學術產出-Theses

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

  • No doi shows Citation Infomation
題名 巨量資料分析應用於顧客關係管理之研究
A Study of Big Data Analytics for Customer Relationship Management
作者 黃盈智
Huang, Ying Chih
貢獻者 尚孝純
Shang, Shari S. C.
黃盈智
Huang, Ying Chih
關鍵詞 巨量資料
海量資料
大數據
顧客關係管理
日期 2013
上傳時間 3-Feb-2015 10:15:44 (UTC+8)
摘要 本研究透過次級資料的收集,探討巨量資料分析在顧客關係管理之應用,並著重於零售業、金融業與醫療業三個產業。近幾年來,巨量資料(Big Data)的浪潮襲捲而來,隨著網際網路的發展與智慧型裝置的普遍,現今每個人在日常生活中不斷產生巨量資料,透過智慧型手機、社群網站、信用卡、全球衛星定位系統(GPS)、感測器等,在無形中製造了大量的數據。而在此同時,企業用來儲存、記憶、處理資料的成本不斷降低,設備越來越便宜,技術越來越先進,再加上新的資料來源,巨量資料分析對企業的重要性不言可喻。
      巨量資料能徹底改變企業的經營方式,大幅提升企業的經營績效,但這些收入是否超過公司內部在巨量資料所投資的成本,為公司帶來獲利?巨量資料分析又是怎樣應用在顧客關係管理,帶來正面效益?本研究從「企業進行巨量資料分析的動機」、「巨量資料的類型與來源」、「巨量資料分析方式」、「巨量資料分析的結果與效益」及「企業在巨量資料分析的投入與調整」五個面向切入,探討巨量資料分析在顧客關係管理的應用。
      研究發現,巨量資料背後的分析學,不僅可以用來解決企業現有的問題,更能協助企業發掘未知的商機,開發新的產品與服務。然而值得注意的是,無論是零售業、金融業或醫療業,巨量資料分析能使企業從競爭對手中脫穎而出的成功關鍵因素,是在於公司本身對資訊的重視程度,以及公司內部能否共同合作,也就是說,從領導階層到第一線員工,不僅都要相信資料,也要懂得如何應用這些巨量資料,使其發揮最大效用,方能在創新的商業模式下,創造企業長期的競爭優勢。
摘要 I
     謝誌 II
     目次 III
     表目錄 VI
     圖目錄 VII
     第壹章 緒論 1
     第一節 研究背景 1
     第二節 研究目的與動機 3
     第三節 研究流程 5
     第貳章 文獻探討 6
     第一節 巨量資料 6
     一、 何謂「巨量資料」 6
     二、 巨量資料的規格 10
     三、 巨量資料的四大特性(4V) 12
     四、 巨量資料與傳統資料的不同 18
     五、 巨量資料分析新思維 21
     第二節 巨量資料應用及管理 24
     一、 巨量資料應用技術 24
     二、 巨量資料的挑戰 35
     第三節 顧客關係管理 40
     一、 顧客關係管理起源 40
     二、 顧客關係管理定義 42
     三、 顧客關係管理三階段 43
     四、 顧客關係管理績效衡量指標 45
     第參章 研究方法 48
     第一節 多重個案研究法 49
     第二節 內容分析研究法 50
     第三節 個案選擇 51
     第四節 資料收集與分析 54
     第肆章 個案介紹 55
     第一節 零售業 55
     一、 美國零售商沃爾瑪(Wal-Mart) 55
     二、 美國辛辛那提動物園(Cincinnati Zoo) 57
     三、 美國零售商塔吉特(Target) 60
     四、 美國Sun World International 62
     五、 美國Papa Gino’s Pizza 64
     六、 澳洲卡夫食品(Kraft Foods)維吉麥(Vegemite) 66
     七、 日本羅森(LAWSON)連鎖便利商店 68
     第二節 金融業 70
     一、 美國第一資本(Capital One)金融公司 70
     二、 英國德溫特資本基金公司(Derwent Capital Markets) 72
     三、 英國英傑華保險公司(Aviva) 74
     第三節 醫療業 77
     一、 加拿大安大略理工學院早產兒健康監護系統 77
     二、 Google流感趨勢預測 78
     三、 IBM Watson醫生診斷輔助系統 81
     第伍章 個案比較分析與發現 83
     第一節 零售業 92
     第二節 金融業 94
     第三節 醫療業 96
     第陸章 結論與建議 98
     第一節 結論 98
     第二節 建議 100
     第三節 研究限制與後續研究建議 102
     參考文獻 103
     中文部分 103
     英文部分 104
參考文獻 中文部分
     1. Davenport, T. H.、Harris, J. G.(2008),魔鬼都在數據裡,胡瑋珊譯,中國生產力中心。
     2. KPMG(2014, 02),「善用海量資料 企業經營邁大步」,取自 https://www.kpmg.com/TW/zh/Documents/Monthly/201402-news3.pdf。
     3. Kumar, R.(2000),研究方法:步驟化學習指南,胡龍騰、黃瑋瑩與潘中道譯,學富文化。
     4. Kumar, V., Reinartz, W. J.(2007),顧客關係管理:資料庫行銷方法之應用,洪育忠與謝佳蓉譯,華泰文化。
     5. Mayer-Schönberger, V.(2014, 07),「大數據帶來決策挑戰」,哈佛商業評論全球繁體中文版,2014年7月號。
     6. 王文科(1990),教育研究法,五南圖書出版公司。
     7. 城田真琴(2013),Big Data大數據的獲利模式:圖解.案例.策略.實戰,鐘慧真與梁世英譯,經濟新潮社。
     8. 胡世忠(2013),雲端時代的殺手級應用:Big Data海量資料分析,天下雜誌。
     9. 財團法人資訊工業策進會前瞻所(2014),「巨量資料簡介」,取自 http://www.nacs.gov.tw/NcsiWebFileDocuments/3c2136cf0801ea10c866aaa770aa3e94.pptx。
     10. 張瑋倫(2005),顧客關係管理-理論與實務,學貫行銷。
