A typical MaxDiff is done in a sequential fashion: design before the data collection and modeling last. Building on the ideas from Sawtooth’s Adaptive and Bandit MaxDiff and the field of computerized adaptive testing, I look for ways to model each individual respondent during the MaxDiff survey to inform the design in real-time for performance gain. Item response theory and machine learning will be explored as model options. Simulations will be carried out for validation.