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題名 基於顧客體驗旅程的搜尋廣告生成
Advertisement generation based on customer experience journey for search advertising作者 張硯筑
Chang, Yen-Chu貢獻者 黃瀚萱<br>陳宜秀
張硯筑
Chang, Yen-Chu關鍵詞 人工智能
自然語言處理
行銷漏斗
搜尋廣告
再行銷
AI
NLP
Marketing Funnel
Search Advertising
Remarketing日期 2020 上傳時間 2-Sep-2020 13:08:33 (UTC+8) 摘要 在顧客自主意識抬頭及注意力稀缺的挑戰之下，該如何成功吸引顧客快速找到其所需要的資訊，將是品牌商所面臨的嚴峻考驗。本研究為行銷自動化提供了一套策略性精準行銷解決方案的演示，將行銷理論框架與廣告操作實務接軌，運用自然語言處理技術自動生成搜尋廣告文案，並以自動化流程篩選出最適合的關鍵字。透過本系統能夠大幅降低製作搜尋廣告的時間成本，在短時間內便能快速生成大量搜尋廣告，避免錯失顧客的最佳購買時機，並突破搜尋廣告不易進行再行銷的困境。本系統以行銷漏斗框架結合顧客旅程，進而優化各階段的關鍵接觸點體驗，為行銷人員在快速變動的廣告市場中，提供最佳化整合行銷綜效的解法，同時創造人工智能技術於搜尋廣告應用的新價值。本研究從發掘關鍵字展開，接著以NER分辨不同類型的關鍵字，再以價值光譜模型與TF-IDF演算法去蕪存菁篩選出含金量高的關鍵字，並以TensorFlow2.0框架構建LSTM生成模型，以便自動生成搜尋廣告文案。本研究採用問卷調查法、深度訪談法及A/B 測試探討如何自動生成匹配顧客意圖的動態搜尋廣告，並深入剖析顧客在不同購買階段的搜尋行為。研究結果發現採用自動生成的搜尋廣告能使轉換率提高83％，同時平均每次轉換費用降低54％，成功讓顧客將消費意圖轉化為具體行動，有助於降低行銷成本並提升生產效能，顛覆既有思維以重塑顧客旅程，進而為顧客打造更多個人化體驗和價值。
Search advertising is a huge online market in which many types of products are recommended and tens of billions of transactions are conducted each day. It has been proven to be a successful business method of online marketing and, consequently, attracting high attention from academics and practitioners. However, in recent years, due to heightened levels of self-awareness and shortened attention span of customers, manually tailoring these advertisements has become a bottleneck in lieu of rapid growth and demand of efficiency.We present a novel approach to automatically generate search advertising copies （texts） that relies on Natural Language Processing （NLP） technology. Unlike most of the previous works that focused on the pricing model, this approach aims to improve the performance of search-based advertising based on the consumer behavioral stages in the marketing funnel model. This work introduced an individual recommender system based on the LSTM auto-encoder model, and implemented it in an A/B testing experiment designed to follow the automated re-marketed strategy, replacing the manual parameters-setting tasks with multiple automated tasks and making search advertising more effective for brand-seeking to user behaviors. To support the experiment, we also conducted a survey and in-depth interviews to discover insight into consumer’s clicking and keyword searching behaviors. Data analyses revealed that automated search advertising improved the conversion rate by 83% and decreased the average cost per conversion by 54%, indicating the promising application of this novel approach to adopt artificial intelligence （AI） in the future of search advertising.參考文獻 一、英文文獻：Abrams Z., Schwarz M.（2007）. Ad Auction Design and User Experience. In: Deng X., Graham F.C.（Eds）, Internet and Network Economics. WINE 2007. Lecture Notes in Computer Science, Vol 4858. Springer, Berlin, Heidelberg.Animesh, Siva Viswanathan, and Ritu Agarwal.（2011）. Competing creatively in sponsored search markets: The effect of rank, differentiation strategy, and competition on performance. Information Systems Research, 22.1, pp.153–169.Agarwal, Ashish and Tridas Mukhopadhyay.（2016）. The Impact of Competing ads on Click Performance in Sponsored Search. Information Systems Research, 27：3, pp.538–557.Alex Jiyoung Kim, Sungha Jang, Hyun S. Shin.（2019）. How should retail advertisers manage multiple keywords in paid search advertising? Journal of Business Research, ISSN 0148–2963, https://doi.org/10.1016/j.jbusres.2019.09.049.Brown, T.