Digital Analytics in the AI Era: Comparative Analysis of 15 Marketing Models and Their Effectiveness

Abstract

The rapid integration of artificial intelligence (AI) into digital marketing has fundamentally transformed how organizations apply and evaluate marketing models. This paper presents a systematic comparative analysis of 15 of the most widely cited marketing models, selected through bibliometric analysis of Google Scholar data, alongside review of major textbooks, university syllabi, and professional resources from HubSpot, McKinsey, Forrester, and Google Digital Garage. The 15 models are classified into three functional categories — audit, planning, and strategy — and analyzed for their core functions, empirical application contexts, and adaptation potential under AI-driven digital analytics conditions. The paper further draws on primary research conducted among Georgian companies and consumers (n=275), providing an emerging-economy perspective on model adoption rates and limitations. Findings reveal that while classical models such as AIDA, 4Ps, and STP remain foundational, AI-era tools demand their integration with data-driven frameworks such as RACE, TAM, and the See-Think-Do-Care model. The paper concludes by proposing an updated classification matrix that links each model to specific AI analytics capabilities, offering both academic and practical value for marketers navigating the digital transformation landscape.