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Advances in artificial intelligence have ushered in new opportunities for marketers in the domain of content generation. We discuss approaches that have emerged to generate text and image content. Drawing on the customer equity framework, we then discuss the potential applications of automated content generation for customer acquisition, relationship development, and customer retention. We conclude by discussing important considerations that businesses must make prior to adopting automated content generation.

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.

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.

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.

This research uses interpretive phenomenology to investigate the effect of visitor–destination interactions and image formation. It seeks to understand the processes that lead the visitor to make sense of his destination experience for her/himself and to others, and transmit that image through his story. A subjective personal introspective SPI and longitudinal observation have been used to collect data and acquire an insider perspective on the image of France as a place experienced by an Asian researcher who is living, experiencing, working, visiting, and traveling in France. The results of this research tend to move the understanding of destination image formation forward by taking a holistic approach that allows researching personal image perception and construction from genuine insider perspective as it is qualified by the individual within his own experiences. The main contribution of this research is to show how the image of a destination might evolve from a tourism destination to a mundane consumption place. This idea emphasizes the transformation of a tourist to a nontourist consumption place.

This paper introduces the development of a new type of sales promotion strategy to create more value for goods and to avoid price discounting. I use a psychological approach designed by creating consumer insight hypotheses based on in-depth interviews, which are then verified by web-motivation research and text-mining. This innovative sales promotion approach is a very hot topic as a new type of promotion development among large companies in Japan and is useful in avoiding price-discounting sales. This paper explains the concrete process used in this type of promotion and reveals the successful case of a large spice company in Japan. The process uses price sensitivity measurement (PSM) as a pricing technique. In the experiment, conducted in nine retail stores, the most successful sales promotion condition saw an increase of 900% in monetary sales without price discounting during the two weeks of the experiment, and 500% in the two weeks after that.