MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03427ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251125111038.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
250506b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0010035 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
PSM .I89 2023 r Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Muhammad Isyhraf Azmin, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Prediction on online retail customer segmentation with rfm model-based in malaysia using machine learning / |
| Statement of responsibility, etc. |
Muhammad Isyhraf Bin Azmin |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiv, 100 pages : |
| Other physical details |
Illustrations (some colour) ; |
| Accompanying material |
1 CD-ROM. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Center for Mathematical Sciences |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Center for Mathematical Sciences |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Final Year Report |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |