Abstract
The aim of this research is to design and explain a model of marketing processes with data-driven innovation capabilities in B2B companies. The research is of a mixed type (qualitative-quantitative). In the qualitative part, the main categories and components of the model were identified by using in-depth semi-structured interviews with 10 of experts in the field of marketing and data technology, using purposive sampling. In the quantitative part, the statistical population included managers, directors, and employees active in the marketing units of B2B companies located in Tehran. Considering the size of the statistical population of 530 people, based on the Morgan table, 219 people were selected as the sample size and were examined using the relative cluster sampling method. The findings were tested using a questionnaire and confirmatory factor analysis. The results showed that causal conditions such as data-driven culture and technological infrastructure have a significant effect on the formation of data-driven marketing processes. These processes also lead to outcomes such as increased marketing innovation, improved business performance, and improved customer satisfaction through data-driven strategies. The relationships between conditions, processes, strategies, and outcomes were statistically significant. As a result, the presented model can be used as a framework for developing innovative data-driven approaches in B2B companies. The findings emphasize that by strengthening a data-driven culture and using predictive analytics, companies will be able to make more effective marketing decisions and gain a more sustainable competitive advantage.
Introduction
In today's world, data-driven marketing is considered one of the key approaches to optimizing marketing processes in B2B companies. This approach is especially important in complex and dynamic competitive conditions, as it is able to improve strategic decision-making based on the analysis of customer data, market trends, and collected information (Rosário et al., 2023). Data-driven marketing allows companies to more accurately identify their customers’ needs and design their marketing strategies based on reliable information and predictive analytics (Akter, 2021; cao et al., 2021). In this regard, B2B companies, especially in complex industries requiring customized solutions, should improve their marketing processes using data-driven innovation (Ghayour baghbani et al., 2024). One of the biggest challenges in this area is implementing data-driven innovation models that not only make optimal use of existing data but also exploit new technologies such as artificial intelligence (AI) and machine learning (De Luca, 2020). Specifically, these models must provide a deep understanding of customers and accurate predictions of their future needs so that marketing processes can be designed effectively and optimally (Länsipuro, 2020). In order for B2B companies to benefit from this approach, they need comprehensive models that include processes such as data collection, data analysis, and strategy development (Mohaisen et al., 2024). These models should be designed in a way that not only helps optimize marketing processes but also improves customer experience and increases overall company efficiency (Johnson et al., 2019). In this regard, Big Data analysis and the use of advanced analytical tools allow companies to adjust their marketing strategies based on more accurate predictions and continuously improve them (Becha et al., 2021). On the other hand, the exploitation of data-driven innovations in B2B companies should be implemented considering the specific characteristics of this type of business and the more complex relationships they have with customers (Bagheri et al., 2024). These characteristics require companies to design their marketing processes based on accurate and reliable analyses of customer and market data to create a sustainable competitive advantage in addition to improving the customer experience (Alghamdi & Agag, 2024). Data-driven marketing models should also consider that data is regularly updated and that they have the ability to respond quickly to customer needs in conditions of rapid market changes (De Luca, 2020).
Considering the above, the present study seeks to present a data-driven and innovative model for optimizing marketing processes in B2B companies; a model based on advanced technologies that both responds to the specific needs of this environment, is simple for companies to implement, and allows for continuous updating and adaptation to rapid market changes. Accordingly, and in accordance with the research objective, this study seeks to answer the main question: How can a comprehensive and implementable model be designed that, by relying on data and technological innovation, significantly increases the effectiveness of B2B marketing processes while reducing costs and remaining flexible to environmental changes? 
Theoretical foundations
In this study, data-driven marketing refers to the use of customer data collected from various sources (including online interactions, CRM systems, social media data, and market research) to formulate marketing strategies and implement advertising campaigns in B2B companies (Al-Khatib, 2025), and data-driven innovation refers to the use of data and their analysis to create new and improved products, services, and marketing processes in B2B companies (Mirzaei and Thompson, 2024).
Paying attention to the capacities of data-driven innovation as one of the most important factors in improving marketing performance can directly affect the promotion of companies' competitive advantage (Mahmoudi, 2024). On the other hand, using data as one of the key resources in marketing decision-making processes can create new opportunities and reduce risks associated with undocumented decisions (Kardani Maliki nejad et al., 2024). In addition, this trend allows marketing strategies to be planned more carefully and allows businesses to gain significant competitive advantages in competition with other companies (Al-Khatib, 2025; lamminparras, 2022).
Al-Khatib et al. (2025) examined “How do big data-based organizational capabilities shape innovation performance?”, empirically found that data-driven organizational capabilities have a positive and significant relationship with intellectual property and play a moderating role in internal and external supply chain integration. This relationship, in addition to the indirect effect of supply chain innovation capabilities, improves firms’ innovation performance.
Torabi et al. (2024) examined “A Critical Review of Intelligent Marketing Strategies: Challenges Between Data-Driven Marketing and Human Experience in the Age of "Encompassing Technologies" used document analysis to find that using data alone without considering the psychological complexities of customers leads to superficial decision-making. Combining data and human insight increases the effectiveness of campaigns and customer satisfaction. Challenges include privacy protection and analyzing customer behavior.
Research Methodology
Research Methodology of this study is based on a mixed approach (qualitative-quantitative) and aims to design a model of marketing processes with data-driven innovation capabilities in B2B companies. In order to achieve this goal, the research was conducted in two consecutive and complementary stages. In the first stage, a qualitative and exploratory approach was used to identify the main dimensions and components of the model. Therefore, the grounded theory method was used. The statistical population in this section included academic experts and senior managers of B2B companies who had significant experience and knowledge in the field of data-driven marketing and organizational innovation. Purposive and snowball sampling was used to select individuals with the most knowledge and insight into the subject. After conducting semi-structured interviews with 10 of these experts, the data were analyzed through open, axial, and selective coding and key concepts were extracted. In the second stage, to verify and test the model obtained from the qualitative section, the research entered the quantitative phase. The statistical population in this section included managers, directors, and employees active in the marketing units of B2B companies located in Tehran. Considering the size of the statistical population (530 people), based on the Morgan table, 219 people were selected as the sample size and the relative cluster sampling method was used. The data collection tool in this stage was a researcher-made questionnaire designed based on the findings of the qualitative stage and was provided to the respondents after confirming the content and construct validity. Cronbach's alpha coefficient was used to measure the reliability of the questionnaire, and the structural equation modeling method in Smart PLS software was used to analyze the data. 
Research findings
The research findings show that effective networking and building ongoing relationships in marketing between companies is a key factor for sustainable success, and that stakeholder identification, a culture of collaboration, and data analytics play a vital role. Also, data-driven innovation capabilities and big data analytics increase organizational efficiency by enhancing marketing agility and competitive advantage, and the convergence of data-driven with human experience makes marketing decision-making more comprehensive and intelligent. Data-driven strategic orientations and the development of organizational capabilities enhance innovation performance and sustainable value creation, and intelligent human resource management and big data provide the necessary infrastructure for the operationalization of innovations. Overall, the integration of networking, data-driven innovation, and a customer-centric approach provides an integrated and effective framework for improving marketing performance and achieving sustainable competitive advantage.
Discussion and Conclusion
The results of this study show that data-driven marketing and data-driven innovation capabilities, as a multidimensional phenomenon, are influenced by a set of causal, contextual, and intervention factors that ultimately lead to improved marketing performance and sustainable competitive advantage through scientific and creative strategies.
In the causal dimension, the results indicate that the existence of technological infrastructure, data-driven culture, and top management support are key prerequisites for the formation of data-driven marketing. This result is consistent with the findings of Al-Khatib et al. (2025) who emphasized that organizational capabilities based on big data are the foundation for the development of intellectual property and innovation in manufacturing companies. In comparison with the research of Koivuniemi (2020), both emphasize the role of human resources and technology as the main resources in applying the data-driven approach.
The main focus of the research is on data-driven marketing innovation, which shows how to transform data into actionable insights. The findings indicate that combining data and human creativity takes marketing from the level of mere numerical analysis to the level of intelligent decision-making. This finding is consistent with the study of Torabi et al. (2024), which emphasized that relying solely on quantitative data without considering human experience leads to superficial decisions.
In the contextual dimension, the findings showed that organizational structure, learning culture, digital maturity, and management policies play an important role in the success of data-driven marketing. Organizations that have an open culture and flexible structure reach data maturity faster. This result is consistent with the research of Länsipuro (2020), who identified structural and cultural barriers as the most important challenges of data-driven marketing. It is also consistent with the findings of Aljumah et al. (2024).
In the intervention conditions, barriers such as resistance to change, weak analytical skills, and lack of technology budget were identified. However, the results showed that targeted training, employee empowerment, and knowledge management can greatly reduce these barriers. This result is similar to the findings of Kardani Maliki Nejad et al. (2024) who emphasized the role of expert validation and training in the implementation of data-driven innovation. It is also in line with the research of Lamminparras (2022) who pointed out the need to develop dynamic capabilities in data-driven decision-making.
The strategies identified in this study include data analysis for service personalization, designing value propositions, and creating targeted communications with customers. Implementing these strategies allows companies to use data not only to describe the past, but also to predict the future.