Improve ROI, cut marketing costs and deliver more effective campaigns
For pharma marketers, nothing has changed the game more than data allowing us to measure the impact of our spend and identify the most valuable channels for messaging, making predictive analytics an appealing development for early adoption. Unlike conventional data-driven marketing which messages an audience based on current needs, advanced predictive analytics powered by machine learning takes data even further by meeting individual patients where they will be with something they didn’t yet know they needed.
When leveraged effectively, a predictive healthcare marketing model means we can not only improve the probability that each impression is delivered to a qualified patient, but also that the message served is tailored to that patient’s needs and preferences based on patterns found via analysis of vast amounts of historical data.
To effectively employ predictive analytics, focus on mapping data to the patient journey and ensure robust ROI tracking is in place to validate the model in market.
Understand the patient journey
What you get out of predictive analytics is only as good as what you put in. An effective model accounts for the patient journey and sources its predictive data across many dimensions. Where online do patients seek information between diagnosis and treatment? What does household income tell us about receptivity to a certain class of drugs? How does zip code correlate with patients’ willingness to ask a doctor to change treatment? On which forums are patients most likely to share their treatment experience with peers? What can be gleaned from claims, doctor visits and self-reported data?
Without knowing every data point about every media consumer, predictive models find patterns in historical data that improve our ability to serve the right message to the right person in a way that contextual, behavioral or even programmatic targeting alone cannot.
Continually analyze ROI
Tracking ROI through the predictive analytic lens should be equally focused. I like to start with my goals in mind – goals based on already known data and insight – to better inform a campaign. At this point, you shouldn’t be shooting in the dark but rather acting on the data culled so far. It’s worth noting that, across a large enough audience sample, analysis of comorbidities (as reflected in disease and symptomatic search terms queried) can be a powerful predictive dimension as some conditions occur frequently in tandem for a variety of reasons yet few options exist for marketers to address such patient subsets directly.
As with any targeting approach, carefully measure every tactic, message and channel delivered via a predictive platform, and continuously compare this to your goals and to performance via traditional channels. Also be mindful of what Yuriv Boykiv calls “nonsense metrics.” If your true goal is behavioral change or Rx lift, pay ultimate attention to the metrics that matter. Perhaps more than any other targeting framework, predictive analytics can yield surprising and sometimes counter-intuitive positive results on the back-end even in the absence of standout leading digital indicators.
Wanting more digital pharma insight? Access my whitepaper for examples of current best practices, case studies and anecdotes illustrating real-world applications of all 8 principals for digital pharma marketing success: