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| Title: | 藉由購買紀錄分析消費者行為
Analyze Consumer Behaviors by Purchasing Records |
| Authors: | 林庭瑋 |
| Contributors: | 洪叔民
林庭瑋 |
| Keywords: | 資料庫行銷
購買行為預測
決策樹
Database marketing
Sales predictions
CHAID
Decision tree |
| Date: | 2017 |
| Issue Date: | 2017-09-13 16:02:59 (UTC+8) |
| Abstract: | 本研究嘗試分析顧客在量販店購物行為，利用歷史購買商品紀錄將購買習慣相同之消費者分類出來，作為業者未來在行銷之依據，以達到提升營收之效果。
本研究乃針對台灣某零售業之量販店作為研究對象,利用該公司之會員消費紀錄中做為本次研究之資料，並利用會員在該年所購買品項挑選出購買次數最多之商品作為因變數，挑選的商品為:牛奶、泡麵、優酪乳及非洋芋片零食四種商種商品並加入年齡、性別、購買頻率共七項做為此自研究之變數，並將資料轉換成CHAID所需資料類型後，運過Chi-Square Automatic Interaction Detector(CHAID)統計分類方式欲探究熱門商品間是否有互相關聯,能更準確的將相關的客戶分類出來，若量販店能夠掌握重要商品之可能購買行為並加以行銷，將能為營收帶來正面效果。
本研究主要成果在於利用決策樹結果提供企業在推出新商品進行銷售前,先針對核心顧客進行購買預測的重要參考,利用此預測結果將商品推出給主要客戶族群,達到最大效益。
This study attempts to analyze the purchasing behavior of consumer in supermarkets. According to the historical purchasing records, customers sharing the same purchasing behavior will be classified and the results will be used for marketing strategies to increase sales.
A retailing firm in Taiwan provided the data for this study. This study acquired the consuming records of its members and identified several most frequently purchased products to be the dependent variables. They are milk, , instant noodles, yogurt and snacks. Furthermore, age, gender and purchase frequency are included as the independent variable. Then this research used Chi- Square Automatic Interaction Detector (CHAID) to classify customers by their purchasing behavior over products. The understanding of purchasing behavior over those popular products can lead to positive effect on the sales of the retailer.
This research used decision-tree analysis and the results can be the important references for companies to predict the purchasing behavior of primary customers in order to maximize the efficiency. |
| Reference: | 中文文獻:
[1.] 城田真琴. (2013). Big Data大數據的獲利模式:圖解案例策略實戰. 臺北市: 經濟新潮社.
[2.] 張冠英. (2012). 利用決策樹分析法探討慢性疾病對腹主動脈瘤患者術前及術後的影響. 淡江大學資訊工程學系碩士班學位論文, 1-76.
[3.] 黃顯欽. (2011). 分析第一當事者影響 A1, A2 類肇事事故因素之研究. 交通大學運輸科技與管理學系學位論文, 1-137.
[4.] 曾淑峰 林志弘 翁玉麟. (2012). 資料採礦應用:以SAS Enterprise Miner為工具. 臺北市: 梅霖文化.
[5.] 資策會. (2015). 運用巨量資料預測技術之現況與未來.
[6.] 資策會. (2016 p14). 台灣批發零售業2016年資訊投資分析與2017年展望.
[7.] 資策會. (2017). 網購調查:2016年台灣人平均消費2.7萬元.
[8.] 資策會，(2015) 運用巨量資料預測技術之現況與未來，產業研究報告，財團法人資訊工程工業策進會產業情報研究所
[9.] 陸昀泓. (2014). 層級貝氏模型應用於消費者之社群與網路購物行為分析之研究. 臺灣大學國際企業學研究所學位論文, 1-66.
[10.] 蔡育儒. (2014). 可處理巨量資料的平行化 CHAID 決策樹. 淡江大學統計學系碩士班學位論文, 1-45.
[11.] 譚磊. (2013 ). 大數據挖掘-從巨量資料發現別人看不到的秘密(初版). 台北市: 上奇時代.
英文文獻:
[1.] Coussement, K., Van den Bossche, F. A., & De Bock, K. W. (2014). Data accuracy`s impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees. Journal of Business Research, 67(1), 2751-2758.
[2.] Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.
[3.] Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied statistics, 119-127.
[4.] Kleissner, C. (1998, January). Data mining for the enterprise. In System Sciences, 1998., Proceedings of the Thirty-First Hawaii International Conference on (Vol. 7, pp. 295-304). IEEE.
[5.] McCarty, J. A., & Hastak, M. (2007). Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression. Journal of business research, 60(6), 656-662.
[6.] Morgan, J. N., & Sonquist, J. A. (1963). Problems in the analysis of survey data, and a proposal. Journal of the American statistical association, 58(302), 415-434. |
| Description: | 碩士
國立政治大學
企業管理研究所(MBA學位學程)
105363046 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0105363046 |
| Data Type: | thesis |
| Appears in Collections: | [企業管理研究所（MBA學位學程）] 學位論文
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