電商平台顧客瀏覽行為與意圖之研究

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：53

、訪客IP：3.142.43.151

姓名

黃予涵(Yu-Han Huang) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

電商平台顧客瀏覽行為與意圖之研究
(Customer’s Browsing Behaviors and Intention in E-Commerce Platform)

相關論文

★ 以第四方物流經營模式分析博連資訊科技股份有限公司	★ 探討虛擬環境下隱性協調在新產品導入之作用--以電子組裝業為例
★ 動態能力機會擷取機制之研究－以A公司為例	★ 探討以價值驅動之商業模式創新－以D公司為例
★ 物聯網行動支付之探討-以Apple Pay與支付寶錢包為例	★ 企業資訊方案行銷歷程之探討-以MES為例
★ B2C網路黏著度之探討-以博客來為例	★ 組織機制與吸收能力關係之研究－以新產品開發專案為例
★ Revisit the Concept of Exploration and Exploitation	★ 臺灣遠距醫療照護系統之發展及營運模式探討
★ 資訊系統與人力資訊科技資源對供應鏈績效影響之研究-買方依賴性的干擾效果	★ 資訊科技對知識創造影響之研究-探討社會鑲嵌的中介效果
★ 資訊科技對公司吸收能力影響之研究－以新產品開發專案為例	★ 探討買賣雙方鑲嵌關係影響交易績效之機制 ─新產品開發專案為例
★ 資訊技術運用與協調能力和即興能力對新產品開發績效之影響	★ 團隊組成多元性影響任務衝突機制之研究─ 以新產品開發專案團隊為例

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]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

隨著網路商店的崛起，瀏覽資料成為商家積極想要分析的部分，而零售商更是我們生活最息息相關的產業，現今人們的購買範圍已經不僅限於線下，本研究的目的即是探討如何藉由零售商的線上瀏覽資料解析出更多背後意涵，並更深入地洞悉和理解每次客戶來訪時的購物行為和意圖，從而使電商能夠根據每次來訪的需求為其提供量身定制而使其滿意的服務。
此研究提出一種結合監督式與非監督式的商業分析方法，先將每筆資料整理成以工作階段(Session)為單位，再以 K-means 為主做分群，將資料分成五群，再根據分群狀況使用決策樹去找出特徵值且命名。
將理論與實務結合，我們擬合此五群與客戶旅程(Customer Journey)之理論，第一群至第五群皆符合察覺(Awareness)階段、思考(Consideration)階段、購買 (Purchase)階段，客戶旅程裡的前三階段，隨後也給予根據客戶旅程的行銷策略建議。此外，在第四章文末更加入預測分析，預測顧客在工作階段內是否進入購物車，藉此更提升本研究對瀏覽資料分析的全面性。

摘要(英)

Online consumer browsing behavior has been widely researched by many companies and researchers. The retail industry is also the most common industry in the world. Nowadays, people not only purchase offline but also purchase online and even more often than before. This study extends the analysis of current research on finding more insights behind browsing data. The goal is also to delve deeper into and comprehend the customers’ shopping behavior and intentions per visit and, thus, enable retailers to provide customers’ satisfactory services tailored to their requirements per visit. We demonstrate the utility of our approach by applying it to a real case of a retailer.
We propose a business analytics approach that utilizes integrated supervised and unsupervised learning. Firstly, we do the session-based data preprocessing, after which we use K-means to find 5 clusters and name them after their rules and patterns by decision tree.
On the integration of theory and practice, we map these 5 clusters with customer journey. The results show group 1 to group 5 just fit “Awareness”, “Consideration”, “Purchase” three phases. We also propose some marketing strategies around the customer journey. In addition, we use supervised learning to predict whether or not a customer in a session will go to the Shopping Cart, which makes this study more comprehensive.

