The use of individual-level browsing data, i.e., the records of a person’s visits to online content through a desktop or mobile browsers and apps, is an increasingly important re- source for social scientists. Browsing data have characteristics that raise many questions for statistical analysis, yet to date, little hands-on guidance on how to handle them exists. Reviewing extant research, and exploring data sets collected through our four research teams spanning seven countries and several years, with over 14,000 participants and 360 million web visits, we derive recommendations along four steps typical for studies using browsing data: preprocessing the raw data; filtering out observations; classifying web vis- its; and modeling browsing behavior. We hope that the recommendations we formulate provides a foundation for discussions about best practices in the field, which so far has paid little attention to justifying the many analytical decisions typical for studies using browsing data.