- 學位論文
以擴充RFM模型探討海峽兩岸消費者在網路購物之再購行為研究
An Augmented RFM Model of the Cross-Strait Consumers’ Repurchase Behavior in Online Shopping
摘要
網路購物成長快速早已成為電子商務業者的兵家必爭之地，了解線上消費者的購物行為成為電子商務業者獲利的必要功課。由於增加顧客留住率可以提高獲利，加上開發一位新顧客的成本遠高於留住一位舊顧客的成本，因此若能掌握消費者向同一個賣家再度購買的可能性以及在同一購物平台再購的機率均有助於業者了解消費者行為，進而掌握有價值的顧客，方便推動目標行銷或精準行銷。本研究是以中國最大的電子商務淘寶網以及台灣前兩大之Yahoo!奇摩拍賣及露天拍賣平台為對象，針對網路購物成交最熱絡的商品類別─女裝，進行兩岸電子商務消費者再購行為的比較分析。研究目的在藉由女裝商品的真實交易資料，建構以RFM模型為基礎的賣家再購和平台再購預測模型，並分析兩岸電子商務中消費者再購行為的異同。 再購行為預測模型的預測變數包括最近交易時間間隔、交易次數、交易總金額、平均交易金額，以及買家最近給的評價等五個變數。本研究結果顯示Yahoo!奇摩拍賣的賣家再購比例和平台再購比例在三個平台中均為最高，其次是露天拍賣，淘寶網的再購比例最低。買家的轉換賣家比例由高至低的順序也和平台的再購比例一樣，顯示Yahoo!奇摩拍賣的消費者行為既有最高的賣家忠誠行為，卻也有很高的賣家轉換比例，呈現多忠誠的消費行為。二元羅吉斯迴歸結果顯示，平台的所有預測變數和賣家再購及平台再購均呈現顯著地相關。本研究也以集群分析找出各平台最有價值顧客的特性。本研究發現是基於網路購物平台的真實交易資料，各平台消費者的再購行為及其預測模型，可供平台業者和電子商務賣家作為顧客關係管理和商品行銷的參考。
並列摘要
The fast growing online shopping has turned into a battlefield for many e-commerce (EC) businesses. They must understand their customers’ purchase behavior in order to make a profit. Given the fact that the increase in customer’s retention rate can lead to higher profit and the cost of acquiring a new customer is higher than that of retention of an existing customer, the EC businesses can understand their customers’ behavior and assess customers’ value in order to initiate target marketing or precision marketing by capturing the probability of revisiting the same seller by a customer and repurchase at the same e-marketplace. Taking China’s largest EC platform—Taobao, and Taiwan’s top two platforms—Yahoo Taiwan Auction and Ruten Taiwan Auction as our research targets, and focusing on the most popular trading categories—women’s apparel, we conduct a comparative analysis on the cross-strait EC consumers’ repurchase behavior. The purpose of this research is to establish a RFM-based prediction model of consumers’ seller repurchase and platform repurchase by analyzing the actual transaction data of women’s apparel and to compare the cross-strait EC consumers’ repurchase behavior. The repurchase behavior prediction model consists of five predictors, including the recency, the freguency, the total amount, the average amount, and the consumer’s last rating. The research findings show that in terms of repurchase rate, Yahoo! is the highest, followed by Ruten, and Taobao is the lowest. Interestingly, the consumer’s seller switching rate in descending order is also Yahoo!, Ruten, and Taobao, which indicates the consumers at Yahoo! exhibit multi-loyalty behavior with both high repurchase rate and high seller switching rate. The Logistic regression shows that all the predictors in the seller repurchase and the platform repurchase prediction model of Yahoo!, Ruten, and Taobao are statistically significant. We also use cluster analysis to identify the characteristics of the most valuable customers at the three different platforms. All of our findings are based on actual transaction data of online shopping web sites, the repurchase behavior of online consumers and its prediction model can be used by EC businesses and platform businesses for consumer relationship management and merchandise sales and marketing.