Business Intelligence Models and Digital Analytics in Digital Marketing Campaigns a Systematic Review

Abstract

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.