We study the design of data analytic policies in a campus dormitory where smart meters are installed to gather usage data. Given the availability of such data, we consider policies to give feedback on comparative usage levels on a daily basis, and give price incentives accordingly. This requires us to divide users into groups according to their behaviors, and set prices that are reasonable. Instead of doing grouping and price setting based on intuition and guesses, which may be ineffective and unfair, we propose a data analytic approach. This requires us to start the design with a clear set of principles; based on these, and the collected data, the user grouping and corresponding pricing are automatically determined, satisfying the agreed-to principles. We show how this design approach works in a real setting, with real world usage data. We also discuss the difficulties in introducing such policies as they are more complicated and involve some uncertainties, and a possible solution by using opt-in (or opt-out) at the first introduction of such new policies. We expect the data analytic policy approach and our experience to be applicable and useful in general settings.