Everyday tasks are increasingly completed with the help of various web-based services, and many users with little technical know-how are using these services. Due to this development, online privacy has emerged as a paramount concern when developing web services. One particular privacy concern involves third-party services such as analytics services that are nowadays commonplace on almost any website. In the current study, we explore the possibilities of automating the data collection in scientific research on personal data leaks related to third-party analytics tools, and build a proof-of-concept implementation of a tool that uses automated traffic analysis to record and analyze potential leaks of personal data to third-party services. The current implementation of the tool is intended to detect URL leaks, and to specifically inspect how this happens in the search functionalities found on the analyzed websites. Our findings indicate that the automation of this kind of data collection is very effective, and could potentially increase the quality of the research significantly as it allows for faster and more wide-spread data collection.