Modelos de Inteligencia de negocios y analítica digital en campañas de marketing digital una revisión sistemática

This article presents a systematic review on the application of Business Intelligence (BI) models and Digital Analytics in digital marketing campaigns, with emphasis on small and medium-sized enterprises (SMEs) in the San Martín region of Peru. Based on indexed and open-access academic literature, t...

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Autores principales: Rolando Junior Rojas-Huamán, Yngue Elizabeth Ramírez-Pezo, George Brayam Murrieta-Patow
Formato: Artículo
Lenguaje:español
Publicado: 2025
Materias:
digital analytics
digital marketing campaigns
small and medium-sized enterprises
predictive analytics
customer segmentation
marketing attribution
return on investment
cost per acquisition
click-through rate
conversion rate
San Martín region
Big Data and Business Intelligence
Digital Marketing and Social Media
SMEs Development and Digital Marketing
Acceso en línea:Enlace permanente (DOI o Handle)
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Sumario:This article presents a systematic review on the application of Business Intelligence (BI) models and Digital Analytics in digital marketing campaigns, with emphasis on small and medium-sized enterprises (SMEs) in the San Martín region of Peru. Based on indexed and open-access academic literature, the study identified the most effective models for optimizing decision-making, personalizing campaigns, and maximizing return on investment (ROI). Inclusion and exclusion criteria were applied consistently with the research design, prioritizing studies with practical applicability focused on BI, digital campaigns, and key performance metrics such as CPA, CTR, CR, and ROI. The main findings highlight the relevance of predictive, segmentation, attribution, and automated optimization models, particularly useful in resource-limited contexts. The study concludes that integrating BI and digital analytics in SMEs is feasible but requires progressive adoption strategies, technological training, and access to affordable digital platforms, thus providing a theoretical foundation for the subsequent validation of a locally adapted BI model.

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