Integrating CRM, data engineering, and data science for unified customer intelligence: A real-time adaptive framework
Independent Researcher, USA.
Review Article
World Journal of Advanced Research and Reviews, 2021, 10(03), 457-470
Article DOI: 10.30574/wjarr.2021.10.3.0185
Publication history:
Received on 21 March 2021; revised on 16 June 2021; accepted on 28 June 2021
Abstract:
The convergence of Customer Relationship Management (CRM), Data Engineering, and Data Science has the potential to revolutionize customer intelligence by providing actionable insights and adaptive solutions in real-time. This paper introduces a unified framework that integrates CRM platforms (e.g., Salesforce) with modern data engineering pipelines and advanced data science models. The framework leverages real-time ETL pipelines built on Apache NiFi and Kafka, combined with predictive models trained using distributed machine learning systems such as Spark MLlib. The architecture is designed to dynamically ingest and process customer interaction data from CRM systems, integrate it with third-party data sources, and generate real-time insights.
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Key innovations include: CRM-Driven Data Enrichment: Real-time integration of CRM data with external public and private datasets for holistic customer profiling.
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Dynamic Customer Segmentation: On-the-fly segmentation using unsupervised learning algorithms (e.g., clustering) combined with CRM-defined attributes.
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Automated Recommendation Systems: Personalized customer engagement strategies derived from reinforcement learning algorithms that adapt based on CRM interaction history.
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Feedback Loop: A self-improving mechanism where customer interaction data feeds back into both CRM and predictive models to improve future recommendations and CRM workflows.
Case studies in e-commerce and B2B sales show that this approach increases conversion rates by 25% and reduces customer churn by 30%, with minimal latency in generating actionable insights. The framework demonstrates how CRM, Data Engineering, and Data Science can synergize to build adaptive and intelligent customer ecosystems for enterprises.
Keywords:
Customer Relationship Management (CRM); Data Engineering; Data Science; Real-Time ETL Pipelines; Apache Nifi
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Copyright information:
Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0