以深度神經網路預測客戶終身價值｜國立中央大學博碩士論文系統

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研究生：	陳伊婷 Yi-Ting Chen
論文名稱：	以深度神經網路預測客戶終身價值 Applying DNN to Predict Customer Lifetime Value
指導教授：	陳炫碩 Shiuann-Shuoh Chen
口試委員:
學位類別：	碩士 Master
系所名稱：	管理學院 - 企業管理學系 Department of Business Administration
論文出版年：	2023
畢業學年度：	111
語文別：	英文
論文頁數：	34
中文關鍵詞：	深度神經網路、顧客生命週期價值、離群值處理、CLV預測
外文關鍵詞：	Customer Lifetime Value (CLV), CLV prediction
相關次數：	點閱：9 下載：0
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顧客生命週期價值（CLV）是衡量顧客對企業長期價值的重要指標。本研究以新鮮食品超市為例，利用深度神經網絡（DNN）模型來預測常客的CLV。通過對超市一年的消費數據進行預處理和特徵選擇，我們採用DNN與Elastic Net回歸相結合的模型，並使用移除離群值的方法來提高預測準確性。我們比較了不同預測期間的預測結果，並發現使用兩個月的消費數據來預測未來五個月的CLV效果最好。此外，我們還研究了各個特徵對CLV的重要性，並發現消費金額、月份、消費頻率以及不同類別購買金額比例對CLV有顯著影響。如果將來有更長時間範圍的數據集，我們可以嘗試去除季節因素，以預測超過一年的CLV。

This research paper explores the application of Deep Neural Networks (DNN) in predicting Customer Lifetime Value (CLV) for regular customers in fresh food supermarkets. Real transaction data spanning 11 months is analyzed, and various data preprocessing techniques, outlier handling methods, and algorithms are compared to develop an accurate CLV prediction model. The findings highlight the importance of outlier removal and feature selection, with the combination of DNN and Elastic Net regression demonstrating the best performance. The study also identifies the optimal forecasting period. The research provides practical insights for businesses seeking to improve CLV forecasts and showcases the potential of DNN in CLV prediction across industries. Overall, this work contributes to advancing CLV prediction methodologies and offers a framework for enhancing accuracy in predicting CLV for regular customers in fresh food supermarkets.

Chinese Abstract ................................i
English Abstract ...............................ii
Contents.......................................iii
List of Figure..................................iv
List of Table...................................iv
Chapter1 Introduction............................1
Chapter2 Literature review ......................2
　2.1 Customer Lifetime Value (CLV) .............2
　2.2 Methods Based on RFM Statistics…………………………….3
　2.3 Methods Based on Machine Learning…………………………3
　　2.3.1 Deep Neural Network (DNN)..............4
　　2.3.2 Regression Tree………………………………….....………………5
　　2.3.3 Gradient Boosting.....………………………..………………5
    2.3.4 Elastic Net Regression................5
Chapter3 Methodology............................6
3.1 Data Pre-processing.........................6
　　3.1.1 Handle Outliers…………………………………….........7
　　3.1.2 Monthly Data…………………………………………………..……………7
　3.2 Identifying Regular Customers using Rules…8
　　3.2.1 Features…………………………………………………………….......11
　3.3 DNN Model………………………………………………………………………………...12
　3.4 Experimental Evaluation……………….............13
　　3.4.1 Comparing Outlier Handling Methods for CLV Prediction……14
　　3.4.2 Comparing Forecasting Performance for Different  Months .........................................16
　　3.4.3 Compare the performance of different algorithms…………17
　　3.4.4 Analysis of Important Features using SHAP Values……….........18
Chapter4 Conclusion.............................21
　4.1Summary of Our Method…………………………………………......21
　4.2 Research Limitations and Future Prospects…22
Chapter5 Reference..............................23
Appendix A : Features…………………………………………………………...……25
                                

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