     11. 陳萬淇(1995),個案研究法,華泰書局。
     12. 葉至誠、葉立誠(1999),研究方法與論文寫作,商鼎文化。
     13. 劉文良(2010),顧客關係管理:新時代的決勝關鍵,碁峯資訊股份有限公司。
     14. 羅敏夏(2012),「用推特情緒賺錢」,南方周末,取自http://www.infzm.com/content/77906。
     15. 蘇蘅(2014),「擁抱大數據 與新石油共舞」,聯合報,取自 http://udn.com/NEWS/OPINION/OPI4/8767536.shtml。
     英文部分
     1. Adrian, M. (2011). “It`s going mainstream, and it`s your next opportunity,” Teradata Magazine. Retrieved from http://www.teradatamagazine.com/v11n01/Features/Big-Data/
     2. Badger, L., Grance, T., Patt-Corner, R., & Voas, J. (2012). “Cloud Computing Synopsis and Recommendations,” National Institute of Standards and Technology Special Publication, May, Vol. 800, 146. Retrieved from https://www.cloudcomputingcaucus.org/pdfs/Cloud_Computing_Synopsis_Recommendations_NIST_May2012.pdf
     3. Baker, S. (2011). Final Jeopardy: Man vs. Machine and the Quest to Know Everything, Houghton Mifflin Harcourt.
     4. Barnes, T. J., & Wilson, M. W. (2014). “Big Data, social physics, and spatial analysis: The early years,” Big Data & Society, Vol.1, Issue 1, 1-14. World Economic Forum : 10.1177/2053951714535365.
     5. Barton, D., & Court, D. (2012). “Making advanced analytics work for you,” Harvard Business Review, Vol.90, 78-83.
     6. Bertram, I. (2014). “What are your analytical priorities this year…?” Gartner Blog Network. Retrieved from http://blogs.gartner.com/ian-bertram/what-are-your-analytical-priorities-this-year/.
     7. Bhatia, A. (1999). A Roadmap to Implementation of Customer Relationship Management (CRM), Business Analyst (ERP) Infosys Technologies Ltd.
     8. Bollen, J., Mao, H., & Zeng, X.-J. (2010). “Twitter mood predicts the stock market,” Journal of Computational Science, Oct 14, 1-8.
     9. Brandom, R. (2013). “IBM`s Watson wants to fix America`s doctor shortage,” The Verge. Retrieved from http://www.theverge.com/2013/10/15/4837828/ibms-watson-wants-to-fix-americas-doctor-shortage.
     10. Brynjolfsson, E., & Macfee, A. (2012). “Big Data: The management revolution,” Harvard Business Review, Vol.90, Issue 10, 61-67.
     11. Brynjolfsson, E., & Mcafee, A. (2011). “The Big Data Boom Is the Innovation Story of Our Time,” The Atlantic. Retrieved from http://www.theatlantic.com/business/archive/2011/11/the-big-data-boom-is-the-innovation-story-of-our-time/248215/
     12. Columbus, L. (2012). “Using Search Analytics To See Into Gartner`s $232B Big Data Forecast,” Forbes. Retrieved from http://www.forbes.com/sites/louiscolumbus/2012/10/15/using-search-analytics-to-see-into-gartners-232b-big-data-forecast/
     13. Davenport, T. H. (2013). “Analytics 3.0.,” Harvard Business Review, December.
     14. Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning (1 ed.), Harvard Business Review Press.
     15. Davenport, T. H., & Patil, D. J. (2012). “Data Scientist: The Sexiest Job of the 21st Century,” Harvard Business Review, Vol. 90, 70-76.
