Buildings are one of the largest consumers of electricity. Dominant electricity consumption within the buildings, contributed by plug loads, lighting and air conditioning, can be significantly improved using Occupancy-based Building Management Systems (Ob-BMS). In this paper, we address three critical aspects of Ob-BMS i.e. 1) Modular sensor node design to support diverse deployment scenarios; 2) Building architecture to support and scale fine resolution monitoring; and 3) Detailed analysis of the collected data for smarter actuation. We present key learning across these three aspects evolved over more than one year of design and deployment experiences. The sensor node design evolved over a period of time to address specific deployment requirements. With an opportunity at the host institute where two dorm buildings were getting constructed, we planned for the support infrastructure required for fine resolution monitoring embedded in the design phase and share our preliminary experiences and key learning thereof. Prototype deployment of the sensing system as per the planned support infrastructure was performed at two faculty offices with effective data collection worth 45 days. Collected data is analyzed accounting for efficient switching of appliances, in addition to energy conservation and user comfort as performed in the earlier occupancy based frameworks. Our analysis shows that occupancy prediction using simple heuristic based modeling can achieve similar performance as more complex Hidden Markov Models, thus simplifying the analytic framework.