Google 在 2022 年將淘汰第三方 cookie 的廣告再行銷追蹤方式，近年來廣告
隱私權問題也讓消費者對廣告的態度產生負面影響，企業憂心未來新顧客的成
本提高導致開發更加困難，因此與顧客保持長久關係，以及擁有自己的顧客資
料庫，妥善建立顧客關係管理(Customer Relationship Management,CRM)，才能
持續增加企業最大利潤。隨著科技的演進，資料分析成為顧客關係管理中重要
的一環，當企業擁有顧客的會員交易資料，便能將大筆的會員資料做有效的顧
客分群，而 CRM 中最常使用的 RFM 模型為，最近一次消費時間(Recency)、消
費頻率(Frequency)、消費總金額(Monetary)，以三種顧客的過往消費行為當作變
數，分成不同屬性的客群，發展顧客關係管理之策略。
過去 RFM 模型在實務運用中，僅靠五分法以及中位數的判斷，粗略地將顧
客分群，而本研究將顧客分群方法做優化，首先加入 A 公司專家建議為 RFM 模
型的分數作定義，在利用非階層式集群的 K-means 法，找出最佳的分群狀態，
如此不僅讓分群能符合 A 公司之產品與產業屬性，更能以客觀科學的方式做有
效的顧客分群。接著以行銷滑水道的架構為顧客做行為分析和命名，而根據
Kumar和 Rajan提出的顧客價值矩陣，為不同的分群給予行銷策略上的建議，制
定不同的顧客發展策略。
完成有效分群以及行銷策略制定後，有鑑於過去的研究僅只停留在此，而
未實際執行行銷活動，以及深度了解不同客群的行為意義，本研究以電話訪談
的方式，訪問不同類型的顧客，了解購買原因和執行行銷活動，觀察活動成效，
探討不同類型顧客的真實想法，並為 A 公司優化初步的顧客關係管理策略，使
其日後能更有效管理會員資料庫，妥善發展顧客關係管理。本研究提供一套有
效分析顧客資料為顧客分群並擬定行銷策略的方法，此方法望能運用在台灣企
業，發展有效顧客關係管理方式。

Google announces end of behavioral targeting for advertising in 2022 and the
privacy concern has also made consumers' attitude towards advertising negatively
affected. Enterprises are concerned about the rising cost of developing new customers
in the future. Therefore, establish customer relationship management which means a
long relationship with customer can continuously increase the maximum profit for
enterprise. With the development of technology, customer data analysis has become an
important part of customer relationship management. When an enterprise has the
transaction data of customers, it can make data as effective customer segmentation. The
most used RFM model in CRM is from: the latest consumption time, frequency, and
total consumption amount, these three kinds of consumer behavior are used as variable,
which are divided into different attributes of customers and develop customer
relationship management.
In the past, RFM model used only five score method and judgment by median,
those methods divide customers roughly. In this study, the method of customer
segmentation is optimism. Firstly, the expert of company will define the score of RFM
model and use Non-hierarchical method: K-mans, which will find the best cluster
condition. This method not only meet the product and industrial of company, but also
can make effective customer segmentation in an objective and scientific way. Next
makes behavior analysis in those customer segmentation and naming for cluster based
on the NES model. And then give different strategic advice based on the customer
management matrix proposed by Kumar and Rajan to develop and retain customer for
company A.
After the effective customer segmentation and marketing strategy are formulated,
the past research that only stop here, but not actually carry out marketing activities,
digging the really mean of customers behavior. This study uses telephone interview to
visit different types of customers, understanding the reason of purchase. The telephone
interview will execute and observe the marketing activities and help understand the real
ideas of different types of customers. In the end this study will develop a preliminary
customer relationship management model for company A, so that it can manage the
member database more effectively in the future. This study provides a set of methods
to analyze customer data for customer segmentation and to formulate marketing
strategy. This method is expected to be used effectively in Taiwan enterprises and
develop effective customer relationship management methods

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