Employing Analytics Data to Guide a Data-Driven Review of LibGuides

This article presents a methodology for conducting an evidence-based review of LibGuides content based on native and non-native analytics data. This methodology uses built-in analytics data from Springshare's platform and data from Google Analytics to investigate LibGuides functionality, use, and design criteria. These criteria, in turn, enable a strategic consideration of how and why we as librarians create LibGuides. Are our guides intended to facilitate reference and research consultations, or do they primarily serve to enable independent research by students? More specifically, who benefits the most from the LibGuides we generate—librarians or researchers? We conclude with a consideration of how analytics data can be leveraged to generate librarian buy-in for reevaluating design criteria of library subject guides and consider implications for practice and further research in this area.