Keywords
RFM; K-Means; data mining; ecommerce
Document Type
Article
Abstract
This study explores the application of machine learning for local market prediction in e-commerce. By leveraging the RFM segmentation method, the model predicts product sales based on user shopping patterns. The RFM score, calculated using recency, frequency, and monetary values of customer purchases, segments customers into distinct categories. The research utilizes a dataset obtained through seven parameters and performs data preprocessing. K-Means clustering then classifies customers into Low, Medium, and High levels based on their RFM scores. Customers in the Low category exhibit low purchase activity but high product browsing. The Medium segment displays consistent purchases of a limited product range. High-level customers demonstrate frequent purchases with significant spending. The identified customer segments enable targeted marketing strategies. For Low-level customers, discounts or product feature promotions can incentivize purchases. Combining product offerings can entice Medium-level customers to explore new products. Finally, High-level customers can be engaged through loyalty programs offering rewards. This approach empowers ecommerce sellers to tailor marketing strategies for each customer segment, enhancing market dominance.
First Page
24
Last Page
37
Page Range
14
Issue
ISSN 2580-6424 (printed) | ISSN 2477-2399 (online)
Volume
Vol 9, No 1(2024)
Digital Object Identifier (DOI)
10.21831/elinvo.v9i1.58671
DOI Link
http://dx.doi.org/10.21831/elinvo.v9i1.58671
Recommended Citation
M. Yahya et al., "A Machine Learning Model for Local Market Prediction Using RFM Model,", vol. Vol 9, No 1(2024), no. ISSN 2580-6424 (printed) | ISSN 2477-2399 (online), pp. 24 - 37, May 2024.
The definitive version is available at https://doi.org/10.21831/elinvo.v9i1.58671
References
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