طراحی مدل بازاریابی داده‌محور مبتنی بر دانش برای توسعه تعامل و خدمات اطلاعاتی در نهادهای فرهنگی

Abstract

Objective:This research aims to design a comprehensive knowledge-based data-driven marketing model for cultural institutions. In the current digital age, cultural institutions face numerous challenges, including changing audience behavior patterns, increasing expectations for personalized services, and the need for an effective presence in the digital space. The main goal of this research is to provide a framework that, by using data analysis capabilities and new technologies such as artificial intelligence, machine learning, and digital platforms, helps cultural institutions deepen and make their interactions with audiences more effective, improve information services, and ultimately achieve sustainable development. This research seeks to answer several key questions: How can audience data be used to improve cultural interactions? What solutions are there for personalizing information services in cultural institutions? And how can the challenges in the digital transformation of these institutions be managed? The proposed model of this study not only improves the audience experience, but also helps cultural institutions maintain and strengthen their competitive position in a rapidly changing environment.

Method:This study was conducted by adopting a qualitative approach and using the grounded theory method. The research population consisted of 17 experts and specialists in the field of cultural institutions who were selected using the snowball sampling method. These individuals included senior managers of cultural organizations, university professors, and researchers in the field of digital marketing who had practical experience and theoretical expertise in the field of the research topic. Data were collected through in-depth semi-structured interviews, and the data collection process continued until reaching theoretical saturation. To ensure the validity and reliability of the data, the interview protocol was reviewed and approved by several university professors. Also, the interview questions were revised and finalized after consulting with experts and conducting two pilot interviews. Data analysis was conducted in two main stages: In the first stage (open coding), the researchers identified 149 initial codes by carefully examining the interviews. In the second stage (axial coding), these codes were organized into 30 main categories, which included six general groups: causal conditions, axial phenomena, contextual conditions, intervening conditions, strategies, and consequences. These categories ultimately led to the design of a comprehensive conceptual model that shows the complex relationships between the various factors affecting data-driven marketing in cultural institutions.

Findings:The findings of this study show that data-driven marketing can create a fundamental transformation in the way cultural institutions interact with their audiences. The most important findings are presented in the form of a paradigm model:

Factors such as increasing technology-driven competition, the emergence of new digital platforms, changing audience behavior towards online experiences, and the need for advanced analytical tools are driving cultural institutions to adopt a data-driven approach.

The core of the model is “Data-driven marketing to enhance engagement and information services,” which includes two main dimensions: a) predictive digital engagement using audience behavior analysis and b) dynamic personalization of services based on collected data.

The success of this model depends on factors such as the existence of a culture of innovation in the organization, political and financial support, the availability of expert human resources, the existence of strong digital infrastructure, and the organization’s readiness for digital transformation.

Barriers such as resistance to change, concerns about data privacy, lack of digital skills in employees, and technical challenges in integrating data systems can affect the implementation of the model.

Suggested solutions include developing integrated data management systems, using advanced analytics technologies such as artificial intelligence, investing in employee training and empowerment, designing personalized user experiences, and creating cross-departmental collaborations.

Successful implementation of this model can lead to results such as strengthening interactions with audiences, optimizing organizational resources, increasing the competitiveness of cultural institutions, creating new revenue models, and developing international collaborations.

Conclusion:This study showed that data-driven marketing can play a transformative role in cultural institutions. The presented model provides a comprehensive framework for the effective use of data to improve interactions and information services. This study has taken an important step towards integrating digital marketing knowledge with the specific needs of cultural institutions. The presented model can serve as a guide for managers and decision-makers of these institutions. However, it is suggested that future studies should empirically examine the effectiveness of this model in different operational environments and analyze the factors affecting its success or failure in different conditions. Also, future research can be devoted to the development of performance measurement tools and indicators for evaluating the effectiveness of this model.

Keywords: Data-driven marketing, cultural interactions, informational services, artificial intelligence, digital platform, cultural institutions.