Key opinion leaders (KOLs) have played an important role in medical affairs for some time. For medical science liaisons (MSLs), a list of vital KOLs might include doctors, university professors and top researchers. To encourage the adoption of new forms of care, the positive regard of KOLs is essential.
Building a strong KOL influencer network takes time, and these networks are constantly changing as various opinion leaders change jobs, adopt new research topics and refine their own knowledge through study and publications. Machine learning offers a way for medical affairs teams to stay on top of these changing patterns, so that they can focus on building mutually beneficial relationships with key opinion leaders.
Challenges for KOL Influencer Networks
Medical affairs teams face challenges in distinguishing among experts, influencers and key opinion leaders. While the three groups frequently overlap, each has its own set of distinguishing features. Understanding these differences can help MA teams choose the right KOL influencer for the right situation.
- Experts know their subject areas intimately. Yet they tend to focus on that subject area rather than on sharing knowledge. They may not engage unless MSLs reach out first.
- Influencers focus on building their own audiences. They may or may not have expertise, but have a passion for sharing information and speaking to interested members of the public.
- Key opinion leaders balance the authority of an expert with the audience of an influencer. Their full-time work may be in their field of expertise, but they’re known for how they express that work to their audiences.
Key opinion leaders “are the subject experts that the press calls when they want a quote or credible soundbite about some current topical issue,” writes Werner Geyser, founder of Influencer Marketing Hub. When they’re not providing insight, KOLs are at work in their field of expertise.
All three of these categories overlap, and none are static in nature. Experts, influencers and KOLs all have the opportunity to change roles over time, and many do.
A hidden fourth category can also cause frustration for MSLs, and that’s the category of the false or overhyped individual. In some cases, a professional can seem to be an expert, influencer or key opinion leader, yet their work doesn’t have the professional or public reach necessary to influence public opinion on a certain topic or issue, writes Andy Demjen, lead strategist at social media agency Kubbco. Artificial intelligence and machine learning offer a way to spot those potential KOLs who simply don’t have the presence or clout to make a difference in the way your patients need.
By understanding how these roles grow and change, MSLs can build stronger relationships and choose the right people to share their message at the right time.
Using Machine Learning To Identify KOL Influencer Networks
Machine learning offers a number of opportunities for improving the ability of medical affairs teams to present their organization’s work and communicate with patients and the public in the most effective ways.
“With the advent of machine learning, the medical affairs team can innovate and perform their many functions better and more efficiently,” write Paul Riley, Colin Baughman and Jeff Catlin in PharmExec. The authors note that machine learning and artificial intelligence won’t magically solve all problems, but can be powerful tools when applied to the right challenges.
One such challenge is identifying KOL influencer networks. Here, technological tools can help teams identify key opinion leaders and see how audiences interact with their work, writes Kristen Baker, senior content marketing manager at marketplace consulting services firm Catalant Technologies.
While conventional software offered early attempts to do this work, machine learning offers greater adaptability and more nuanced insights. Programs that don’t use artificial intelligence “often find it difficult to sift through substantial big data sets,” note Pratap Khedkar and Saby Mitra at professional services firm ZS.
Hampered by these limitations, the software may struggle or fail to make the associations necessary to highlight a certain expert as a potential key opinion leader. The program has the data, but it doesn’t have the capacity to spot patterns or to identify certain relationships that arise within that data.
Machine learning solves this problem by equipping MSLs with the ability to glean insights from large data sets. As the software encounters more examples of key opinion leaders succeeding professionally and in front of public audiences, it identifies patterns common to various examples. From these patterns, it can predict various paths for future KOLs to succeed working with a particular MSL team on a particular communication challenge.
Machine learning does not replace the expertise of MSLs in understanding the work key opinion leaders do or in building relationships. Rather, the software enhances that work by suggesting avenues that might not otherwise have come up.
Mapping KOL Influencer Networks With Machine Learning
Key opinion leaders are driven by their own professional interests, not by a desire to build an audience. While they don’t mind sharing what they do, they also don’t need to share it in order to feel personally and professionally fulfilled.
An important part of encouraging KOL participation is “learning what drives and motivates them first, and then aligning their interests with your marketing goals—not the other way around,” writes Donna Short, executive vice president of professional relations at MJH Life Sciences.
When the focus is on a handful of KOLs, understanding their passions and looking for intersections is simpler. As more KOLs join the list, however, keeping track of all these professional interests and their conversations with your team’s own work becomes more challenging. Machine learning can play an important role in mapping KOL networks.
Machine learning can also be used to understand which key opinion leaders contribute to professional and public conversations and to extract the parts that resonate most with your own team’s message and audiences. Bernard Sarel, director of product management at intelligent commerce and digital ad platform Skai, recommends focusing on two factors: diversity and reach.
Key opinion leaders who converse in a number of ways before a variety of audiences demonstrate more diversity. They’re able to reach more audiences. Machine learning can help MSLs quickly spot which KOLs are reaching the same audiences that MSLs need to address.
When KOLs have a large reach, they can synthesize information to produce more meaningful insights, writes Sarel. Here, machine learning helps MSLs understand a KOL’s reach by showing how various ideas are connected and where a key opinion leader might be just the right person to opine on a new topic or idea.
In nearly all cases, training MSL teams on the use of artificial intelligence and machine learning in improving KOL engagement is a must. Medical affairs teams that have already put machine learning to use can offer insight and training to teams embracing the technology for the first time.