03427ntm a2200337 i 4500 0 0 lcc 0 6 10000 10000 2025-05-06 0 CD13596 T000003194 2025-05-06 00:00:00 2025-05-06 PSM 102501 102507 MY-KuUP 20251125111038.0 t||||fr|||| 000 0 ta 250506b ||||| |||| 00| 0 eng d THE0010035 (Local) Hardback UMPSA eng UMPSA rda PSM .I89 2023 r Bc. Muhammad Isyhraf Azmin, author. Prediction on online retail customer segmentation with rfm model-based in malaysia using machine learning / Muhammad Isyhraf Bin Azmin Kuantan Pahang : UMPSA, 2023 © 2023 xiv, 100 pages : Illustrations (some colour) ; 1 CD-ROM. rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Center for Mathematical Sciences Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023 Includes bibliographical references This research looks at the impact of e-commerce on consumer behaviour and tries to establish effective marketing strategies through customer segmentation and analysis. Data-driven methodologies like RFM modelling, k-means clustering, and decision tree modelling provide useful insights into client behaviour, loyalty levels, and purchase patterns. RFM ratings are used to create unique customer categories, demonstrating considerable differences in terms of recency, frequency, and monetary values. The decision tree model outperforms other models in properly forecasting client classes, offering useful insights into the distribution and characteristics of various consumer groups. The investigation yielded practical recommendations such as the deployment of personalised marketing tactics that respond to the individual demands and preferences of each consumer category. This includes building focused marketing efforts that address the unique qualities of each group, as well as concentrating on enhancing the entire customer experience across all touchpoints. Businesses may optimise consumer interaction, promote revenue development, and achieve long-term success in the increasingly competitive ecommerce industry by implementing these guidelines. Furthermore, the study emphasises the significance of data-driven decision making and ongoing customer data analysis in order to keep up with changing consumer preferences and market dynamics. Establishing feedback systems and responding quickly to client complaints are critical in cultivating a customer-centric culture. It is also essential to regularly analyse and monitor consumer data in order to successfully adjust plans to changing customer wants and market trends. Overall, this study offers significant insights and a practical roadmap for organisations to exploit data-driven insights, allowing them to customise their marketing strategies, efficiently satisfy client wants, and stay ahead of the changing business landscape. Businesses may improve consumer engagement, increase revenue growth, and achieve long-term success in the increasingly competitive e-commerce industry by applying these tips. Center for Mathematical Sciences Dissertations Universities and colleges Dissertations Final Year Report Dissertations lcc PSM