手機應用軟體使用者瀏覽行為分群之研究

The Study of Mobile Phone APP Software User Browsing Behavior Clustering

廖如閔(Ju-Min Liao)

指導教授 ： 邱垂昱 ； 范書楷

國立臺北科技大學/管理學院/工業工程與管理研究所/碩士(2014年)

https://doi.org/10.6841/NTUT.2014.00029

摘要

面對智慧型手機不斷的推陳出新，使用者對手機的使用方式已經從鍵盤式轉為觸控式，在這轉變的過程中，除了科技外觀上不同於過去外，重要的是智慧型手機安裝的應用程式(Application、一般簡稱為App)，本研究由好書服文創事業有限公司提供使用者使用手機APP瀏覽點閱之紀錄資料，來進行使用者集群分析， 本研究建構一個以手機軟體之使用者網路瀏覽行為為基礎，以進行使用者忠誠度群集分析模式，以RFM (recency, frequency and monetary)分析架構於使用者瀏覽行為之描述，並運用資料探勘上資料分群技術，其採用PSOSOM (particle swarm optimization and self-organizing map)演算法對使用者瀏覽行為進行集群分析，目的在於分析顧客價值，找出市場區隔，達到管理者掌握影響瀏覽行為的因素，再藉由管理者整合資料並且不斷更新軟體，使其更有效能與效率於使用者在應用軟體上接受優惠資訊的正確性及速度，進而提升使用者對該應用程式的忠誠度。

關鍵字

資料探勘 ； RFM分析 ； 集群分析 ； 市場區隔

並列摘要

With development of information technology, finding useful information existed in vast data has become an important issue. Company must understand their customers and customer value analysis turns to be an important tool. In this research, we used PSOSOM (particle swarm optimization and self-organizing map) clustering to analyze user loyalty of mobile phone App (application can be abbreviated as App) software, and then RFM (recency, frequency and monetary) analysis described the user behavior. According to the market segmentation, special offers to the varied consumer group can stimulate purchasing behavior. Let customers from online to offline physical channel then drives sales and enhances brand. Finally, improve click-and-mortar conversion rate effectively.

並列關鍵字

Data mining ； RFM analysis ； Cluster analysis ； Market segmentation

參考文獻

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