| Article ID: | iaor200948254 |
| Country: | United Kingdom |
| Volume: | 4 |
| Issue: | 1 |
| Start Page Number: | 3 |
| End Page Number: | 15 |
| Publication Date: | Dec 2009 |
| Journal: | International Journal of Services Operations and Informatics |
| Authors: | Liu Weiguo, Zhong Shi, Chaudhary Mayank, Kapur Shyam |
| Keywords: | e-commerce, datamining, forecasting: applications |
Like any marketing campaigns, online advertisement campaigns need to be monitored, analysed and optimised. The quantitative methods are more crucial to online campaigns because of their dynamic pricing and highly interactive nature. Not only can marketing effectiveness be measured almost instantly in terms of measures such as click through rate and/or the acquisition/conversion rate, but a rich set of user data can also be collected and used by learning algorithms. The huge sets of dynamic data raise many challenging problems. In order to run a successful campaign, any serious advertiser, publisher or ad exchange network need a system that combines forecasting, data mining and optimisation techniques. In this paper, we propose such a methodology for a systematic analysis of the relevant problems and describe techniques that work on real world data as satisfactory solutions.