（2009）. Change by design: How design thinking transforms organizations and inspires innovation. New York: Harper Collins.Batra, R. and Keller, K.L.（2016）. Integrating marketing communications：new findings, new lessons, and new ideas. Journal of Marketing, Vol. 80, No. 6, pp. 122–145.B. Chen, T. Liu and Y. Liu. （2019）. Stereo Source Separation in the Frequency Domain: Solving the Permutation Problem by a Sliding K–means Method. ICASSP 2019 – 2019 IEEE International Conference on Acoustics, Speech and Signal Processing（ICASSP）, Brighton, United Kingdom, pp.4250–4254.Court, D., Elzinga, D., Mulder, S., & Vetvik, O. J.（2009）. The consumer decision journey. McKinsey Quarterly, June.Chan, T.Y., Wu, C. and Xie, Y.（2011）. Measuring the lifetime value of customers acquired from Google search advertising. Marketing Science, Vol. 30 No. 5, pp.835–850.Diana I. Cordova and Mark R. Lepper.（1996）. Intrinsic Motivation and the Process of Learning：Beneficial Effects of Contextualization, Personalization, and Choice. Journal of Educational Psychology, 88.4, pp.715.Desai, P., Shin, W., & Staelin, R.（2014）. The company that you keep：When to buy a competitor’s keyword. Marketing Science, 33（4）, pp.485–508.David C. Edelman and Marc Singer.（2015）. Competing on Customer Journeys. Harvard Business Review, November, pp.88–100.Daly, Angela and Scardamaglia, Amanda.（2017）. Profiling the Australian Google Consumer：Implications of Search Engine Practices for Consumer Law and Policy. Journal of Consumer Policy. Available at SSRN： https://ssrn.com/abstract=2958653.Evans, David S.（2008）. The Economics of the Online Advertising Industry. Review of Network Economics, Vol. 7, No. 3. Available at SSRN: https://ssrn.com/abstract=1086473.Ertemel, Adnan Veysel and Peyk, Peyvent.（2018）. The Impact of Zero Moment of Truth on Consumer Buying Decision. An Exploratory Research in Turkey 5th International Conference on Social Sciences and Education Research. Available at SSRN：https://ssrn.com/abstract=3350368.Howard, J. A., & Sheth, J. N.（1969）. The theory of buyer behavior, 14. New York: Wiley.Jansen, B.J., Flaherty, T.B., Baeza–Yates, R., Hunter, L., Kitts, B. and Murphy, J. （2009）. The components and impact of sponsored search. Computer, Vol. 42 No. 5, pp.98–101.Jansen, B. J., & Schuster, S.（2011）. Bidding on the buying funnel for sponsored search and keyword advertising. Journal of Electronic Commerce Research, 12（1）, pp.1–18.Jerath, K., Ma, L., & Park, Y.（2014）. Consumer click behavior at a search engine：The role of keyword popularity. Journal of Marketing Research, 51（4）, pp.480–486.Jafarzadeh, H., Aurum, A., D`Ambra, J. and Ghapanchi, A.（2015）. A systematic review on search engine advertising. Pacific Asia Journal of the Association for Information Systems, Vol. 7, No. 3.Kevin Gallagher.（2018）. AI in Marketing. Washington DC：Business Insider.Laffey, D.（2007）. Paid search: The innovation that changed the Web. Business Horizons, Vol 50, No. 3, pp.211–218.Lemon, K.N. and Verhoef, P.C.（2016）. Understanding customer experience throughout the customer journey. Journal of Marketing, Vol. 80, No. 6, pp.69–96.Lemon, Katherine N. & Peter C. Verhoef.（2016）. Understanding Customer Experience Throughout the Customer Journey. Journal of Marketing: AMA/MSI Special Issue, Vol. 80, pp.69–96, DOI: 10.1509/jm.15.0420.Michael Luca.（2015）. User–Generated Content and Social Media. the Handbook of Media Economics. Available at SSRN: https://ssrn.com/abstract=2549198 or http://dx.doi.org/10.2139/ssrn.2549198.Michelle Cheng.