RELATIONSHIP MARKETING IN THE ONLINE ENVIRONMENT AS AN INSTRUMENT FOR INFLUENCING CONSUMER BEHAVIOR AND MARKETING PRICING
Abstract
The article is devoted to solving the scientific and practical problem of the fragmented use of data on digital consumer behavior in the modern business environment. In the context of the rapid development of the digital economy, companies are shifting from a transactional approach to relationship marketing, where long-term interaction with the client becomes a key asset. However, a significant conceptual gap remains between the accumulation of vast amounts of behavioral information and its practical application. Businesses often face the "data silo" problem, lacking a systematic mechanism for converting raw data into effective, automated pricing and sales strategies. The purpose of the research is to develop a comprehensive conceptual model that integrates digital behavior analysis, Customer Relationship Management (CRM) systems, adaptive pricing mechanisms, and automated sales technologies into a single ecosystem. The methodological basis of the study involves a systematic analysis of scientific literature, the method of synthesis to combine disparate concepts into a unified framework, and business process modeling to design the final cyclical structure. The study classifies key digital behavior metrics into explicit categories (transaction history, search queries) and implicit categories (clickstream data, time on page, abandoned carts), defining them as the primary raw material for personalization. It is substantiated that the modern CRM system transcends its traditional role as a passive database, becoming an analytical core for aggregating and segmenting customer data using methods like RFM analysis and churn prediction algorithms. The key scientific result is the development of an integrated, cyclical conceptual model. This model demonstrates a continuous process where a consumer's "digital footprint" is captured and analyzed within the CRM to power a "pricing engine". This engine generates personalized or dynamic prices based on the customer’s estimated willingness to pay, which are then delivered via automated sales technologies. The consumer's reaction creates a new data point, feeding back into the CRM and refining future algorithms. The practical value of the research lies in providing a strategic roadmap for data monetization, allowing companies to significantly enhance customer loyalty, increase Customer Lifetime Value (LTV), optimize marketing budgets, and build sustainable competitive advantages in the online environment.
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