LMM-as-a-Judge , providing reliable scores comparable to or better than commercial models like GPT, and second, for Preference Learning , generating...

We examine the potential feasibility of using GPT-4V, a multimodal AI , to predict an individual's likelihood of clicking on an ad image based on a detailed psychological profile ("persona") rather than just historical behavior. The analysis breaks down how GPT-4V could process ad images and extensive persona text to infer connections, noting the complexity of matching visual element...

This paper explores how the rise of algorithms, particularly machine learning , is profoundly changing the field of economics, going beyond just impacting the economy itself. It argues that unlike previous technological innovations, algorithms represent a fundamental shift in how decisions are made , which is the core of economics. The author highlights that traditional econometric tools are desig...

This document explores how Google's implementation of the BERT algorithm in October 2019 impacted the sponsored search market. Analyzing changes in bidder competition and cost-per-click (CPC) , the authors found that BERT generally increased the number of bidders but had varied effects on CPC depending on search query length . Shorter queries saw increased CPC , while longer queries experience...

This paper explores a novel framework called LR-Recsys, which enhances black-box recommender systems by integrating Large Language Model (LLM)-based contrastive explanations . Instead of relying solely on past user behavior or explicit product details, LR-Recsys uses LLMs to generate both positive and negative reasons why a user might or might not like a product. These generated explanations, conv...

We detail research into how platforms like Mercari can guide sellers' pricing decisions. The research, a collaboration with Mercari, involved a large-scale field experiment where sellers were shown different suggested prices or no suggestions at all. Key findings indicate that suggested prices significantly influence seller listing prices , particularly for inexperienced sellers or those listi...

This paper examines how AI systems are likely to incorporate advertising , similar to web search and social media, but with new complexities due to their dynamic, personalized nature. The authors discuss potential methods for delivering commercial content , differentiating between static ads and "generative advertisements" seamlessly integrated into AI responses through product recommend...

The sources discuss how large language models (LLMs) form internal representations or "beliefs" about users based on conversational interactions, primarily through in-context learning . While LLMs can personalize responses and recall information using different memory architectures like context windows and external storage, they face significant challenges in tracking evolving user state...

We analyze the assertion that Prompt Engineering will be automated by Agentic AI within ten years , specifically validating claims understood to be made on a user-referenced webpage. It defines prompt engineering as a sophisticated skill for optimizing human-AI communication and describes Agentic AI as systems with autonomous, goal-directed capabilities like planning and self-correction. The core...

We discuss experimental design to study how tailoring text prompts for diffusion models , which generate ad visuals and text, can improve ad effectiveness. The core idea is to use the Twin-2K-500 dataset , a publicly available resource containing detailed demographic, psychological, and behavioral data from over 2,000 individuals, to define nuanced target personas . By comparing advertisements gen...

This paper describes the profound transformation of marketing driven by autonomous AI, which moves beyond simple automation to independent decision-making systems. It details key enabling frameworks like AutoGPT, Baby-AGI, and LangChain , highlighting their unique capabilities for tasks like end-to-end campaign management and hyper-personalization. However, the text also emphasizes the significant...

This academic paper introduces Augmented Marginal outcome density Ratio (AMR) , a novel approach for estimating average treatment effects (ATE) from observational data that addresses limitations of existing methods, particularly in settings with high-dimensional covariates and weak overlap . Unlike covariate-focused adjustment techniques prone to sensitivity in complex scenarios, AMR employs outco...

Causal Representation Learning with Generative ArtificialIntelligence: Application to Texts as Treatments∗This academic paper explores a novel approach to causal inference with unstructured data like text, focusing on how generative AI, specifically Large Language Models (LLMs) , can improve the process. The core idea is to leverage the internal representation of text generated by LLMs to disentan...

Instruments.

We explore the significant risk of collider bias when using covariates derived from unstructured data , such as text or images, in causal analysis. It explains that collider bias occurs when adjusting for a variable that is a common effect of two or more other variables , including the exposure and outcome of interest or their correlates. The text details how features from unstructured data, chara...