A Study on Potential Head Advertisers in Sponsored Search
This paper studies the advertisers from whom the search engine may increase the revenue by offering an advanced sponsored search service. We divide them into head and tail advertisers according to their contributions to the search engine revenue. Data analysis shows that some tail advertisers have large amount of budgets and low budget usage ratios, who aimed to achieve the planned campaign goals (e.g., a large number of clicks), but they finally failed in doing so due to wrongly-selected bid keywords, inappropriate bid prices, and/or low-quality ad creatives. In this paper, we conduct a deep analysis on these advertisers. Specially, we define the measures to distinguish potential head advertisers from tail advertisers, and then run simulation experiments on the potential head advertisers by applying different improvements. Encouraging results have been achieved by our diagnosing approaches. We also show that a decision tree model can be implemented for a better improvement to those advertisers.
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- 2.Anastasakos, T., Hillard, D., Kshetramade, S., Raghavan, H.: A collaborative filtering approach to ad recommendation using the query-ad click graph. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 1927–1930. ACM, New York (2009)CrossRefGoogle Scholar
- 8.Graepel, T., Candela, J., Borchert, T., Herbrich, R.: Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine. In: Proceedings of the 27th International Conference on Machine Learning (2009)Google Scholar
- 13.Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)Google Scholar
- 14.Radlinski, F., Broder, A., Ciccolo, P., Gabrilovich, E., Josifovski, V., Riedel, L.: Optimizing relevance and revenue in ad search: a query substitution approach. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 403–410. ACM, New York (2008)CrossRefGoogle Scholar
- 16.Wang, C., Zhang, P., Choi, R., D’Eredita, M.: Understanding consumers attitude toward advertising. In: Proceedings of the Eighth Americas Conference on Information Systems (2002)Google Scholar