應用關鍵特徵值分析、分群和預測模型進行零售業顧客購買行為的縱向研究

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李結衣(Chieh-Yi Lee) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

應用關鍵特徵值分析、分群和預測模型進行零售業顧客購買行為的縱向研究
(Longitudinal Study of Retail Customer Purchase Behavior Using Feature Importance Analysis, Clustering, and Predictive Modeling)

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摘要(中)

了解顧客行為對於零售業的成功至關重要，在競爭激烈且瞬息萬變的市場環境中，企業需要深入洞察顧客需求和購買行為，以制定有效的行銷策略和提升顧客滿意度。顧客分群和顧客購買次數預測是了解顧客行為的兩種重要方法，然而過去針對零售業的研究中對於關鍵特徵值分析的應用較少，這是一個值得深入探討的領域，因為它能幫助企業辨識影響顧客購買行為的關鍵因素，並能夠進一步提升行銷策略的準確性和有效性。
　　過去的研究未能充分整合關鍵特徵值分析、顧客分群和顧客購買次數預測這三個重要面向，因此本研究旨在整合這三種模型，並透過台灣某連鎖零售業的個案進行驗證。本研究之具體研究目標有三：(1) 使用XGBoost模型對顧客縱向資料進行關鍵特徵值分析，確認是否存在其他對顧客行為具有高度影響的變數；(2) 針對不同情境，採用兩階段分群法進行顧客分群，並了解各情境下的顧客特性與行為特徵；(3) 探討BG/NBD模型在零售業中的應用，預測顧客購買次數並評估其在實際應用中的表現。應用研究架構於台灣某連鎖零售業結果顯示，關鍵特徵值分析在所有情境下均得到相同結果，除RFM模型的三個變數（Recency、Frequency、Monetary）外，顧客的購買週期、促銷使用比例及類一產品購買比例也是影響顧客購買行為的重要變數；在顧客分群方面，各情境下的兩階段分群方法均有效地將顧客分群，並展示了其在提升分群品質和解釋力方面的優越性；在顧客購買次數預測方面，計算出的R-squared為 0.699，顯示預測模型具有良好的表現，能準確捕捉顧客購買次數的分佈趨勢。

摘要(英)

Understanding customer behavior is crucial for the success of the retail industry. In a highly competitive and rapidly changing market environment, companies need deep insights into customer needs and purchasing behaviors to develop effective marketing strategies and enhance customer satisfaction. Customer segmentation and purchase frequency prediction are two essential methods for understanding customer behavior. However, past research on the retail industry has rarely applied key feature analysis, which is a worthwhile area for further exploration as it can help companies identify critical factors influencing customer purchasing behavior and further improve the accuracy and effectiveness of marketing strategies.
Previous studies have not sufficiently integrated key feature analysis, customer segmentation, and purchase frequency prediction. Therefore, this study aims to integrate these three models and verify them through a case study of a chain retailer in Taiwan. The specific research objectives of this study are threefold: (1) to use the XGBoost model for key feature analysis on longitudinal customer data to identify other highly influential variables on customer behavior; (2) to conduct customer segmentation using a two-stage clustering method for different scenarios and understand customer characteristics and behavioral traits under each scenario; (3) to explore the application of the BG/NBD model in the retail industry to predict customer purchase frequency and evaluate its performance in practical applications. The application of the research framework to a chain retailer in Taiwan shows that key feature analysis consistently identifies the same critical variables across all scenarios. In addition to the three variables of the RFM model (Recency, Frequency, Monetary), customer purchasing cycle, promotion usage rate, and proportion of category 1 product purchases are also important variables influencing customer purchasing behavior. For customer segmentation, the two-stage clustering method effectively segmented customers in all scenarios, demonstrating its superiority in improving segmentation quality and explanatory power. For purchase frequency prediction, the calculated R-squared is 0.699, indicating that the prediction model performs well and accurately captures the distribution trend of customer purchase frequency.

關鍵字(中)

★ 關鍵特徵值
★ 顧客分群
★ 顧客購買次數
★ RFM 模型
★ XGBoost模型
★ BG/NBD模型

關鍵字(英)

★ Key Feature
★ Customer Segmentation
★ Purchase Frequency
★ RFM model
★ XGBoost model
★ BG/NBD model

論文目次

中文摘要 i
ABSTRACT ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章緒論 1
1.1　研究背景與動機 1
1.2　研究目的 3
1.3　論文結構 5
第二章文獻探討 6
2.1　關鍵特徵值分析應用 6
2.2　零售業顧客分群 9
2.3　顧客購買次數預測模型 13
第三章研究方法 15
3.1　關鍵特徵值分析 15
3.2　兩階段集群分析 18
3.2.1　第一階段離群值分析 18
3.2.2　第二階段集群分析 20
3.3　顧客購買次數預測模型 23
3.3.1 　BG/NBD 模型 23
3.3.2　模型準確度分析 27
3.4　顧客流動分析 28
第四章案例分析 29
4.1　情境一：第n年分析 30
4.1.1　第n年顧客資料的XGBoost特徵值分析 30
4.1.2　第n年顧客的兩階段分群分析 32
4.1.3　應用BG/NBD模型預測第n +1年購買次數 37
4.2　情境二：第n +1年分析 39
4.2.1　基於第n年分群標準對第n +1年顧客進行分群及顧客流動分析 39
4.2.2　第n年及第n +1年顧客資料的XGBoost特徵值分析 43
4.2.3　第n +1年顧客的兩階段分群分析 44
4.2.4　第n年及第n +1年顧客的兩階段分群分析 49
4.2.5　比較BG/NBD模型預測結果與第n +1年實際數據的準確性 53
第五章結論與建議 57
5.1　研究結論 57
5.2　研究限制與建議 59
參考文獻 60

參考文獻

Abirami, M., & Pattabiraman, V. (2016). Data mining approach for intelligent customer behavior analysis for a retail store. Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC–16’),
Alhawarat, M., & Hegazi, M. (2018). Revisiting k-means and topic modeling, a comparison study to cluster arabic documents. IEEE Access, 6, 42740-42749.
Anitha, P., & Patil, M. M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University-Computer and Information Sciences, 34(5), 1785-1792.
Bell, D. E., & Lal, R. (2002). The impact of frequent shopper programs in grocery retailing. Review of Marketing Science (ROMs) Working Paper.
Carbonell, P., Rodríguez‐Escudero, A. I., & Pujari, D. (2009). Customer involvement in new service development: An examination of antecedents and outcomes. Journal of product innovation management, 26(5), 536-550.
Chen, M.-C., Chiu, A.-L., & Chang, H.-H. (2005). Mining changes in customer behavior in retail marketing. Expert Systems with Applications, 28(4), 773-781.
Chen, M., Liu, Q., Chen, S., Liu, Y., Zhang, C.-H., & Liu, R. (2019). XGBoost-based algorithm interpretation and application on post-fault transient stability status prediction of power system. IEEE Access, 7, 13149-13158.
Coates, A., & Ng, A. Y. (2012). Learning feature representations with k-means. In Neural Networks: Tricks of the Trade: Second Edition (pp. 561-580). Springer.
Dhandayudam, P., & Krishnamurthi, I. (2014). Rough set approach for characterizing customer behavior. Arabian Journal for Science and Engineering, 39, 4565-4576.
Fader, P. S., Hardie, B. G., & Lee, K. L. (2005). “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Marketing science, 24(2), 275-284.
Hiziroglu, A. (2013). A neuro-fuzzy two-stage clustering approach to customer segmentation. Journal of Marketing Analytics, 1, 202-221.
Jen, L., Chou, C.-H., & Allenby, G. M. (2003). A Bayesian approach to modeling purchase frequency. Marketing Letters, 14, 5-20.
Joia, L., & Sanz, P. (2004). Purchase Frequency And Transaction Profitability: An Empirical Investigation Into The Brazilian Home Appliance eRetailing Sector.
Koutroumbas, K., & Theodoridis, S. (2008). Pattern recognition. Academic Press.
Lee, J., Jung, O., Lee, Y., Kim, O., & Park, C. (2021). A comparison and interpretation of machine learning algorithm for the prediction of online purchase conversion. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1472-1491.
Liu, Y., & Zhao, H. (2017). Variable importance‐weighted random forests. Quantitative Biology, 5(4), 338-351.
MacQueen, J. B. (1965). On the Asymptotic Behavior of k-means. Defense Technical Information Center, 10.
Marcus, C. (1998). A practical yet meaningful approach to customer segmentation. Journal of consumer marketing, 15(5), 494-504. https://doi.org/10.1108/07363769810235974
Meng, Y., Liang, J., Cao, F., & He, Y. (2018). A new distance with derivative information for functional k-means clustering algorithm. Information Sciences, 463, 166-185.
Munusamy, S., & Murugesan, P. (2020). Modified dynamic fuzzy c-means clustering algorithm–Application in dynamic customer segmentation. Applied Intelligence, 50(6), 1922-1942.
Platzer, M., & Reutterer, T. (2016). Ticking away the moments: Timing regularity helps to better predict customer activity. Marketing science, 35(5), 779-799.
Puccinelli, N. M., Goodstein, R. C., Grewal, D., Price, R., Raghubir, P., & Stewart, D. (2009). Customer experience management in retailing: understanding the buying process. Journal of retailing, 85(1), 15-30.
Qiu, J., Lin, Z., & Li, Y. (2015). Predicting customer purchase behavior in the e-commerce context. Electronic commerce research, 15, 427-452.
Riehmann, P., Hanfler, M., & Froehlich, B. (2005). Interactive sankey diagrams. IEEE Symposium on Information Visualization, 2005. INFOVIS 2005.,
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53-65.
Rout, D., Kotangale, A., Nath, S., & Roy, B. (2023). An Association Based Approach to Elicit and Measure Impact of Features on Sales of a Garment Retail. 2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1),
Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987). Counting your customers: Who-are they and what will they do next? Management science, 33(1), 1-24.
Shi, X., Wong, Y. D., Li, M. Z.-F., Palanisamy, C., & Chai, C. (2019). A feature learning approach based on XGBoost for driving assessment and risk prediction. Accident Analysis & Prevention, 129, 170-179.
Shin, H. (2022). XGBoost regression of the most significant photoplethysmogram features for assessing vascular aging. IEEE Journal of Biomedical and Health Informatics, 26(7), 3354-3361.
Sinaga, K. P., & Yang, M.-S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716-80727.
Spoor, J. M. (2023). Improving customer segmentation via classification of key accounts as outliers. Journal of Marketing Analytics, 11(4), 747-760.
Tarczynski, T. (2011). Document Clustering: Concepts, Metrics and Algorithms. International journal of Electronics and Telecommunications, 57(3), 271-277.
Turkmen, B. (2022). Customer Segmentation with machine learning for online retail industry. The European Journal of Social & Behavioural Sciences.
Vosough, Z., Kammer, D., Keck, M., & Groh, R. (2018). Mirroring Sankey Diagrams for Visual Comparison Tasks. VISIGRAPP (3: IVAPP),
Wübben, M., & Wangenheim, F. v. (2008). Instant customer base analysis: Managerial heuristics often “get it right”. Journal of marketing, 72(3), 82-93.
Xie, Q. (2023). Machine Learning on Wine Quality: Prediction and Feature Importance Analysis. alcohol, 8(9.5), 10.11.
Xingang, W., & Chao, W. (2019). Application of Xgboost feature extraction in fault diagnosis of rolling bearing. Mechanical Engineering Science, 1(2).
Zheng, H., Yuan, J., & Chen, L. (2017). Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation. Energies, 10(8), 1168.
Zhu, J., Jiang, Z., Evangelidis, G. D., Zhang, C., Pang, S., & Li, Z. (2019). Efficient registration of multi-view point sets by K-means clustering. Information Sciences, 488, 205-218.

指導教授

沈建文

審核日期

2024-7-26

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