大數據行銷研究 Big Data Marketing Research

Presentation on theme: "大數據行銷研究 Big Data Marketing Research"— Presentation transcript:

1 大數據行銷研究 Big Data Marketing Research
Tamkang University 大數據行銷研究課程介紹 (Course Orientation for Big Data Marketing Research) 1051BDMR01 MIS EMBA (M2262) (8638) Thu, 12,13,14 (19:20-22:10) (D409) Min-Yuh Day 戴敏育 Assistant Professor 專任助理教授 Dept. of Information Management, Tamkang University 淡江大學 資訊管理學系 tku.edu.tw/myday/

2 淡江大學105學年度第1學期 課程教學計畫表 Fall 2016 (2016.09 - 2017.01)
課程名稱：大數據行銷研究 (Big Data Marketing Research) 授課教師：戴敏育 (Min-Yuh Day) 開課系級：資管所碩專班 (TLMXJ1A) 開課資料：選修 單學期 3 學分 (3 Credits, Elective) 上課時間：週五 12,13,14 (Fri 19:20-22:10) 上課教室： D409 (淡江大學台北校園)

3 課程簡介 本課程介紹大數據行銷研究的 基本概念及研究議題。 課程內容包括 資料科學與大數據行銷 大數據行銷分析與研究 測量構念測量與量表
探索性因素分析、確認性因素分析 社群運算與大數據分析 社會網路分析、量測與實務 大數據情感分析 金融科技行銷研究 大數據行銷個案分析

4 Course Introduction This course introduces the fundamental concepts and research issues of big data marketing research. Topics include Data Science and Big Data Marketing Big Data Marketing Analytics and Research Measuring the Construct Measurement and Scaling Exploratory Factor Analysis Confirmatory Factor Analysis Social Computing and Big Data Analytics Measurements and Practices of Social Network Analysis Big Data Sentiment Analysis FinTech Marketing Research Case Study on Big Data Marketing

5 課程目標 (Objective) 瞭解及應用大數據行銷研究 基本概念與研究議題。 (Understand and apply the fundamental concepts and research issues of big data marketing research.) 進行大數據行銷研究相關之資訊管理研究。 (Conduct information systems research in the context of big data marketing research.)

6 課程大綱 (Syllabus) 週次 (Week) 日期 (Date) 內容 (Subject/Topics) /09/16 中秋節 (調整放假一天) (Mid-Autumn Festival Holiday)(Day off) /09/23 大數據行銷研究課程介紹 (Course Orientation for Big Data Marketing Research) /09/30 資料科學與大數據行銷 (Data Science and Big Data Marketing) /10/07 大數據行銷分析與研究 (Big Data Marketing Analytics and Research) /10/14 測量構念 (Measuring the Construct) /10/21 測量與量表 (Measurement and Scaling)

7 課程大綱 (Syllabus) 週次 (Week) 日期 (Date) 內容 (Subject/Topics) /10/28 大數據行銷個案分析 I (Case Study on Big Data Marketing I) /11/04 探索性因素分析 (Exploratory Factor Analysis) /11/11 確認性因素分析 (Confirmatory Factor Analysis) /11/18 期中報告 (Midterm Presentation) /11/25 社群運算與大數據分析 (Social Computing and Big Data Analytics) /12/02 社會網路分析 (Social Network Analysis)

8 課程大綱 (Syllabus) 週次 (Week) 日期 (Date) 內容 (Subject/Topics) /12/09 大數據行銷個案分析 II (Case Study on Big Data Marketing II) /12/16 社會網絡分析量測與實務 (Measurements and Practices of Social Network Analysis) /12/23 大數據情感分析 (Big Data Sentiment Analysis) /12/30 金融科技行銷研究 (FinTech Marketing Research) /01/06 期末報告 I (Term Project Presentation I) /01/13 期末報告 II (Term Project Presentation II)

9 教學方法與評量方法 教學方法 講述、討論、賞析、模擬、問題解決 評量方法 實作、報告、上課表現

10 教材課本 教材課本 講義 (Slides) 大數據行銷研究相關個案與論文 (Cases and Papers related to Big Data Marketing Research)