     16. Duhigg, C. (2012). “How Companies Learn Your Secrets,” New York Times, Vol. 16.
     17. Farr, C. (2013). “IBM Watson fires its own cancer-fighting moonshot,” VentureBeat. Retrieved from http://venturebeat.com/2013/10/18/ibm-watson-fires-its-own-cancer-fighting-moonshot/
     18. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). “From Data Mining to Knowledge Discovery in Databases,” AI MAGAZINE, Vol. 17, Issue 3, 37.
     19. Franks, B. (2012). Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics (1 ed.), Wiley.
     20. Gantz, J., & Reinsel, D. (2011). “Extracting Value from Chaos,” IDC, June. Retrieved from http://www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf
     21. Gartner. (2011). ”Gartner Says Solving Big Data Challenge Involves More Than Just Managing Volumes of Data.” Retrieved from http://www.gartner.com/newsroom/id/1731916
     22. Gartner. (2012). “Gartner Says Big Data Creates Big Jobs: 4.4 Million IT Jobs Globally to Support Big Data By 2015,” Analysts Discuss Key Issues Facing the IT Industry During Gartner Symposium. Retrieved from http://www.gartner.com/newsroom/id/2207915
     23. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). “Detecting influenza epidemics using search engine query data,” Nature, Vol. 457, Issue 7232, 1012-1014. doi: 10.1038/nature07634
     24. Global Pulse. (2012). “Big Data for Development: Challenges & Opportunities.” Retrieved from http://www.unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf
     25. Grimes, S. (2007). “Defining Text Analytics,” InformationWeek. Retrieved from http://www.informationweek.com/software/information-management/defining-text-analytics/d/d-id/1051763?
     26. Halevi, G., & Moed, H. (2012). “The Evolution of Big Data as a Research and Scientific Topic: Overview of the Literature,” Research Trends, Issue 30. Retrieved from http://www.researchtrends.com/issue-30-september-2012/the-evolution-of-big-data-as-a-research-and-scientific-topic-overview-of-the-literature/
     27. Hays, C. L. (2004). “What Wal-Mart knows about customers’ habits,” The New York Times, Vol. 14. Retrieved from http://www.nytimes.com/2004/11/14/business/yourmoney/14wal.html?_r=0
     28. IBM. (2012a). “Cincinnati Zoo transforms customer experience and boosts profits.” Retrieved from http://public.dhe.ibm.com/common/ssi/ecm/en/ytc03380usen/YTC03380USEN.PDF?ce=ISM0179&ct=swg&cmp=ibmsocial&cm=h&cr=sa&ccy=us
     29. IBM. (2012b). “Global Technology Outlook.” Retrieved from http://www.research.ibm.com/files/pdfs/gto_booklet_executive_review_march_12.pdf
     30. IBM. (2012c). “Solutions Big Data IBM.” Retrieved from http://www-05.ibm.com/fr/events/netezzaDM_2012/Solutions_Big_Data.pdf
     31. Isson, J. P., & Harriott, J. (2013). Win with Advanced Business Analytics: Creating Business Value from Your Data, John Wiley & Sons.
     32. Jones, T. O., & Sasser, W. E. (1995). “Why satisfied customers defect,” Harvard Business Review, Vo. 73, Issue 6, 88.
     33. Kalakota, R., & Robinson, M. (1999). E-business 2.0: Roadmap for Success, Addison-Wesley Professional.
     34. Kumar, V., & Reinartz, W. (2005). Customer Relationship Management: A Databased Approach (1st ed.), Wiley.
     35. Laney, D. (2001). “3D Data Management: Controlling Data Volume, Velocity and Variety,” Meta Group (Gartner), Feb 6. Retrieved from http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
     36. Laney, D., & Beyer, M. A. (2012). “The Importance of Big Data: A Definition,” Gartner. Retrieved from https://www.gartner.com/doc/2057415/importance-big-data-definition
     37. Linoff, G. S., & Berry, M. J. A. (1997). Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Wiley.
     38. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). “Big data: The next frontier for innovation, competition, and productivity,” McKinsey Global Institute. Retrieved from http://www.mckinsey.com/~/media/McKinsey/dotcom/Insights%20and%20pubs/MGI/Research/Technology%20and%20Innovation/Big%20Data/MGI_big_data_full_report.ashx
     39. Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think (1 ed.), Eamon Dolan/Houghton Mifflin Harcourt.
