行動裝置、網路傳輸速度及社群應用程式的快速崛起，且尤其2020年起新冠病毒肆虐，人們被限制活動範圍後線上購物頻率上升，進而帶動各式商品廣告行銷的數位化。不同的數位廣告平台可以觸及的顧客群及人數不同，所得到的廣告效益也會有所差異。透過文獻探討及商業背景資料的蒐集整理可以發現以廣告行銷的觀點來看，企業及廣告主所追求的就是曝光次數及將數位廣告的瀏覽人次轉換為商品或服務訂單的數量。本研究將數位廣告預算分配問題轉換為背包問題，再利用最短路徑求解。
本研究參考東森數位行銷、創億學堂等電商平台所提供的網路公開資料來進行數據範圍的假設，並利用測試數據，分析在不同預算條件的情況下對數位廣告的曝光次數及轉換率的影響。進行數位廣告預算分配的模擬實驗後，得出並非一昧提高投入資金可獲得的廣告效益會最大，相反地，在低預算的數據組平均點擊次數或平均轉換次數的表現反而更好。以曝光次數較多的數據在預算為80,000到90,000元時可獲得較好結果；以轉換率較高的數據資料更可以明顯觀察出，預算為50,000元時獲得結果最好，且因企業規模大小、品牌知名度高低等不同的廣告主所追求的廣告效益也並不相同。期望本研究結果可以提供企業或商家廣告投放時初步的決策依據，或是對於過去廣告投放結果進行優化。

The development of mobile devices, network transmission speeds, social media applications and in particular the epidemic of COVID-19 in 2020, make the frequency of online shopping has increased after people are restricted to activities. And then, it drives the digitalization of advertisements for all kinds of products. Different digital advertising platforms can reach different target audience and the benefit they get as well. Through literature review and background knowledge collection we can know, as the companies and advertisers, what they want is the exposures and the conversion rate. This research converts the digital advertising budget allocation problem into a knapsack problem, and then uses the shortest path to solve it.
This study refers to the online public information provided by the e-commerce platform to make assumptions about the data range. Using the simulation data which generated by random numbers to analyze the impact on the number of exposures and conversion rates of digital advertisements under different budget conditions. After conducting a simulation experiment, it is concluded that the advertising benefits will not be the largest one by keep increasing the investment funds. On the contrary, the performance of average clicks or average conversions in a low-budget data set is better. The data with more impression get better result from 80000 to 90000 dollars; the data with more conversion get the best result at 50000 dollars. And because of the size of the company or the level of brand awareness, the consequence of advertising benefits they pursued are also different. It is hoped that this research can provide a preliminary decision-making basis for companies or businesses when they are advertising, or optimize their past advertising results.

第一章 緒論 1
1-1 研究背景 1
1-2 研究動機 1
1-3 研究目的 2
1-4 研究架構 2
第二章 文獻回顧 4
2-1 廣告行銷 4
2-2 關鍵字廣告 8
2-2-1 關鍵字廣告投放流程 9
2-2-2 關鍵字廣告投放流程圖 11
2-3 即時競價系統 11
2-4 廣告效益之衡量指標 12
2-5 最短路徑問題 16
2-6 標籤設置法與標籤校正法 16
2-7 背包問題 17
2-8 網絡圖 19
第三章 研究方法 24
3-1 數位廣告預算分配問題敘述 24
3-2 數位廣告預算分配路徑示意圖 25
3-3 Dequeue Implementation 27
第四章 實證分析 29
4-1 數位廣告投放預算分配模式建構 29
4-2 模擬環境設置 30
4-3 模擬數位廣告預算分配之求解 30
第五章 結論與建議 40
5-1 結論 40
5-2 建議 41
參考文獻 42
參考書籍 42
期刊文獻 42
中文文獻 43
英文文獻 44
網路資料 46

參考書籍
1. Ravindra K. Ahuja, Thomas L. Magnanti, James B. Orlin. (1993). Network Flows - Theory, Algorithms, and Applications. New Jersey: Prentice Hall
2. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. (1990). Introduction to Algorithms. United States: MIT Press
3. Stefan Edelkamp Stefan Schrodl. (2011). Heuristic Search. United States: Morgan Kaufmann

期刊文獻
1. Dijkstra, E. W. (1959). A Note on Two Problems in Connexion with Graphs. Numer. Math., P.269–271.
2. Evans, D. S. (2009). The online advertising industry: Economics, evolution, and privacy. Journal of Economic Perspectives, 23(3), P.37–60.
3. 林隆儀、林岳民 (2005). 廣告比較策略與廣告訴求方式對購買意願的影響－產品涉入之干擾效果. 企業管理學報, 65期(2005/06/01), P1–29.
4. 江義平、江岱衛 (2010). 關鍵字廣告點擊行為探究. 電子商務研究, P.407–432.
5. 蔡政宏、莊翊健 (2011). 以層級分析法分析影響廣告效益的重要因素. 全球商業經營管理學報, P.67–77.

