For some, the term machine learning may conjure up images of virtual assistants or automated tools. In reality, it’s a highly effective way to utilize marketing data.
While some pharma marketers still consider it a futuristic concept, many others are already using it.
In machine learning, the data guides the analyst on which road should be taken. Often, it’s one the analyst may not have anticipated.
This allows for a more customized user experience: As machine learning identifies patterns and makes predictions in real time, each engagement and journey is different from the next.
Need an example? When a financial organization contacts a customer to report a suspicious charge, it’s machine learning that likely prompts the contact. Companies use it to better understand purchasing behavior at the individual level and flag instances that seem to violate established patterns.
For marketers, machine learning is about finding nuggets of “predictive” knowledge in the waves of structured and unstructured data. They stand to benefit as we move toward a world of hyper-converged data, channels, content, and context.
Marketers must begin with an understanding of client needs and goals — and, of course, a sharing of data. Each branded touchpoint, whether paid or organic, needs to be entered into a database. In turn, the database will examine the content with which the person was engaging, as well as the duration of that engagement and the content that was consumed next.
Once the individual’s process and patterns are captured, the tech will learn their paths and predict what might come next. The database will then take this information and trigger future messaging or engagements.
Machine learning can also help uncover surprises. In one instance, it revealed to us that journal ads were more impactful at the HCP level than originally anticipated, particularly with HCPs who already showed a strong affinity to journals. This led us to increase the program’s investment in journals.
In that same example, the tech helped optimize our email frequency and message cadence based on particular triggers and actions.
Machine learning enables us to understand what’s working and what isn’t much faster than before. Based on the algorithms, we can make stronger recommendations for the next marketing cycle. This allows pharma marketers to be proactive rather than reactive.
With this enhanced understanding, marketers and media will reach HCPs in a new way and provide a unique experience to each individual.
Paul Kallukaran, EVP, performance analytics and data science, CMI/Compas.
Senior media planners Nikki Jengeleski and Gia Lanzetta contributed to this column.