RFMVDA: An Enhanced Deep Learning Approach for Customer Behavior Classification in E-Commerce Environments
Customer Relationship Management (CRM) systems, widely used in enterprises, have evolved into Software-as-a-Service (SaaS) platforms. With the advent of Customer Data Platforms (CDP), these systems continuously store customer behavior data for purposes such as creating single customer profiles, anal...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10839360/ |
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| author | Kwanhee Kim Mingyu Jo Ilkyeun Ra Sangoh Park |
| author_facet | Kwanhee Kim Mingyu Jo Ilkyeun Ra Sangoh Park |
| author_sort | Kwanhee Kim |
| collection | DOAJ |
| description | Customer Relationship Management (CRM) systems, widely used in enterprises, have evolved into Software-as-a-Service (SaaS) platforms. With the advent of Customer Data Platforms (CDP), these systems continuously store customer behavior data for purposes such as creating single customer profiles, analyzing, tracking, and managing customer interactions from various perspectives. With the global expansion of the e-commerce market, research on customer analysis and classification optimized for the e-commerce environment has been actively conducted. The RFM (Recency, Frequency, Monetary) model is a straightforward method for classifying customers and is applied across various industries. However, in the e-commerce environment, where customers can access services at any time, there are limitations in collecting, storing, and reflecting customer behavior data for classification. To resolve these limitations, this paper proposes the RFMVDA (Recency, Frequency, Monetary, Visits, Durations, Actions) model. This model is designed to capture customer data, sessions, and behavior units suitable for the e-commerce environment. By utilizing the RFMVDA model for customer behavior-based segmentation and classification, we constructed a Deep Neural Network (DNN) to predict customer behavior-based classifications. As a result, the proposed model demonstrated a segmentation prediction accuracy of 92.98% for customers in the e-commerce environment. |
| format | Article |
| id | doaj-art-11b871b5adfd44d7b2cb62cec9058512 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-11b871b5adfd44d7b2cb62cec90585122025-01-25T00:01:31ZengIEEEIEEE Access2169-35362025-01-0113125271254110.1109/ACCESS.2025.352902310839360RFMVDA: An Enhanced Deep Learning Approach for Customer Behavior Classification in E-Commerce EnvironmentsKwanhee Kim0https://orcid.org/0009-0001-6154-2984Mingyu Jo1https://orcid.org/0009-0002-5667-5392Ilkyeun Ra2https://orcid.org/0000-0002-9277-5780Sangoh Park3https://orcid.org/0000-0002-1832-3532School of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaSchool of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaDepartment of Computer Science and Engineering, University of Colorado Denver, Denver, CO, USASchool of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaCustomer Relationship Management (CRM) systems, widely used in enterprises, have evolved into Software-as-a-Service (SaaS) platforms. With the advent of Customer Data Platforms (CDP), these systems continuously store customer behavior data for purposes such as creating single customer profiles, analyzing, tracking, and managing customer interactions from various perspectives. With the global expansion of the e-commerce market, research on customer analysis and classification optimized for the e-commerce environment has been actively conducted. The RFM (Recency, Frequency, Monetary) model is a straightforward method for classifying customers and is applied across various industries. However, in the e-commerce environment, where customers can access services at any time, there are limitations in collecting, storing, and reflecting customer behavior data for classification. To resolve these limitations, this paper proposes the RFMVDA (Recency, Frequency, Monetary, Visits, Durations, Actions) model. This model is designed to capture customer data, sessions, and behavior units suitable for the e-commerce environment. By utilizing the RFMVDA model for customer behavior-based segmentation and classification, we constructed a Deep Neural Network (DNN) to predict customer behavior-based classifications. As a result, the proposed model demonstrated a segmentation prediction accuracy of 92.98% for customers in the e-commerce environment.https://ieeexplore.ieee.org/document/10839360/Customer segmentationcustomer classificationmachine learningdeep neural network (DNN)customer data platform (CDP)customer relationship management (CRM) |
| spellingShingle | Kwanhee Kim Mingyu Jo Ilkyeun Ra Sangoh Park RFMVDA: An Enhanced Deep Learning Approach for Customer Behavior Classification in E-Commerce Environments IEEE Access Customer segmentation customer classification machine learning deep neural network (DNN) customer data platform (CDP) customer relationship management (CRM) |
| title | RFMVDA: An Enhanced Deep Learning Approach for Customer Behavior Classification in E-Commerce Environments |
| title_full | RFMVDA: An Enhanced Deep Learning Approach for Customer Behavior Classification in E-Commerce Environments |
| title_fullStr | RFMVDA: An Enhanced Deep Learning Approach for Customer Behavior Classification in E-Commerce Environments |
| title_full_unstemmed | RFMVDA: An Enhanced Deep Learning Approach for Customer Behavior Classification in E-Commerce Environments |
| title_short | RFMVDA: An Enhanced Deep Learning Approach for Customer Behavior Classification in E-Commerce Environments |
| title_sort | rfmvda an enhanced deep learning approach for customer behavior classification in e commerce environments |
| topic | Customer segmentation customer classification machine learning deep neural network (DNN) customer data platform (CDP) customer relationship management (CRM) |
| url | https://ieeexplore.ieee.org/document/10839360/ |
| work_keys_str_mv | AT kwanheekim rfmvdaanenhanceddeeplearningapproachforcustomerbehaviorclassificationinecommerceenvironments AT mingyujo rfmvdaanenhanceddeeplearningapproachforcustomerbehaviorclassificationinecommerceenvironments AT ilkyeunra rfmvdaanenhanceddeeplearningapproachforcustomerbehaviorclassificationinecommerceenvironments AT sangohpark rfmvdaanenhanceddeeplearningapproachforcustomerbehaviorclassificationinecommerceenvironments |