     40. McKinsey. (2009). “Clearing the Air on Cloud Computing.” Retrieved from http://www.isaca.org/Groups/Professional-English/cloud-computing/GroupDocuments/McKinsey_Cloud%20matters.pdf
     41. Oliver, R. L. (1999). “Whence Consumer Loyalty?” Journal of Marketing, Vol. 63, 33-44.
     42. Oracle. (2012). “Oracle Big Data Platform.” Retrieved from http://www.oracle.com/in/corporate/events/big-data-executive-event-1986617-en-in.pdf
     43. Podesta, J., Pritzker, P., Moniz, E., Holdren, J., & Zients, J. (2014). “Big data: seizing opportunities, preserving values.” Retrieved from http://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_may_1_2014.pdf
     44. Rao, S. S. (1998). “Diaper-beer syndrome,” Forbes, April 6. Retrieved from http://www.forbes.com/forbes/1998/0406/6107128a.html
     45. Redman, T. C. (2013). “Data’s Credibility Problem.” Harvard Business Review, Vol. 91, Issue 12, 84.
     46. Rijmenam, M. V. (2014). Think bigger: developing a successful big data strategy for your business, AMACOM.
     47. Rosenfield, J. R. (2002). “Customer Relationship Management: A Brief History, and A Big Mystery.” Retrieved from http://www.jrosenfield.com/articles/CRM-History.htm
     48. Scism, L., & Maremont, M. (2010a). “Inside Deloitte`s Life-Insurance Assessment Technology,” Wall Street Journal, Nov 19. Retrieved from http://online.wsj.com/news/articles/SB10001424052748704104104575622531084755588
     49. Scism, L., & Maremont, M. (2010b). “Insurers Test Data Profiles to Identify Risky Clients,” Wall Street Journal, Nov 19. Retrieved from http://nhhealthins.com/wp-content/uploads/2010/11/health_profiles.11.27.pdf
     50. Shah, S., Horne, A., & Capellá, J. (2012). “Good Data Won`t Guarantee Good Decisions,” Harvard Business Review, April.
     51. Sicular, S. (2013). “Gartner’s Big Data definition consists of three parts, not to be confused with three ‘V’s,” Forbes. Retrieved from http://www.forbes.com/sites/gartnergroup/2013/03/27/gartners-big-data-definition-consists-of-three-parts-not-to-be-confused-with-three-vs/
     52. Siegel, E. (2013). Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (1 ed.), Wiley.
     53. Swift, R. S. (2001). Accelerating Customer Relationships: Using CRM and Relationship Technologies, Prentice Hall Professional.
     54. Temple, K. (2012). “What Happens in an Internet Minute?” Inside Scoop, Mar 13. Retrieved from http://scoop.intel.com/what-happens-in-an-internet-minute/
     55. Ward, J. S., & Barker, A. (2013). “Undefined By Data: A Survey of Big Data Definitions,” arXiv preprint arXiv:1309.5821. Retrieved from http://arxiv.org/pdf/1309.5821.pdf
     56. WHO, Dimes, M. o., PMNCH, & Children, S. t. (2012). “Born Too Soon: The Global Action Report on Preterm Birth,” World Health Organization. Retrieved from http://www.healthynewbornnetwork.org/sites/default/files/resources/BornTooSoon-Report-April2012.pdf
     57. World Economic Forum. (2012). “Big Data, Big Impact: New Possibilities for International Development,” World Economic Forum. Retrieved from http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf
     58. Yin, R. K. (1994). Case study research: Design and methods, Sage publications.
     59. Zikopoulos, P., Parasuraman, K., Deutsch, T., Giles, J., & Corrigan, D. (2013). Harness the Power of Big Data: The IBM Big Data Platform, McGraw Hill Professional.
