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Journal : JOIV : International Journal on Informatics Visualization

Design of Prediction Model using Data Mining for Segmentation and Classification Customer Churn in E-Commerce Mall in Mall Huda, Ilham; Achmad Suhendra, Agus; Arif Bijaksana, Moch
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2414

Abstract

The classification of churn is driven by the potential risks e-commerce companies face, such as losing customers who discontinue their service usage or churn. Marketing specialists have shifted their efforts from acquiring new customers to retaining existing ones in order to mitigate customer churn. Predictive models are created using data mining techniques to identify customer churn patterns. This study proposes a data mining model aimed at predicting customer behavior, with the processed results utilized as suggestions for improvements and company strategies in customer retention through segmentation and classification. Segmentation and classification involve several variables: Session, Interaction with Application, Actions taken during the interaction, purchasing, claim, and discount. This study employs a clustering technique based on the Recency, Frequency, and Monetary (RFM) model, which considers factors such as the time since the last visit, the number of visits, and the total amount spent by the customer. The classification algorithm model was evaluated by comparing three classification algorithms: decision tree and Support Vector Machine (SVM). The decision tree algorithm had the highest accuracy, achieving an impressive 87% accuracy rate in customer classification. Factors influencing customer churn include purchasing behavior, session activity, claim feature utilization, adding products to cart, and discounts. Improving stock management is crucial to prevent stock shortages, likely to cause churn. Additional measures like sending emails/notifications and offering vouchers/loyalty points can be implemented for customers who added products to their carts but didn't complete the purchase, with a focus on popular products.
Co-Authors Abd. Karim Basir Adhi Tio Rachman Agnar Mokhammad Akbar Bima Nugrahanto Amelia Kurniawati Angga Darmawan Aprilia, Hafiza Arif Bijaksana, Moch Arnol Hendra Manyu Ayu Amanda Hapsari Ayuni, Nurul Boby Hera Sagita Budi Praptono Cahyaningtyas, Rosa Virginia Clarisa Presty Pangeran Devayanti , Komang Sriasih Laksmi Dewi , Nadia Aprilia Deyana Prastika Putri Dipta Raga Pratama Dityo, Daffa Raihan Efrata Denny Saputra Yunus Eliza Anggraini Joefatha Endang Chumaidiyah Esa Purnama Fahrani , Anisa Fajri Rahman Qusyaifi Farantino, Richy Fauzan Haeqal Arifin Firdho, Muhammad Ghaisani, Inten Ayuning Haikal, Muhammad Fikri Hamidah, Siti Nur Huda, Ilham Ika Arum Puspita Ilma Mufidah Ima Normalia Kusmayanti Iman Nur Hidayat Irzaputri, Khalista Muthia Ismail, Muhammad Ravi Jasmine Raisya Salsabila Jodia Ridha Arrozak Kurniawan, Tiara Fadhila Lian, Rayhan Bagus Listrianto, Redi Isman Luciana Andrawina M. Yazid Dhiyaulhaq Mardhiyyah Mardhiyyah Maria Apsari Sugiat Martini , Sri Meldi Rendra Muhammad Dary Fauzan Muhammad Iqbal Muhammad Surya Dwi Kurniawan Narendra, Erlangga Nadhif Dwi Nina Kurnia Hikmawati Noeya, Adisty Azzahra Ozki Septariadi Pradipta, Fenta Pretty Prima Roza Putra Fajar Alam Putra, Muhammad Aldio Rozan Rifa Rizka Anisah Rio Aurachman Rizkan Iskandar Wigena Rizki Wicaksono Parasto Rosad Ma'ali El Hadi Sari Wulandari Sari Wulandari Sari Wulandari Sevy Safira Handini Sri Martini Syahputra, Arnanda Eka Syeren Anastasya Syeren Anastasya Trisyan Admaja Kuncoro Vietra Shauma Ranabilla Wali Hakim Pradana Wawan Tripiawan Winda Hariani Br Munthe Yati Rohayati