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| 研究生： |
黃振萬 Chen-Wan Huang |
|---|---|
| 論文名稱： | Methodologies for Discovering Sets of Critical Products |
| 指導教授： |
許秉瑜
Ping-Yu Hsu |
| 口試委員: | |
| 學位類別： |
博士 Doctor |
| 系所名稱： |
管理學院 - 企業管理學系 Department of Business Administration |
| 論文出版年： | 2021 |
| 畢業學年度： | 109 |
| 語文別： | 英文 |
| 論文頁數： | 62 |
| 中文關鍵詞： | 關鍵商品 、數據挖掘 、垂直數據庫 、頻繁項目集挖掘 、RFM |
| 外文關鍵詞： | critical products, data mining, vertical database, frequent itemsets mining, RFM |
| 相關次數： | 點閱：12 下載：0 |
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在數位轉型時代，零售業者能夠識別出重要客戶與其相關之關鍵商品來提升公司營運績效至關重要。關鍵商品是重要客戶之關鍵購買項目，但在一般消費客戶群中並非具有影響性。儘管關鍵商品之銷售量可能偏低，但仍建議應持續保留在零售店內貨架上，藉以留住店家之重要客戶。過去文獻中少有研究考慮關鍵商品並找出識別模型。本研究提出一種利用垂直數據庫架構來識別出關鍵商品之改良演算法，並將該演算法應用於實際零售超市之交易數據庫，進行實驗以驗證其有效性。經過三種創新過濾條件設計，Precision rate分別達到80.55%、82.15% 與82.35％。本研究是第一個能夠結合多種過濾方法來發掘零售關鍵商品之研究。
Identifying critical products and important customers to strengthen company performance is vitally important in the digital transformation era. Critical products are itemsets that are preferred by important customers and yet not popular among ordinary customers. As a result, critical products should be kept on the shelf even if their sales volume is lower than that of other popular products. However, few studies have considered identifying critical products or their potentially valuable customer patterns. Therefore, an innovative algorithm that takes advantage of vertical databases to identify critical products was designed in this study. The proposed algorithm is applied to a transactions database of a midsize supermarket to verify the performance. The result showed that the precision of identifying critical products reached 80.55%, 82.15%, and 82.35% for three different filtering criteria. To the best of our knowledge, this dissertation is the first to use multiple filtering criteria to identify critical products.
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