從預測為流失的顧客中找出特徵以識別出較易挽留的顧客群集

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：23

、訪客IP：13.59.77.83

姓名

陳俊亨(Chun-Heng Chen) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

從預測為流失的顧客中找出特徵以識別出較易挽留的顧客群集
(Find Out the Feature to Identify the Cluster in Churn Customer that is More Possible to be Retained)

相關論文

★ 在社群網站上作互動推薦及研究使用者行為對其效果之影響	★ 以AHP法探討伺服器品牌大廠的供應商遴選指標的權重決定分析
★ 以AHP法探討智慧型手機產業營運中心區位選擇考量關鍵因素之研究	★ 太陽能光電產業經營績效評估－應用資料包絡分析法
★ 建構國家太陽能電池產業競爭力比較模式之研究	★ 以序列採礦方法探討景氣指標與進出口值的關聯
★ ERP專案成員組合對績效影響之研究	★ 推薦期刊文章至適合學科類別之研究
★ 品牌故事分析與比較-以古早味美食產業為例	★ 以方法目的鏈比較Starbucks與Cama吸引消費者購買因素
★ 探討創意店家創業價值之研究- 以赤峰街、民生社區為例	★ 以領先指標預測企業長短期借款變化之研究
★ 應用層級分析法遴選電競筆記型電腦鍵盤供應商之關鍵因子探討	★ 以互惠及利他行為探討信任關係對知識分享之影響
★ 結合人格特質與海報主色以類神經網路推薦電影之研究	★ 資料視覺化圖表與議題之關聯

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2029-6-1以後開放)

摘要(中)

隨著人們越來越重視的身體的健康，社會對於保健食品的需求也愈發強烈，需求也帶來了更激烈的產業競爭，這意味著消費者有著更多元的商家選擇。顧客流失指的是顧客在特定的期間不再向企業購買產品，而對企業來說，挽留顧客是一個非常重要的環節，因為它有著相較於獲取新顧客更低的成本，所需付出的成本差異達20倍之多，而如何找到較易挽留的顧客更是一大課題。

在保健食品銷售的眾多通路中，以電話行銷這個通路最為常見，傳統上電話行銷人員需要針對名單上的顧客大量撥打電話來促使成交，然而盲目的撥打是一種不必要的人力成本耗費。相較會持續保持忠誠的顧客，企業應針對未來會流失的顧客進行挽留，但當中又有些顧客是堅定的流失而無法挽留，也就是說，企業應針對那些未來會流失，但卻較可能被挽留的顧客群體展開行動。

本研究揭示了從真實的公司交易、通話資料解析出可用特徵，到增加特徵、篩選特徵、決定採用期數的過程，結合了預測模型和六種SMOTE方法找出流失顧客，其中L2正則化的羅吉斯迴歸搭配ADASYN在不同的超參數k之下，Precision平均可達82.224%，意即我們預測為流失的顧客，達到八成以上的比例確實為流失。後再以K-MEANS方法對預測為流失的顧客進行分群，並找出特徵去識別出當中較易被挽留的顧客群集。

摘要(英)

As people pay more attention to their health, society′s demand for health supplements has become stronger. The demand has also brought about more intense industrial competition, which means that customers have more diverse merchant choices. Customer churn refers to the customer no longer purchasing products from the company in a specific period. For companies, retaining customers is very important because it has a lower cost than acquiring new customers. The cost difference is as much as 20 times.

Among the many channels for selling health supplements, telemarketing is the most common. Traditionally, telemarketers need to make a large number of calls to customers on the list to promote purchase. However, blind calling is an unnecessary waste of labor costs. Compared with customers who will continue to remain loyal, companies should target customers who will churn in the future, but some of them are determined to churn and cannot be retained. In other words, companies should target and take action to the customers who will churn in the future but are more likely to be retained.

