User browsing behavior is tracked by search providers in order to construct activity profiles that are used to fine-tune searches and present user-specific advertisements. When a search input matches a commercial product or service offering, ads based on the previously-saved interests, likes and dislikes are displayed. The number of web searches from mobile devices has exceeded those conducted from desktops. Mobile devices are being used for critical business tasks such as e-commerce, banking transactions, video conferences, email communications and confidential data storage. Companies are moving towards mobile-app-only strategies and advertisers are displaying ads on mobile apps as well. Mobile device ads can often reveal information such as location, gender, age and other valuable data about users. This chapter describes a methodology for extracting and analyzing ads on mobile devices to retrieve user-specific information, reconstruct a user profile and predict user identity. The results show that the methodology can identify a user even if he or she uses the same device, multiple devices, different networks or follows different usage patterns. The methodology can be used to support a digital forensic readiness framework for mobile devices. Additionally, it has applications in context-based security and proactive and reactive digital forensic investigations.