有鑑於巨大的經濟壓力，多數的休旅業者已經面臨到如何尋找赚取高利潤機會的議題，面對這樣的議題時，業者有效的顧客關係管理將明顯地影響到這種議題的執行成效。一般來説，潛藏性消失客源所具有的資訊在進行行銷分析時通常是被忽略的，但是這些資説應該是具有某種程度的參考價値，因此在這份研究中，我們將把這個特質納入分析考量。如我們所知，近年來類神經網路方法已經被應用在許多領域上，例如：聚類分析、預测、辨識等，而且也呈現出很好的應用成效。因此，在我們的研究中，將引用類神經網路之非監督式模式（自適應共振理論模式）來進行必要的群聚解析。有鑑於群聚解析的結果並不能直接應用，因此我們也設計了一個同時考量群聚解析和行銷資説挖掘的整合性程序來解析我們所考量的議題。此外，對於非潛藏性消失客源的群聚，我們也在文中設計一個程序來判斷哪些客群是休旅業者値得投資和開發的。最後，我們也透過一個個案實例來展現本文所提方法的合理性和可用性。

Due to enormous economic pressure, most tourism organizations have to address the issue of finding opportunities to earn high profits. In addressing this issue, the efficiency of Customer Relationship Management (CRM) will significantly affect the company's performance. Generally, the information is hided in the potential loss-customer clusters will be omitted to the marketing analysis. However, such information should be available for marketing analysis. Therefore, we will take this property into consideration in this article. As is known, Artificial Neural Networks (ANNs) were employed in many applications, e.g. clustering, prediction, recognition, and so on. The effectiveness of applications was also verified as well. Hence, in this study, we will apply an unsupervised model of ANNs, which is the Adaptive Resonance Theory (ART) model, to our clustering analysis. Since the results derived from clustering analysis can not be directly used for the issues we focused on, we designed an integrated solution to combine the procedure of clustering analysis and marketing information mining. As for the nonpotential loss-customer clusters, we also designed a procedure to address the issue of judging which clusters would be suitable for investing and developing for tourism organizations. Finally, an illustrative example is presented to demonstrate the rationality and availability for our proposed approach in this article.