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Millions of users on the Internet discuss a variety of topics on Question-and-Answer (Q8A) instances. However, not all instances and topics receive the same amount of attention, as some thrive and achieve self-sustaining levels of activity, while others fail to attract users and either never grow beyond being a small niche community or become inactive. Hence, it is imperative to not only better understand but also to distill deciding factors and rules that define and govern sustainable Q8A instances. We aim to empower community managers with quantitative methods for them to better understand, control, and foster their communities, and thus contribute to making the Web a more efficient place to exchange information. To that end, we extract, model, and cluster a user activity-based time series from 50 randomly selected Q8A instances from the Stack Exchange network to characterize user behavior. We find four distinct types of user activity temporal patterns, which vary primarily according to the users' activity frequency. Finally, by breaking down total activity in our 50 Q8A instances by the previously identified user activity profiles, we classify those 50 Q8A instances into three different activity profiles. Our parsimonious categorization of Q8A instances aligns with the stage of development and maturity of the underlying communities, and can potentially help operators of such instances: We not only quantitatively assess progress of Q8A instances, but we also derive practical implications for optimizing Q8A community building efforts, as we, e.g., recommend which user types to focus on at different developmental stages of a Q8A community.