Designing an Intelligent Pattern of Digital Consumer Behavior Based on Big Data

This research aims to design an intelligent model of digital consumer behavior knowledge based on big data. This research was conducted using a qualitative approach. First, the qualitative method of thematic analysis was used, followed by the application of big data analysis techniques. The statistical population includes experts in the field of marketing who specialize in qualitative analysis. The sample size was determined to be 10 people using the snowball method and theoretical saturation. The data collection tool includes interviews with experts, which were analyzed using the thematic analysis technique in MAXQDA. In the following, the customer's behavioral trend has been studied based on the Big Data technique model, using the data available in the Digikala company. Coding in MATLAB is done based on specific formulas. The results showed seven components and 48 indicators that were identified and approved by experts in designing consumer behavior patterns using a digital marketing approach. These components include 1. Marketing Practices. 2- Innovation, 3- Digital marketing strategy, 4- Dynamic digital marketing, 5- Customer management, 6- Consumer cooperative behavior, and 7- Consequences of consumer response. The business management has finally decided to expand the intelligent system for consumer behavior. The main evaluation index is relatively unique and cannot effectively stimulate the acquisition of new customers. The only evaluation comes from consumers who have a recorded history of financial behavior on the digital platform. The value network model relies on digital technology because it facilitates interaction between end consumers as a relational medium.