Thoroughly vetting data before building propensity models is a critical first step. User error, bias, accidental deletion, and improper coding all contribute to a problematic propensity model, and ultimately to problematic campaigns. Healthcare marketers and data analysts must carefully monitor data quality and deal with issues as soon as detected.
