Mapping e-commerce trends in the USA: a time series and deep learning approach

Filipe R. Ramos (), Luisa M. Martinez (), Luis F. Martinez (), Ricardo Abreu () and Lihki Rubio ()
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Filipe R. Ramos: Universidade de Lisboa
Luisa M. Martinez: Instituto Português de Administração de Marketing—IPAM Lisboa
Luis F. Martinez: Universidade Nova de Lisboa
Ricardo Abreu: Instituto Português de Administração de Marketing—IPAM Lisboa
Lihki Rubio: Universidad del Norte

Journal of Marketing Analytics, 2025, vol. 13, issue 3, No 4, 606-634

Abstract: Abstract Driven by digitalization and accelerated by the COVID-19 pandemic, e-commerce has experienced strong growth, especially in the last 4 years. This transformation has reshaped consumer behavior, business models, and workplace dynamics, where digitalization, such as artificial intelligence and automation, has improved operational efficiency, personalization, and market reach. This study explores these dynamics and provides an overview of e-commerce in the U.S. through a time series approach, analyzing five key variables: sales, employment, hours worked, costs, and the producer price index. It also models and forecasts sales and the producer price index using classic, deep learning, and hybrid methods. The results show that while sales have increased, employment and labor hours have fallen, alongside stable production costs and a reduction in the producer price index over the past 2 years. In forecasting, deep neural networks offer superior predictive performance, although classic methods provide similarly accurate results in series with clear trends and seasonality, making them a more computationally efficient alternative. This research contributes to decision-making in e-commerce by exploring the relationships between sales growth and labor market dynamics, evaluating the effectiveness of different forecasting methods, and highlighting the need for strategic adaptability in a digitalized sector.

Keywords: E-commerce; Trends; Digitalization; Time series; Deep Learning; Hybrid models; Forecasting; Prediction error (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1057/s41270-025-00392-9

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