Predictive models for organic traffic growth: applying machine learning in digital analytics

Abstract

This study builds certified predictive models of organic traffic expansion through incorporation of machine learning techniques to streamline online engagement techniques. The work defines a methodological taxonomy of the process of choosing the right algorithms as well as offers practical guidelines on the implementation of the correct prediction systems depending on the situation of search-driven user acquisition. In the study, a systematic comparative analysis of twenty recent publications published in 2021-2026 will be performed, representing the current progress in the area. The research methodology integrates the outcomes of research studies assessing the performance of algorithms based on statistical models, supervised learning algorithms, deep neural networks, and hybrid ensemble algorithms considering the analysis of feature engineering and modeling temporal dependencies. Combination of ensemble algorithms (hybrid ensemble) shows better accuracy in prediction compared to individual algorithm. Attention mechanisms in recurrent neural networks have been shown to achieve good results in modeling temporal dependencies among sequential patterns. Graph neural networks are useful in learning spatial dependencies. The combination of content quality indicators, technical performance measures, and search ranking factors via feature engineering can be seen as the keys to the best accuracy. First taxonomy for methodology specific to needs of organic traffic forecasting which is different than general network prediction. First evidence-based taxonomy that integrates explainable artifical intelligence with search-driven context and connected advanced machine learning theory with digital marketing application through evidence based algorithm selection frameworks that take into account constraints of enterprise. Actionable criteria for selecting a deployment architecture for enterprise based on requirements of enterprise. Evidence based guidelines support an iterative model development process from statistical approaches at bottom level to advanced deep learning capabilities match current resource capabilities. Framework provides optimization of resources by accurately forecasting and enhancing content strategy and reducing technical barriers to marketing allocation.