描述 碩士
國立政治大學
企業管理研究所
102363046
102
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102363046
資料類型 thesis
dc.contributor.advisor 尚孝純zh_TW
dc.contributor.advisor Shang, Shari S. C.en_US
dc.contributor.author (Authors) 黃盈智zh_TW
dc.contributor.author (Authors) Huang, Ying Chihen_US
dc.creator (作者) 黃盈智zh_TW
dc.creator (作者) Huang, Ying Chihen_US
dc.date (日期) 2013en_US
dc.date.accessioned 3-Feb-2015 10:15:44 (UTC+8)-
dc.date.available 3-Feb-2015 10:15:44 (UTC+8)-
dc.date.issued (上傳時間) 3-Feb-2015 10:15:44 (UTC+8)-
dc.identifier (Other Identifiers) G0102363046en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/73224-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 企業管理研究所zh_TW
dc.description (描述) 102363046zh_TW
dc.description (描述) 102zh_TW
dc.description.abstract (摘要) 本研究透過次級資料的收集,探討巨量資料分析在顧客關係管理之應用,並著重於零售業、金融業與醫療業三個產業。近幾年來,巨量資料(Big Data)的浪潮襲捲而來,隨著網際網路的發展與智慧型裝置的普遍,現今每個人在日常生活中不斷產生巨量資料,透過智慧型手機、社群網站、信用卡、全球衛星定位系統(GPS)、感測器等,在無形中製造了大量的數據。而在此同時,企業用來儲存、記憶、處理資料的成本不斷降低,設備越來越便宜,技術越來越先進,再加上新的資料來源,巨量資料分析對企業的重要性不言可喻。
      巨量資料能徹底改變企業的經營方式,大幅提升企業的經營績效,但這些收入是否超過公司內部在巨量資料所投資的成本,為公司帶來獲利?巨量資料分析又是怎樣應用在顧客關係管理,帶來正面效益?本研究從「企業進行巨量資料分析的動機」、「巨量資料的類型與來源」、「巨量資料分析方式」、「巨量資料分析的結果與效益」及「企業在巨量資料分析的投入與調整」五個面向切入,探討巨量資料分析在顧客關係管理的應用。
      研究發現,巨量資料背後的分析學,不僅可以用來解決企業現有的問題,更能協助企業發掘未知的商機,開發新的產品與服務。然而值得注意的是,無論是零售業、金融業或醫療業,巨量資料分析能使企業從競爭對手中脫穎而出的成功關鍵因素,是在於公司本身對資訊的重視程度,以及公司內部能否共同合作,也就是說,從領導階層到第一線員工,不僅都要相信資料,也要懂得如何應用這些巨量資料,使其發揮最大效用,方能在創新的商業模式下,創造企業長期的競爭優勢。
zh_TW
dc.description.abstract (摘要) 摘要 I
     謝誌 II
     目次 III
     表目錄 VI
     圖目錄 VII
     第壹章 緒論 1
     第一節 研究背景 1
     第二節 研究目的與動機 3
     第三節 研究流程 5
     第貳章 文獻探討 6
     第一節 巨量資料 6
     一、 何謂「巨量資料」 6
     二、 巨量資料的規格 10
     三、 巨量資料的四大特性(4V) 12
     四、 巨量資料與傳統資料的不同 18
     五、 巨量資料分析新思維 21
     第二節 巨量資料應用及管理 24
     一、 巨量資料應用技術 24
     二、 巨量資料的挑戰 35
     第三節 顧客關係管理 40
     一、 顧客關係管理起源 40
     二、 顧客關係管理定義 42
     三、 顧客關係管理三階段 43
     四、 顧客關係管理績效衡量指標 45
     第參章 研究方法 48
     第一節 多重個案研究法 49
     第二節 內容分析研究法 50
     第三節 個案選擇 51
     第四節 資料收集與分析 54
     第肆章 個案介紹 55
     第一節 零售業 55
     一、 美國零售商沃爾瑪(Wal-Mart) 55
     二、 美國辛辛那提動物園(Cincinnati Zoo) 57
     三、 美國零售商塔吉特(Target) 60
     四、 美國Sun World International 62
     五、 美國Papa Gino’s Pizza 64
     六、 澳洲卡夫食品(Kraft Foods)維吉麥(Vegemite) 66
     七、 日本羅森(LAWSON)連鎖便利商店 68
     第二節 金融業 70
     一、 美國第一資本(Capital One)金融公司 70
     二、 英國德溫特資本基金公司(Derwent Capital Markets) 72
     三、 英國英傑華保險公司(Aviva) 74
     第三節 醫療業 77
     一、 加拿大安大略理工學院早產兒健康監護系統 77
     二、 Google流感趨勢預測 78
     三、 IBM Watson醫生診斷輔助系統 81
     第伍章 個案比較分析與發現 83
     第一節 零售業 92
     第二節 金融業 94
     第三節 醫療業 96
     第陸章 結論與建議 98
     第一節 結論 98
     第二節 建議 100
     第三節 研究限制與後續研究建議 102
     參考文獻 103
     中文部分 103
     英文部分 104
-
dc.description.tableofcontents 摘要 I
     謝誌 II
     目次 III
     表目錄 VI
     圖目錄 VII
     第壹章 緒論 1
      第一節 研究背景 1
      第二節 研究目的與動機 3
      第三節 研究流程 5
     第貳章 文獻探討 6
      第一節 巨量資料 6
     一、 何謂「巨量資料」 6
     二、 巨量資料的規格 10
     三、 巨量資料的四大特性(4V) 12
     四、 巨量資料與傳統資料的不同 18
     五、 巨量資料分析新思維 21
      第二節 巨量資料應用及管理 24
     一、 巨量資料應用技術 24
     二、 巨量資料的挑戰 35
      第三節 顧客關係管理 40
     一、 顧客關係管理起源 40
     二、 顧客關係管理定義 42
     三、 顧客關係管理三階段 43
     四、 顧客關係管理績效衡量指標 45
     第參章 研究方法 48
      第一節 多重個案研究法 49
      第二節 內容分析研究法 50
      第三節 個案選擇 51
      第四節 資料收集與分析 54
     第肆章 個案介紹 55
