AI-Driven Marketplace Intelligence for Commercial Spaces

Abstract

The rapid expansion of digital commercial space marketplaces has significantly increased the complexity of managing bookings, pricing strategies, and service quality across multi-vendor platforms. Traditional marketplace systems rely heavily on manual decision-making and static historical data, proving insufficient in responding to real-time market trends, shifting customer preferences, and fluctuating demand. As a result, commercial space owners frequently face challenges such as inaccurate demand forecasting, revenue loss from rigid pricing, delayed customer support, and poor inventory utilization. This survey paper analyses how the strategic integration of Predictive Analytics and Generative Artificial Intelligence (GenAI) can enhance the efficiency, scalability, and customer experience of commercial space marketplaces. Specifically, the study evaluates AI-driven demand forecasting, dynamic pricing recommendations, automated customer support through intelligent agents, personalized property listing generation, and automated review analysis. The methodology involves reviewing AI-based marketplace use cases and technological frameworks currently shaping the industry. The proposed system utilizes historical and real-time data — including booking records, customer interactions, and urban mobility trends to generate actionable insights and automate critical operations. The framework also supports scalable deployment and intelligent decision-making for long-term marketplace optimization and business growth. Techniques such as Machine Learning, Natural Language Processing (NLP), BER-Topic clustering, and Large Language Models (LLMs) are examined for contextual automation and intelligent reasoning. Key findings indicate that AI-driven systems significantly reduce manual effort, improve pricing precision, and optimize occupancy rates for vendors. The study concludes that AI-powered marketplace intelligence provides a robust and scalable solution for building smarter, customer-centric commercial platforms, and establishes a foundation for future research into real-time data integration, continuous model learning, and advanced analytics in commercial real estate.