Volume 14, Issue 54 (Spring 2026)                   IUESA 2026, 14(54): 99-110 | Back to browse issues page

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Karimi Kundzi M, parvini Y. Designing a customer behavior prediction model using social media interaction data. IUESA 2026; 14 (54) :99-110
URL: http://iueam.ir/article-1-2253-en.html
Designing a customer behavior prediction model using social media interaction data
Mohammadreza Karimi Kundzi , Yaser Parvini *1
1- , yaserparvini4261@gmail.com
Abstract:   (7 Views)
The present study was conducted with the aim of designing a customer behavior prediction model using social media interaction data. This study used a qualitative approach and grounded theory method with the systematic process of Strauss and Corbin to extract concepts and patterns in the interview data with experts. The data were collected through in-depth interviews with 9 digital marketing managers and experts and analyzed after open coding, axial and selective coding. 16 subcategories were identified, which were categorized into background conditions, causal conditions, intervening conditions, strategies and consequences. The results showed that users' initial interest, active interaction with content, the influence of others' experiences and hidden behavioral patterns act as background conditions of customer behavior and the timing of content, the attractiveness of the content presented and the cross-sectional behavior of users play the role of causal conditions. Also, negative feedback and strategy modification, behavioral prediction indicators, the use of mixed data, and data challenges as intervening conditions affect user behavior, and targeted strategies including advertising and specialized content, advanced analytics tools, and real-world case studies improve the accuracy of predicting customer behavior. The main outcome of this model includes increasing customer loyalty and improving the behavior prediction model, which can help with smart marketing decisions and effective strategies. By providing a systematic and comprehensive model for analyzing user interaction data, this research paves the way for businesses to use social networks in a targeted manner, and enables the identification of hidden behavioral patterns and the prediction of long-term customer behavior.
Keywords: Customer behavior prediction, interactive data, social media, digital marketing, digital marketing.
Full-Text [PDF 777 kb]   (5 Downloads)    
Type of Study: Research | Subject: General
Received: 2025/12/22 | Accepted: 2026/03/30 | Published: 2026/03/30 | ePublished: 2026/03/30
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