Poverty is a powerful context that affects billions of consumers around the world. An appreciation of this context and the ways it shapes thoughts, feelings, and behaviors is essential to understanding the vulnerabilities of low-SES consumers. We synthesize research on consumption in poverty by reviewing some of the social vulnerabilities and frequent neglect, discrimination, and stigmatization encountered by low-SES consumers, as well as the cognitive challenges emerging from the experience of financial scarcity. These social, cognitive, and societal vulnerabilities highlight the importance of behaviorally informed programs and policies to address consumer vulnerability in contexts of poverty.
Almost every aspect of consumers are subject to digitization and storage — physical movement, product and media consumption, online search and shopping habits, body metrics and social interactions. Using cookies, offline beacons and data trading, these so-called “digital exhaust” of individual behavior are collated to obtain “360 degree,” real-time view of consumers. Advances in computer science in the sub-fields of natural language processing and computer vision allows for sense making of qualitative unstructured data involving text, images, video and various forms of multi-modal data from consumers and the marketplace. Collectively, these advances in real time and varied data collection, algorithmic methods that make sense of data, and cheap computational power have led to significant advances in data-driven artificial intelligence. For quantitative marketers, these advances in AI have led to exciting opportunities to improve marketing — through real time and contextualized personalization of all elements of the marketing mix (the 4Ps), automation of many marketing actions, learning about customer needs and consumer feedback to products and services in the marketplace. AI can improve marketing activities (e.g., market research) and decisions by making them faster, cheaper or better. Marketers and marketing scholars have mostly taken a firm-centric profit maximizing perspective in the application of AI. But as AI has made greater inroads in the marketplace and society, considerable debate has risen about the terms of that trade underlying the sharing of individual and societal data with firms. This debate has become especially strong when it raises concerns about data privacy, bias and fairness. To the extent that consumer and societal data are the raw material of ongoing AI-based innovation, marketers have to address these issues in a balanced manner to ensure the long-term sustainability in the use of AI for marketing — as these have implications for consumer data-sharing with firms and societal regulations governing the use of data and AI. The purpose of this volume is to provide a deep and expansive overview of the state of the art of AI research in marketing. The next ten chapters in this volume cover managerially relevant applications of AI in marketing, while also describing relevant methods. The chapters also point scholars to exciting new areas of research at the interface of AI and marketing. In this introductory chapter, we seek to provide an organizing structure for AI scholarship and use it to situate the research described in the handbook chapters. We also hope the structure will aid scholars in identifying gaps in the literature to make new contributions.
The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.
The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.
This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.
In this paper, we aim to provide a comprehensive overview of customer feedback literature, highlighting the burgeoning role of artificial intelligence (AI). Customer feedback has long been a valuable source of customer insights for businesses and market researchers. While previously survey focused, customer feedback in the digital age has evolved to be rich, interactive, multimodal, and virtually real time. Such explosion in feedback content has also been accompanied by a rapid development of AI and machine learning technologies that enable firms to understand and take advantage of these high-velocity data sources. Yet, some of the challenges with traditional surveys remain, such as self-selection concerns of who chooses to participate and what attributes they give feedback on. In addition, these new feedback channels face other unique challenges like review manipulation and herding effects due to their public and democratic nature. Thus, while the AI toolkit has revolutionized the area of customer feedback, extracting meaningful insights requires complementing it with the appropriate social science toolkit. We begin by touching upon conventional customer feedback research and chart its evolution through the years as the nature of available data and analysis tools develop. We conclude by providing recommendations for future questions that remain to be explored in this field.
This paper reviews the recent developments at the intersection of personalization and AI in marketing and related fields. We provide a formal definition of personalized policy and review the methodological approaches available for personalization. We discuss scalability, generalizability, and counterfactual validity issues and briefly touch upon advanced methods for online/interactive/dynamic settings. We then summarize the three evaluation approaches for static policies -- the Direct method, the Inverse Propensity Score estimator, and the Doubly Robust method. Next, we present a summary of the evaluation approaches for special cases such as continuous actions and dynamic settings. We then summarize the findings on the returns to personalization across various domains, including content recommendation, advertising, and promotions. Next, we discuss the work on the intersection between personalization and welfare. We focus on four of these welfare notions that have been studied in the literature: (1) search costs, (2) privacy, (3) fairness, and (4) polarization. We conclude with a discussion of the remaining challenges and some directions for future research.
The expansion of marketing data is encouraging the growing use of deep learning (DL) in marketing. I summarize the intuition behind deep learning and explain the mechanisms of six popular algorithms: three discriminative (convolutional neural network (CNN), recurrent neural network (RNN), and Transformer), two generative (variational autoencoder (VAE) and generative adversarial networks (GAN)), and one RL (DQN). I discuss what marketing problems DL is useful for and what fueled its growth in recent years. I emphasize the power and flexibility of DL for modeling unstructured data when formal theories and knowledge are absent. I also describe future research directions.
We provide an overview of how artificial intelligence is transforming the identification, structuring, and prioritization of customer needs – known as the voice of the customer (VOC). First, we summarize how the VOC helps firms gain insights on using user-generated data. Second, we discuss the types of user-generated data and the challenges associated with analyzing each type of data. Third, we describe common methods, matched to the firms' goals and the structure of the data, that are used to analyze the VOC. Fourth, and most importantly, we map the methods to relevant applications, providing guidance to select the appropriate method to address the desired research questions.
