Since the inception of the web searching technology, people have been searching for almost everything and anything on the internet. The ever-increasing dependency of users on these search engines and the dynamic nature of the World Wide Web has reduced the accuracy of the search results and increased the search time of an individual. Today, more than ever before, there is a need for search engines to be relevant and precise to the user's needs and to be able to make decisions about what the user wants to search, and should be able to suggest him similar or related topics of his interest. This increasing need of the search engine to become a decision engine [1] (term coined by Stefan Weitz [2]) brought to fore various creative technological ideas like Tag clouds and AutoComplete [3]. For a better and more relevant search experience, it is crucial that we study the present search behavior of users and its corresponding response by the search engine. This work contemplates the nature of searches made and how they evolve from time to time. In this paper we examine and construe data from various angles and then provide our suggestions and conclusions for a better, more personalized and relevant search. © 2010 IEEE.