This study reveals how to parse available features from real company transactions and call data, to the process of adding features, filtering features, and deciding the number of periods to adopt. It combines predictive models and six SMOTE methods to find churn customers, among which L2 regularization using Logistic regression and ADASYN under different k, the average precision can reach 82.224%, which means that more than 80% of the customers predicted to be churn are actually churn. Then, cluster the customers predicted to be churn through K-MEANS method, and find out the feature to identify the customer cluster that is more possible to be retained.

關鍵字(中)

★ 顧客流失
★ 顧客挽留
★ SMOTE
★ K-MEANS

關鍵字(英)

★ Customer Churn
★ Customer Retention
★ SMOTE
★ K-MEANS

論文目次

中文摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章緒論 1
1-1 研究背景 1
1-2 研究動機 1
1-3 研究目的 3
1-4 研究架構 3
第二章文獻探討 5
2-1 研究之商業場域 5
2-2 流失定義 6
2-3 預測流失顧客的方法與研究目的 8
第三章研究方法 11
3-1 研究流程 11
3-2 流失定義與特徵決定 12
3-2-1流失定義 13
3-2-2 特徵決定 13
3-3 流失預測與SMOTE 16
3-3-1 流失預測 17
3-3-2 SMOTE 22
3-4 分群方法與群集解析 27
3-4-1分群方法 27
3-4-2 群集解析 28
第四章研究實驗 32
4-1 資料搜集 32
4-2 資料前處理 32
4-3 實驗設定 33
4-4 實驗結果與分析 35
4-4-1 特徵篩選 35
4-4-2 流失顧客預測 38
4-4-3 分群方法 46
第五章結論與建議 52
5-1 研究結論 52
5-2 研究限制與未來研究建議 53
參考文獻 54

參考文獻

Adhikary, D. D., & Gupta, D. (2021). Applying over 100 classifiers for churn prediction in telecom companies. Multimedia Tools and Applications, 80(28), 35123-35144.

Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020). A survey on churn analysis in various business domains. IEEE Access, 8, 220816-220839.

Agresti, A. (2012). Categorical data analysis (Vol. 792). John Wiley & Sons.

Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.

Berger, P., & Kompan, M. (2019). User modeling for churn prediction in E-commerce. IEEE Intelligent Systems, 34(2), 44-52.

Banerjee, T., Mukherjee, G., Dutta, S., & Ghosh, P. (2019). A large-scale constrained joint modeling approach for predicting user activity, engagement, and churn with application to freemium mobile games. Journal of the American Statistical Association.

Cenggoro, T. W., Wirastari, R. A., Rudianto, E., Mohadi, M. I., Ratj, D., & Pardamean, B. (2021). Deep learning as a vector embedding model for customer churn. Procedia Computer Science, 179, 624-631.

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.

Chou, P., Chuang, H. H. C., Chou, Y. C., & Liang, T. P. (2022). Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning. European Journal of Operational Research, 296(2), 635-651.

Chen, M. (2019, October). Music streaming service prediction with MapReduce-based artificial neural network. In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 0924-0928). IEEE.

Coussement, K., Benoit, D. F., & Van den Poel, D. (2010). Improved marketing decision making in a customer churn prediction context using generalized additive models. Expert systems with Applications, 37(3), 2132-2143.

Douzas, G., Bacao, F., & Last, F. (2018). Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Information sciences, 465, 1-20.

Guitart, A., Chen, P. P., & Periáñez, Á. (2018). The winning solution to the IEEE CIG 2017 game data mining competition. Machine Learning and Knowledge Extraction, 1(1), 252-264.

Han, H., Wang, W. Y., & Mao, B. H. (2005, August). Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In International conference on intelligent computing (pp. 878-887). Berlin, Heidelberg: Springer Berlin Heidelberg.

He, H., Bai, Y., Garcia, E. A., & Li, S. (2008, June). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence) (pp. 1322-1328). Ieee.

Hu, X., Shi, Z., Yang, Y., & Chen, L. (2020, April). Classification method of internet catering customer based on improved RFM model and cluster analysis. In 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) (pp. 28-31). IEEE.

