Artificial Intelligence-Driven CRM: Enhancing Customer Engagement and Performance in the Iranian Retail Industry

Authors

  • Ali Hedayati K. N. Toosi University of Technology, Master of Business Administration (MBA), Marketing,Tehran,Iran

DOI:

https://doi.org/10.63053/ijmea.40

Keywords:

Artificial Intelligence, Customer Relationship Management, Retail Industry, Iran

Abstract

The landscape of Customer Relationship Management (CRM) is undergoing a profound transformation driven by advancements in artificial intelligence (AI) and automation technologies. Progressive organizations increasingly leverage AI to analyze customer behavior, refine brand communication strategies, and cultivate stronger relationships. Modern CRM platforms generate large volumes of data through customer interactions and behavioral patterns. AI algorithms mine this data to uncover latent insights into individual preferences, predict future behaviors, and generate personalized recommendations.

AI-powered automation has streamlined CRM workflows by facilitating rapid, tailored interactions that improve responsiveness and reinforce customer loyalty. Through consistent engagement and value creation, businesses can maximize customer lifetime value and sustain long-term relationships.

This study investigates the transformative role of AI in CRM, with a specific focus on the retail sector in Iran. The integration of machine learning (ML) and AI technologies into CRM solutions has enabled Iranian retail businesses to gain a more granular understanding of customer preferences, automate routine operations, and deliver hyper-personalized experiences. For instance, ML models can efficiently process complex customer datasets to identify behavioral trends and forecast purchase intentions—informing adaptive marketing strategies and customized product offerings.

Moreover, AI-driven virtual assistants and chatbots enable continuous customer support, effectively handling inquiries and issues beyond standard business hours. This paper highlights the critical impact of AI and ML in enhancing CRM functionality and customer engagement within the Iranian retail market. A survey of 213 participants across Iran’s retail sector was conducted to evaluate the impact of AI on CRM transformation. The findings confirm a substantial influence of AI on reshaping the CRM experience in Iran’s retail industry.

References

Raval, R., Singh, A., & Sharma, P. (2014). Online storefronts and their role in B2C e-commerce transformation. Journal of Internet Commerce, 13(3), 210–225.

Frasquet, M., Miquel-Romero, M. J., & Gil-Saura, I. (2015). Consumer trust in online retailing: The role of website design and customer service. International Journal of Retail & Distribution Management, 43(3), 214–230. https://doi.org/10.1108/IJRDM-05-2014-0056.

Jami Pour, M., Irani, H. R., & Yaghobi, A. (2024). Exploring the Challenges of migration towards Software-as-a-Service in Iran: the case study of Cloud-based CRM using a multidimensional perspective. Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies), 18(1), 173-193.

Ha, H.-Y., & Youl, L. (2004). Factors influencing consumer perceptions of brand trust online. Journal of Product & Brand Management, 13(2), 97–113. https://doi.org/10.1108/10610420410529730.

Katawetawaraks, C., & Wang, C. L. (2011). Online shopper behavior: Influences of online shopping decision. Asian Journal of Business Research, 1(2), 66–74.

Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2020). The next generation of CRM: Understanding the adoption of AI-integrated CRM systems. International Journal of Information Management, 54, 102189. https://doi.org/10.1016/j.ijinfomgt.2020.102189.

Kumar, V., & Reinartz, W. (2018). Customer Relationship Management: Concept, Strategy, and Tools (3rd ed.). Springer International Publishing. https://doi.org/10.1007/978-3-319-98285-1.

Ngai, E. W. T., Xiu, L., & Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592–2602. https://doi.org/10.1016/j.eswa.2008.02.021

Arora, A. S., & Rahman, Z. (2018). Information technology capability as a strategic tool to enhance CRM performance: A study of select Indian firms. Journal of Strategic Marketing, 26(7), 601–618. https://doi.org/10.1080/0965254X.2017.1377199.

Amini, S., Zarei, H., & Faraji, M. (2022). The impact of AI chatbots on customer service efficiency: Evidence from Iranian online retail. Journal of E-Business and Intelligent Systems, 7(1), 45–61.

Hosseini, M., & Mohammadi, M. (2020). Evaluating the effectiveness of AI recommendation systems in Iranian e-commerce platforms. Iranian Journal of Management Studies, 13(2), 123–140.

Rahman, M. S., Alam, M. Z., & Azad, M. A. K. (2021). Predicting customer churn using machine learning: A review and case study. Journal of Retailing and Consumer Services, 61, 102560. https://doi.org/10.1016/j.jretconser.2021.102560

Sadati, S., Razavi, S. H., & Akbari, M. (2020). The role of AI in fostering trust and engagement in Iranian e-commerce. Journal of Retailing and Consumer Behavior, 25(4), 202–215.

Lungu, D. C., Grigorescu, A., & Yousaf, Z. (2024). The Ethical Concerns of AI Technologies. Europe in the New World Economy: Opportunities and Challenges, 253.

Dastjerdi, M., Keramati, A., & Keramati, N. (2023). A novel framework for investigating organizational adoption of AI-integrated CRM systems in the healthcare sector; using a hybrid fuzzy decision-making approach. Telematics and Informatics Reports, 11, 100078.

Published

2025-04-07

How to Cite

Hedayati, A. (2025). Artificial Intelligence-Driven CRM: Enhancing Customer Engagement and Performance in the Iranian Retail Industry. International Journal of Applied Research in Management, Economics and Accounting, 2(2), 47–55. https://doi.org/10.63053/ijmea.40

Issue

Section

Articles