Optimizing sales processes with Artificial Intelligence (AI) and Machine Learning (ML): a scientometric analysis

• Autores: Ramirez Soto, Alison Nathaniel, Sandoval Ríos, José Elías, Yaranga Vite, Italo Paul, Reyes Castañeda, Pedro Efigenio
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Cesar Vallejo
• Repositorio: UCV-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ucv.edu.pe:20.500.12692/175510
• Enlace del recurso: https://journals.sapienzaeditorial.com/index.php/SIJIS/article/view/870; https://hdl.handle.net/20.500.12692/175510; https://doi.org/10.51798/sijis.v5i4.870
• Nivel de acceso: acceso abierto
• Materia: Bibliometric analysis; Sales automation; Machine learning; Artificial intelligence; Sales process; https://purl.org/pe-repo/ocde/ford#5.02.04

This study explores the literature that evaluates how artificial intelligence (AI) and machine learning (ML) can affect the optimization of sales processes, using a scientometric and bibliometric approach. Through keyword co-occurrence analysis in the scientific literature, the main trends and patterns in AI and ML research applied to sales were identified. VOSviewer software was used to map the relationships between key terms and visualize the predominant focus areas in the field. The results reveal that the adoption of AI and ML technologies is highly correlated with improvements in the efficiency of sales processes, highlighting the growing importance of these technologies in the development of business strategies. However, limited participation of researchers from developing countries was observed in this cutting-edge field, underscoring the need for greater inclusion and international collaboration. This study provides a comprehensive view of the current state of AI and ML research in sales, identifying both the advances made and the gaps in the literature that require further attention. The findings provide a solid basis for future research seeking to delve into the practical applications of these technologies in different industrial and geographical contexts, as well as for the development of policies that promote a more equitable distribution of knowledge and resources in this emerging area.