The landscape of the Internet is continually evolving. This creates huge opportunities for different industries to optimize vital channels online, resulting in various-forms of new Internet services. As a result, digital users are interacting with many digital systems and they are exhibiting dynamic behaviors. Their shopping behaviors are drastically different today than it used to be, with offline and online shopping interacting with each other. They have many channels to access online media but their consumption patterns on different channels are quite different. They do philanthropy online to help others but their heterogeneous motivations and different fundraising campaigns leads to distinct path-to-contribution. Understanding the digital user’s decision making process behind their dynamic behaviors is critical as they interact with various digital systems for the firms to improve user experience and improve their bottom line. In this thesis, I study digital users’ decision journeys and the corresponding digital technology firms’ strategies using inter-disciplinary approaches that combine econometrics, economic structural modeling and machine learning. The uncovered decision journey not only offer empirical managerial insights but also provide guideline for introducing intervention to better serve digital users.