INNOVATIVE APPROACHES TO PRICING AND ASSORTMENT MANAGEMENT BASED ON ARTIFICIAL INTELLIGENCE
DOI:
https://doi.org/10.37332/Keywords:
marketing pricing policy, product policy, artificial intelligence, Big Data, dynamic pricing, assortment management, product life cycle, price personalizationAbstract
Okrepkyi R.B., Dudar V.T. INNOVATIVE APPROACHES TO PRICING AND ASSORTMENT MANAGEMENT BASED ON ARTIFICIAL INTELLIGENCE
Purpose. The aim of the article is to study the impact of marketing pricing policy on product assortment management in the context of digitalization and to substantiate the feasibility of introducing artificial intelligence (AI) into pricing processes to improve business performance.
Methodology of research. The article applies a systematic analysis of scientific sources and practical case studies of AI implementation in pricing. Theoretical generalization methods are used to develop a conceptual model for integrating AI into marketing pricing policy; the comparative method is applied to contrast traditional and AI-oriented approaches; statistical and analytical methods are employed to interpret quantitative data on the effectiveness of AI pricing.
Findings. It has been established that traditional pricing methods (cost-based, competition-based, value-based) have limited adaptability to rapid changes in market conditions, reducing their effectiveness. The concept of AI pricing is proposed, which takes into account consumer behavioral data, demand dynamics, competitive actions, and seasonal factors in real time. The main functions of pricing policy and the role of Big Data in personalizing pricing decisions are identified.
Originality. An integrated model of marketing pricing policy using AI tools has been developed, which ensures the synchronization of assortment management, dynamic pricing, and consumer behavioral analytics. This model allows you to simultaneously optimize your product portfolio and adjust pricing strategies in real time based on streaming data, predictive analytics, and target segment clustering. The integration of machine learning algorithms ensures deep individualization of commercial offers at the level of individual consumers, increasing the accuracy of pricing decisions, demand elasticity, and the overall profitability of the enterprise.
Practical value. The main conclusions can be used by companies to develop adaptive pricing strategies, optimize assortment management, increase profitability, and improve marketing activities through personalized offers and prompt responses to changes in market conditions.
Key words: marketing pricing policy, product policy, artificial intelligence, Big Data, dynamic pricing, assortment management, product life cycle, price personalization.
References
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