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Optimizing Salesforce CRM Workflows Through Machine Learning and API-Led Connectivity for Enhanced Customer Experience

Dr. ALI EL-MOURSY

Department of Electrical & Computer Engineering, University of Sharjah, United Arab Emirates

Abstract

Salesforce Customer Relationship Management (CRM) platforms play a significant role in managing customer interactions, automating business processes, and improving organizational performance. However, conventional CRM workflows often encounter challenges related to manual task execution, fragmented enterprise applications, delayed customer responses, inconsistent data synchronization, and limited predictive capabilities. This research proposes an integrated framework for optimizing Salesforce CRM workflows through machine learning and API-led connectivity to enhance customer experience and operational efficiency. The proposed framework combines predictive analytics, intelligent workflow automation, customer behavior analysis, and modular application programming interfaces to facilitate seamless integration between Salesforce and external enterprise systems. Machine learning models are employed to predict customer preferences, classify service requests, identify potential customer churn, prioritize sales opportunities, and recommend personalized engagement strategies. API-led connectivity enables secure, scalable, and reusable communication among Salesforce, enterprise resource planning systems, marketing platforms, customer service applications, and external data sources. The methodology incorporates data preprocessing, feature engineering, model development, integration design, workflow automation, and performance evaluation. Key evaluation dimensions include prediction accuracy, workflow execution time, integration reliability, customer response time, and user satisfaction. The framework aims to establish an intelligent, interoperable, and customer-centric CRM environment capable of supporting real-time decisions and proactive engagement. The proposed approach provides a foundation for transforming traditional Salesforce operations into adaptive digital workflows that improve productivity, strengthen customer relationships, and support sustainable business growth across diverse enterprise environments.

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 8 No. 6 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2023.0806009

Pages

14753-14761

Published

December 11, 2023
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Dr. ALI EL-MOURSY (%2023). Optimizing Salesforce CRM Workflows Through Machine Learning and API-Led Connectivity for Enhanced Customer Experience. International Journal of Emerging Trends in Engineering and Management Research , Vol. 8 No. 6 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 14753-14761. https://doi.org/10.15662/ijetemr.2023.0806009

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