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Articles

Optimal Keywords Grouping in Sponsored Search Advertising Under Uncertain Environments

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References

  • Abhishek, V., and Hosanagar, K. Keyword generation for search engine advertising using semantic similarity between terms. In Proceedings of the Ninth International Conference on Electronic Commerce. Minneapolis, MN: ACM, 2007, pp. 89–94.
  • Abou Nabout, N. A novel approach for bidding on keywords in newly set-up search advertising campaigns. European Journal of Marketing, 49, 5/6 (2015), 668–691.
  • Agarwal, A., and Mukhopadhyay, T. The impact of competing ads on click performance in sponsored search. Information Systems Research, 27, 3 (2016), 538–557.
  • Amaldoss, W.; Jerath, K.; and Sayedi, A. Keyword management costs and “broad match” in sponsored search advertising. Marketing Science, 35, 2 (2015), 259–274.
  • Bing Ads. Create a new ad group. Available at https://help.bingads.microsoft.com/apex/index/3/en/53097. Accessed on May 23, 2019.
  • Brynjolfsson, E.; Hu, Y.; and Simester, D. Goodbye pareto principle, hello long tail: The effect of search costs on the concentration of product sales. Management Science, 57, 8 (2011), 1373–1386.
  • Burns, K. New technology briefing: Ten golden rules to search advertising. Interactive Marketing, 6, 3 (2005), 248–252.
  • Chatwin, R.E. An overview of computational challenges in online advertising. In 2013 American Control Conference. Washington, DC: IEEE, 2013, pp. 5990–6007.
  • Chen, J.; Liu, D.; and Whinston, A.B. Auctioning keywords in online search. Journal of Marketing, 73, 4 (2009), 125–141.
  • Chen, J., and Stallaert, J. An economic analysis of online advertising using behavioral targeting. MIS Quarterly, 38, 2 (2010), 429–449.
  • Chen, Y.; Xue, G. R.; and Yu, Y. Advertising keyword suggestion based on concept hierarchy. In Proceedings of the 2008 international conference on web search and data mining. Palo Alto, CA: ACM, 2008, pp. 251–260.
  • Cholette, S.; Özlük, Ö.; and Parlar, M. Optimal keyword bids in search-based advertising with stochastic advertisement positions. Journal of Optimization Theory and Applications, 152, 1 (2012), 225–244.
  • Clausen, J. Branch and bound algorithms—Principles and examples. Department of Computer Science, University of Copenhagen, 1999.
  • Du, X.; Su, M.; Zhang, X.; and Zheng, X. Bidding for multiple keywords in sponsored search advertising: Keyword categories and match types. Information Systems Research, 28, 4 (2017), 711–722.
  • Edelman, B., and Ostrovsky, M. Strategic bidder behavior in sponsored search auctions. Decision Support Systems, 43, 1 (2007), 192–198.
  • Ghose, A., and Yang, S. An empirical analysis of search engine advertising: Sponsored search in electronic markets. Management Science, 55, 10 (2009), 1605–1622.
  • Gong, J.; Abhishek, V.; and Li, B. Examining the impact of keyword ambiguity on search advertising performance: A topic model approach. MIS Quarterly, 42, 3 (2018), 1–40.
  • Google AdWords. How adgroups work. Available at https://support.google.com/google–ads/answer/2375404?hl=en (accessed on October 28, 2018).
  • Google AdWords. Organize your account with ad groups. Available at https://support.google.com/google-ads/answer/6372655?hl=en (accessed on May 23, 2019).
  • Gopal, R.; Li, X.; and Sankaranarayanan, R. Online keyword based advertising: Impact of ad impressions on own-channel and cross-channel click-through rates. Decision Support Systems, 52, 1 (2011), 1–8.
  • Gotter, A. How to Create the Most Effective Adgroups for Google AdWords. Available at https://www.disruptiveadvertising.com/adwords/ad-groups/ (accessed on October 28, 2018).
  • Hillard, D.; Schroedl, S.; Manavoglu, E.; Raghavan, H.; and Leggetter, C. Improving ad relevance in sponsored search. In Proceedings of the Third ACM International Conference on Web Search and Data Mining. New York: ACM, 2010, pp. 361–370.
  • Holthausen, D.M., and Assmus, G. Advertising budget allocation under uncertainty. Management Science, 28, 5 (1982), 487–499.
  • Hou, L. A hierarchical Bayesian network-based approach to keyword auction. IEEE Transactions on Engineering Management, 62, 2 (2015), 217–225.
