近年來,網際網路於商業的應用蓬勃發展,網路本身所具備強大的連結功能與豐富的網頁資訊已經逐漸成為企業界矚目的焦點。網際網路的發展讓傳統的行銷工具有更多的發展空間,使得各式各樣的電子商務營運模式逐漸成形,例如:B2B、B2C、C2C、C2B等營運類型。其中,發展電子商務的虛擬通路不但可以彌補實體零售店舖的不足,亦可透過網際網路接觸更多的潛在顧客並且提高品牌知名度。因此企業必須建立出一套自動化的資料庫探勘機制,適時對近期的網路購物消費者行為做適當的解讀與分析,再回饋到電子商務網站本身的設計或者產品特性,如此才能準確掌握不同於實體店面消費者的行為模式,實現有效的顧客關係管理(Customer relationship management),進而不斷延續整體商務網站的產業競爭力。 本研究以B2C電子商務網站為研究對象,使用RFM模型來發掘價值會員,再利用關聯式規則(Association rule)針對價值網路會員的網路點閱資料(Clickstream data)做資料採礦分析,嘗試探勘出網站會員的點閱規則,並利用序列型樣(Sequential pattern)建構會員的瀏覽行為模式,以作為網站經營者實務上規劃網站及擬定行銷策略之依據。
Rich connected links and information on the internet have changed enterprises’ ways to do business. Internet also enlarges the field of marketing and forms many creative business models, such as B2B, B2C, C2B, and C2C. Nowadays, enterprises can access more potential customers through broadcasting product and brand information by internet. As a result, how to build an automatic system to help enterprises find out valuable customers and understand customer’s online behaviors is an extremely important issue now. This research is to build a systematic mechanism to find out valuable customers and understand their online browsing and shopping behaviors. RFM model, the association rule and the sequential pattern method are adopted to mine the internet clickstream data of a B2C e-commerce website. The empirical results show our mechanism can well achieve our research purpose.