摘要(英) |
Amongst the various different types of business industries, building a successful private brand is a goal for many enterprises. On the Internet, any form of behavior will produce a data trajectory. Hidden within the fragmented scattered data is the actual requirements of consumers at the other end of the network. How to provide insights into the meaning of these data, and using this information to set customized marketing strategy, should be one of the key requirements of successful brand management today.
The emergence of Apache Hadoop, used for various big data analysis, provides a simple, low cost solution. By using Apache Hadoop technology, brand operators can setup a Data Management Platform (DMP) that allows management and marketing staff to have sufficient information for effective decision making from the perspective of business strategy, reflecting on how to view from a consumer’s perspective, conveying the brand’s core idea and consumer value, and providing insight into the consumer’s profile. This in turn unleashes the true business value of the data.
This study uses a Taiwanese automobile brand as the case for investigation. The study investigates the process of DMP implementation, how the data of individuals can be combined from different aspects, how multi-community trajectory provides analysis for the characteristics of customer behavior, profiles of users, and the benefits of marketing channel. All these can serve as a basis for delivering an efficient marketing strategy in the future.
Through the introduction of a data management platform, integrating online and offline information, the firm can carry out quantitative analysis on marketing effectiveness with appropriate data analytics. The analysis results of big data can ensure whether brands and products can really meet consumer needs, and drive marketing strategies with data, thereby enhancing brand value. |
參考文獻 |
【中文文獻】
1. 中華郵政官網 (2019),107年郵政統計要覽一、概況,https://www.post.gov.tw/post/internet/Message/index.jsp?ID=1461828033477,存取時間:2020/4/3。
2. 石路遙,(2015),廣告投放數據管理平台的設計與實現,北京交通大學軟體工程系碩士論文。
3. 台灣網路資訊中心 (2019),2019台灣網路報告,https://report.twnic.tw/2019/,存取時間:2020/2/21。
4. 交通部公路總局 (2019),新車領牌數,https://stat.thb.gov.tw/hb01/webMain.aspx?sys=100&funid=11200,存取時間:2020/5/1。
5. 自由時報 (2016),特斯拉Model 3 訂單飆25萬輛,https://ec.ltn.com.tw/article/paper/975453,存取時間:2020/4/23。
6. 牧田幸裕、柯依芸譯,(2018),數位行銷教科書—大數據的獲利管理學、虛實全通路導入,幸福文化出版公司,新北市。
7. 納智捷 (2020),台灣顧客滿意度調查第一名,https://www.luxgen-motor.com.tw/luxgen_protection/Index,存取時間:2020/5/13。
8. 黃裕文、黃光璿,(2015),基於 MapReduce 之架構解單一來源最短路徑問題 ,國立暨南國際大學資訊工程系碩士論文。
9. 鈕文英、吳裕益,(2015),單一個案研究法:研究設計與後設分析,心理出版社,新北市。
10. 意藍科技官網 (2019),什麼是 PeopleView 人群資料庫?,https://www.eland.com.tw/peopleview_1.html,存取時間:2020/4/23。
11. 駱德廉,(2017),巨量資料分析與智能應用,深石數位科技,台北市。
【英文文獻】
1. Apache (2020), “Apache Hadoop” (Accessed 2020/5/1, available at https://hadoop.apache.org).
2. Chang, F., Dean, J., Ghemawat, S., Hsieh, W., Wallach, D., Burrows, M., Chandra, T., Fikes, A., & Gruber, R. (2006), “Bigtable: A Distributed Storage System for Structured Data,” in Proc. 7th USENIX Symp. Operating Systems Design and Implementation (OSDI’06), Seattle,WA.
3. Dean, J., & Ghemawat, S. (2004), “MapReduce: Simplified Data Processing on Large Clusters,” in Proc. 6th Conference on Symposium on Opearting Systems Design & Implementation, San Francisco, CA.
4. Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1992), “Knowledge Discovery in Databases: An Overview,” AI Magazine, Vol. 13, No. 3. , pp. 57-70.
5. Ghemawat, S., Gobioff, H., & Leung, S. T. (2003), “The Google File System,” in Proc. Nineteenth ACM Symposium on Operating Systems Principles, New York, NY.
6. Han, J., Kamber, M., & Jian, P. (2012), Data Mining: Concepts and Techniques, Morgan Kaufmann, San Franscisco, CA.
7. LOTAME (2019), “1st Party Data, 2nd Party Data, 3rd Party Data: What Does It All Mean?” (Accessed 2020/5/8, available at https://www.lotame.com/1st-party-2nd-party-3rd-party-data-what-does-it-all-mean/ ).
8. Microsoft Azure (2020), “What is NoSQL?” (Accessed 2020/4/4, available at https://azure.microsoft.com/zh-tw/overview/nosql-database).
9. White, T. (2012), Hadoop: The Definitive Guide, O′Reilly Media, Sebastopol, CA.
10. Wikipedia (2020), “Kafka,” (Accessed 2020/3/20, available at https://zh.wikipedia.org/wiki/Kafka). |