Big data has become the ”promised land” for healthcare – yet the industry is still learning how to put that valuable data to use, including when marketing to patients.

Most health systems have accumulated massive quantities of patient, provider, and market claims data at this point. However, they often lack robust health analytics to understand the behavior of current patients and populations they’d like to target. 

Fortunately, healthcare-specific marketing technologies are becoming more sophisticated, and most healthcare CRM platforms include built-in predictive analytics. Propensity modeling in particular is a must-have for successful digital marketing teams. It’s a statistical approach that’s used to predict the likelihood that a specific event will occur. Marketers may apply a range of diagnostic, demographic, encounter history, and other variables to predict the likelihood that a patient is, say, a viable candidate for bariatric surgery. If yes, the patient is added to the target audience for a bariatric campaign and receives messages according to rules for that audience. 

Propensity modeling makes the wealth of data available to health systems more actionable. First, propensity models consider a wide array of variables and mathematically condense them. Next, the models derive patterns and relationships from discrete datasets and translate them into key insights. These insights allow marketers to build more strategic, impactful campaigns. 

The process of building propensity models, however, is not simple. Analysts who don’t carefully vet their data sources or who succumb to so-called “overfitting” their models see campaigns fall flat, wasting a great deal of time, money, and other resources.
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