NGHIÊN CỨU PHÂN TÍCH HÀNH VI NGƯỜI DÙNG TRÊN WEBSITE BÁN TRÁI CÂY, PHÁT TRIỂN GIẢI PHÁP HỖ TRỢ QUYẾT ĐỊNH KINH DOANH THÔNG MINH=Researching and analyzing - user behavior on websites that sell fruit, giving solutions for supporting smart business decisions

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Tác giả: Đình Hoàng Nguyễn, Hoàng Giang Nguyễn, Hữu Hiếu Nguyễn, Thái Cường Nguyễn, Tiến Duy Nguyễn, Tuấn Tú Nguyễn, Hữu Hiếu Vũ, Thị Dương Vũ

Ngôn ngữ: vie

Ký hiệu phân loại:

Thông tin xuất bản: Tạp chí Khoa học Tài nguyên và Môi trường, 2024

Mô tả vật lý: tr.18-29

Bộ sưu tập: Metadata

ID: 119140

Thêm vào giỏ

“Dữ liệu là dầu mới” - hàm ý nói về sự dồi dào của cả hai nguồn tài nguyên: Dầu và dữ liệu, tuy nhiên nếu cả hai ở trạng thái “thô” đều sẽ không có giá trị, do vậy việc thu thập dữ liệu chính xác, đầy đủ và phân tích chúng sẽ tạo nên giá trị cốt lõi của dữ liệu. Đồng thời giúp tổ chức phân loại, làm rõ các nhu cầu khách hàng, từ đó xây dựng được nhiều bước đột phá mới trong kinh doanh. Để tối ưu quá trình phân tích dữ liệu từ những thành phần thô ban đầu, phương pháp phân cụm được sử dụng như một phương pháp để tiếp cận tốt hơn với những giá trị mà nó mang lại. Bài báo sử dụng phương pháp phân cụm thông qua thuật toán K-Means. Phân tích dựa trên hơn 1.000 đơn hàng và thu được các cụm khách hàng có hành vi mua sắm tương tự. Nhờ vậy, thông qua quá trình phân loại các tổ chức có thể phát triển nhiều chiến lược kinh doanh tương ứng với từng nhóm khách hàng riêng biệt, cũng như cách tiếp cận với ngành kinh doanh dịch vụ nói chung.The phrase “Data is the new oil” implies the abundance of both resources: Oil and data. However, if both remain in their “raw” states, they bring little value. Therefore, accurate data collection and thorough analysis form the core value of data. Simultaneously, this process helps organizations classify and clarify customer needs, leading to innovative breakthroughs in business. To optimize the analysis of data from its raw components, clustering methods are employed as an approach to better engage with the inherent values they offer. This article explores clustering methods using the K-Means algorithm. The analysis is based on over 1,000 orders, resulting in clusters of customers with similar shopping behaviors. Consequently, through this classification process, organizations can develop distinct business strategies tailored to each customer group, as well as adopt effective approaches to the broader service industry.

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