研究生: |
陳思妤 Chen, Szu-Yu |
---|---|
論文名稱: |
應用集群分析於精準行銷之研究-以企業軟體為例 Applying the Cluster Analysis Techniques to Precision Marketing: The Case of Enterprise Software |
指導教授: |
劉立行
Liu, Li-Hsing |
口試委員: |
莊伯仲
Chuang, Po-Chung 張晏榕 Chang, Yen-Jung 劉立行 Liu, Li-Hsing |
口試日期: | 2022/02/09 |
學位類別: |
碩士 Master |
系所名稱: |
圖文傳播學系 Department of Graphic Arts and Communications |
論文出版年: | 2022 |
畢業學年度: | 110 |
語文別: | 中文 |
論文頁數: | 62 |
中文關鍵詞: | 精準行銷 、RFM 指標 、集群分析 、CART 決策樹 |
英文關鍵詞: | precision marketing, RFM model, cluster analysis, CART decision tree |
DOI URL: | http://doi.org/10.6345/NTNU202200368 |
論文種類: | 學術論文 |
相關次數: | 點閱:219 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
隨著訂閱授權並交付軟體的 SaaS(Software as a Service,簡稱 SaaS)軟體即
服務出現,預測模型的應用將可以為企業軟體業者提升競爭力。企業軟體業在目
標客戶的預測上,常常面臨資料蒐集不易之困境。倘若能依循零售業的方式,利
用資料庫中的顧客購買紀錄,作為預估未來市場的決策依據。本研究採用 RFM
指標中三項指標進行顧客價值之兩階段集群分析,再運用 CART 決策樹將客戶
進行分析,建構出預測模型,進而探討各集群間的差異性。透過透過 UCI 公開資
料庫的某英國批發零售商銷售總筆數 530108 之交易資料,建立預測模型,分析
該企業的顧客特徵值。根據結果,給予企業軟體業者、廣告業者以及後續相關領
域參考。
茲將本研究重要發現分述如下:
一、精準行銷與廣告策略為正相關,行銷目標在於消費者體驗上能更進階,同時
降低廣告成本並創造更高的收益,最終進行付費購買。
二、RFM 模型與兩階段集群分析將線上零售商客戶進行分群,從客戶變動的消費
行為對其產生特徵值標籤後,將顧客分為「高消費型客戶」、「潛力型消費型
客戶」、「流失型客戶」等三種類型。
三、建立模型方面,使用「分類與回歸數」(Classification and Regression Tree,簡
稱 CART)決策樹算法建構模型,結果發現決策樹的顯著度為 95 %,顯示決
策樹能提供對應的解釋規則。
With the emergence of SaaS (Software as a Service) that licenses and delivers
software, the application of predictive models will enhance the competitiveness of
enterprise software providers. The enterprise software industry is often faced with
the difficulty of data collection in the prediction of target customers. If we can follow the way of the retail industry, the customer purchase records in the database can be used as the basis for decision-making in estimating the future market. This research uses the three indicators in the RFM index to conduct a two-stage cluster analysis of customer value and then uses the CART decision tree to analyze the customer, construct a prediction model, and then explore the differences between the clusters.
Based on the transaction data of a UK wholesale retailer with a total number of
530,108 sales through the UCI public database, a predictive model was established
to analyze the customer characteristics of the company. According to the results, it
is given to the enterprise software industry, advertising industry, and subsequent
related fields for reference. The Major findings are as follows:
1. Precision marketing is positively related to advertising strategies. The marketing
goal is to make the consumer experience more advanced, while reducing
advertising costs and creating higher income, and finally making paid purchases.
2. The RFM model and two-stage cluster analysis group online retailer customers,
and after generating eigenvalue labels from customers' changing consumption
behaviors, customers are divided into "high consumption customers", "potential
consumption customers", and "churning customers".
3. In terms of model building, the "Classification and Regression Tree" (CART)
decision tree algorithm was used to build the model. The results showed that the
significance of the decision tree was 95%, indicating that the decision tree can
provide corresponding explanation rules.
