Market Segmentation with Novel Machine Learning

While pharmaceutical marketers have long used attitudinal and behavioral segmentation approaches to identify potential customers and tailor marketing activities, traditional methods lack utility for healthcare-level commercial operations and tactics. Behavioral segmentation uses administrative data to segment physicians. So, for example, you might learn which physicians are early adopters, who prescribes what treatment options most, or whether a physician is based in a hospital or clinic setting – but segmentation factors are ultimately limited to data constructs available in administrative data. These data sources provide an accurate representation of certain specific behaviors, but cannot provide insights into motivations or triggers of their behavior, meaning the "why" and/or thought processes remain indeterminable. In addition, results are not always data-driven, because researchers often inject their own personal biases when defining healthcare provider characteristics and segments. Attitudinal segmentation uses surveys tailored to the exact business need -- measuring such healthcare provider characteristics as peer influence, industry friendliness, perception of safety signals, mechanics of decision making and therapy choice, or receptiveness to channels of communication, and treatment selection making, for example -- to understand what messages or information are most likely to resonate with a physician.