11 參考書籍 Big Data Marketing: Engage Your Customers More Effectively and Drive Value, Lisa Arthur, Wiley, 2013. Marketing Research, Carl McDaniel Jr. and Roger Gates, Wiley, 2011. Data Science for Business: What you need to know about data mining and data-analytic thinking, Foster Provost and Tom Fawcett, O'Reilly, 2013 Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python, Thomas W. Miller, Pearson FT Press, 2015 Creating Value with Big Data Analytics: Making Smarter Marketing Decisions, Peter C. Verhoef and Edwin Kooge, Routledge, 2016 Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data, Omer Artun and Dominique Levin, Wiley, 2015 Digital Marketing Analytics: Making Sense of Consumer Data in a Digital World, Chuck Hemann and Ken Burbary, Que

12 作業與學期成績計算方式 作業篇數 學期成績計算方式 3篇 期中評量：30 ％ 期末評量：30 ％
期末評量：30 ％ 其他（課堂參與及報告討論表現）： 40 ％

13 Big Data Marketing Research

14 Big Data Marketing Source:

15 Big Data Marketing: Engage Your Customers More Effectively and Drive Value, Lisa Arthur, Wiley, 2013. Source:

16 Creating Value with Big Data Analytics: Making Smarter Marketing Decisions, Peter C. Verhoef and Edwin Kooge, Routledge, 2016 Source:

17 Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data, Omer Artun and Dominique Levin, Wiley, 2015 Source:

18 Data Science for Business: What you need to know about data mining and data-analytic thinking, Foster Provost and Tom Fawcett, O'Reilly, 2013 Source:

19 Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python, Thomas W. Miller, Pearson FT Press, 2015 Source:

20 Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data, Omer Artun and Dominique Levin, Wiley, 2015 Source:

21 The FINTECH Book: The Financial Technology Handbook for Investors, Entrepreneurs and Visionaries, Susanne Chishti and Janos Barberis, Wiley, 2016. Source:

22 Marketing

23 “Meeting needs profitably”
Marketing “Meeting needs profitably” Source: Philip Kotler & Kevin Lane Keller, Marketing Management, 14th ed., Pearson, 2012

24 Source: Kotler and Keller (2008)
Marketing “Marketing is an organizational function and a set of processes for creating, communicating, and delivering value to customers and for managing customer relationships in ways that benefit the organization and its stakeholders.” (Kotler & Keller, 2008) Source: Kotler and Keller (2008)

25 Source: Kotler and Keller (2008)
Marketing Management “Marketing management is the art and science of choosing target markets and getting, keeping, and growing customers through creating, delivering, and communicating superior customer value.” (Kotler & Keller, 2008) Source: Kotler and Keller (2008)

26 Source: McDaniel Jr. and Gates (2009)
Marketing Research “Marketing Research is the planning, collection, and analysis of data relevant to marketing decision making and the communication of the results of this analysis to management.” Source: McDaniel Jr. and Gates (2009)

27 The Nature of Marketing Research
Goals Customer Marketing Environment Opportunistic Nature Marketing Concept Marketing Mix Systems Source: McDaniel Jr. and Gates (2009)

28 Marketing Research systematic design, collection, analysis, and reporting of data and findings relevant to a specific marketing situation facing the company. Source: Philip Kotler and Kevin Keller, Marketing Management, 14th Edition, 2011, Prentice Hall

29 Architecture of Big Data Analytics
Big Data Analytics Applications Big Data Sources Big Data Transformation Big Data Platforms & Tools * Internal * External * Multiple formats * Multiple locations * Multiple applications Middleware Hadoop MapReduce Pig Hive Jaql Zookeeper Hbase Cassandra Oozie Avro Mahout Others Queries Transformed Data Big Data Analytics Raw Data Extract Transform Load Reports Data Warehouse OLAP Traditional Format CSV, Tables Data Mining Source: Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications

30 Social Big Data Mining (Hiroshi Ishikawa, 2015)
Source:

31 Source: http://mattturck

32 Summary This course introduces the fundamental concepts and research issues of big data marketing research. Topics include Data Science and Big Data Marketing Big Data Marketing Analytics and Research Measuring the Construct Measurement and Scaling Exploratory Factor Analysis Confirmatory Factor Analysis Social Computing and Big Data Analytics Measurements and Practices of Social Network Analysis Big Data Sentiment Analysis FinTech Marketing Research Case Study on Big Data Marketing

33 Contact Information 戴敏育 博士 (Min-Yuh Day, Ph.D.) 專任助理教授 淡江大學 資訊管理學系 電話： #2846 傳真： 研究室：B929 地址： 新北市淡水區英專路151號 ： 網址：