Marketing Optimization: Purchase Data-Based Customer Segmentation Decision Support System

Abstract—In the era of technological development and changes in shopping culture, e-commerce is increasingly dominating the market, and customer purchase data is becoming a valuable source of information for companies. To address the challenges of inappropriate targeting, customer retention, customer satisfaction, and measuring the effectiveness of marketing campaigns, this research aims to design a decision support system for customer segmentation based on purchase data, identify the optimal parameters of clustering algorithms, and develop appropriate marketing strategies for each group of customers generated from clustering. By using tools such as Matplotlib, Numpy, and Pandas, this research is expected to provide valuable guidance for companies in optimizing their marketing strategies in the competitive e-commerce market.

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Publication Details

Journal
Matics Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology)
Published
2025-03-25
DOI
https://doi.org/10.18860/mat.v17i1.24274
Primary Topic
Customer churn and segmentation
Type
article
Field-Weighted Citation Impact
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article

Marketing Optimization: Purchase Data-Based Customer Segmentation Decision Support System

Yusi Tyroni Mursityo, Djoko Santoso, Veronica Wong, Muhammad Althaaf Fadhiilah et al.
Matics Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology)
Customer churn and segmentation
article

Marketing Optimization: Purchase Data-Based Customer Segmentation Decision Support System

Yusi Tyroni Mursityo, Djoko Santoso, Veronica Wong, Muhammad Althaaf Fadhiilah, Luthfia Rahmi Setyorini, Andi Ahyar Almuhajir Amrani
article en

Abstract

Abstract—In the era of technological development and changes in shopping culture, e-commerce is increasingly dominating the market, and customer purchase data is becoming a valuable source of information for companies. To address the challenges of inappropriate targeting, customer retention, customer satisfaction, and measuring the effectiveness of marketing campaigns, this research aims to design a decision support system for customer segmentation based on purchase data, identify the optimal parameters of clustering algorithms, and develop appropriate marketing strategies for each group of customers generated from clustering. By using tools such as Matplotlib, Numpy, and Pandas, this research is expected to provide valuable guidance for companies in optimizing their marketing strategies in the competitive e-commerce market.

Matics Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology)Vol. 17(1)
University of Brawijaya (ID)
Openalex Percentile: Top 94%
Customer churn and segmentation
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Marketing Optimization: Purchase Data-Based Customer Segmentation Decision Support System — Yusi Tyroni Mursityo, Djoko Santoso, et al. · Matics Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) (2025) | TGRS Research Map | TGRS