電子商務是現代B2B marketing的一種具體且主要的表現形式，廣泛稱之為企業對企業並透過電子商務的方式進行交易。除了典型電子商務主事線上交易外，企業普遍做法是將企業官網視為品牌網站經營，並將銷售工作賦予訓練有素的業務人員，品牌網站的經營也將視客戶在整體供應鏈上的位置做出有限度的行銷。企業需從價值鏈的角度來思考企業官網的意義，不斷檢視與提高官網能為企業帶來的價值。 過往論文在資料探勘的研究多應用於B2C及B2B電子商務市場上，將會員資料與網站瀏覽行為作比對，進而提供顧客關係管理及個人化推薦服務，相較之下，卻看不到B2B企業官網相關的研究探討，因此本研究使用資料探勘技術結合關聯法則將企業官網的日誌檔進行探勘研究及分析，試圖在沒有會員資料的典型企業官網下藉由網站使用者的瀏覽行為發掘出顧客價值及隱藏的商機。 本研究資料範圍取自T公司官方網站之日誌檔在2017年網站改版重新上線後的資料並運用Apriori和Hots Pot兩種演算法得出實驗結果，Apriori演算法可發掘網站使用者觀看網頁的行為模式，從中發現到熱門網頁及了解到網頁點選的順暢度增加了網頁的點閱率，發掘的資訊提供了網站設計或管理人員檢視企業官網建置的成效。而Hot Spot演算法發掘出依國別為關鍵欄位探勘出各國家所代表的網站使用者在線上的瀏覽行為，分別得出T公司主要營運的18個主要客戶所隸屬的國家而得出的實驗結果，再從瀏覽行為判讀網站使用者因企業人士身份在觀看網頁顯得目標導向及重點閱讀。整體而言本研究實驗結果所發掘的結論具正向及可回饋於公司作貢獻。 ;E-commerce is the primary form of B2B marketing in modern society, which involves selling products or services between businesses through electronic commerce. Besides typical online e-commerce transactions, the most popular method adopted by companies now is to manage their corporate official websites as brand websites and provide sales tools to well-trained salespeople. The management of the brand website also involves limited marketing depending on the customer’s position in the supply chain. Businesses need to contemplate the meaning of the official website from the value chain point of view to constantly review and generate values for the official website. Past literature mostly apply data mining research to the B2C and B2B e-commerce markets, where members’ information is compared with website browsing behavior to facilitate customer relationship management and personalized recommendation services. In contrast, there is a lack of research related to the B2B official websites, therefore the study utilizes data mining technique in conjunction with association rules to explore and analyze the official website log file in order to discover customer values and hidden business opportunities via user browsing behavior on a classic corporate official website that does not store any membership information. The study derives data from the log file of T company’s official website, which was updated in 2017. The data was subjected to Apriori and HotSpot algorithms. Apriori algorithm ascertained the website users’ browsing behavior to identify the most popular web pages as well as the relationship between the increased click-through rate and browsing responsiveness. The information discovered was provided to website designers or managers to review the benefits of the corporate website. HotSpot algorithm identified representative website users’ online browsing behavior of different countries, where the customers of T company in 18 countries were analyzed to reveal that most of them were businesspeople who exhibited goal-oriented and keyword spotting browsing behaviors. In general, the conclusions derived by the study provided positive feedback to the company.
