Being able to accurately predict what a customer will purchase next is of paramount importance to successful online retailing. In practice, customer purchase history data is readily available to make such predictions, sometimes complemented with customer characteristics. Given the large assortments maintained by online retailers, scalability of the prediction method is just as important as its accuracy. We study two classes of models that use such data to predict what a customer will buy next: A novel approach that uses latent Dirichlet allocation (LDA), and mixtures of Dirichlet-Multinomials (MDM). A key benefit of a model-based approach is the potential to accommodate observed customer heterogeneity through the inclusion of predictor variables. We show that LDA can be extended in this direction while retaining its scalability. We apply the models to purchase data from an online retailer and contrast their predictive performance with that of a collaborative filter and a discrete choice model. Both LDA and MDM outperform the other methods. Moreover, LDA attains performance similar to that of MDM while being far more scalable, rendering it a promising approach to purchase prediction in large assortments.

A scalable model-based approach to gain insights in dynamic purchase behavior for large product assortments and customer bases.

In modern retail contexts, retailers sell products from vast product assortments to a large and heterogeneous customer base. Understanding purchase behavior in such a context is very important. Standard models cannot be used due to the high dimensionality of the data. We propose a new model that creates an efficient dimension reduction through the idea of purchase motivations. We only require customer-level purchase history data, which is ubiquitous in modern retailing. The model handles large-scale data and even works in settings with shopping trips consisting of few purchases. As scalability of the model is essential for practical applicability, we develop a fast, custom-made inference algorithm based on variational inference. Essential features of our model are that it accounts for the product, customer and time dimensions present in purchase history data; relates the relevance of motivations to customer-and shopping-trip characteristics; captures interdependencies between motivations; and achieves superior predictive performance. Estimation results from this comprehensive model provide deep insights into purchase behavior. Such insights can be used by managers to create more intuitive, better informed, and more effective marketing actions. We illustrate the model using purchase history data from a Fortune 500 retailer involving more than 4,000 unique products.

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Devido à grande busca por qualidade das uvas, pesquisadores e produtores intensificam a descoberta de novas tecnologias e manejos. Sendo a desfolha uma técnica comum nos vinhedos, a cobertura inorgânica do solo é uma alternativa a ser aliada, a fim de melhorar as características físicas e químicas das bagas, refletindo na qualidade do mosto e do vinho. Por isso, este trabalho teve como objetivo avaliar os efeitos de um material inorgânico como cobertura do solo em conjunto com a desfolha das plantas sobre o rendimento dos frutos, qualidade do mosto e qualidade do vinho. Foram trabalhados os tratamentos com lona plástica na cor branca disposta no período de frutificação e desfolha parcial, na safra 2020. Pode-se concluir através dos resultados, que o tratamento com cobertura do solo sem desfolha teve maior massa dos cachos. As demais variáveis do mosto e do vinho não apresentaram diferença estatística. Portanto para estas condições, a desfolha não teve influência sobre os tratamentos, sendo que a cobertura do solo pode ser uma alternativa para melhorar as características físicas da uva Tannat.