資料科學家曾被《哈佛商業評論》評為「21世紀最誘人的新興職位」。行銷學界也因為資料科學的導入，對行銷研究產生了重大的影響，許多國際期刊也開始要求研究者使用資料科學式的行銷研究方法。本課程希望介紹人工智慧與大數據分析如何實際應用在行銷資料分析的具體方法，並涵蓋以下基本範疇: 一、介紹何謂「行銷資料科學」以及行銷資料的類型、來源與管理手法。二、資料科學的基礎知識與商業應用範疇。三、如何整合行銷資料科學與行銷研究，為企業創造商業價值。四、如何應用大數據行銷分析工具，諸如：A/B 測試、再行銷、行為側寫、推薦系統、全球定位系統與行動分析、競爭智慧、情感分析、營收狀態相關數據的彙總與分析、使用者行為相關數據的彙總與分析、網站指標相關數據的彙總與分析...等。其中部份的行銷大數據分析案例，我們將配合實際的程式進行解釋，以便學生了解及實際應用。最後，本課程將探討以下議題: 一、行銷資料科學如何協助企業進行價值創新與價值溝通。二、如何透過行銷資料科學發展以分析為基礎的策略 (analysis-based strategy)，並逐步建構企業的競爭優勢。
Data scientists have been rated as "the most attractive emerging position in the 21st century" by Harvard Business Review. The introduction of data science in the marketing academic community has also had a significant impact on marketing research. Many international journals have also begun to require researchers to use data science-based marketing research methods. This course hopes to introduce specific methods of how artificial intelligence and big data analysis are actually applied in marketing data analysis, and covers the following basic areas: 1. Introduction to what "marketing data science" is and the types, sources and management techniques of marketing data. 2. Basic knowledge and business application areas of data science. 3. How to integrate marketing data science and marketing research to create business value for enterprises. 4. How to apply big data marketing analysis tools, such as: A/B testing, remarketing, behavioral profiling, recommendation systems, global positioning systems and action analysis, competitive intelligence, sentiment analysis, summary and analysis of data related to revenue status, Summary and analysis of data related to user behavior, summary and analysis of data related to website indicators...etc. We will explain some of the marketing big data analysis cases with actual programs to facilitate students' understanding and practical application. Finally, this course will explore the following topics: 1. How marketing data science can assist companies in value innovation and value communication. 2. How to scientifically develop an analysis-based strategy through marketing data and gradually build the company's competitive advantage.
1. 羅凱揚、蘇宇暉、鍾皓軒，行銷資料科學｜大數據 x 市場分析 x 人工智慧，碁峰出版社，2019 年 7 月。
2. 加嵜長門、田宮直人，大數據時代一定要會的 SQL 商業資料分析術，旗標出版社，2018 年 5 月。
1. Luo Kaiyang, Su Yuhui, and Zhong Haoxuan, Marketing Data Science | Big Data x Market Analysis x Artificial Intelligence, Qifeng Publishing House, July 2019.
2. Nagato Kasaki and Naoto Tamiya, SQL business data analysis techniques that must be mastered in the era of big data, Banner Publishing House, May 2018.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 課堂參與課堂參與 class participation |
10 | |
| 作業作業 Homework |
50 | |
| 期中報告期中報告 interim report |
20 | |
| 期末專題期末專題 Final topic |
20 |