關鍵字(中)

★ 電商平台
★ 顧客瀏覽行為與意圖

關鍵字(英)

★ E-Commerce Platform
★ Customer’s Browsing Behaviors and Intention

論文目次

中文摘要 I
Abstract II
目錄 III
圖目錄 VI
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
第二章文獻回顧 3
2.1 零售業與電子商務之發展 3
2.2 客戶分群 4
2.3 購物籃分析 4
2.4 瀏覽資料分析 6
2.5 監督式學習(Supervised Learning) 6
2.5.1 邏輯式迴歸 (LogisticRegression) 7
2.5.2 支持向量機(Support Vector Machine) 7
2.5.3 決策樹(Decision Tree) 8
2.6 非監督式學習(Unsupervised Learning) 8
2.6.1 主成份分析（Principle Component Analysis) 9
2.6.2 K-均值（K-Means）9
2.7 客戶旅程(Customer Journey) 10

第三章研究方法 11
3.1 研究方法流程 11
3.2 研究分析工具與資料來源 12
3.2.1 研究分析工具 12
3.2.2 資料來源與介紹 13
3.3 資料前處理 13
3.3.1 將資料整理成以工作階段(Session)為單位 13
3.3.2 將瀏覽網址類別(Category)轉置成欄位名稱 15
3.3.3 標準化 15
3.4 方法與模型 16
3.4.1 Elbow Method 16
3.4.2 K-means 17
3.4.3 決策樹 19
3.4.4 客戶旅程 21
3.5 預測與模型表現準則 22
第四章實證結果 26
4.1 分群結果與特徵命名 26
4.1.1 各群透過決策樹之結果 26
4.1.2 與平均值比較後命名 29
4.2 結合客戶旅程與行銷建議 31
4.2.1 結合客戶旅程 31
4.2.2 行銷建議 32
4.3 使用決策樹預測 33
第五章結論與建議 36
5.1 研究結論 36
5.2 未來建議 37
參考文獻 38

圖目錄
圖 1 美國2010-2019線上線下佔比圖 1
圖 2 研究方法流程圖 12
圖 3 原始數據之資料結構 13
圖 4 兩個工作階段 14
圖 5 一個工作階段 14
圖 6 將 category 轉置 15
圖 7 使用 Elbow Method 決定分群數 17
圖 8 K-means 下群心的迭代過程 19
圖 9 Gini Curve 20
圖 10 客戶旅程圖 22
圖 11 混淆矩陣 23
圖 12 ROC Curve 25
圖 13 群1之決策樹 27
圖 14 群2之決策樹 27
圖 15 群3之決策樹 28
圖 16 群4之決策樹 28
圖 17 群5之決策樹 28
圖 18 與平均值比較 30
圖 19 放棄購物車率之比較 31
圖 20 結合客戶旅程圖 32
圖 21 決策樹預測結果 34
圖 22 本預測之混淆矩陣 35
圖 23 本預測之 ROC 35

