| DC 欄位 |
值 |
語言 |
| DC.contributor | 企業管理學系 | zh_TW |
| DC.creator | 劉彥廷 | zh_TW |
| DC.creator | Ya-Ting Liu | en_US |
| dc.date.accessioned | 2022-8-26T07:39:07Z | |
| dc.date.available | 2022-8-26T07:39:07Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=109421031 | |
| dc.contributor.department | 企業管理學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 現今在變化動盪的食品電商零售產業中，企業更需要保持競爭力同時與顧客建立起長期互動的關係，藉此增加公司的利潤。顧客分群對於多數企業而言，能使企業夠有效理解到顧客特徵與特性，提供企業在不同顧客之間下來分配合適資源；企業同時必須因應消費者行為模式的改變，面對消費者各式各樣的需求時，必須掌握顧客消費行為模式來洞察先機。
本研究提出兩大研究目的，第一部份建立LRFM模型，採用資料探勘的技術，將顧客劃分為數種族群，並使用多維度顧客組合模式來挖掘出最高價值的顧客族群。第二部份建立機率模型，BG/NBD與Gamma/Gamma模型來預測顧客購買行為之特徵，模型在非契約市場環境中，藉此衡量顧客的長期獲利能力。
最後，分群模型歸類為五群，挖掘出兩大高價值忠誠顧客族群，其他群族分別為高消耗顧客、不確定型新顧客、不確定型流失顧客等。模型特徵評估水準上，預期購買次數為R-squared為0.850、預期購買金額為 R-squared為0.877，預測特徵上有著良好的表現。將預測模型結合分群模型，分析不同群體間的特徵表現，為企業制定客製化行銷策略，提升企業的營運績效並降低整體成本，維護長期與顧客之互動關係，作為企業參考的依據。 | zh_TW |
| dc.description.abstract | In today’s turbulent food e-commerce retail industry, businesses need to remain just as competitive while simultaneously establish long-term interaction with customers to increase profits. For many enterprises, customer segmentation enables them to effectively understand customer characteristics and allocate appropriate resources between different customers. Enterprises must also simultaneously respond to changes in consumer behavior and discover insights by applying probabilistic models.
There are two major objectives in this research. First is to develop LRFM model with data mining approach for customer segmentation. Second is to apply BG/NBD and Gamma/Gamma models to predict customer purchase behavior. Both models are generally used in non-contractual market environments to measure customers’ long-term profitability. Results indicate that customer profiles can be classified into five groups, including high-cost consuming group, uncertain new customer group, uncertain lost customer group, and two groups of loyal high value customers. Regarding the accuracy of prediction models, the R-squared of the BG/NBD model for purchase frequency is 0.850, while the R-squared of the Gamma/Gamma model for purchase amount is 0.877. The results of the prediction models are integrated with segmentation model to analyze feature performance between different groups. The proposed models can help enterprise develop customized marketing strategies to bolster operational performance, while lower overall costs and maintain long-term interaction with customers. | en_US |
| DC.subject | 集群分析 | zh_TW |
| DC.subject | 顧客分群 | zh_TW |
| DC.subject | LRFM模型 | zh_TW |
| DC.subject | BG/NBD模型 | zh_TW |
| DC.subject | Gamma/Gamma 模型 | zh_TW |
| DC.subject | Customer segmentation | en_US |
| DC.subject | LRFM model | en_US |
| DC.subject | BG/NBD model | en_US |
| DC.subject | Gamma/Gamma model | en_US |
| DC.subject | Clustering analysis | en_US |
| DC.title | 應用 LRFM、BG/NBD、Gamma/Gamma 模型於零售電商顧客分群及購買行為預測 之研究 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Using LRFM, BG/NBD, and Gamma/Gamma models for Customer Segmentation and Purchase Behavior Prediction on E-commerce Retailer | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |