Preprint Notice This is the author-accepted manuscript (AAM) of the paper "LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior, " accepted for publication in the IEEE International Conference on e-Business Engineering (ICEBE 2025), to be held 10-12 November 2025 at Mustaqbal University, Buraydah, Saudi Arabia. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting or republishing, or for creating derivative Abstract--Simulating consumer decision-making is vital for designing and evaluating marketing strategies before costly real-world deployment. However, post-event analyses and rule-based agent-based models (ABMs) struggle to capture the complexity of human behavior and social interaction. We introduce an LLM-powered multi-agent simulation framework that models consumer decisions and social dynamics. Building on recent advances in large language model simulation in a sandbox environment, our framework enables generative agents to interact, express internal reasoning, form habits, and make purchasing decisions without predefined rules. In a price-discount marketing scenario, the system delivers actionable strategy-testing outcomes and reveals emergent social patterns beyond the reach of conventional methods. This approach offers marketers a scalable, low-risk tool for pre-implementation testing, reducing reliance on time-intensive post-event evaluations and lowering the risk of underperforming campaigns.

Large-scale social digital twinning projects are complex with multiple objectives. For example, a social digital twinning platform for innovative last-mile delivery solutions may aim to assess consumer delivery method choices within their social environment. However, no single tool can achieve all objectives. Different simulators exist for consumer behavior and freight transport. Therefore, we propose a high-level architecture and present a blueprint for a generic modelling framework. This includes defining modules, input/output data, and interconnections, while addressing data suitability and compatibility risks. We demonstrate the framework's effectiveness with two real-world case studies.

Artificial Intelligence (AI) has revolutionized food marketing by providing advanced techniques for personalized recommendations, consumer behavior prediction, and campaign optimization. This paper explores the shift from traditional advertising methods, such as TV, radio, and print, to AI-driven strategies. Traditional approaches were successful in building brand awareness but lacked the level of personalization that modern consumers demand. AI leverages data from consumer purchase histories, browsing behaviors, and social media activity to create highly tailored marketing campaigns. These strategies allow for more accurate product recommendations, prediction of consumer needs, and ultimately improve customer satisfaction and user experience. AI enhances marketing efforts by automating labor-intensive processes, leading to greater efficiency and cost savings. It also enables the continuous adaptation of marketing messages, ensuring they remain relevant and engaging over time. While AI presents significant benefits in terms of personalization and efficiency, it also comes with challenges, particularly the substantial investment required for technology and skilled expertise. This paper compares the strengths and weaknesses of traditional and AI-driven food marketing techniques, offering valuable insights into how marketers can leverage AI to create more effective and targeted marketing strategies in the evolving digital landscape.

In an era dominated by the Internet of Things, ensuring the longevity and sustainability of IoT devices has emerged as a pressing concern. This study explores the various complex difficulties which contributed to the early decommissioning of IoT devices and suggests methods to improve their lifespan management. By examining factors such as security vulnerabilities, user awareness gaps, and the influence of fashion-driven technology trends, the paper underscores the need for legislative interventions, consumer education, and industry accountability. Additionally, it explores innovative approaches to improving IoT longevity, including the integration of sustainability considerations into architectural design through requirements engineering methodologies. Furthermore, the paper discusses the potential of distributed ledger technology, or blockchain, to promote transparent and decentralized processes for device provisioning and tracking. This study promotes a sustainable IoT ecosystem by integrating technology innovation, legal change, and social awareness to reduce environmental impact and enhance resilience for the digital future

The COVID-19 pandemic has had a long-term impact on industries worldwide, with the hospitality and food industry facing significant challenges, leading to the permanent closure of many restaurants and the loss of jobs. In this study, we developed an innovative analytical framework using Hamiltonian Monte Carlo for predictive modeling with Bayesian regression, aiming to estimate the change point in consumer behavior towards different types of restaurants due to COVID-19. Our approach emphasizes a novel method in computational analysis, providing insights into customer behavior changes before and after the pandemic. This research contributes to understanding the effects of COVID-19 on the restaurant industry and is valuable for restaurant owners and policymakers.

