Machine Learning Techniques Deliver Granular Insights to Enable Improved HCP Marketing

Companies have turned to analytics to help them drive more effective campaigns, and to rules-based programs to help reps decide when to execute different tactics. The challenge with most measurement programs is that they suffer from aggregation bias, washing out granularity as they estimate the overall response to a given tactic. They also can be challenged to provide critical insights around differences across specialties and geographies. When we look at rules-based execution aids, they are inflexible and do not tie to changing responses over time.

At the recent PMSA conference, Jane Portman, VP Health Analytics at Merkle, and Brian Demitros, VP of Analytics at Merkle, delivered a presentation designed to educate conference attendees on what's being done out there in terms of using machine learning and analytics to track the behavior of HCPs in response to past and current marketing efforts in order to predict future behavior, and also to gauge the efficacy of those efforts.

The goal for marketers is to find the optimal solution to evaluate and optimize the performance of new digital tools and a new addressable marketing ecosystem designed to provide continued support to sales reps within a multi-channel customer experience. Traditional measurement would not be able to get the level of insights that marketers need. We took a page out of our Digital Media Measurement practice to apply supervised machine learning techniques to the challenge to rapidly and consistently deliver granular insights to enable improved sales force execution and optimal multi-channel customer experience.

Why is it important? Because pharma marketers are facing common challenges in the industry:
  • Customers are saturated with promotional messages.
  • Outbound promotional engagement and access is declining.
  • Customers have channel preferences and propensity.
  • Value-add content and beyond-the-pill programs spark interest and brand engagement.
One key to addressing those challenges is to use a "marketing ecosystem" of various digital tools, including:
  • Digital leave behind
  • Targeted banners
  • Interactive sales aids
  • On-demand content
  • Virtual portals
  • Alerts/SMS
  • Rep-triggered EM
These digital tools require new methods to optimize the customer experience for multi-channel effectiveness, interaction effects of numerous tactics, and content-level optimization.

Meanwhile, existing or traditional marketing methods and approaches have their shortcomings, which machine learning can solve. The problem is, pharma has been slow to adopt it. According to 2018 data by Burtch Works LLC, healthcare and pharma has a 60% rate of machine learning use, which is lower than financial services, gaming, consulting, corporate, retail, general advertising and marketing, and technology. Why? Much of the reason involves pharma industry constraints, like medical legal approval, lack of CTAs or offers, limited content variation, and low campaign activity.

Key takeaways from the presentation?

Pharma marketing has changed to become more addressable. Given constraints in the industry, the analytics hasn't evolved at the same rate, but there's an opportunity in taking learnings and best practices from other industries and applying them to pharma. In the end, it's about reaching each individual HCP in the right way, at the right time, with the right message. Using machine learning, that goal is easier to achieve.


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