      第一節 零售業 55
     一、 美國零售商沃爾瑪(Wal-Mart) 55
     二、 美國辛辛那提動物園(Cincinnati Zoo) 57
     三、 美國零售商塔吉特(Target) 60
     四、 美國Sun World International 62
     五、 美國Papa Gino’s Pizza 64
     六、 澳洲卡夫食品(Kraft Foods)維吉麥(Vegemite) 66
     七、 日本羅森(LAWSON)連鎖便利商店 68
      第二節 金融業 70
     一、 美國第一資本(Capital One)金融公司 70
     二、 英國德溫特資本基金公司(Derwent Capital Markets) 72
     三、 英國英傑華保險公司(Aviva) 74
      第三節 醫療業 77
     一、 加拿大安大略理工學院早產兒健康監護系統 77
     二、 Google流感趨勢預測 78
     三、 IBM Watson醫生診斷輔助系統 81
     第伍章 個案比較分析與發現 83
      第一節 零售業 92
      第二節 金融業 94
      第三節 醫療業 96
     第陸章 結論與建議 98
      第一節 結論 98
      第二節 建議 100
      第三節 研究限制與後續研究建議 102
     參考文獻 103
      中文部分 103
      英文部分 104
zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102363046en_US
dc.subject (關鍵詞) 巨量資料zh_TW
dc.subject (關鍵詞) 海量資料zh_TW
dc.subject (關鍵詞) 大數據zh_TW
dc.subject (關鍵詞) 顧客關係管理zh_TW
dc.title (題名) 巨量資料分析應用於顧客關係管理之研究zh_TW
dc.title (題名) A Study of Big Data Analytics for Customer Relationship Managementen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 中文部分
     1. Davenport, T. H.、Harris, J. G.(2008),魔鬼都在數據裡,胡瑋珊譯,中國生產力中心。
     2. KPMG(2014, 02),「善用海量資料 企業經營邁大步」,取自 https://www.kpmg.com/TW/zh/Documents/Monthly/201402-news3.pdf。
     3. Kumar, R.(2000),研究方法:步驟化學習指南,胡龍騰、黃瑋瑩與潘中道譯,學富文化。
     4. Kumar, V., Reinartz, W. J.(2007),顧客關係管理:資料庫行銷方法之應用,洪育忠與謝佳蓉譯,華泰文化。
     5. Mayer-Schönberger, V.(2014, 07),「大數據帶來決策挑戰」,哈佛商業評論全球繁體中文版,2014年7月號。
     6. 王文科(1990),教育研究法,五南圖書出版公司。
     7. 城田真琴(2013),Big Data大數據的獲利模式:圖解.案例.策略.實戰,鐘慧真與梁世英譯,經濟新潮社。
     8. 胡世忠(2013),雲端時代的殺手級應用:Big Data海量資料分析,天下雜誌。
     9. 財團法人資訊工業策進會前瞻所(2014),「巨量資料簡介」,取自 http://www.nacs.gov.tw/NcsiWebFileDocuments/3c2136cf0801ea10c866aaa770aa3e94.pptx。
     10. 張瑋倫(2005),顧客關係管理-理論與實務,學貫行銷。
     11. 陳萬淇(1995),個案研究法,華泰書局。
     12. 葉至誠、葉立誠(1999),研究方法與論文寫作,商鼎文化。
     13. 劉文良(2010),顧客關係管理:新時代的決勝關鍵,碁峯資訊股份有限公司。
     14. 羅敏夏(2012),「用推特情緒賺錢」,南方周末,取自http://www.infzm.com/content/77906。
     15. 蘇蘅(2014),「擁抱大數據 與新石油共舞」,聯合報,取自 http://udn.com/NEWS/OPINION/OPI4/8767536.shtml。
     英文部分
     1. Adrian, M. (2011). “It`s going mainstream, and it`s your next opportunity,” Teradata Magazine. Retrieved from http://www.teradatamagazine.com/v11n01/Features/Big-Data/
     2. Badger, L., Grance, T., Patt-Corner, R., & Voas, J. (2012). “Cloud Computing Synopsis and Recommendations,” National Institute of Standards and Technology Special Publication, May, Vol. 800, 146. Retrieved from https://www.cloudcomputingcaucus.org/pdfs/Cloud_Computing_Synopsis_Recommendations_NIST_May2012.pdf
     3. Baker, S. (2011). Final Jeopardy: Man vs. Machine and the Quest to Know Everything, Houghton Mifflin Harcourt.
     4. Barnes, T. J., & Wilson, M. W. (2014). “Big Data, social physics, and spatial analysis: The early years,” Big Data & Society, Vol.1, Issue 1, 1-14. World Economic Forum : 10.1177/2053951714535365.
     5. Barton, D., & Court, D. (2012). “Making advanced analytics work for you,” Harvard Business Review, Vol.90, 78-83.
     6. Bertram, I. (2014). “What are your analytical priorities this year…?” Gartner Blog Network. Retrieved from http://blogs.gartner.com/ian-bertram/what-are-your-analytical-priorities-this-year/.
     7. Bhatia, A. (1999). A Roadmap to Implementation of Customer Relationship Management (CRM), Business Analyst (ERP) Infosys Technologies Ltd.
