The authors propose a new, exploratory approach for analyzing market structures that leverages two recent methodological advances in natural language processing and machine learning. They customize a neural network language model to derive latent product attributes by analyzing the co-occurrences of products in shopping baskets. Applying dimensionality reduction to the latent attributes yields a two-dimensional product map. This method is well-suited to retailers because it relies on data that are readily available from their checkout systems and facilitates their analyses of cross-category product complementarity, in addition to within-category substitution. The approach has high usability because it is automated, is scalable and does not require a priori assumptions. Its results are easy to interpret and update as new market basket data are collected. The authors validate their approach both by conducting an extensive simulation study and by comparing their results with those of state-of-the-art, econometric methods for modeling product relationships. The application of this approach using data collected at a leading German grocery retailer underlines its usefulness and provides novel findings that are relevant to assortment-related decisions.

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Psychology and economics (the mixture of which is known as behavioral economics) are two fundamental disciplines underlying marketing. Various marketing studies document the nonrational behavior of consumers, even though behavioral biases might not always be consistently termed or formally described. In this review, we identify empirical research that studies behavioral biases in marketing. We summarize the key findings according to three classes of deviations (i.e., non-standard preferences, non-standard beliefs, and non-standard decisionmaking) and the marketing mix instruments (i.e., product, price, place, and promotion). We thereby introduce marketing researchers to the theoretical foundation of and terminology used in behavioral economics. For scholars from behavioral economics, we provide ready access to the rich empirical, applied marketing literature. We conclude with important managerial implications resulting from the behavioral biases of consumers, and we present avenues for future research.

The authors would like to thank two anonymous referees for their helpful and constructive comments as well as the co-editor of the special section, Udo Wagner, for his detailed feedback and suggestions. Furthermore, the authors benefitted from critical discussions with participants at the 5 th French-Austrian-German Workshop on Consumer Behavior. Daniel Guhl gratefully acknowledges support by the Deutsche Forschungsgemeinschaft (DFG) through CRC TRR 190.

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