本研究目的為應用資料探勘技術挖掘會員資料庫與交易資料庫在行銷上之知識，並
以改良顧客價值RFM 模型為市場導向。本研究藉由迅速成長之 85℃咖啡連鎖店說明應
用之流程。本文以台北市之 85℃咖啡連鎖店消費顧客為研究對象。研究方法首先採用
RFMD模型變數(基於 RFM 模型)將消費顧客進行市場區隔分析，並以決策樹 C4.5 演算
法進行顧客社會經濟資料分類與分群結果之分類，結果將建構 4項分類規則，其可應用
於資料庫行銷規劃中。相較於以消費利益變數進行市場區隔與規劃之傳統行銷區隔模
式，本研究之顧客價值分類規則更能精確的挖掘高價值顧客出並加以歸類，以達到目標
市場之行銷規劃。

The objective of this research is to apply the data mining theories on
membership/transaction databases for marketing knowledge discovering. The research uses a
fast-growing coffee chain shop: 85℃ Coffee as an empirical case. The researcher proposes a
RFMD model (based on the RFM model) to cluster the customers of 85℃ Coffee chain
shops in Taipei. The study apply the C4.5 algorithm on the result of clustering and
socioeconomic variables. As a result, there are four classification rules are created by this
research. These rules can be applied in database marketing plan. In contrast with traditional
market segmentation method (which employs customers benefit variables), this research
discovers high value customer precisely via the classification rules. Hence, the results can
easily achieve the target markets for marketing projects.