（2019）. JPMorgan Chase has an AI copywriter that writes better ads than humans can. Retrieved from https://qz.com/work/1682579/jpmorgan–chase–chooses–ai–copywriter–persado–to–write–ads/.（Feb 22, 2020）Philip Kotler, Hermawan Kartajaya & Iwan Setiawan.（2016）. Marketing 4.0: Moving from Traditional to Digital. America：John Wiley & Sons Inc.Rutz, Oliver J. and Bucklin, Randolph E.（2007）. A Model of Individual Keyword Performance in Paid Search Advertising. Available at SSRN: https://ssrn.com/abstract=1024765.Rutz, O.J. and Bucklin, R.E.（2011）. From generic to branded：a model of spillover in paid search advertising. Journal of Marketing Research, Vol. 48, No. 1, pp.87–102.Rekettye, Jr. and Gábor.（2019）. The Effects of Digitalization on Customer Experience 2019 ENTRENOVA Conference Proceedings. Available at SSRN： https://ssrn.com/abstract=3491767.Sharp, B.（2010）. How Brand Grow what marketers don’t know. Australia & New Zealand：Oxford University Press. pp. vii. 17, 78, 82.Sayedi, A., Jerath, K., and Srinivasan, K.（2014）. Competitive poaching in sponsored search advertising and its strategic impact on traditional advertising. Marketing Science, 33（4）, pp.586–608.Schultz, Carsten & Holsing, Christian.（2017）. Differences across Device Usage in Search Engine Advertising. 10.4018/978–1–5225–3114–2.ch010.Sherice Jacob.（2019）. How Airbnb Uses Data Science to Improve Their Product and Marketing. Retrieved from https://neilpatel.com/blog/how–airbnb–uses–data–science/.（Feb 11, 2020）Simonov, A., & Hill, S.（2019）. Competitive Advertising on Brand Search: Traffic Stealing and Customer Selection.Sahni, Navdeep S. and Zhang, Charles.（2020）. Search Advertising and Information Discovery：Are Consumers Averse to Sponsored Messages? Stanford University Graduate School of Business Research Paper No. 3441786. Available at SSRN：https://ssrn.com/abstract=3441786.Ursu, Raluca and Dzyabura, Daria.（2019）. Retailers` Product Location Problem with Consumer Search. Quantitative Marketing and Economics. Available at SSRN: https://ssrn.com/abstract=3284615.Vincent, N., Johnson, I.L., Sheehan, P., & Hecht, B.J.（2019）. Measuring the Importance of User–Generated Content to Search Engines. ArXiv, abs/1906.08576.Wedel, M. and Kannan, P.K.（2016）. Marketing analytics for data–rich environments. Journal of Marketing, Vol. 80 No. 6, pp.97–121.Yang, Sha & Ghose, A.（2010）. Analyzing the Relationship Between Organic and Sponsored Search Advertising：Positive, Negative, or Zero Interdependence? Marketing Science. 29. 602–623. 10.2139/ssrn.1491315.Yoo, Chan. （2014）. Branding Potentials of Keyword Search Ads：The Effects of Ad Rankings on Brand Recognition and Evaluations. Journal of Advertising. 43. 85–99. 10.1080/00913367.2013.845541.Yang, Yanwu and Jansen, Bernard and Yang, Yinghui and Guo, Xunhua and Zeng, Daniel Dajun.（2018）. Keyword Optimization in Sponsored Search Advertising： A Multi–Level Computational Framework. Available at SSRN: https://ssrn.com/abstract=3393235.Yang, Xiao & Sun, Daren & Zhu, Ruiwei & Deng, Tao & Guo, Zhi & Ding, Zongyao & Qin, Shouke & Zhu, Yanfeng.（2019）. AiAds: Automated and Intelligent Advertising System for Sponsored Search. 1881–1890. 10.1145/3292500.3330782.Yahoo. Arria Natural Language Generation Technology Expands BBC`s Coverage of UK Elections（Dec 17, 2019）. Retrieved from https://finance.yahoo.com/news/arria–natural–language–generation–technology–140000289.html（Feb 14, 2020）Zhou, Guorui & Mou, Na & Fan, Ying & Pi, Qi & Bian, Weijie & Zhou, Chang & Zhu, Xiaoqiang & Gai, Kun.