參考文獻

Adar, E., Teevan, J., & Dumais, S. T. (2009). Resonance on the web: Web dynamics and revisitation patterns. Conference on Human Factors in Computing Systems - Proceedings, 1381–1390. https://doi.org/10.1145/1518701.1518909
Bloch, P. H., & Richins, M. L. (1983). Shopping Without Purchase: an Investigation of Consumer Browsing Behavior. ACR North American Advances, NA-10.
Bolton, R. N., & Shankar, V. (2003). An empirically derived taxonomy of retailer pricing and promotion strategies. Journal of Retailing, 79(4), 213–224. https://doi.org/10.1016/j.jretai.2003.09.005
Boone, D. S., & Roehm, M. (2002). Retail segmentation using artificial neural networks. International Journal of Research in Marketing, 19(3), 287–301. https://doi.org/10.1016/S0167-8116(02)00080-0
Brown, M., Pope, N., & Voges, K. (2003). Buying or browsing? European Journal of Marketing, 37(11/12), 1666–1684. https://doi.org/10.1108/03090560310495401
Buckinx, W. ., & Poel, D. Van Den. (2003). Predicting Online Purchasing Behavior.
Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium.
Burt, S., & Sparks, L. (2003). E-commerce and the retail process: A review. Journal of Retailing and Consumer Services, 10(5), 275–286. https://doi.org/10.1016/S0969- 6989(02)00062-0
Chen, Y. L., Hsu, C. L., & Chou, S. C. (2003). Constructing a multi-valued and multi- labeled decision tree. Expert Systems with Applications, 25(2), 199–209. https://doi.org/10.1016/S0957-4174(03)00047-2
Cheung, K. W., Kwok, J. T., Law, M. H., & Tsui, K. C. (2003). Mining customer product ratings for personalized marketing. Decision Support Systems, 35(2), 231– 243. https://doi.org/10.1016/S0167-9236(02)00108-2
Close, A. G., & Kukar-Kinney, M. (2010). Beyond buying: Motivations behind consumers’ online shopping cart use. Journal of Business Research, 63(9–10), 986–992. https://doi.org/10.1016/j.jbusres.2009.01.022
38
Cox, L. A. (2002). Data mining and causal modeling of customer behaviors. In Telecommunication Systems (Vol. 21, Issues 2–4, pp. 349–381). Springer. https://doi.org/10.1023/A:1020911018130
Davidson, I. (2002). UnderstandingK-MeansNon-hierarchicalClustering IanDavidson. In pdfs.semanticscholar.org. https://pdfs.semanticscholar.org/eff6/7c0447c1c37bbbeb7638813848d2206ddace.p df
Davies, G. (1993). Trade marketing strategy.
Dennis, C., Marsland, D., & Cockett, T. (2001). Data mining for shopping centres – customer knowledge-management framework. Journal of Knowledge Management, 5(4), 368–374. https://doi.org/10.1108/13673270110411797
FORGY, & W., E. (1965). Cluster analysis of multivariate data : efficiency versus interpretability of classifications. Biometrics, 21, 768–769.
Goel, S., Hofman, J. M., & Sirer, M. I. (2012). Who Does What on the Web: A Large- Scale Study of Browsing Behavior. In Sixth International AAAI Conference on Weblogs and Social Media. www.aaai.org
Grewal, D., Retailing, A. R.-J. of, & 2017, U. (2017). AL & Nordfält, J. 2017.’The future of retailing’.
Griva, A., Bardaki, C., Pramatari, K., & Papakiriakopoulos, D. (2018). Retail business analytics: Customer visit segmentation using market basket data. Expert Systems with Applications, 100, 1–16. https://doi.org/10.1016/j.eswa.2018.01.029
Halvorsrud, R., Kvale, K., & Følstad, A. (2016). Improving service quality through customer journey analysis. Journal of Service Theory and Practice, 26(6), 840– 867. https://doi.org/10.1108/JSTP-05-2015-0111
Hansen, T. H., & Skytte, H. (1998). Retailer buying behaviour: A review. International Journal of Phytoremediation, 21(1), 277–301. https://doi.org/10.1080/095939698342788
Hartigan, J. (1975). Clustering algorithms. https://dl.acm.org/doi/abs/10.5555/540298 Jonathan Reynolds, Malin Sundström, I. A. et al. (2014). Expert Group on Retail Sector
Innovation Retail Innovation Six perspectives on. https://doi.org/10.2777/60660 39

