大數據行銷時代下顧客關係管理 ─以國內超市為例

文化大學機構典藏 CCUR > 商學院 > 企業管理學系暨國際企業管理研究所 > 博碩士論文 > Item 987654321/44967

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題名:	大數據行銷時代下顧客關係管理 ─以國內超市為例 Customer Relationship Management in the Age of Big Data Marketing Domestic Supermarket as an Example
作者:	唐國維
貢獻者:	國際企業管理學系
關鍵詞:	顧客關係行銷資料庫行銷顧客價值購物籃分析 custonmer relationship markrting database marketing customer value market basket analysis
日期:	2019
上傳時間:	2019-09-09 10:17:21 (UTC+8)
摘要:	大數據時代來臨，企業結合資訊應用其擁有的數據進行分析，了解消費者行為並進行顧客關係管理，可為企業帶來新商業模式，增加獲利。本研究目的：1.整合顧客價值分析和顧客活躍性指標針對顧客購買行為進行預測與分群。2.透過購物籃分析檢驗顧客購買行為間關聯性及推薦價值。3.建置顧客個人化的產品推薦模型。本研究以超市為研究標的，針對1,079位會員共1,547,494筆銷售資料進行調查，資料期間於2018年4月至11月。結果顯示，整合顧客價值和顧客活躍性將顧客區分成「忠誠-活躍」、「忠誠-不活躍」、「不忠誠-活躍」及「不忠誠-不活躍」等四種類型，超市管理者能針對群體異質性進行客製化管理。第二，針對會員購物資料進行購物籃分析，能預測顧客購買行為，並協助門市規劃陳設，提高能見度。第三，本研究針對會員購買肉鬆產品行為進行顧客推薦分析，結果顯示顧客效用模型能瞭解個別顧客對產品整體效用偏好值，進行精準行銷。其次，顧客願付價格分析能有效分析個別顧客之產品屬性偏好，進而規劃個人化推廣策略。此外，本研究發現進行成本效益分析和最適產品組合分析能協助超市業者以最小成本發展品牌，並獲得最大效益。最適定價策略能協助企業制訂適當價格，以獲取高額之利潤。 With the advent of the era of big data, how to apply the huge data possessed by enterprises with information technology to conduct analysis, understand consumer behaviors and demands and conduct customer relationship management can bring new business models to enterprises and increase profits. The main purposes of this study are as follows: 1. Integrate customer value analysis and customer activity ndicators to predict and classify customer buying behaviors. 2. Examine the correlation and recommendation value of customers' purchasing behaviors through basket analysis. 3. Build personalized product recommendation model for customers. This study took supermarkets as research objects, and conducted a survey on 1,547,494 sales data of 1,079 members, covering the period from April to November 2018. The results show that the integration of customer value and customer activity performance effectively divides customers into four types: "loyalty - active", "loyalty - inactive", "disloyalty - active" and "disloyalty - inactive". The supermarket managers can carry out customized management according to the heterogeneity of the group. Secondly, the shopping basket analysis based on shopping data of members can effectively predict customers' purchasing behavior, and assist the store to plan the display of goods and improve visibility. Thirdly, this research conducts customer recommendation analysis on members' purchase behavior of meat floss products, and the results show that the customer utility model can understand individual ustomers' preference value of the overall utility of products, and then conduct accuratev marketing. Therefore, the analysis of customer willing to pay price can effectively analyze the product attribute preference of individual customers, and then plan the personalized promotion strategy. In addition, this study found that the cost benefit analysis and the optimal product combination analysis can help supermarket operators to develop the brand at the minimum cost and obtain the maximum benefit. The optimal pricing strategy can assist enterprises to set appropriate prices to obtain high profits.
顯示於類別:	[企業管理學系暨國際企業管理研究所] 博碩士論文

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