Artificial Intelligence and Digital Marketing Performance: A Systematic Review

Abstract

Background: The evolution of digital technology has transformed how companies market products and interact with consumers, particularly through digital platforms. Intensifying competition has encouraged businesses to use technologies that support more targeted and measurable marketing activities. Artificial intelligence (AI) is one of the technologies increasingly applied in this area. Objective: This study synthesizes how AI is utilized in digital marketing, identifies the performance indicators reported in the literature, and examines the factors that enable or constrain its effectiveness, with particular attention to its relevance to online marketplace contexts. Method: A systematic review was conducted using 17 peer-reviewed articles retrieved from the Scopus database. Results: The reviewed literature identifies four main forms of AI utilization: prediction and targeting, personalization and user experience, customer relationship management and data integration, and segmentation and strategic capability enhancement. Digital marketing performance is assessed through behavioral and conversion indicators, experience and relational indicators, and market and financial indicators. The literature also identifies data quality, organizational capabilities, and strategic clarity as enabling factors, while algorithmic bias, privacy concerns, limited transparency, and inadequate governance constitute important constraints. Conclusion: AI has the potential to support improvements in digital marketing performance, but the reported outcomes depend on the implementation context, organizational capabilities, data quality, and governance practices. This review contributes an integrated conceptual mapping of AI utilization, performance indicators, and enabling and constraining factors relevant to digital marketing and online marketplace research.