     8. Bollen, J., Mao, H., & Zeng, X.-J. (2010). “Twitter mood predicts the stock market,” Journal of Computational Science, Oct 14, 1-8.
     9. Brandom, R. (2013). “IBM`s Watson wants to fix America`s doctor shortage,” The Verge. Retrieved from http://www.theverge.com/2013/10/15/4837828/ibms-watson-wants-to-fix-americas-doctor-shortage.
     10. Brynjolfsson, E., & Macfee, A. (2012). “Big Data: The management revolution,” Harvard Business Review, Vol.90, Issue 10, 61-67.
     11. Brynjolfsson, E., & Mcafee, A. (2011). “The Big Data Boom Is the Innovation Story of Our Time,” The Atlantic. Retrieved from http://www.theatlantic.com/business/archive/2011/11/the-big-data-boom-is-the-innovation-story-of-our-time/248215/
     12. Columbus, L. (2012). “Using Search Analytics To See Into Gartner`s $232B Big Data Forecast,” Forbes. Retrieved from http://www.forbes.com/sites/louiscolumbus/2012/10/15/using-search-analytics-to-see-into-gartners-232b-big-data-forecast/
     13. Davenport, T. H. (2013). “Analytics 3.0.,” Harvard Business Review, December.
     14. Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning (1 ed.), Harvard Business Review Press.
     15. Davenport, T. H., & Patil, D. J. (2012). “Data Scientist: The Sexiest Job of the 21st Century,” Harvard Business Review, Vol. 90, 70-76.
     16. Duhigg, C. (2012). “How Companies Learn Your Secrets,” New York Times, Vol. 16.
     17. Farr, C. (2013). “IBM Watson fires its own cancer-fighting moonshot,” VentureBeat. Retrieved from http://venturebeat.com/2013/10/18/ibm-watson-fires-its-own-cancer-fighting-moonshot/
     18. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). “From Data Mining to Knowledge Discovery in Databases,” AI MAGAZINE, Vol. 17, Issue 3, 37.
     19. Franks, B. (2012). Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics (1 ed.), Wiley.
     20. Gantz, J., & Reinsel, D. (2011). “Extracting Value from Chaos,” IDC, June. Retrieved from http://www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf
     21. Gartner. (2011). ”Gartner Says Solving Big Data Challenge Involves More Than Just Managing Volumes of Data.” Retrieved from http://www.gartner.com/newsroom/id/1731916
     22. Gartner. (2012). “Gartner Says Big Data Creates Big Jobs: 4.4 Million IT Jobs Globally to Support Big Data By 2015,” Analysts Discuss Key Issues Facing the IT Industry During Gartner Symposium. Retrieved from http://www.gartner.com/newsroom/id/2207915
     23. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). “Detecting influenza epidemics using search engine query data,” Nature, Vol. 457, Issue 7232, 1012-1014. doi: 10.1038/nature07634
     24. Global Pulse. (2012). “Big Data for Development: Challenges & Opportunities.” Retrieved from http://www.unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf
     25. Grimes, S. (2007). “Defining Text Analytics,” InformationWeek. Retrieved from http://www.informationweek.com/software/information-management/defining-text-analytics/d/d-id/1051763?
     26. Halevi, G., & Moed, H. (2012). “The Evolution of Big Data as a Research and Scientific Topic: Overview of the Literature,” Research Trends, Issue 30. Retrieved from http://www.researchtrends.com/issue-30-september-2012/the-evolution-of-big-data-as-a-research-and-scientific-topic-overview-of-the-literature/
     27. Hays, C. L. (2004). “What Wal-Mart knows about customers’ habits,” The New York Times, Vol. 14. Retrieved from http://www.nytimes.com/2004/11/14/business/yourmoney/14wal.html?_r=0
     28. IBM. (2012a). “Cincinnati Zoo transforms customer experience and boosts profits.” Retrieved from http://public.dhe.ibm.com/common/ssi/ecm/en/ytc03380usen/YTC03380USEN.PDF?ce=ISM0179&ct=swg&cmp=ibmsocial&cm=h&cr=sa&ccy=us
     29. IBM. (2012b). “Global Technology Outlook.” Retrieved from http://www.research.ibm.com/files/pdfs/gto_booklet_executive_review_march_12.pdf
     30. IBM. (2012c). “Solutions Big Data IBM.” Retrieved from http://www-05.ibm.com/fr/events/netezzaDM_2012/Solutions_Big_Data.pdf
     31. Isson, J. P., & Harriott, J. (2013). Win with Advanced Business Analytics: Creating Business Value from Your Data, John Wiley & Sons.
     32. Jones, T. O., & Sasser, W. E. (1995). “Why satisfied customers defect,” Harvard Business Review, Vo. 73, Issue 6, 88.