中文參考文獻
1. 許順富 (2000). 網路廣告特性類型與廣告效果之探討. 國立台灣大學商學研究所碩士論文.
2. 趙玉琪 (2006). 廣告之中央路徑與周邊路徑決策對消費者品牌態度之影響. 逢甲大學國際貿易學系碩士班碩士論文.
3. 楊瑜君 (2006). 幽默廣告類型、消費者生活型態對廣告效果之影響. 東吳大學企業管理學系碩士論文.
4. 黃偉龍 (2007). 一個傳送大量多媒體電子廣告資料的方法 --- 使用多重路徑及多種子的傳送機制. 國立交通大學資訊學院資訊學程碩士班.
5. 黎育熙 (2010). 網路資訊搜尋之信任度對消費意願的影響 - 以民宿為例. 臺北科技大學管理學院工業工程與管理EMBA專班學位論文, 1~72.
6. 林宏恩、黃乙白 (2010). 新型2D-3D 整合式數位廣告看板應用設計. 國立交通大學平面顯示技術碩士學位學程.
7. 郭家宇 (2010). 探討線上廣告的位置與相對性對品牌回憶與辨識的影響. 國立中正大學企業管理學系碩士班, 1~41.
8. 陳思含、蔡孟龍、林芷涵、陳佳伶 (2016). 社群軟體行銷廣告效益之研究. 元培醫事科技大學, 1~47.
9. 韓忠憲 (2019). 即時競價廣告對企業廣告效益的影響 - 以飲料產業為例. 80. 國立臺灣科技大學管理學院MBA碩士學位論文
10. 洪柏青 (2020). 數位廣告平台比較分析與策略運用-以Facebook和Google Ads為例. 國立政治大學商學院經營管理碩士學程文科資創組碩士論文.
11. 陳璐 (2020). 大資料應用對廣告行銷業的影響研究. 國立台灣大學管理學院復旦EMBA境外專班碩士論文.

英文參考文獻
1. Asdemir, K., Kumar, N., & Jacob, V. S. (2012). Pricing models for online advertising: CPM vs. CPC. Information Systems Research, 23(3 PART 1), 804–822. https://doi.org/10.1287/isre.1110.0391
2. Athey, S., Calvano, E., & Gans, J. (2013). The Impact of The Internet on Advertising Markets for News Media.
3. Bekoglu, F. B. (2016). Strategic Approach in Social Media Marketing and a Study on Successful Facebook Cases. 12(7), 261–274. https://doi.org/10.19044/esj.2016.v12n7p261
4. Captivo, M. E., Clímaco, J., Figueira, J., Martins, E., & Santos, J. L. (2003). Solving bicriteria 0-1 knapsack problems using a labeling algorithm. Computers and Operations Research, 30(12), 1865–1886. https://doi.org/10.1016/S0305-0548(02)00112-0
5. Goldsmith, R. E., & Lafferty, B. A. (2002). Consumer response to Web sites and their influence on advertising effectiveness. Internet Research, 12(4), 318–328. https://doi.org/10.1108/10662240210438407
6. Gallo, G., and S. P. (1986). Shortest path methods: a unifying approach. Mathematical Programming Study, 26, 38–64.
7. Goos, P., Dens, N., Pelsmacker, P. De, & Aleksandrovs, L. (2019). Using mixture-amount modeling to optimize the advertising media mix and quantify cross-media synergy for specific target groups. May, 1–25. https://doi.org/10.1002/asmb.2470
8. June, M., & Mehta, A. (2000). Advertising Attitudes and Advertising Effectiveness. 40(3).
9. Kao, G. (2013). Protal Advertisement Scheduling with Distinct Banners.
10. Kumar, V., & Gupta, S. (2016). Conceptualizing the Evolution and Future of Advertising Conceptualizing the Evolution and Future of Advertising. 3367(July). https://doi.org/10.1080/00913367.2016.1199335
11. Mero, J., Karjaluoto, H., & Jayawardhena, C. (2012). Digital and Social Media Marketing Usage in B2B Industrial Section. May 2016.
12. Paixão, J. M., & Santos, J. L. (2013). Labeling methods for the general case of the multi-objective shortest path problem – A computational study. Intelligent Systems, Control and Automation: Science and Engineering, 61(2), 489–502. https://doi.org/10.1007/978-94-007-4722-7_46
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14. Wang, Y.-M. (2013). Converting Web Traffic to Advertising Revenue: A Two-Sided Network Effect Perspective. 152.
15. Wu, B. Y., & Chao, K.-M. (2013). Shortest-Paths Trees. Spanning Trees and Optimization Problems, 36–53. https://doi.org/10.1201/9780203497289-9

網路資料
1. Google Ads 說明 (2021/03)
https://support.google.com/google-ads/answer/2615875?hl=zh-Hant
2. Ad Balance (2021/04)
https://adbalance.wpcdn-a.com
3. Ad Network vs. Ad Exchange: What’s the Difference? (2021/04)
https://www.inmobi.com/blog/2020/08/20/ad-network-vs-ad-exchange-whats-the-difference-between-the-two
4. 印度經濟日報 (2021/05)
https://economictimes.indiatimes.com/definition/advertising
5. DMA台灣數位媒體應用暨行銷協會 (2021/05)
https://www.dma.org.tw
6. 為何影音廣告的焦點從In-Stream, 轉向Out-Stream ? (2021/05)
https://www.onead.com.tw/news/from-in-stream-out/
7. KOL行銷 (2021/05)
https://digitalpr.tw/kol行銷/
8. 中華經濟研究院 (2021/05)
https://www.cier.edu.tw/news/detail/70129
9. 台灣2020年GDP成長率 (2021/05)
https://www.thenewslens.com/article/146741
10. 行政院主計總處 (2021/05)
https://www.dgbas.gov.tw/mp.asp?mp=1
11. 行政院重要政策：台灣5G行動計畫 (2021/05)
https://www.ey.gov.tw/Page/5A8A0CB5B41DA11E/087b4ed8-8c79-49f2-90c3-6fb22d740488
12. 創億學堂：網路廣告預算 (2021/06)
https://imjaylin.com/ad-budget/
13. 數位廣告起源 (2021/06)
https://www.bnext.com.tw/article/24715/BN-ARTICLE-24715
14. Queue 實作 (2021/06)
https://alrightchiu.github.io/SecondRound/queue-introjian-jie-bing-yi-linked-listshi-zuo.html