Role Of Artificial Intelligence And Machine Learning In CRM And Business Management

This study proposes a robust framework for improving Customer Relationship Management (CRM) and business management systems via the combination of Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies. The suggested architecture tackles the increasing issues of data quality, real-time processing, and scalability through IoT-enabled data gathering, AI-enhanced data integration, and ML-driven predictive analytics. Essential characteristics comprise actionable insights, automation, and a feedback mechanism for ongoing enhancement. The framework illustrates its capacity to customize client encounters, enhance resource allocation, and refine decision-making processes. This study establishes a solid basis for enhancing CRM tactics in the digital age by examining innovative methodologies and their consequences. Future research avenues encompass explainable AI, cost-effective solutions for SMEs, and privacy-preserving methodologies to guarantee secure and transparent data management

Authors

Publication Details

Journal
IOSR Journal of Computer Engineering
Published
2024-12-01
DOI
https://doi.org/10.9790/0661-2606030105
Primary Topic
Big Data and Business Intelligence
Type
article
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article

Role Of Artificial Intelligence And Machine Learning In CRM And Business Management

Deepti Garg
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IOSR Journal of Computer Engineering
Big Data and Business Intelligence
article

Role Of Artificial Intelligence And Machine Learning In CRM And Business Management

Deepti Garg
article en

Abstract

This study proposes a robust framework for improving Customer Relationship Management (CRM) and business management systems via the combination of Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies. The suggested architecture tackles the increasing issues of data quality, real-time processing, and scalability through IoT-enabled data gathering, AI-enhanced data integration, and ML-driven predictive analytics. Essential characteristics comprise actionable insights, automation, and a feedback mechanism for ongoing enhancement. The framework illustrates its capacity to customize client encounters, enhance resource allocation, and refine decision-making processes. This study establishes a solid basis for enhancing CRM tactics in the digital age by examining innovative methodologies and their consequences. Future research avenues encompass explainable AI, cost-effective solutions for SMEs, and privacy-preserving methodologies to guarantee secure and transparent data management

IOSR Journal of Computer EngineeringVol. 26(6)
Openalex Percentile: Top 60%
Big Data and Business Intelligence
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