一、中文文獻
王智立、郭若宣(2021)。應用資料探勘技術於 RFM 顧客價值區隔之研究。 智慧科技與應用統計學報,19(1),36-64。
王彥欽(2011)。華德法分群後運用差分演化演算法預估軟體工作量(未出版 之碩士論文)。大同大學,台北市。
尹相志(2009)。SQL Server 2008 Data Mining 資料採礦(第二版)。臺北市: 悅知文化。
吳明隆、涂金堂(2012)。SPSS (PASW)與統計應用分析 I。台北市:五南。 吳奇廷(2018)。應用機器學習於精準行銷之研究(未出版之碩士論文)。國立成功大學,台南市。
邱皓政(2008)。量化研究方法(一):研究設計與資料分析(修訂初版)。臺北市:雙葉書廊。
邱創鈞、曾柏健、張炳騰(2019)。應用資料探勘分類法於多屬性 ABC 存貨分類。創新與經營管理學刊,8(1),46-60。
許仲廷(2020)。運用機器學習方法推廣綜合券商大財管業務(未出版之碩士論文)。國立政治大學,台北市。
陳隆輝、李昶良(2015)。應用決策樹探討研究所補教業者之電話行銷策略。 高雄師大學報,39,49-72。
黃寶容(2014)。企業軟體開發之影響因素分析—以 A 公司為例(未出版之碩 士論文)。國立交通大學,新竹市。
郭瀚揚(2019)。資料探勘應用之研究:零售業的 RFM 分析架構(未出版之碩 士論文)。國立臺灣師範大學,台北市。
趙素儀、陳坤虎(2020)。以集群分析探討樂悲觀雙向度模式與心理適應之關 係。中華輔導與諮商學報,58,85-125。
蔡瑞木(2013)。資料探勘之應用—以某百貨公司為例(未出版之碩士論文)。 國立臺北大學,台北市。
賴琴文(2015)。以資料探勘與模糊邏輯技術建置乳癌疾病診斷系統(未出版 之碩士論文)。義守大學,高雄市。
韓怡臻 (2021)。應用自動文字探勘於臺灣中文饒舌音樂歌詞之研究(未出版 之碩士論文)。國立臺灣師範大學,台北市。
二、英文文獻
Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product features by mining consumer. Management Science, 57(8), 1485-1509.
Arromba, I. F., Martin, P. S., Cooper Ordoñez, R., Anholon, R., Rampasso, I. S., Santa-Eulalia, L. A., Martins, V. W. B. & Quelhas, O. L. G. (2021). Industry 4.0 in the product development process: benefits, difficulties and its impact in marketing strategies and operations. Journal of Business & Industrial Marketing, 36(3), 522-534.
Berry, S. L., Maas, J., Kirk J. H., Reynolds, J. P., Gardner, I. A. & Ahmadi, A. (1997). Effects of antimicrobial treatment at the end of lactation on milk yield, somatic cell count, and incidence of clinical mastitis during the subsequent lactation in a dairy herd with a low prevalence of contagious mastitis. Journal of American Veterinary Medicine Association, 211, 207–210.
Brieman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth Publishing Company.
Carusoa, G., Gattoneb, S. A., Fortunac, F. & Battistab, T. (2021). Cluster analysis for mixed data: an application to credit risk evaluation. Socio-Economic Planning Sciences, 73, 1-7.
Chatterjee, S., Ghosh, S. K., Chaudhuri, R. & Nguyen, B. (2019). Are CRM systems
ready for AI integration? a conceptual framework of organizational readiness
for effective AI-CRM integration. The Bottom Line, 33(2), 144-157.
Cheng, J. H. & Liu, S. F. (2017). A study of innovative product marketing strategies
for technological SMEs. Journal of Interdisciplinary Mathematics, 20(1), 319-
337.
Clarke, A. E., Bloch, D. A., Danoff, D. S., & Esdaile, J. M. (1984). Decreasing costs
and improving outcomes in systemic lupus erythematosus: using regression trees
to develop health policy. Journal of Rheumatology. 21, 2246-2253.
Cortez, R. M. & Johnston, W. J. (2019). Marketing role in B2B settings: evidence
from advanced, emerging and developing markets. Journal of Business
Industrial Marketing, 34(3), 605-617.
Dai, M. & Wang, Y. (2020). Research on the application of business intelligence
based on data mining technology in the new industry. Journal of Simulation, 8(6),
85-89.
Damghani, K. K., Abdi, F. & Abolmakarem, S. (2018). Hybrid soft computing
approach based on clustering, rule mining, and decision tree analysis for customer segmentation problem: a real case of customer centric industries. Applied Soft Computing, 73, 816-828.
Ducange, P., Pecori, R. & Mezzina, P. (2017). A glimpse on big data analytics in the
framework of marketing strategies. Soft Computing: Methodologies and
Applications, 22(5), 325-342.
Erevelles, S., Fukawa, N. & Swayne, L. (2016). Big Data consumer analytics and the
transformation of marketing. Journal of Business Research, 69, 897-904.
Fowler M. (2014). Patterns of Enterprise Application Architecture. Addison-Wesley
Professional.
Gamrot, W., Świtała, M., Reformat, B. & Bilińska-Reformat, K. (2018). The
influence of brand awareness and brand image on brand equity-an empirical study of logistics service providers. Journal of Economics and Management, 33(3), 96-119.
Gan, Z. (2021). Research on precision marketing based on clustering algorithm. Data Engineering and Communications Technologies, 97, 1129-1135.
Ghiasi, M. M., Zendehboudi, S. & Mohsenipour, A. A. (2020). Decision tree based diagnosis of coronary artery disease: CART model. Computer Methods and Programs in Biomedicine, 192, 1-14.
Guha, S., Harrigan, P. & Soutar, G. (2018). Linking social media to customer relationship management (CRM): a qualitative study on SMEs. Journal of Small Business & Entrepreneurship, 30(3), 193-214.
Hartigan, J. A. (1975). Clustering algorithms. John Wiley & Sons.
Heimbach, I., Kostyra, D. S. & Hinz, O. (2015). Marketing automation. Business &
Information Systems Engineering, 57(2), 129-133.
Heldt, R., Silveira C. S. & Lucea, F. B. (2021). Predicting customer value per product:
From RFM to RFM/P. Journal of Business Research, 127, 444-453.
Henk, H., Hillerton, J. E. & Berry, A.E. (2004). Decision tree analysis to evaluate dry
cow strategies under UK conditions. Journal of Dairy Research, 71, 409-418. Hongping, L. (2021). Big data precision marketing and consumer behavior analysis
based on fuzzy clustering and PCA model, Journal of Intelligent & Fuzzy Systems, 40(4), 6529-6539.
Hu, Y. (2018). Marketing and business analysis in the era of big data, American
Journal of Industrial and Business Management, 8(7), 1747-1756.
Hughes, A. H. (1994). Strategic database marketing. Probus Publishing Company. Jacobsen, S. (2018). Why did I buy this? the effect of WOM and online reviews on
post purchase attribution for product outcomes. Journal of Research in Interactive Marketing, 12(3), 370-395.
Järvinen, J. & Taiminen, H. (2015). Harnessing marketing automation for B2B content marketing. Industrial Marketing Management 54, 164-175.
Jena, A. B. & Snehasis, P. (2017). Role of marketing automation software tools in
improving or boosting sales. Splint International Journal of Professionals, 4(7),
30-35.
Park, H. S. & Baik, D. W. (2006). A study for control of client value using cluster
analysis. Journal of Network and Computer Applications, 29(4), 262-276.
Picken, J. C. (2017). From startup to scalable enterprise: laying the foundation.
Business Horizons, 60(5), 587-595.
Khan, S. & Iqbal, M. (2020). AI-Powered customer service: Does it optimize customer
experience? Proceedings of the 2020 8th International Conference on Reliability
Infocom Technologies and Optimization, 590-594.
Krishnan, R. & Nair, P. R. (2021). RFM based customer analysis and product
recommendation system. Advanced Computing and Intelligent Technologies, 218, 159-164.
Lakshmanarao, A., Sekhar, K. C. & Kumar, V. (2018). Using machine learning clustering algorithms for analysing wholesale customers data. International Journal of Scientific Research in Computer Science Applications and Management Studies, 7(4), 1-3.
Leroi-Werelds, S. (2019). An update on customer value: state of the art, revised typology, and research agenda. Journal of Service Management, 30(5), 650-680.
Lipyanina H., Sachenko A., Lendyuk T., Serhiy N. & Grodskyi S. (2019). Decision Tree Based Targeting Model of Customer Interaction with Business Page. [Unpublished master’s thesis]. Ternopil National Economic University.
Li, X., Shi, M. & Wang, X. (2019). Video mining: measuring visual information using automatic methods. International Journal of Research in Marketing, 36(2), 216- 231.
Lihua, L. & Shiyu, G. (2019). Power marketing big data precision user profile. Proceedings of the 2019 International Conference on Virtual Reality and Intelligent Systems, 117-120.
L'Oréal Manufactures Cosmetics. (2017, November). L'Oréal Paris delivers precision marketing through DoubleClick.
https://www.thinkwithgoogle.com/intl/en-cee/future-of-marketing/digital- transformation/loreal-paris-delivers-precision-marketing-through-doubleclick/
Lu, J., Wu, D., Mao, M., Wang, W. & Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74, 12-32.
Ma, X. (2005). Growth in mathematics achievement: analysis with classification and regression trees. The Journal of Educational Research, 99(2), 78-86.
Maraghi, M., Adibi, M. A. & Mehdizadeh, E. (2020). Using RFM model and market basket analysis for segmenting customers and assigning marketing strategies to resulted segments. Journal of Applied Intelligent Systems & Information Sciences, 1(1), 35-43.
Martínez-López, J. F. & Casillas, J. (2013). Artificial intelligence based systems applied in industrial marketing: an historical overview, current and future insights. Industrial Marketing Management, 42(4), 489-495.
Metsola, T. (2018). A framework for understanding the usage of the customer journey in marketing automation. [Unpublished master’s thesis]. Lappeenranta University of Technology.
Mitra, D., Chu, Y. & Cetin, K. (2021). Cluster analysis of occupancy schedules in
residential buildings in the United States. Energy and Buildings, 236(1), 1-13. Mudjahidin, I. (2022). Development of conceptual model to increase customer interest using recommendation system in E-commerce. Procedia Computer Science, 197, 727-733.
Nguyen, V., Zhou T., Chong, L., Yee, A., Boyingc, L. L. & Xiaodiec, P. (2020).
Predicting customer demand for remanufactured products: a data-mining
approach, European Journal of Operational Research, 281(3), 543-558.
Noerwijati, K., Sholihin, S., Wahyuni, T. S., Budiono, R., Minh, N. V., Setyobudi, R. H., Vincēviča-Gaile, Z. & Husna, L. (2021). Cluster Analysis based selection in
seedling population of Cassava Clones, Sarhad Journal of Agriculture , 37(2),
331-713.
Park, H. S., & Baik, D. K. (2006). A study for control of client value using Cluster
Analysis. Journal of Network and Computer Applications, 29(4), 262-276.
Paschen, J., Kietzmann, J. & Kietzmann, T.C. (2019). Artificial intelligence (AI) and its implications for market knowledge in B2B marketing. Journal of Business &
Industrial Marketing, 34(7), 1410-1419.
Power, B. & Weinman, J. (2018). Revenue growth is the primary benefit of the cloud. IEEE Cloud Computing, 5(4), 89-94.
Quinlan, J. R. (1996). Improved use of continuous attributes in C4.5. Journal of
Artificial Intelligence Research, 4, 77-90.
Rostek, K. & Zawistowska, A. (2019). Marketing automation in the process of
communication on the B2B market. Business Informatics, 1(51), 72-90.
Sakunthala, A. (2020). Role of information technology on CRM implementation in
selected industries, Asian Journal of Management, 11(4), 413-418.
Stone, B. (1995). Successful direct marketing methods. NTC Business Books. Sun, Y. (2021). Research on precision marketing of Big Data in small and medium sized
E-commerce enterprises, smart innovation. Systems and Technologies, 218,
121-127.
Telus International. (2021, March). Customer experience priorities in a post-
pandemic world.
https://assets.ctfassets.net/3viuren4us1n/1Q9DV4UT8MDmbfVz2S4rBP/5dc61 ea87ccc30ed8954347c8e20d818/TELUS_International_2021_CX_Survey_Rep ort.pdf
Thorpe, H. (2018). Creating an integrated digital marketing strategy: using cross
channel data to build intelligent strategies. Journal of Digital & Social Media
Marketing, 6(1), 28-39.
Torrico, B. H. & Frank, B. (2017). Consumer desire for personalisation of products
and services: cultural antecedents and consequences for customer evaluations.
Total Quality Management & Business Excellence, 30(3), 355-369. Truonga, V. N., Li, Z., Alain Yee Loongn, C., Boying, L., Xiaodie P. (2020).
Predicting customer demand for remanufactured products: a data-mining
approach, European Journal of Operational Research 281(3), 543-558.
Uysal, U. C. (2019) Rfm-based Customer Analytics in Public Procurement Sector.
[Unpublished master’s thesis]. Ankara Yıldırım Beyazıt University.
Vakulenkoa, Y., Shamsb, P., Hellströma, D. & Hjorta, K. (2019). Online retail
experience and customer satisfaction: the mediating role of last mile delivery. The International Review of Retail, Distribution and Consumer Research, 29(3), 306-320.
Veronica, P. & Silvia, M. (2018). Traditional versus Online Marketing for B2B Organizations: where the line blurs, Ovidius University of Constanta: Economic Sciences Series, 18(1), 382-387.
Vishnoi, S. K., Bagga, T., Sharma, A. & Wani, S. N. (2019). Artificial intelligence enabled marketing solutions: a review. Indian Journal of Economics & Business, 17(4), 167-177.
Wachter, S., Mittelstadt, B. & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the general data protection
regulation, International Data Privacy Law, 7(2), 76-99.
Wang, C., Li, R., Wang P. & Chen, Z. (2017). Partition cost-sensitive CART based
on customer value for Telecom customer churn prediction. [Paper presentation].
The 36th Chinese Control Conference, Dalian, China.
Wang, X., Arnett, D. B. & Hou, L. (2016). Using external knowledge to improve
organizational innovativeness: understanding the knowledge leveraging process,
Journal of Business & Industrial Marketing, 31(2), 164-173.
Wen, X., & Ye, Y. (2022). An analysis of customer change, government support, and cash holdings. International Journal of Engineering Business Management, 14, 1-11.
Wright, L. T., Robin, R., Stone, M. & Aravopoulou, E. (2019). Adoption of Big Data Technology for innovation in B2B marketing, Journal of Business-to-Business, 26(3), 281-293.
Wu, J., Shi, L., Lin, W.P., Tsai, S. B., Li, Y. Yang, L & Xu, G. (2020). An empirical study on customer segmentation by purchase behaviors using a RFM Model and K-Means algorithm. Mathematical Problems in Engineering, 2020, 1-7.
Wunderman, L. (1999). Being Direct: Making Advertising Pay. Random House. Xue, B. (2020). An optimization scheme of network marketing based on Big Data.
[Paper presentation]. 2020 5th International Conference on Smart Grid and
Electrical Automation, Zhangjiajie, China.
Yang, C. & Wei, Y. (2017). Research on precision marketing of university teaching
materials in big data era, Education and Humanities Research, 80, 262-266. You, Z., Si, Y. W., Zhang, D., Zeng, X., Leung, C.H., & Li, T. (2015). A decision-making framework for precision marketing. Expert Systems with Applications, 42(7), 3357-3367.
Zhang, B. & Zhang, B. (2018). Precise marketing of precision marketing value chain
process on the H group line based on Big Data. Journal of Intelligent & Fuzzy Systems, 35(3), 2837-2845.
Zhang, J., Wu T.,1 & Fan Z. (2019). Research on precision marketing model of
tourism industry based on user’s mobile behavior trajectory, Mobile Information
System, 2019, 1-14.
Zhang, S., Pauwels K., & Peng C. (2019). The impact of adding online-to-offline
service platform channels on firms' offline and total sales and profits. Journal of Interactive Marketing, 47, 115-128.