Influencer analysis done the right way
Influencer identification that relies solely on data from mentions, tweets, retweets, likes, etc. is easily manipulated and lead to misidentification of true influencers. That is the main reason we have built and developed our proprietary algorithm that we believe is the most accurate and trustworthy way identify the true influencers in any healthcare topic, large or small.
- Key Opinion Leaders
- Symplur Signals is powered by the world's largest healthcare Key Opinion Leader database. The Healthcare Social Graph® provides segmentation across 18 stakeholder categories. Easily filter any conversation by who the most influential voices are.
The Symplur algorithm is a recursive algorithm that is made possible because of the Healthcare Social Graph, a neural network made up of billions of data points on the hundreds of millions of unique users whose activity we have recorded and categorized in this space since 2011. Our computations do not just rely on how often you are being mentioned, but it considers how influential are those parties who are mentioning you. And, how influential are those parties that are mentioning them, and how influential are the parties mentioning those parties, and so forth. The algorithm loops through itself in this fashion about a thousand times over and results in a score that measures your level of influence, we call it your Healthcare Social Graph Score. This algorithm is very similar to Google’s PageRank algorithm that determines the rank of their search results.
Additionally, our algorithm takes into account what healthcare stakeholders are mentioning you. If it’s a known doctor, patient, government public health account, etc., then it will give more weight to that mention. If, in contrast, a mention is coming from an unknown party, then that mention is significantly discounted.
Lastly, our influencer analysis is highly focused on influence in very specific topical areas. For instance, if your goal is to identify influential cardiologists you are most likely analyzing cardiology related discussions. In this case, the Healthcare Social Graph Score gives greater weight to conversations and mentions from parties who themselves are specifically influential in cardiology. Conversations and mentions from parties who are influential in non-related areas, like dermatology, are not given as much weight.
This same dynamic weight assignment applies to even more specific topics as well. So you’re able to identify KOL’s on the broad topic of cardiology and inside more specific topics within cardiology such as hypertension, arrhythmias, or hyperlipidemia.