KETCHEN, D. J., & SHOOK, C. L. (1996). THE APPLICATION OF CLUSTER ANALYSIS IN STRATEGIC MANAGEMENT RESEARCH: AN ANALYSIS AND CRITIQUE. Strategic Management Journal, 17(6), 441–458. https://doi.org/10.1002/(SICI)1097-0266(199606)17:6<441::AID- SMJ819>3.0.CO;2-G
Kim, J. K., Song, H. S., Kim, T. S., & Kim, H. K. (2005). Detecting the change of customer behavior based on decision tree analysis. Expert Systems, 22(4), 193– 205. https://doi.org/10.1111/j.1468-0394.2005.00310.x
Kitts, B., Freed, D., & Vrieze, M. (2000). Cross-sell: A Fast Promotion-Tunable Customer-item Recommendation Method Based on Conditionally Independent Probabilities.
Kukar-Kinney, M., & Close, A. G. (2010). The determinants of consumers’ online shopping cart abandonment. Journal of the Academy of Marketing Science, 38(2), 240–250. https://doi.org/10.1007/s11747-009-0141-5
Kumar, R., & Tomkins, A. (2010). A characterization of online browsing behavior. Proceedings of the 19th International Conference on World Wide Web, WWW ’10, 561–570. https://doi.org/10.1145/1772690.1772748
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420
Likas, A., Vlassis, N., & J. Verbeek, J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451–461. https://doi.org/10.1016/S0031- 3203(02)00060-2
Lloyd, S. P. (1982). Least Squares Quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. https://doi.org/10.1109/TIT.1982.1056489
Macqueen, J. (1967). SOME METHODS FOR CLASSIFICATION AND ANALYSIS OF MULTIVARIATE OBSERVATIONS. In books.google.com. https://books.google.com/books?hl=zh- TW&lr=&id=IC4Ku_7dBFUC&oi=fnd&pg=P A281&dq=Some+Methods+for+cla ssification+and+Analysis+of+Multivariate+Observations&ots=nO- dEYKeoL&sig=eQSjmY5HBURcMirHRn3Bd4E6e9E
40

Michael J. A. Berry, G. S. L. (2004). Data Mining Techniques: For Marketing, Sales, and Customer Relationship. https://books.google.com.tw/books?hl=zh- TW&lr=&id=Ni5nMDO1OfEC&oi=fnd&pg=PR19&dq=Data+mining+techniques +second+edition+- +for+marketing,+sales,+and+customer+relationship+management.&ots=v9d4mm EPJK&sig=94xtRZcKCAtXhwiX7RnErT5PUz8&redir_esc=y#v=onepage&q=Dat
Ngai, E. W. T., Xiu, L., & Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. In Expert Systems with Applications (Vol. 36, Issue 2 PART 2, pp. 2592–2602). Elsevier Ltd. https://doi.org/10.1016/j.eswa.2008.02.021
Rosenbaum, M. S., Otalora, M. L., & Ramírez, G. C. (2017). How to create a realistic customer journey map. Business Horizons, 60(1), 143–150. https://doi.org/10.1016/j.bushor.2016.09.010
Swindley, D. (1992). Retail Buying in the United Kingdom. The Service Industries Journal, 12(4), 533–544. https://doi.org/10.1080/02642069200000063
Tang, K., Chen, Y. L., & Hu, H. W. (2008). Context-based market basket analysis in a multiple-store environment. Decision Support Systems, 45(1), 150–163. https://doi.org/10.1016/j.dss.2007.12.016
Therneau, T. M., & Atkinson, E. J. (2019). An Introduction to Recursive Partitioning Using the RPART Routines.
Torres de Oliveira, R., Indulska, M., Steen, J., & Verreynne, M. L. (2020). Towards a framework for innovation in retailing through social media. Journal of Retailing and Consumer Services, 54, 101772. https://doi.org/10.1016/j.jretconser.2019.01.017
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. In The Nature of Statistical Learning Theory. Springer New York. https://doi.org/10.1007/978-1- 4757-2440-0
Vapnik, & Vladimir. (1998). The Support Vector Method of Function Estimation. In Nonlinear Modeling. Springer US. https://doi.org/10.1007/978-1-4615-5703-6_3
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., ... Yutani,
41

H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686
Wong, M., & Hartigan, J. (1979). A k-means clustering algorithm.
42

指導教授

陳炫碩(Shiuann-Shuoh Chen)

審核日期

2022-7-29

推文