  • Interactive Advertising Bureau. 2018 IAB Internet Ad Revenue Full Year Report. Available at https://www.iab.com/wp-content/uploads/2019/05/Full-Year-2018-IAB-Internet-Advertising-Revenue-Report.pdf (accessed on May 10, 2019).
  • Jansen, B.J. Understanding Sponsored Search: Core Elements of Keyword Advertising. Cambridge: Cambridge University Press, 2011.
  • Jansen, B.J., and Clarke, B. Conversion potential: A metric for evaluating search engine advertising Performance. Journal of Research in Interactive Marketing, 11, 2 (2017), 142–159.
  • Jansen, B. J.; Sobel, K.; and Zhang, M. The brand effect of key phrases and advertisements in sponsored search. International Journal of Electronic Commerce, 16, 1 (2011), 77–106.
  • Kiritchenko, S., and Jiline, M. Keyword optimization in sponsored search via feature selection. In New Challenges for Feature Selection in Data Mining and Knowledge Discovery. Antwerp, Belgium: JMLR, 2008, pp. 122–134.
  • Kosuch, S., and Lisser, A. Upper bounds for the 0–1 stochastic knapsack problem and a B&B algorithm. Annals of Operations Research, 176, 1 (2010), 77–93.
  • Li, H.; Kannan, P.K.; Viswanathan, S.; and Pani, A. Attribution strategies and return on keyword investment in paid search advertising. Marketing Science, 35, 6 (2016), 831–848.
  • Lobo, M.S.; Vandenberghe, L.; Boyd, S.; and Lebret, H. Applications of second-order cone programming. Linear Algebra and Its Applications, 284, 1–3 (1998), 193–228.
  • Lu, X., and Zhao, X. Differential effects of keyword selection in search engine advertising on direct and indirect sales. Journal of Management Information Systems, 30, 4 (2014), 299–326.
  • Luo, W.; Cook, D.; and Karson, E. J. Search advertising placement strategy: Exploring the efficacy of the conventional wisdom. Information & Management, 48, 8 (2011), 404–411.
  • Lutze, H. F. The Findability Formula: The Easy, Non-Technical Approach to Search Engine Marketing. Hoboken, NJ: Wiley & Sons, 2009.
  • Mohr, J.J.; Sengupta, S.; and Slater, S.F. Marketing of High-Technology Products and Innovations. Upper Saddle River, NJ: Pearson Prentice Hall, 2010.
  • Muthukrishnan, S.; Pál, M.; and Svitkina, Z. Stochastic models for budget optimization in search-based advertising. Algorithmica, 58, 4 (2010), 1022–1044.
  • Nie, H.; Yang, Y.; and Zeng, D. Keyword generation for sponsored search advertising: Balancing coverage and relevance. IEEE Intelligent Systems, September 3, 2019.
  • Ortiz-Cordova, A., and Jansen, B. J. Classifying web search queries to identify high revenue generating customers. Journal of the American Society for Information Science and Technology, 63, 7 (2012), 1426–1441.
  • Pin, F., and Key, P. Stochastic variability in sponsored search auctions: Observations and models. In Proceedings of the 12th ACM conference on Electronic Commerce. San Jose, CA: ACM, 2011, pp. 61–70.
  • Prekopa, A. Stochastic Programming. Dordrecht, the Netherlands: Kluwer Academic, 1995.
  • Qiao, D.; Zhang, J.; Wei, Q.; and Chen, G. Finding competitive keywords from query logs to enhance search engine advertising. Information & Management, 54, 4 (2017), 531–543.
  • Ravi, S.; Broder, A.; Gabrilovich, E.; Josifovski, V.; Pandey, S.; and Pang, B. Automatic generation of bid phrases for online advertising. In Proceedings of the Third ACM International Conference on Web Search and Data Mining. New York: ACM, 2010, pp. 341–350.
  • Regelson, M., and Fain, D. Predicting click-through rate using keyword clusters. In Proceedings of the Second Workshop on Sponsored Search Auctions. Ann Arbor, MI: ACM Press, 2006, pp. 1–6.
  • Rusmevichientong, P., and Williamson, D.P. An adaptive algorithm for selecting profitable keywords for search-based advertising services. In Proceedings of the 7th ACM Conference on Electronic Commerce. Ann Arbor, MI: ACM Press, 2006, pp. 260–269.
  • Rutz, O.J., and Bucklin, R.E. A model of individual keyword performance in paid search advertising. SSRN 1024765, June. https://pdfs.semanticscholar.org/b44d/ded8d39b05763547048c5d47f37fcbf3099e.pdf
  • Rutz, O. J.; Bucklin, R. E.; and Sonnier, G. P. A latent instrumental variables approach to modeling keyword conversion in paid search advertising. Journal of Marketing Research, 49, 3 (2012), 306–319.
