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August 25, 2025Journal of Informatics Education and Research0 citationsOpen Access

Artificial Intelligence (Ai) Driven Solutions For Intelligent Crm (Customer Relationship Management) In Quick Commerce: An Empirical Study

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JPJyotsna L PandeySJSonia Jain

Key Points

  • AI-powered CRM systems enhance customer satisfaction and operational efficiency, driving growth in quick commerce.
  • The study found that the integration of AI tools significantly improves customer interaction and retention rates.
  • This research involved empirical analysis across multiple sectors, gathering insights from a sample of 229 participants.
  • Adopting AI-driven CRM technologies poses challenges like data protection issues and a lack of skilled professionals.

Abstract

Customer Relationship Management (CRM) is at the forefront of modern market practice as AI-driven Customer Relationship Management (CRM) systems are transforming how companies engage with their customers by making interactions in a more intelligent, structured and personalised manner. “With the integration of Artificial Intelligence (AI), CRM systems have progressed remarkably by empowering organisations to procure deeper insights into customers' behaviour, communicate more constructively, as well as come up with improved services tailored to individual needs.” This research paper examines how AI-powered Customer Relationship Management (CRM) systems are utilised in the quick commerce industry, offering insight into the challenges, advantages, and best practices for adopting such cutting-edge technology. Still, quick commerce faces major hurdles, including protecting user data, handling complex system integrations, and the shortage of skilled professionals to operate and manage AI-powered CRM systems effectively. This paper explores how "AI-powered tools-such as chatbots, customer segmentation, personalised marketing, predictive analytics, and sentiment analysis-affect customer satisfaction and drive business growth in the fast-paced world of quick commerce.” A sample of 229 was collected from different sectors. The factors identified in the study are Personalisation & Predictive insights, Customer Interaction Automation, Customer Retention & Loyalty and Operational Intelligence.

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Cite This Study

Pandey et al. (2025) studied this question.

synapsesocial.com/papers/68af5d75ad7bf08b1eae137bhttps://doi.org/10.52783/jier.v5i3.3504
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