The retail industry is transitioning toward a phygital ecosystem driven by the rapid growth of data, where machine learning plays a critical role in transforming raw data into actionable insights. These applications aim to analyze customer purchasing behavior, predict future trends, and actively guide consumer decisions. Despite extensive research, the literature lacks a comprehensive and structured technology roadmap that clarifies the sequence of technological investments and operational processes required to achieve these goals.
To address this gap, this study adopts a qualitative meta-synthesis approach. Data were collected from 85 relevant academic articles and analyzed using open coding. The findings resulted in a technology roadmap for the retail industry consisting of three interrelated layers: goals, processes, and infrastructure. The goals layer includes customer behavior analysis, personalization, business performance improvement, technological innovation, ethical considerations, and market adaptability. The process layer comprises data collection, analysis, decision-making, interaction, and feedback learning. The infrastructure layer includes data-driven hardware, digital networks, artificial intelligence algorithms, digital platforms, and customer interaction interfaces.
Overall, the proposed model helps retail managers align machine learning investments with measurable financial outcomes and ethical requirements, thereby fostering sustainable competitive advantage.