Designing a Customer Behavior Model for Online Social Media Using Big Databases

Customers' use of technology-based intelligent information can put forth some key decision variables as a good starting point for investor decisions in financial and banking markets. On the other hand, the increasing number of commercial banks has raised concerns about customer turnover, especially for old and well-known banks. In this regard, the data about the interactions of banks and customers on online social media and mobile phones have always been an important source of banking marketing research. Analysis in banking research is generally based on questionnaires. Nowadays, however, macro-databases include numerous words, images, videos, and non-numeric outputs in terms of the volume, speed and variety of digital processes and often obscure traditional statistical analyses. Researchers have become interested in macro-databases for marketing decisions and creative marketing campaigns, and some have suggested them for the analysis of customer behavior. The use of macro-databases has, thus, significantly become a successful approach in today's marketing. In order to conduct this research, first, the behavior of customers in creating a large database, especially in the social media environment, is examined through surveys and by studying customers' attitudes toward a product, service or bank. Then, the relationship between the customer-centric banking marketing mixed with the data-driven macro-database and the referral system is examined. Finally, the paradigm model of research is presented for the behavior of banking customers in online social media.

This research seeks to provide a new model for analyzing the behavior of bank customers using the big data method. It is applied in terms of purpose and descriptive in terms of data collection. In order to finalize the analytical model of bank customers' behavior using big data, a qualitative research method was used. Also, the data theory method was used as a basis for tthe construction of the theory. The statistical population of the study consisted of two groups. The first group contained the customers who were significantly active in online social networking cyberspace, and the second group was for the bank managers who were well acquainted with the social media space. Accordingly, the statistical sample of the study included 15 bank customers who had extensive online financial and credit transactions with the bank as well as 10 bank managers who were well acquainted with the social media communication environment. The sampling method in this research is theoretical. For this purpose, the sampling continued until the model reached the saturation. The researcher collected and selected the data in such a way that he could discover the hidden background of various texts and images of online social interactions using the NVIVO10 software to formulate the final theory.

In intervening situations, the customer behavior was created using the psychological characteristics which represented five factors of personality. In this case, the internal and external sources of control and certain complex concepts were identified and explored. There were two categories of customer behavior to deal with. It was found that positive advertisement can increase online space-based negotiations using three types of knowledge in this area. Customers seek knowledge about others by looking at their profiles, photos and texts and also try to motivate others by sending them texts and comments. Thus, they make product and theoretical comparisons that result in mutual awareness.

Based on an operational model, this study suggests that the netnography method (blogging) be used to analyze the content of users' comments on virtual networks and discover the hidden layers and underpinnings of comments and texts written by users and members of virtual networks. Also, the culture of bank customers in relation to values, customs, and Iranian cultural meanings should be analyzed so that banks can have the knowledge of common concepts in this area. It is also suggested that a combination of social media data and transaction records be used to study the impact of social media behavior on banking purchasing behavior. Another suggestion is that, before commercializing their products and services, banks should place them in the online space to notify the groups there and use their opinions. They can thus offer better products and services according to the views and behaviors of customers. In addition, it is suggested that banks invest in marketing intelligence to become a competitive source for customers.