Using Machine Learning to Identify Strategic Brand Growth Points

Abstract

The study presents a systematic arrangement and theoretical decomposition of machine-learning (ML) approaches with the aim of identifying and testing a brand’s key growth levers. Its objective is to construct a unified conceptual framework that integrates diverse ML algorithms into a closed analytical loop for processing market data, uncovering latent insights, and forecasting opportunities for brand expansion. The methodological foundation is built on an analysis and synthesis of leading publications in predictive analytics, natural language processing (NLP), and clustering techniques applied to marketing. The outcome is a multi-layer architecture that enables a staged progression from raw-data collection and aggregation to the formulation of growth hypotheses and their virtual validation. Scientific novelty lies in the description of a framework that eliminates fragmented ML usage in brand management by embedding these techniques into a single strategic process. The results are expected to benefit other researchers as well as strategy and marketing directors seeking to adopt data-driven approaches to brand governance.