Hello from the future, where robots are working behind the scenes to enable things we only previously dreamed of. Machine Learning is one aspect of Artificial Intelligence (AI) that is most utilized in marketing—yes, even in pharma—but many don’t even know it exists, or the extent of its power.
For pharma marketers who want to harness the power of machine learning, the first step is to define the business problem, collect the appropriate data, and use a variety of machine learning algorithms to identify patterns of behaviors. That information is then ultimately used to engage with the right customer, with the right message, at the right time.
Example Case Study
Machine learning has the ability to provide a personalized user experience to HCPs based on their media consumption patterns, in conjunction with the business problems pharma marketers are trying to solve in real time. Say, for instance, a brand has multiple creative messages that ultimately provide a complete product journey for HCPs. With machine learning, pharma marketers now have the opportunity to weave the path HCPs take to complete the product journey tailored to each individual HCP’s consumption habits.
For example, if Dr. Smith receives an email containing Brand Message A and fully engages with the content, the next time she is on an endemic site, Brand Message B will be served. Once Dr. Smith has engaged with Brand Message B, Brand Message C will be delivered via eNewsletter.
Machine learning provides an understanding of what media Dr. Smith has consumed, which provides the ability to anticipate what she needs next in the product journey, deploying the next wave of creative messaging when she is ready to receive it.
Getting Started with Machine Learning
A few best practices to follow to ensure your first jump into machine learning is successful include:
- Partner with an agency with strong experience in utilizing machine learning for your ideal audiences, whether HCP or patient.
- Begin with a discussion around what data your agency needs to access in order to help you reach your goals. The most common missteps come from using the wrong data.
- Define your business objectives and cadence of messaging prioritization—think about your “ifs” and “thens.”
- Be patient. Not all targets are going to engage at the rate you wish. You need to keep in mind while this may be a longer process, it is a more efficient one!
