| dc.contributor.advisor | 詹文男 | zh_TW |
| dc.contributor.advisor | Zhan, Wennan | en_US |
| dc.contributor.author (Authors) | 應淇帆 | zh_TW |
| dc.contributor.author (Authors) | Ying, Ci-Fan (Lisa YIng) | en_US |
| dc.creator (作者) | 應淇帆 | zh_TW |
| dc.creator (作者) | Ying, Ci-Fan (Lisa YIng) | en_US |
| dc.date (日期) | 2018 | en_US |
| dc.date.accessioned | 2-Mar-2018 11:44:04 (UTC+8) | - |
| dc.date.available | 2-Mar-2018 11:44:04 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-Mar-2018 11:44:04 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0104932100 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/116040 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 經營管理碩士學程(EMBA) | zh_TW |
| dc.description (描述) | 104932100 | zh_TW |
| dc.description.abstract (摘要) | 企業在行銷產品時,第一需透過大數據資訊了解消費者,第二更要善 用大數據選擇適合的媒體投放,再從投放效果進一步抓到更精準的消費者 進行再行銷,不斷測試以提升廣告投資效率(ROI)。 大數據的趨勢已經改變全球商業環境,大眾傳播行銷不再有效,企業 行銷已經需要走向個人化行銷,台灣企業更必須有扭轉運作思維、調整行 銷策略。根據本研究顯示,大部份企業已經開始掌握大數據的趨勢,不僅 有一半以上的企業在內部成立大數據相關部門,更自行建立資料庫,甚至 自行成立廣告投放平台,另外也有公司會透過資訊部門與行銷部門成立跨 部門專案或是與外部專業團隊策略聯盟。 但是目前缺乏相關領域人才資料,資訊太多無法整合、資料老舊格式 不一,是目前企業主管普遍認為大數據較難推動的地方。本研究從新的4 P理論,認為大數據可以進行新4P:人(People):精準分析消費者因人而異的狀態,成效(Performance):找到能帶動成效的 行銷方式,步驟(Process)運用數據優先處理危急問題預測,預測(Prediction)精準預測顧客下次回購時間。本研究顯示出 台灣各企業主管針對大數據帶動新4P理論和成效多半表示認同,不過, 企業主管認為要解決缺乏相關領域人才的問題,更需加強資訊整合工作, 並且採用適合的軟體工具。企業主管更需有改變領導風格、且需投入相當成本進行大數據分析的決心。 | zh_TW |
| dc.description.abstract (摘要) | Serving TV media and corporate marketing department nearly one decade, I found that the use of big data would become a significant issue. Firstly, enterprises need to understand consumers through big data, secondly should make good use of big data to select appropriate media, and then from the delivery results to further capture more accurate consumers to improve efficiency. Big data trends have changed the global business environment, mass media marketing is no longer valid, mass marketing has to move toward personalized marketing. Taiwanese companies must reverse thinking in terms of marketing strategies. According to this study, most enterprises have started to grasp the trend of big data. Not only are more than half of them setting up their own big data-related departments internally, but also establishing their own databases or even setting up their own advertising platforms. In addition, some companies set up cross-departmental projects or strategic alliances with external professional teams. However, the current lack of relevant personnel, too much information that cannot be integrated, the old format, it is generally believed that corporate executives more difficult to promote big data. From the new 4P theory, think big data can make a new 4P: People (Precise analysis of the consumer status), Performance (Find ways to promote the effectiveness of marketing), Process (the use of data-priority), and Prediction (Precisely predict the next customer repurchase time). At present, most business executives in Taiwan are agreeing to drive the new 4P theory and effectiveness in response to big data. However, business executives think that it is necessary to solve the problem of lack of qualified personnel in related fields and to strengthen information integration, and use the appropriate software tools. Business executives need to change leadership style, and need to invest considerable cost of big data analysis. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與問題 2 第三節 論文章節 2 第四節 研究流程 3 第二章 文獻探討 4 第一節 大數據之定義與範疇 4 第二節 企業廣告行銷策略的定義與範疇 6 第三節 新4P理論的定義與範疇 10 第四節 企業的大數據策略 15 第三章 研究方法 18 第一節 研究架構 18 第二節 研究流程與操作步驟 20 第四章 分析與討論 23 第一節 研究分析 23 第二節 研究發現 29 第五章 結論與建議 31 第一節 結論 31 第二節 建議 32 第三節 研究限制 32 第四節 未來研究方向 33 參考文獻 34 附錄A GOOGLE問卷內容 36 | zh_TW |
| dc.format.extent | 1426499 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0104932100 | en_US |
| dc.subject (關鍵詞) | 大數據 | zh_TW |
| dc.subject (關鍵詞) | 數位行銷 | zh_TW |
| dc.subject (關鍵詞) | 策略分析 | zh_TW |
| dc.subject (關鍵詞) | Big data | en_US |
| dc.subject (關鍵詞) | Digital marketing | en_US |
| dc.subject (關鍵詞) | Strategic analysis | en_US |
| dc.title (題名) | 大數據時代廣告行銷策略分析 | zh_TW |
| dc.title (題名) | An Exploratory Study on Advertising & Marketing Strategies of Big Data Age | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 一、中文文獻: 參考文獻 1. 陳傑豪(2015)。大數據玩行銷。第一版:台北市。遠見天下。 2. 劉幼琍主編(2016)。大數據與未來傳播。第一版:台北市。五 南書局。 3. 李郁怡(2015)。尋找品牌行銷引爆點,哈佛商業週刊2015 年11月號。 4. 廖晨旭(2014)。「大數據分析時代壽險業之因應對策」。國立政 治大學經營管理碩士學程(EMBA) 。 5. 鄭美華(2017) 。數據分析與個人資料保護之衝突: 從收視 行為調查談起。國立政治大學法律科際整合研究所。 6. 陳怡安(2015)。大數據思維下行銷傳播。台北科技大學經營管 理系碩士班。 7. 李郁怡等(2015),「大數據 再進化」,數位時代,83期20 15年5月號。 8. 劉文良(2017)。顧客關係管理:新時代的決勝關鍵。第一版: 台北。碁峰 。 9. 江裕真譯(2014)。大數據@工作力:如何運用巨量資料,打造 個人與企業競爭優勢。臺北:天下文化。 10. 社群X電商-賣什麼都狂銷(2016)。臺北:今周刊。 二、英文文獻 1. Mcquail, d., (1992). Media performance: mass communication and the public interest. 2. Mayer-schönberger, v. & cukier, k., (2013). big data. 3. Surdak, c., (2014). Data crush: how the information tidal wave is driving new business opportunities. 4. Burke, Mary A., and Ali Ozdagli.(2013).“Household inflation expectations and consumer spending: evidence from panel data.” No.13-25. Working Papers, Federal Reserve Bank of Boston. 5. Choi, Hyunyoung, and Hal Varian. (2012). “Predicting the present with google trends.” Economic Record, 88(s1):2-9. 6. Davenport, Thomas H., and Gilbert J. Probst. (2002). Knowledge management casebook: Siemens best practices. John Wiley & Sons,Inc. 7. Da, Zhi, Joseph Engelberg, and Pengjie GAO. (2011) “In search ofattention.” The Journal of Finance 66.5: 1461-1499. 8. DePaolo, Concetta A., and Kelly Wilkinson. (2014). “Recurrent online quizzes: Ubiquitous tools for promoting student presence, participation and performance.” Interdisciplinary Journal of E-Learning and Learning Objects, 10:75-91. 9. Hamid, Alain, and Moritz Heiden (2015). “Forecasting Volatility with Empirical Similarity and Google Trends.” Journal of Economic Behavior & Organization, No. 117: 62–81. | zh_TW |
