Traditional metrics in online advertising such as the click-through rate often take into account the users' search activities separately and do not consider any interactions between them. In understanding online search behavior, this fact may favor a certain group of search type and, therefore, may mislead managers in allocating their financial spending efficiently. We analyzed a large query log for the occurrence of user-specific interaction patterns within and across three different industries (clothing, healthcare, hotel) and were able to show that users' online search behavior is indeed a multi-stage process, whereas e.g. a product search for sneakers typically begins with general, often referred to as generic, keywords which becomes narrowed as it proceeds by including more specific, e.g. brand-related ("sneakers adidas"), keywords. Our method to analyze the development of users' search process within query logs helps managers to identify the role of specific activities within a respective industry and to allocate their financial spending in paid search advertising accordingly.