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Title: | 顧客族群消長之分析與應用 Analysis and Applications of Dynamic of Customer Clusters |
Authors: | Tsung-Han Tsai 蔡宗翰 |
Advisor: | 任立中(Li-Chung Jen) |
Keyword: | 資料庫行銷,消費者動態性,集群分析,馬可夫鏈, Database Marketing,Customer Dynamic,Cluster Analysis,Markov Chain, |
Publication Year : | 2011 |
Degree: | 碩士 |
Abstract: | 隨著時代的變遷,企業之間的競爭強度日益增加,企業要如何了解消費者的異質性(Heterogeneity)與動態性(Dynamic)永遠都是行銷人員面對的最核心問題與挑戰。從前的企業想著如何降低成本來增加獲利,但在行銷的知識傳遞下,企業開始思考如何了解自己的顧客,進而提供更符合客戶需求的產品與服務,如此不但能增加獲利,確立長久的關係,更能建立一個高效率、可維持的獲利模式。
透過資料庫的建置,企業可以進行成本、存貨的控管;但如何從資料庫中獲得有效的資訊,幫助企業了解市場需求與顧客族群未來的趨勢,才真正考驗著企業的行銷思維與分析能力、經營策略是否與時俱進。本研究針對此問題,利用集群分析(Clustering Analysis),針對通路商之交易資料庫,分析現有顧客的族群消長,試圖預測顧客未來的動態,作為一對一的顧客關係管理基礎。 本研究提供一個系統化的方法,透過集群分析分類顧客,使用邏輯思回歸(Logistic Regression)確認分群變數均顯著有意義後,產生馬可夫鏈移轉機率矩陣(Markov Chain ),探討顧客族群之消長,從中捕捉顧客的動態性,並提出通路商以及品牌商如何應用此方法,提升行銷資源之使用效率,達成一對一行銷的目標。 針對M通路商資料庫,將兩年內的顧客分群,發現通路商有數群不同特性之顧客,利用兩年的資料預測顧客之動態性,發現顧客正在流失,未來若能根據此結果制訂經營策略與搭配的行銷活動,並持續追蹤顧客族群之動態,相信可以改善此情形。 Consumer dynamic and consumer Heterogeneity are two core issue of marketing researchers. Companies have begun thinking about how to understand their customers deeper, and prepare better product and service before head. to meet consumers’ or customers’ needs. By doing this, they can not only increase profits, but also build long-term relationship with individual customer, and increase the efficiency of marketing spending. Through the invention of computers and information systems, database construction became possible and valuable. By using databases, companies can control cost and inventory easily; but how to get valuable information s from the database to help understanding future demands of market and individual customer’s potential behavior are really testing the companies’ ability in marketing research area. This research focuses on customer dynamic issue, using Cluster Analysis method which tested by Logistic Regression to develop a Markov Chain transportation probability matrix to analyze the dynamic of customers of a firm. . In order to understand the dynamic trends of any customer, analyze target will be trace month by month. By tracing which group he/she belongs to, we can build the Markov Chain Matrix to predict future situation of this target customer. This study provide a systematization method for distributors to predict future customers’ distribution , so distributors can develop marketing projects to affect customers and check the effect of those projects .Brand vendors will also benefit from better opportunities to target those customers potentially going to be brand vendors’ target by cooperate with distributors. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16580 |
Fulltext Rights: | 未授權 |
Appears in Collections: | 國際企業學系 |
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