The purpose of this study was to design and explain a model of data-driven marketing strategies in the digital marketing of knowledge-based companies with emphasis on contextual, behavioral, and structural dimensions as well as the implementation stages of data-driven strategies. This study was conducted using a qualitative approach within an interpretivist paradigm. The research population consisted of experts, managers, and specialists active in knowledge-based companies with at least five years of experience in digital marketing and data-driven marketing. Participants were selected through snowball sampling, and the sample size was determined based on theoretical saturation. Data were collected through semi-structured interviews and analyzed using directed content analysis and MAXQDA software. To assess the validity of the extracted subcategories, the Content Validity Ratio (CVR) was calculated with the participation of 10 experts. Ultimately, 43 subcategories were identified and classified into five main categories, including contextual factors, behavioral factors, structural factors, strategy implementation stages, and strategy outcomes. The findings indicated that contextual factors such as environmental data-driven analysis, utilization of emerging technologies, competitive data analysis, and development of information technology infrastructure played a significant role in shaping data-driven marketing strategies. Behavioral factors including managers’ data-oriented skills, human resource expertise, data-driven organizational culture, and cross-functional collaboration facilitated effective implementation of these strategies. Structural dimensions such as data-based workflow design, system integration, data management, and internal policy development were also identified as critical elements. Furthermore, the implementation process of data-driven marketing was conceptualized as a six-stage operational cycle consisting of data-driven research, data cleansing and analysis, campaign design, customer acquisition, performance evaluation, and campaign optimization. The outcomes of these strategies included accurate customer targeting, increased conversion rates, enhanced customer loyalty, improved customer experience, and higher return on investment. The results demonstrated that data-driven marketing in knowledge-based companies is not solely dependent on technology, but rather emerges from the interaction of contextual, behavioral, and structural organizational factors. The proposed model can serve as a practical framework for guiding digital marketing decision-making and improving the effectiveness of marketing strategies in knowledge-based companies.