Big data applications in the banking sector and its use in marketing insights

The main aim of this study is to determine how and in what areas structural and non-structural high-volume data obtained from customer transactions in the banking sector and various sources are used in marketing predictions for customers. The study utilizes a case study design and purposive sampling technique. Accordingly, the sample of this study consists of individuals working in deposit and participation banks in the banking sector who are knowledgeable about big data processes, selected based on purposive sampling technique. In this context, a total of 38 banks, including 32 deposit banks and 6 participation banks listed on the BDDK website, were contacted, and appointments were requested from individuals knowledgeable about big data processes. Meetings were conducted with 20 participants from 13 different banks that accepted the appointment request. A semi-structured interview technique was used as the data collection method. The obtained data were analyzed through descriptive and content analysis. As a result of the analysis, it was determined that the 168 codes were grouped into 22 categories, forming 6 main themes. Examining the results obtained, it was revealed that big data is used in the banking sector, there are internal and external big data sources, and big data is utilized in areas such as risk management, fraud detection, and customer relationship management. Furthermore, it was found that banks actively leverage big data in marketing predictions in areas such as product determination, pricing, distribution, promotion, people, physical evidence, and processes. Keywords: Data, Big Data, Banking Sector, Service Marketing, Case Study