Comprehending how consumers react to advertising inputs is essential for marketers aiming to optimize advertising strategies and improve campaign effectiveness. This study examines the complex nature of consumer behaviour by applying theoretical frameworks derived from physics and social psychology. We present an innovative equation that captures the relation between spending on advertising and consumer response, using concepts such as symmetries, scaling laws, and phase transitions. By validating our equation against well-known models such as the Michaelis-Menten and Hill equations, we prove its effectiveness in accurately representing the complexity of consumer response dynamics. The analysis emphasizes the importance of key model parameters, such as marketing effectiveness, response sensitivity, and behavioural sensitivity, in influencing consumer behaviour. The work explores the practical implications for advertisers and marketers, as well as discussing the limitations and future research directions. In summary, this study provides a thorough framework for comprehending and forecasting consumer reactions to advertising, which has implications for optimizing advertising strategies and allocating resources.

The flexibility of choosing the ad action as a function of the consumer state is critical for modern-day marketing campaigns. We study the problem of identifying the optimal sequential personalized interventions that maximize the adoption probability for a new product. We model consumer behavior by a conversion funnel that captures the state of each consumer (e.g., interaction history with the firm) and allows the consumer behavior to vary as a function of both her state and firm's sequential interventions. We show our model captures consumer behavior with very high accuracy (out-of-sample AUC of over 0.95) in a real-world email marketing dataset. However, it results in a very large-scale learning problem, where the firm must learn the state-specific effects of various interventions from consumer interactions. We propose a novel attribution-based decision-making algorithm for this problem that we call model-free approximate Bayesian learning. Our algorithm inherits the interpretability and scalability of Thompson sampling for bandits and maintains an approximate belief over the value of each state-specific intervention. The belief is updated as the algorithm interacts with the consumers. Despite being an approximation to the Bayes update, we prove the asymptotic optimality of our algorithm and analyze its convergence rate. We show that our algorithm significantly outperforms traditional approaches on extensive simulations calibrated to a real-world email marketing dataset.

The COVID-19 pandemic has severely disrupted the retail landscape and has accelerated the adoption of innovative technologies. A striking example relates to the proliferation of online grocery orders and the technology deployed to facilitate such logistics. In fact, for many retailers, this disruption was a wake-up call after which they started recognizing the power of data analytics and artificial intelligence (AI). In this article, we discuss the opportunities that AI can offer to retailers in the new normal retail landscape. Some of the techniques described have been applied at scale to adapt previously deployed AI models, whereas in other instances, fresh solutions needed to be developed to help retailers cope with recent disruptions, such as unexpected panic buying, retraining predictive models, and leveraging online-offline synergies.

The digital revolution has led to the digitization of human behavior, creating unprecedented opportunities to understand observable actions on an unmatched scale. Emerging phenomena such as crowdfunding and crowdsourcing have further illuminated consumer behavior while also introducing new behavioral patterns. However, the sheer volume and complexity of this data present significant challenges for marketing researchers and practitioners. Traditional methods used to analyze consumer data fall short in handling the breadth, precision, and scale of emerging data sources. To address this, computational methods have been developed to manage the "big data" associated with consumer behavior, which typically includes structured data, textual data, audial data, and visual data. These methods, particularly machine learning, allow for effective parsing and processing of multi-faceted data. Given these recent developments, this review article seeks to familiarize researchers and practitioners with new data sources and analysis techniques for studying consumer behavior at scale. It serves as an introduction to the application of computational social science in understanding and leveraging publicly available consumer data.

AI in Manufacturing to surpass USD 42.5 billion by 2031 from USD 1.7 billion in 2021 at a CAGR of 37.6% in the coming years, i.e., 2021-31. Artificial Intelligence technology is widely being adopted in manufacturing industries to analyze complex sets of data, changes in consumer behavior, and demand for detecting anomalies and improving supply chains and distribution networks. Further, AI can improve decision making by using advanced software to gain more insights and visibility in the operation process, which is driving the market growth of AI in Manufacturing. Based on Offering, the AI in Manufacturing Market is divided into Hardware, Software, and Services, of which the Software segment is expected to lead. Specific programs can be run by software alone without the need for additional hardware.