Development of a pricing strategy and profitable business model for person RE-ID it products

The aim of the article. The article aims to develop a comprehensive pricing strategy for the Person Re-Identification (Person Re-ID) product, a passive video analytics system designed for tracking individuals across cameras without relying on facial recognition. The study seeks to ensure the product’s profitability, competitiveness, and broad market penetration by addressing the diverse needs of market segments such as corporations, retail, government, small and medium-sized businesses (SMB), and Smart City initiatives. The objectives include analyzing the perceived value and pricing expectations across these segments, evaluating competitors’ pricing models, defining an optimal subscription structure, proposing price levels and package parameters, and outlining strategies for dynamic price adaptation throughout the product’s lifecycle. Analysis results. The research employs a multi-faceted methodology, combining qualitative and quantitative approaches, including market environment analysis, competitive benchmarking, value-based pricing, cost analysis, and financial modeling. The analysis identifies key market segments — corporations, government, SMBs, and Smart City projects and evaluates their unique needs, such as scalability for large enterprises, regulatory compliance for government, and affordability for SMBs. Competitive analysis reveals that existing video analytics solutions, such as Video Management Systems (VMS), Video Surveillance as a Service (VSaaS), facial recognition platforms, and broader AI-driven analytics, often rely on per-camera licensing, subscription models, or server-based licenses. Person Re-ID’s competitive edge lies in its passive identification, resource efficiency, and compatibility with existing VMS, which reduces infrastructure costs and addresses privacy concerns by avoiding biometric data collection. A value-based pricing approach, supported by expert evaluations and customer feedback, highlights the product’s ability to deliver high return on investment (ROI) through faster search times, reduced manual oversight, and compliance with privacy regulations like GDPR. A three-tier subscription model (Basic, Pro, Enterprise) is proposed, tailored to varying customer needs and budgets, with prices ranging from $100–150/month for SMBs to $1500+/month for enterprise clients. The model incorporates flexibility, allowing customers to adjust service levels dynamically, and is validated through financial projections, including break-even analysis and ROI estimates. Ethical considerations, such as transparency in data handling and customizable privacy settings, further enhance the product’s market positioning. Conclusions and directions for further research. The study develops a flexible, value-driven, multi-tiered pricing strategy for Person Re-ID that balances profitability with market accessibility. The proposed subscription model effectively targets diverse segments, from cost-sensitive SMBs to large corporations and government entities, by aligning prices with perceived value and ensuring compliance with ethical and regulatory standards. The strategy adapts to the product lifecycle, advocating penetration pricing during the launch phase, gradual price increases during growth, and profit optimization at maturity, with potential freemium offerings to attract new users. The emphasis on privacy and resource efficiency positions Person Re-ID as a competitive, ethically sound alternative to biometric-based systems. Future research directions include exploring dynamic pricing models using machine learning to adapt to demand fluctuations, investigating the impact of transparent pricing on customer trust, and formalizing ROI evaluation methodologies for video analytics solutions. Additionally, deeper analysis of market reactions to privacy-focused features and their influence on pricing perceptions could further refine the strategy. Continuous validation through A/B testing, pilot sales, and customer feedback is recommended to ensure the pricing model remains responsive to market dynamics.