（2019）. Deep Interest Evolution Network for Click–Through Rate Prediction. Proceedings of the AAAI Conference on Artificial Intelligence. 33. 5941–5948. 10.1609/aaai.v33i01.33015941.二、中文文獻：林俊宏（譯）（2014）。奧美廣告教父教你用數據找到潛在客戶：性感小數字（原作者：Dimitri Maex, Paul B. Brown）。台灣：哈林文化出版社。周雪君（2015）。又是手機惹的禍？人類專注力比金魚還要弱。檢自https://www.thenewslens.com/article/16837（Feb 12, 2020）陳愷新（2018）。大數據持續升勢，經驗法則正轉軌「智能法則」。證券服務，661，頁34–38。徐瑞珠（譯）（2019）。數位行銷的10堂課｜SEO x 廣告 x 社群媒體 x facebook洞察報告 x Google Analytics （原作者：Ian Dodson）。台灣：碁峰。蕭瑞麟（2016）。思考的脈絡 : 創新，可能不擴散。臺北市 : 遠見天下文化出版。Kimberly Chin（2018）。從搜尋看見台灣人的農曆新年。檢自https://www.thinkwithgoogle.com/intl/zh–tw/consumer–insights/search/cny–search–insights/（Feb 12, 2020） 描述 碩士
國立政治大學
數位內容碩士學位學程
107462011資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107462011 資料類型 thesis dc.contributor.advisor 黃瀚萱<br>陳宜秀 zh_TW dc.contributor.author (Authors) 張硯筑 zh_TW dc.contributor.author (Authors) Chang, Yen-Chu en_US dc.creator (作者) 張硯筑 zh_TW dc.creator (作者) Chang, Yen-Chu en_US dc.date (日期) 2020 en_US dc.date.accessioned 2-Sep-2020 13:08:33 (UTC+8) - dc.date.available 2-Sep-2020 13:08:33 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2020 13:08:33 (UTC+8) - dc.identifier (Other Identifiers) G0107462011 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131903 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 數位內容碩士學位學程 zh_TW dc.description (描述) 107462011 zh_TW dc.description.abstract (摘要) 在顧客自主意識抬頭及注意力稀缺的挑戰之下，該如何成功吸引顧客快速找到其所需要的資訊，將是品牌商所面臨的嚴峻考驗。本研究為行銷自動化提供了一套策略性精準行銷解決方案的演示，將行銷理論框架與廣告操作實務接軌，運用自然語言處理技術自動生成搜尋廣告文案，並以自動化流程篩選出最適合的關鍵字。透過本系統能夠大幅降低製作搜尋廣告的時間成本，在短時間內便能快速生成大量搜尋廣告，避免錯失顧客的最佳購買時機，並突破搜尋廣告不易進行再行銷的困境。本系統以行銷漏斗框架結合顧客旅程，進而優化各階段的關鍵接觸點體驗，為行銷人員在快速變動的廣告市場中，提供最佳化整合行銷綜效的解法，同時創造人工智能技術於搜尋廣告應用的新價值。本研究從發掘關鍵字展開，接著以NER分辨不同類型的關鍵字，再以價值光譜模型與TF-IDF演算法去蕪存菁篩選出含金量高的關鍵字，並以TensorFlow2.0框架構建LSTM生成模型，以便自動生成搜尋廣告文案。本研究採用問卷調查法、深度訪談法及A/B 測試探討如何自動生成匹配顧客意圖的動態搜尋廣告，並深入剖析顧客在不同購買階段的搜尋行為。研究結果發現採用自動生成的搜尋廣告能使轉換率提高83％，同時平均每次轉換費用降低54％，成功讓顧客將消費意圖轉化為具體行動，有助於降低行銷成本並提升生產效能，顛覆既有思維以重塑顧客旅程，進而為顧客打造更多個人化體驗和價值。 zh_TW dc.description.abstract (摘要) Search advertising is a huge online market in which many types of products are recommended and tens of billions of transactions are conducted each day. It has been proven to be a successful business method of online marketing and, consequently, attracting high attention from academics and practitioners. However, in recent years, due to heightened levels of self-awareness and shortened attention span of customers, manually tailoring these advertisements has become a bottleneck in lieu of rapid growth and demand of efficiency.We present a novel approach to automatically generate search advertising copies （texts） that relies on Natural Language Processing （NLP） technology. Unlike most of the previous works that focused on the pricing model, this approach aims to improve the performance of search-based advertising based on the consumer behavioral stages in the marketing funnel model. This work introduced an individual recommender system based on the LSTM auto-encoder model, and implemented it in an A/B testing experiment designed to follow the automated re-marketed strategy, replacing the manual parameters-setting tasks with multiple automated tasks and making search advertising more effective for brand-seeking to user behaviors. To support the experiment, we also conducted a survey and in-depth interviews to discover insight into consumer’s clicking and keyword searching behaviors. Data analyses revealed