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  Published Paper Details:

  Paper Title

MACHINE LEARNING TECHNIQUES FOR CUSTOMER RELATIONSHIP MANAGEMENT

  Authors

  Arun Velu

  Keywords

Machine Learning, Management, CRM, Data Analysis and

  Abstract


For a long time now, customer relationship management has been a crucial part of organizations aiming at enhancing their customer experience. In recent years, machine learning and its varying techniques have changed how organizations interact with their customers through data analytics or analysis. Machine learning is a branch of artificial intelligence that has increased its reputation and evolved into a very successful and powerful technology in recent years. As such, the use of machine techniques in customer relationship management systems can prove very advantageous and robust, which can ultimately lead to higher customer satisfaction rates, reduce the churn rate, and even increase revenues. This research paper examines the ways that machine learning techniques are used in customer relationship management. Further, the article also discusses the advantages of using machine learning in CRM and how customer relationship management has changed in recent years. Therefore, this paper major aims at answering the questions of how and why machine learning and its techniques are used in customer relationship management.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2106331

  Paper ID - 208740

  Page Number(s) - c753-c763

  Pubished in - Volume 9 | Issue 6 | June 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Arun Velu,   "MACHINE LEARNING TECHNIQUES FOR CUSTOMER RELATIONSHIP MANAGEMENT", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 6, pp.c753-c763, June 2021, Available at :http://www.ijcrt.org/papers/IJCRT2106331.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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