Hudaib, A., Dannoun, R., Harfoushi, O., Obiedat, R., & Faris, H. (2015). Hybrid data mining models for predicting customer churn. International Journal of Communications, Network and System Sciences, 8(5), 91-96.

Keiningham, T. L., Cooil, B., Aksoy, L., Andreassen, T. W., & Weiner, J. (2007). The value of different customer satisfaction and loyalty metrics in predicting customer retention, recommendation, and share‐of‐wallet. Managing service quality: An international Journal, 17(4), 361-384.

Lee, E., Kim, B., Kang, S., Kang, B., Jang, Y., & Kim, H. K. (2018). Profit optimizing churn prediction for long-term loyal customers in online games. IEEE Transactions on Games, 12(1), 41-53.

Lee, E., Jang, Y., Yoon, D. M., Jeon, J., Yang, S. I., Lee, S. K., ... & Kim, K. J. (2018). Game data mining competition on churn prediction and survival analysis using commercial game log data. IEEE Transactions on Games, 11(3), 215-226.

Martínez, A., Schmuck, C., Pereverzyev Jr, S., Pirker, C., & Haltmeier, M. (2020). A machine learning framework for customer purchase prediction in the non-contractual setting. European Journal of Operational Research, 281(3), 588-596.

Ko, Y. H., Hsu, P. Y., Cheng, M. S., Jheng, Y. R., & Luo, Z. C. (2019). Customer retention prediction with CNN. In Data Mining and Big Data: 4th International Conference, DMBD 2019, Chiang Mai, Thailand, July 26–30, 2019, Proceedings 4 (pp. 104-113). Springer Singapore.

Milošević, M., Živić, N., & Andjelković, I. (2017). Early churn prediction with personalized targeting in mobile social games. Expert Systems with Applications, 83, 326-332.

Nguyen, H. M., Cooper, E. W., & Kamei, K. (2011). Borderline over-sampling for imbalanced data classification. International Journal of Knowledge Engineering and Soft Data Paradigms, 3(1), 4-21.

Nie, G., Rowe, W., Zhang, L., Tian, Y., & Shi, Y. (2011). Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications, 38(12), 15273-15285.

Radosavljevik, D., van der Putten, P., & Larsen, K. K. (2010). The impact of experimental setup in prepaid churn prediction for mobile telecommunications: What to predict, for whom and does the customer experience matter?. Trans. Mach. Learn. Data Min., 3(2), 80-99.

Sharma, H., & Kumar, S. (2016). A survey on decision tree algorithms of classification in data mining. International Journal of Science and Research (IJSR), 5(4), 2094-2097.

Ullah, I., Raza, B., Malik, A. K., Imran, M., Islam, S. U., & Kim, S. W. (2019). A churn prediction model using random forest: analysis of machine learning techniques for churn prediction and factor identification in telecom sector. IEEE access, 7, 60134-60149.

Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas, K. C. (2015). A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, 1-9.

Wu, S., Yau, W. C., Ong, T. S., & Chong, S. C. (2021). Integrated churn prediction and customer segmentation framework for telco business. Ieee Access, 9, 62118-62136.

Wang, Q. F., Xu, M., & Hussain, A. (2019). Large-scale ensemble model for customer churn prediction in search ads. Cognitive Computation, 11, 262-270.

Yang, W., Huang, T., Zeng, J., Yang, G., Cai, J., Chen, L., ... & Liu, Y. E. (2019, August). Mining player in-game time spending regularity for churn prediction in free online games. In 2019 ieee conference on games (cog) (pp. 1-8). IEEE.

Zhang, R., Li, W., Tan, W., & Mo, T. (2017, June). Deep and shallow model for insurance churn prediction service. In 2017 IEEE international conference on services computing (SCC) (pp. 346-353). IEEE.

指導教授

許秉瑜

審核日期

2024-7-6

推文