that automated search advertising improved the conversion rate by 83% and decreased the average cost per conversion by 54%, indicating the promising application of this novel approach to adopt artificial intelligence （AI） in the future of search advertising. en_US dc.description.tableofcontents 謝辭 IIABSTRACT III摘要 IV目錄 V表目錄 VII圖目錄 VIII第一章 緒論 1第一節 研究背景 1第二節 研究動機 3第三節 研究目的 5第四節 研究問題 7第五節 研究架構 11第二章 文獻回顧 13第一節 搜尋廣告的生態 13第二節 由顧客旅程思考需求落差 17第三節 強權競爭下的關鍵字規劃策略 19第四節 廣告場域中的自然語言生成應用 22第三章 系統設計 25第一節 資料蒐集與預處理 27第一項 訓練資料來源 27第二項 資料預處理 29第二節 命名實體識別 30第三節 價值光譜模型 31第四節 TF-IDF演算法 32第五節 LSTM AUTO-ENCODER模型生成 32第六節 搜尋廣告之動態再行銷機制 35第四章 研究方法 37第一節 研究個案 37第二節 實驗設計 38第一項 AI廣告生成技術可行性問卷調查 38第二項 搜尋廣告之市場測試（A/B testing） 39第三項 深度訪談 44第五章 研究結果 46第一節 NLG生成模型訓練及評比 46第二節 BLEU自動成效評估 48第三節 問卷調查統計與分析 50第四節 A/B測試統計與分析 52第五節 深度訪談內容分析 57第六章 結論 61第一節 研究發現 61第二節 研究限制 64第三節 研究貢獻 65第四節 未來發展 65參考文獻 67附錄一 訪談題綱 73 zh_TW dc.format.extent 2135506 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107462011 en_US dc.subject (關鍵詞) 人工智能 zh_TW dc.subject (關鍵詞) 自然語言處理 zh_TW dc.subject (關鍵詞) 行銷漏斗 zh_TW dc.subject (關鍵詞) 搜尋廣告 zh_TW dc.subject (關鍵詞) 再行銷 zh_TW dc.subject (關鍵詞) AI en_US dc.subject (關鍵詞) NLP en_US dc.subject (關鍵詞) Marketing Funnel en_US dc.subject (關鍵詞) Search Advertising en_US dc.subject (關鍵詞) Remarketing en_US dc.title (題名) 基於顧客體驗旅程的搜尋廣告生成 zh_TW dc.title (題名) Advertisement generation based on customer experience journey for search advertising en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 一、英文文獻：Abrams Z., Schwarz M.（2007）. Ad Auction Design and User Experience. In: Deng X., Graham F.C.（Eds）, Internet and Network Economics. WINE 2007. Lecture Notes in Computer Science, Vol 4858. Springer, Berlin, Heidelberg.Animesh, Siva Viswanathan, and Ritu Agarwal.（2011）. Competing creatively in sponsored search markets: The effect of rank, differentiation strategy, and competition on performance. Information Systems Research, 22.1, pp.153–169.Agarwal, Ashish and Tridas Mukhopadhyay.（2016）. The Impact of Competing ads on Click Performance in Sponsored Search. Information Systems Research, 27：3, pp.538–557.Alex Jiyoung Kim, Sungha Jang, Hyun S. Shin.（2019）. How should retail advertisers manage multiple keywords in paid search advertising? Journal of Business Research, ISSN 0148–2963, https://doi.org/10.1016/j.jbusres.2019.09.049.Brown, T.（2009）. Change by design: How design thinking transforms organizations and inspires innovation. New York: Harper Collins.Batra, R. and Keller, K.L.（2016）. Integrating marketing communications：new findings, new lessons, and new ideas. Journal of Marketing, Vol. 80, No. 6, pp. 122–145.B. Chen, T. Liu and Y. Liu. （2019）. Stereo Source Separation in the Frequency Domain: Solving the Permutation Problem by a Sliding K–means Method. ICASSP 2019 – 2019 IEEE International Conference on Acoustics, Speech and Signal Processing（ICASSP）, Brighton, United Kingdom, pp.4250–4254.Court, D., Elzinga, D., Mulder, S., & Vetvik, O. J.（2009）. The consumer decision journey. McKinsey Quarterly, June.Chan, T.Y., Wu, C. and Xie, Y.（2011）. Measuring the lifetime value of customers acquired from Google search advertising. Marketing Science, Vol. 30 No. 5, pp.835–850.Diana I. Cordova and Mark R. Lepper.（1996）. Intrinsic Motivation and the Process of Learning：Beneficial Effects of Contextualization, Personalization, and Choice. Journal of Educational Psychology, 88.4, pp.715.Desai, P., Shin, W., & Staelin, R.（2014）. 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