Decision support system for search engine advertising campaign management by determining negative keywords

Official URL: http://risc01.sabanciuniv.edu/record=b1817191 (Table of Contents)

Search engine advertisers need to determine the best keyword set for their campaigns. Every company has particular constraints and expectations from the Search Engine Advertising (SEA). In this research we worked on a Decision Support System (DSS) that can be used in SEA campaign management. The DSS determines the negative keywords (which should be eliminated from the keyword set in order to improve the performance) based on the data obtained from the earlier campaigns. Current metrics used to determine the negative keywords are not sufficient/adequate, since they don’t incorporate other important aspects such as bounce rate, quality score etc. which are often used by the advertisers in order to evaluate the traffic but rely mostly to conversion rate. In our research first we analyze the keywords at unigram level (similar to some of the existing approaches available in the literature) in order to identify the set of unigrams which are negatively and/or positively effecting the campaign by using various machine learning techniques (either as is or used the core concepts associated with them) such as Naïve Bayes, Decision Trees, Logistic Regression. We further extended these algorithms by incorporating ideas borrowed from Greedy Randomized Adaptive Search (GRASP). We also introduced novel metrics which incorporate more aspects used in real life SEA campaigns by the advertisers as part of this process. The performance of our approach is evaluated with an experimental analysis conducted on real life data obtained from a major FMCG producer.