基於巨量資料架構分析社交行為與主題模型預測顧客流失

網路的方便性，讓顧客於線上購物之轉換成本較低，而企業要如何利用有限資源，進行顧客流失的預測以及挽留，對企業是相當重要的議題。過往研究中主要以顧客價值進行顧客流失預測，而現今的線上購物平台，大多提供了能讓顧客撰寫評論的平台以及社交的空間，因此過去的顧客流失預測方式，隨著資料特性的不同，已顯得較不適用。顧客藉由購買資訊的分享與交流，包括了產品評論、評價等做為購買決策的考量，並影響未來是否持續消費的可能，而能夠做為考量依據的決策，通常受到具有關係的人所影響。購買紀錄以及產品評論也會隨著時間的累積，令企業能夠分析的資料量逐漸增加。因此本研究提出了基於巨量資料架構分析社交行為與主題模型的顧客流失預測模型，考量了顧客在網路上的社交行為，以及顧客所撰寫的評論中，文字訊息隱含的資訊，而透過主題模型的建置，能將顧客常表達的字詞進行主題分類，以此來建置出顧客本身的偏好特徵，並且以Hadoop平台為實驗基礎架構，節省運算較大量的資料量所需花費的時間。而研究結果也表明，本研究所提出之顧客流失預測模型，與顧客價值進行流失預測的方法比較，擁有較佳的預測結果，並且在Hadoop平台上的執行效率，也明顯高於一般電腦上的執行效率。

關鍵字

顧客流失預測；顧客價值分析； RFM 模型；主題模型；社交行為；信任關係

並列摘要

Because the convenience of the network, so that customers in the online shopping conversion costs are lower. For enterprises, how to use limited resources to prediction of customer churn and customer retention, is a very important issue. In the past research, customer churn was mainly predicted by customer value. Today's online shopping platform provides a platform for customers to write reviews and social. Therefore, the past customer churn prediction method, because the different data characteristics, has gradually become not applicable. Customers share and exchange information about purchase, including product reviews, ratings as a consideration for purchasing decisions and influence the possibility of continued consumption in the future. Such decisions may be influenced by the opinions of their friends who have a relationship. Purchase records and product reviews will be with the accumulation of time, so that enterprises can analyze the amount of data gradually increased. Therefore, this project proposes a prediction of customer churn model based on social behavior analysis and topic model with big data. It considers the social behavior of customers on the Internet and the information implied in the reviews written by the customers. And through the topic model of the building, customers often express the words can be topic classified, and in order to build the customer’s own preferences, and to Hadoop platform for the experimental infrastructure, saving a large amount of data required to calculated the amount of time. The experimental results show that our prediction of customer churn model has better prediction results compared with the other customer churn prediction method, and the execution efficiency on the Hadoop platform is obviously higher than that on the general computer implementation efficiency.

並列關鍵字

Customer Churn Prediction ； Customer Value Analysis ； RFM Model ； Topic Model ； Social behavior ； Trust Relationship

參考文獻

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基於巨量資料架構分析社交行為與主題模型預測顧客流失

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