Optimizing Customer Relationship Management with Surprise Program: A Quantitative Approach
DOI:
https://doi.org/10.54099/ijmdb.v3i2.1131Keywords:
CRM, , Quantitative, , Clustering, , K-MeansAbstract
This research explores the implementation of customer relationship marketing strategies through the a program launched by PT XYZ. The program was designed to enhance customer loyalty with personalized special offers and exclusive promotions. This study employs quantitative methods and the data were obtained from the analysis of company reports and customer demographics, geography, behaviour, and psychology. Using K-means clustering, this study analyses data collected from XYZ's CRM surprise program, focusing on customer interests in video, games, and music, as well as their payment methods (prepaid or postpaid). The findings reveal significant variations in digital content consumption between prepaid and postpaid users. This segmentation enables XYZ to tailor its service offerings more effectively. The study highlights the potential for increasing customer satisfaction and loyalty through personalized offers, underscoring the importance of behavioural and psychographic data in optimizing service delivery.
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