  • Samuelson, P.A., and Nordhaus, W.D. Microeconomics. New York: McGraw-Hill/Irwin, 2001.
  • Sen, R. Optimal search engine marketing strategy. International Journal of Electronic Commerce, 10, 1 (2005), 9–25.
  • Shi, S.W., and Dong, X. The effects of bid pulsing on keyword performance in search engines. International Journal of Electronic Commerce, 19, 2 (2015), 3–38.
  • Shin, W. Keyword search advertising and limited budgets. Marketing Science, 34, 6 (2015), 882–896.
  • Skiera, B., and Abou Nabout, N. Practice prize paper—PROSAD: A bidding decision support system for profit optimizing search engine advertising. Marketing Science, 32, 2 (2013), 213–220.
  • Telang, R.; Rajan, U.; and Mukhopadhyay, T. The market structure for Internet search engines. Journal of Management Information Systems, 21, 2 (2004), 137–160.
  • Wächter, A., & Biegler, L.T. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming, 106, 1 (2006), 25–57.
  • Weber, I. Top 3 Benefits of Using Keyword Grouping. https://seranking.com/blog/top-3-benefits-of-using-keyword-grouping/ (accessed on June 19, 2019).
  • Wiley, D.L. Outsmarting Google: SEO Secrets to Winning New Business. Hoboken, NJ: Pearson, 2011.
  • WordStream. Keywords Grouping: How to Group Your Keywords in AdWords. https://www.wordstream.com/adwords-keyword-grouping (accessed on October 28, 2018).
  • Wu, H.; Qiu, G.; He, X.; Shi, Y.; Shen, J.; Shen, J.; Bu, J.; and Chen, C. Advertising keyword generation using active learning. International Conference on World Wide Web. Madrid, Spain: ACM, 2009, pp. 1095–1096.
  • Xue, L.; Ray, G.; and Gu, B. Environmental uncertainty and IT infrastructure governance: A curvilinear relationship. Information Systems Research, 22, 2 (2011), 389–399.
  • Yang, Y.; Jansen, B.J.; Yang, Y., Guo, X.; and Zeng, D. Keyword optimization in sponsored search advertising: A multilevel computational framework. IEEE Intelligent Systems, 34, 1, (2019), 32–42.
  • Yang, Y.; Li, X.; Zeng, D.; and Jansen, B.J. Aggregate effects of advertising decisions: A complex systems look at search engine advertising via an experimental study. Internet Research, 28, 4 (2018), 1079–1102.
  • Yang, Y.; Qin, R.; Jansen, B.J.; Zhang, J.; and Zeng, D. Budget planning for coupled campaigns in sponsored search auctions. International Journal of Electronic Commerce, 18, 3 (2014), 39–66.
  • Yang, Y.; Yang, Y.C.; Jansen, B.J.; and Lalmas, M. Computational advertising: A paradigm shift for advertising and marketing? IEEE Intelligent Systems, 32, 3 (2017), 3–6.
  • Yang, Y.; Zeng, D.; Yang, Y.; and Zhang, J. Optimal budget allocation across search advertising markets. INFORMS Journal on Computing, 27, 2 (2015), 285–300.
  • Yang, Y.; Zhang, J.; Qin, R.; Li, J.; Liu, B.; and Liu, Z. Budget strategy in uncertain environments of search auctions: A preliminary investigation. IEEE Transactions on Services Computing, 6, 2 (2013), 168–176.
  • Zenetti, G.; Bijmolt, T.H.; Leeflang, P.S.; and Klapper, D. Search engine advertising effectiveness in a multimedia campaign. International Journal of Electronic Commerce, 18, 3 (2014), 7–38.
  • Zhang, X., and Feng, J. Cyclical bid adjustments in search-engine advertising. Management Science, 57, 9 (2011), 1703–1719.
  • Zhang, Y.; Zhang, W.; Gao, B.; Yuan, X.; and Liu, T.Y. Bid keyword suggestion in sponsored search based on competitiveness and relevance. Information Processing & Management, 50, 4 (2014), 508–523.
  • Zhou, Y.; Huang, F.; and Chen, H. Combining probability models and web mining models: a framework for proper name transliteration. Information Technology and Management, 9, 2 (2008), 91–103.
  • Zhou, Y., and Naroditskiy, V. Algorithm for stochastic multiple-choice knapsack problem and application to keywords bidding. In Proceedings of the 17th International Conference on World Wide Web. Beijing, China: ACM, 2008, pp. 1175–1176.

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