Key Opinion Leader Mapping is an integral part of pharmaceutical marketing. Despite pharma being the most knowledgeable about their products, a respected third party physician is often a preferred source of information for other physicians as it seems more independent. The success of many drugs is dependent on the views of the key opinion leaders in their therapy area. So pharmaceutical companies all invest a lot of time in ensuring that they map all the thought leaders in their therapy space.
The way this is done traditionally is essentially a review of all publications, affiliations, associations, meetings and clinical trial participation to identify the most influential physician in the therapy area. These are then ranked and scored and sometimes market research is also conducted to validate the desk research. After this profiles are created and relationships mapped – often with the help of software.
This has been done for many decades and has been successful to a point. However, are you working with the right opinion leaders? Because you do not know what you do not know, it is difficult to tell if you are investing your marketing dollars in the right opinion leaders, and how much better your results would be if different opinion leaders were sought. One of the challenges is that the process is based on a variety of factors that get scored on factors such as credibility, relevance, reach, e.g. how many times they speak at influential congresses, how many followers they have. The factors being chosen may be misleading as you may find someone with massive social media followings but having minimal influence on clinical issues as they may be tweeting about other issues. Conversely you may have someone very influential with a private group and less than 80 members who themselves are very influential and they discuss relevant clinical practice issues that would be powerful influencers for your brand. Traditional Key Opinion Leader (KOL) and Thought Leader (TL) mapping provides detailed information on what specific physicians are doing in a specific specialty, but activity and thought leadership are not always the same thing. In our work in this space, we have discovered that it is not unusual for some of the most active ‘pharma-perceived’ thought leaders to also be perceived by peers as not credible. By using Artificial Intelligence to uncover the top KOLs and up-and-coming KOLs, we have discovered that activity does not equal influence. We have identified physicians mapped in other approaches as opinion leaders because they are very active at conferences and online but they actually are not well respected or influential. They are often called LOLs (Loud Opinion Leaders).
On top of that, we have also identified other factors that are often overlooked in more traditional analytics approaches to KOL identification. For example, the size of a physicians practice can be a factor influencing their level of influence in their region. In addition, for physicians working in hospitals or outpatient facilities, adding in data about the recorded number of specific procedures they carry out from claims data, and their complication rates for surgeons (which has an impact on their reputation), or the number of patients with a relevant diagnosis can be a powerful indicator of influence as well. This, when combined with other data also uncovers their influence within their hospital network as well.
Clearly, the more data gathered and combined, more intelligent decisions can be made. By incorporating big data (data requiring a supercomputer to open) for KOL selection, on top of regular conference, publications, trial data, and social media data, far greater insights into the level of influence can be identified and leveraged appropriately. Supercomputing and Artificial Intelligence are able to detect relationships and patterns that other traditional analytics (e.g. statistics, and linear approaches) and humans could not. Despite trillions of constantly updated data sources, the output can be cut in numerous ways, is very granular, and can be used in far more ways that assist a brand than just determining who to include on clinical trials, advisory boards and speaker programs. Our clients use the results of the analysis in numerous ways including precision targeting, stronger segmentation, and understanding why specific physicians are making the treatment choices they do. This can have significant benefit to product commercialization plans and tactics. KOL identification and mapping with AI can provide innovative thinking needed for unmet needs around a product challenge as well.
The biggest risk of using traditional KOL mapping is if it is not thorough enough to be accurate, nor updated regularly. The more data collected (big and thorough data), the more accurate the results. However, big data cannot be opened on a computer and requires a supercomputer due to the size, and also cannot be analyzed without Artificial Intelligence. On top of that, despite clinical trial and publication data being collected and analyzed, the data has no long-term shelf life. When using AI, for example in oncology, all 5000 weekly journal articles can be analyzed as they are published and the results integrated with all the other data being analyzed (patient population, claims data etc) to provide up-to-date timely information that is always relevant.
When you do require absolute certainly that you are targeting the optimal KOLs, utilizing big data and Artificial Intelligence is the optimal way to achieve superior outcomes from constantly updated data, thorough and accurate relationships identified, and more efficient use of resources.
For more information on how Eularis utilize big data and AI to identify and map Key Opinion Leaders/Thought Leaders, please read our case study here:
And contact us at Eularis http://www.eularis.com/contact