Role of Data Fusion on Customer Profiling and their Lifetime in Retail Sector and it’s Performance Evaluation.
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Role of Data Fusion on Customer Profiling and their Lifetime in Retail Sector and it’s Performance Evaluation
Authors:
Nainsi1, Priyavrat Raghuvanshi2, VaibhavTyagi3, Rohit Kumar Singh4
1,2,3Department of Information Technology
Meerut Institute of Engineering and Technology, Meerut UP, India Pin-250005 nainsi.krishan.itl.2021@miet.ac.in,
priyavrat.raghuvanshi.itl.2021@miet.ac.in vaibhav.tyagi.itl.2021@miet.ac.in
4Department of Electronics & Communication Engineering
Meerut Institute of Engineering and Technology, Meerut UP, India Pin-250005 rohit.singh@miet.ac.in
Abstract- Being profitable was a crucial goal for the banking sector's long-term pros- perity, and building solid, enduring connections with customers is largely dependent on their level of satisfaction. Through the analysis of buyer information, companies could offer customized services. Banks may improve customer experiences, personalize their product lines, and locate growth possibilities through the use of data analysis methods. The accomplishment of managing client relationships (CRM), which permits ongoing client development and retention, depends on customer classification and profiles. By employing these tactics, banks can increase their clientele, offer personalized products, make money, and maximize cross-selling and up-selling campaigns.
Keywords: K-means clustering, hierarchical clustering, data mining, customer profiles, lifetime value and RFM analysis.
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