Designing a Model for Analyzing Customer Behavior on Big Data Using Meta-Synthesis Method and Delphi Method

The financial industry has always been a data-driven industry. Recent technological advances along with several other factors such as changing customer priorities and changing business needs have resulted in large quantities of production and consumption. Analysis of customers' behavior with Big data over the past 20 years has attracted the attention of banking marketers. Banks have used customer behavior analysis to optimally exploit opportunities, resources and avoid risks in turbulent market conditions as a powerful alternative. This concept is very dynamic and despite extensive research, it has not yet been adequately explored. The aim of this study was to develop a comprehensive model of customer analysis based on Big data by examining 156 works in nine reliable scientific databases and experts' opinions. This research has tried to develop theoretical foundations of customer behavior analysis by combining researches. To analyze the research literature, the method of transcending was used. Then, using two-stage Delphi method, the opinions of experts of the western banks of the country (Kurdistan, Kermanshah, West Azarbaijan, Hamedan) were taken and seventeen categories were classified into forty-nine concepts. The results show that customer behavior analysis model consists of five categories: factors shaping behavior, macro data, strategies, challenges and consequences.