以遞迴式類神經網絡探討顧客信任及忠誠度對於再購行為預測之研究;Predicting customer behavior with Recurrent Neural Network based on trust and loyalty

NCUIR > School of Management at National Central University > Graduate Institute of Business Administration > Electronic Thesis & Dissertation > Item 987654321/76842

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/76842

Title:	以遞迴式類神經網絡探討顧客信任及忠誠度對於再購行為預測之研究;Predicting customer behavior with Recurrent Neural Network based on trust and loyalty
Authors:	謝沂歆;Hsieh, Yi-Hsin
Contributors:	企業管理學系
Keywords:	資料驅動;信任;忠誠度;再購行為;預測;遞迴式類神經網絡;Data driven;Trust;Loyalty;Repurchase;Prediction;Recurrent Neural Networks(RNN)
Date:	2018-07-05
Issue Date:	2018-08-31 11:50:55 (UTC+8)
Publisher:	國立中央大學
Abstract:	隨著健康意識提升、人口結構趨向高齡化，預防保健格外受到重視，使得全球對於營養保健食品需求漸增，因此也造就了不同多元的銷售通路，其中電子商務與電話行銷通路最為成長快速。根據研究顯示，信任與忠誠度被視為重要的影響因素之一，因為這些因素也會影響著顧客是否會有購買及再購的行為，而企業經營最重要的績效就是顧客購買。在過去的研究中，信任與忠誠度都是透過問卷來取得資料，但實際上收集問卷是很困難，因為客戶往往填寫意願不高、時間區隔長甚至會有填寫偏誤。此外，透過問卷並不能實際預測顧客未來的再購行為，因此近期有研究透過使用系統上的資料來進行分析顧客過去的行為來預測未來行為，但預測出來的結果並不能利用管理上的手段來改變顧客的行為。所以本研究利用資料驅動的方式來成功取得顧客的信任與忠誠度，隱含著資料驅動的方式能彌補問卷的缺失與提升資料收集效率。本研究以營養保健食品產業在電話行銷通路上的通話紀錄與交易紀錄為基礎，建立操作性定義與資料轉換公式，將信任與忠誠度量化以利於衡量與分析，並利用遞迴式類神經網絡的方式，將信任與忠誠度作為輸入變數，預測顧客未來的再購行為，達70%的準確度，比傳統常用的分類方法SVM還要準確。此外，透過本研究可以探討到顧客信任以及忠誠度對再購行為的影響，未來企業可以針對這幾項指標作為KPI調整企業銷售手段或改善與顧客間的互動，同時對於未來的銷售量也能有預估及提升。 ;In the age of rising awareness in health, preventative medication has brought many attentions. In addition, this concept has resulted in the demand of nutrition and food supplements. The sales of these products highly depend on customer trust and loyalty. Therefore, trust and loyalty may be used to predict repurchase behavior. In the past studies, trust and loyalty has to be analyzed with data collected with questionnaires. The common problem with this approach is low returning rate and less reliable data. This study is collect data through call records and transaction records of the nutrition and health industry through telemarketing channel. The data are used to quantify trust and loyalty. With these values, Recurrent Neural Networks(RNN) method is utilized to predict customer′s future repurchase behavior. The results show that the accuracy can reach 70%, which is higher than the accuracy derived from SVM. Companies can use these indicators (trust and loyalty) as KPIs to adjust corporate sales methods and improve the interactions with customers.
Appears in Collections:	[Graduate Institute of Business Administration] Electronic Thesis & Dissertation

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