PREDICTIVE ANALYTICS FOR CUSTOMER RETENTION IN BANKING: A FUSION OF MARKETING AND BLOCKCHAIN
DOI:
https://doi.org/10.29121/shodhkosh.v5.i6.2024.6357Keywords:
Predictive Analytics, Customer Retention, Crm, Client LoyaltyAbstract [English]
Finding ways to utilise analytics to improve retention of clients in the banking sector is the primary goal of this master's project. Both theoretical and practical components make up this work. The theoretical portion addresses managing client relationships from an analytical standpoint, outlining several factors that influence customer retention, discussing predictive modelling of customer attrition, and presenting potential customer retention initiatives. The primary way of gathering data for the empirical portion is a qualitative research approach that involves four semi-structured thematic interviews with the management of the case firm. The primary conclusions show that the organisation's operations have a significant impact on client retention, and analytics will play a bigger part in the banking sector going forward. The findings also indicate that analytics might enhance specific branch or staff analyses, improve client identification, provide more precise data, and facilitate faster and more efficient retention and CRM activities.
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Copyright (c) 2024 Kumari Tripti, Shirish Mishra, Dr. Avneesh Kumar
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