ВИКОРИСТАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В ПРОМИСЛОВОМУ МАРКЕТИНГУ

Анотація

The article provides a comprehensive analysis of the possible use of artificial intelligence in industrial marketing as a tool for increasing the effectiveness of personalization of B2B communications in the context of the digital transformation of the economy, increased global competition and increased requirements of corporate clients for the quality of service, speed of response and individualization of commercial offers. The tools for applying artificial intelligence in industrial marketing are disclosed, in particular big data analytics, machine learning, intelligent CRM systems, chatbots and recommendation systems that provide segmentation of B2B clients, personalization of requests, personalization of commercial offers and optimization of marketing forecast budgets. It is substantiated that the use of artificial intelligence tools contributes to increasing the accuracy of targeting, reducing deal cycles, reducing transaction costs and forming long-term partnerships with corporate clients. A conceptual model of artificial intelligence integration with corporate information systems and digital communication channels is proposed, which ensures the coordination of marketing decisions with the production, logistics and financial processes of an industrial enterprise. Such integration allows synchronizing the demand forecast with production capacities, optimizing equipment loading planning, inventory management and supply logistics, which ensures the operational efficiency of the enterprise and the level of satisfaction of B2B customers. It is shown that the use of AI-based analytics creates the prerequisites for the transition from reactive marketing to proactive management of interaction with customers and the formation of individualized technical and commercial offers. Key limitations and risks of implementing artificial intelligence in industrial marketing have been identified, including low quality and fragmentation of data, limited level of digital maturity of enterprises, shortage of data analytics and artificial intelligence specialists, high costs of implementing and maintaining intelligent systems, as well as increasing cyber risks and the threat of commercial information leakage.

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