     33. Kalakota, R., & Robinson, M. (1999). E-business 2.0: Roadmap for Success, Addison-Wesley Professional.
     34. Kumar, V., & Reinartz, W. (2005). Customer Relationship Management: A Databased Approach (1st ed.), Wiley.
     35. Laney, D. (2001). “3D Data Management: Controlling Data Volume, Velocity and Variety,” Meta Group (Gartner), Feb 6. Retrieved from http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
     36. Laney, D., & Beyer, M. A. (2012). “The Importance of Big Data: A Definition,” Gartner. Retrieved from https://www.gartner.com/doc/2057415/importance-big-data-definition
     37. Linoff, G. S., & Berry, M. J. A. (1997). Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Wiley.
     38. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). “Big data: The next frontier for innovation, competition, and productivity,” McKinsey Global Institute. Retrieved from http://www.mckinsey.com/~/media/McKinsey/dotcom/Insights%20and%20pubs/MGI/Research/Technology%20and%20Innovation/Big%20Data/MGI_big_data_full_report.ashx
     39. Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think (1 ed.), Eamon Dolan/Houghton Mifflin Harcourt.
     40. McKinsey. (2009). “Clearing the Air on Cloud Computing.” Retrieved from http://www.isaca.org/Groups/Professional-English/cloud-computing/GroupDocuments/McKinsey_Cloud%20matters.pdf
     41. Oliver, R. L. (1999). “Whence Consumer Loyalty?” Journal of Marketing, Vol. 63, 33-44.
     42. Oracle. (2012). “Oracle Big Data Platform.” Retrieved from http://www.oracle.com/in/corporate/events/big-data-executive-event-1986617-en-in.pdf
     43. Podesta, J., Pritzker, P., Moniz, E., Holdren, J., & Zients, J. (2014). “Big data: seizing opportunities, preserving values.” Retrieved from http://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_may_1_2014.pdf
     44. Rao, S. S. (1998). “Diaper-beer syndrome,” Forbes, April 6. Retrieved from http://www.forbes.com/forbes/1998/0406/6107128a.html
     45. Redman, T. C. (2013). “Data’s Credibility Problem.” Harvard Business Review, Vol. 91, Issue 12, 84.
     46. Rijmenam, M. V. (2014). Think bigger: developing a successful big data strategy for your business, AMACOM.
     47. Rosenfield, J. R. (2002). “Customer Relationship Management: A Brief History, and A Big Mystery.” Retrieved from http://www.jrosenfield.com/articles/CRM-History.htm
     48. Scism, L., & Maremont, M. (2010a). “Inside Deloitte`s Life-Insurance Assessment Technology,” Wall Street Journal, Nov 19. Retrieved from http://online.wsj.com/news/articles/SB10001424052748704104104575622531084755588
     49. Scism, L., & Maremont, M. (2010b). “Insurers Test Data Profiles to Identify Risky Clients,” Wall Street Journal, Nov 19. Retrieved from http://nhhealthins.com/wp-content/uploads/2010/11/health_profiles.11.27.pdf
     50. Shah, S., Horne, A., & Capellá, J. (2012). “Good Data Won`t Guarantee Good Decisions,” Harvard Business Review, April.
     51. Sicular, S. (2013). “Gartner’s Big Data definition consists of three parts, not to be confused with three ‘V’s,” Forbes. Retrieved from http://www.forbes.com/sites/gartnergroup/2013/03/27/gartners-big-data-definition-consists-of-three-parts-not-to-be-confused-with-three-vs/
     52. Siegel, E. (2013). Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (1 ed.), Wiley.
     53. Swift, R. S. (2001). Accelerating Customer Relationships: Using CRM and Relationship Technologies, Prentice Hall Professional.
     54. Temple, K. (2012). “What Happens in an Internet Minute?” Inside Scoop, Mar 13. Retrieved from http://scoop.intel.com/what-happens-in-an-internet-minute/
     55. Ward, J. S., & Barker, A. (2013). “Undefined By Data: A Survey of Big Data Definitions,” arXiv preprint arXiv:1309.5821. Retrieved from http://arxiv.org/pdf/1309.5821.pdf
     56. WHO, Dimes, M. o., PMNCH, & Children, S. t. (2012). “Born Too Soon: The Global Action Report on Preterm Birth,” World Health Organization. Retrieved from http://www.healthynewbornnetwork.org/sites/default/files/resources/BornTooSoon-Report-April2012.pdf
     57. World Economic Forum. (2012). “Big Data, Big Impact: New Possibilities for International Development,” World Economic Forum. Retrieved from http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf
     58. Yin, R. K. (1994). Case study research: Design and methods, Sage publications.
     59. Zikopoulos, P., Parasuraman, K., Deutsch, T., Giles, J., & Corrigan, D. (2013). Harness the Power of Big Data: The IBM Big Data Platform, McGraw Hill Professional.
zh_TW