結合RFM模式及信心，抵抗性心理指標建構分群顧客之特性

The Research of Characteristics of Classified Customers Based on RFM Model UsingPsychological Indices of Confidence and Resistance

黃慧新(Hui-Hsin Huang)

《行銷評論》 9卷1期 (2012/03) Pp. 19-41

https://doi.org/10.29931/MR.201203.0002

摘要

本研究主要目的為建立既有的市場佔有率當中屬於本品牌顧客之消費者心理特質，以作為未來的消費行為判別之參考依據，此議題的重要性在於過去以行銷資料庫為基礎的數學模式，以可觀察的消費者行為（如購買次數、購買間隔等）去預測市場佔有率、顧客價值，鮮少有結合個體消費心理特質之研究。若能對總體消費行為做個體消費心理觀測，在行為發生之前做偵測與規劃，則使用資料庫行銷不只是推估消費者行為之遷移過程，更可從心理層面了解造成此行為之原因，並預先做偵測。因此本研究首先使用既有的消費者忠誠度行為指標（如RFM等）將本品牌顧客分群，由每一群體中歸納出其人口統計資料，再藉由抽樣了解每一群體的心理特質，心理特質部份以「信心」和「抵抗性」兩個變項，作為了解這群既有消費者心理狀態標準，「信心」指涉的為消費者對自己判斷的確定性與消費者對此品牌態度之方向性，「抵抗性」為消費者對其他競爭品牌行銷活動引誘的抵抗程度；當此顧客對本品牌信心程度越高，並對他牌抵抗性越強烈時，意味此顧客有較高的忠誠度。研究最後並以羅吉斯迴歸估計其轉換機率，分析屬於本品牌市佔率中的顧客價值，作為未來新顧客進入時，以其人口統計資料將之歸屬於現有分群、了解並預測其信心和抵抗性，進一步決定其顧客品質。

關鍵字

RFM ； 信心 ； 抵抗性

並列摘要

The purpose of this research is to set up a model which characterizes a consumer's psychological behavior to predict the consumer's intention of certain brand. The topic is significantly important because there are quite few researches which combine consumer's psychological behaviors and a mathematic model of its associated data base for marketing study. If we can offer a delineation of an individual consumer's psychology on aggregate level, we can set up a marketing plan or strategy before the marketing share is changed and which is due to a change of a consumer's purchase behavior. This research method is organized as follows: Firstly, we use RFM model to classify customers for each brand and find the fitness of demographics variables in each group. Secondly, we survey the customer of each group to explore their psychological characteristic. This research tries to quantify ”confidence” and ”resistance” as the customer's psychological variables. Here confidence refers to the belief strength and belief certainty of a consumer toward certain brand and resistance means the level of a consumers' strength toward certain brand against that of competing brands. When the customer has more confidence toward the brand and resistance against the competing brand strongly, it means the higher loyalty the customer possesses. After sampling, we use Logistic regression to predict brand switching probability. And, finally we can analyze the customer value of each group .We can also estimate the value of a new entrance customer according to his (her) demography and classify him (her) into a group to find his (her) corresponding confidence and resistance values.

並列關鍵字

RFM ； confidence ； resistance

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