Key opinion leader (KOL) identifying and mapping is a critical part of business marketing, particularly in the pharmaceutical industry. AI and big data are making a huge difference in speeding up the process of spotting the right influencers/thought leaders and making sure only the optimal targets for a certain project are reached out to, saving money and effort.
But how exactly are these emerging technologies making a difference in improving the efficiency of these complicated research tasks?
This article explores the methodology behind KOL mapping and how it can turn your influencer marketing strategy on its head.
What is KOL Mapping?
A key opinion leader (KOL), or “thought leader,” is a highly knowledgeable and experienced influencer who has a respected reputation in a given field or industry. Generally sought after in particular in the health care sector, KOLs have distinguished themselves by performing some notable activities that set them apart from their peers during their career. These activities could range from sitting on an influential organization’s board, speaking at national or international congresses and/or publishing their research results in peer-reviewed journals. (Also read: ‘Everything Is Solvable’: Advice From Female CEOs in Tech.)
Identifying KOLs and their digital counterparts, known as “digital opinion leaders” (DOLs), is critical for an organization’s marketing efforts. This is especially true for pharmaceutical companies, as trusted and independent physicians and health care professionals’ opinions influence people more strongly than those of other doctors when it comes to choosing a drug, medical device or therapy. The success or failure of many medications—especially newer or less-known ones—often depends on KOLs’ opinions. Hence, pharmaceutical companies invest significant efforts in mapping KOLs.
But things are rarely that simple in practice.
Choosing the right people—those who are influential enough, may want to hear your message and are available for a conversation—out of millions of practicing physicians and other experts is literally like looking for a needle in a haystack. If that haystack were the size of Mars.
So how do you choose the right people?
Artificial Intelligence: The Key to Finding Relevant KOLs
The sheer number of data sets, especially in health care, that we can access in our modern, interconnected world is simply breathtaking.
Thousands of research papers are published on a weekly basis in all kinds of journals, and the amount of data coming from electronic medical records, claims data and daily practice can be simply too overwhelming for any human to analyze. Even the smallest piece of information can, however, be critically important when it may affect a supposed KOL’s reputation—for example, a surgeon’s rates of complications or necessary re-operations.
However, not all data has the same value: Does a congress presentation in a regional conference hold the same weight as, say, being a featured contributor in a World Health Organization guideline? Obviously not.
So, given all this data, how can you decide who is the most influencing KOL in a given area? The short answer is AI.
Crucial Applications for AI in Key Opinion Leader Mapping
1. Data Organization.
AI is able to scan through data much faster than a human could—referring to a set of rules to assign scores to the more important data, organize them and present them legibly so the analysts can study each KOL’s profile before it’s presented to the clients. (Also read: Robotic Process Automation: What You Need to Know.)
In other words, AI is able to scrape this immense amount of data, identify relevant or important info and discard everything unnecessary so humans can refine their searches. It can combine data from different sources to determine an expert’s degree of influence at international, national, and local levels.
2. Correctly Identifying KOLs.
People hardly ever give their name in exactly the same way for different activities on different websites.
For example, when collecting info about a particular influencer, you may find him as “Claudio Buttice” as a paper author, as “C.A. Butticè” on a website and as “Buttice, C” in a textbook. When thousands of names are collected, and the number of people sharing the same initials is high, things can become quite confusing.
AI can make obvious decisions based on simple rules and determine when it does not need to worry about an accent missing or a one-character difference. It can also look up to databases on healthcare professionals for final verification. If properly instructed, a machine learning algorithm can eventually start to recognize specific KOLs by integrating data about their affiliations, the town they come from and/or the organizations they are members of to make it sure a given activity is assigned to the right person. (Also read: 3 Amazing Examples of Artificial Intelligence in Action.)
Searching for specific activities that set a KOL apart might become much harder when these experts work in non-English-speaking countries, and even more so when their language is based on non-Latin alphabets (such as Japanese kanji, for example).
Experts from different countries might be found in specific databases only, and some documents—such as national guidelines—may exist only in their native languages. The only ways to overcome these language barriers are to hire an army of international specialists fluent in every language on Earth or to enlist the help of a single, very efficient automatic translation tool.
The most advanced translation tools, such as DeepL, make full use of their neural networks to capture even the slightest nuances of human language. The application of machine learning to translation has allowed these tools to make leaps and bounds in recent years: Now, the right tools can translate document in mere seconds; and even basic translators that come with your internet browser allows you to make sense of foreign websites in an instant.
4. Improved Communication
Machine learning can extract many unexpectedly useful insights from the big data vault.
It can identify patterns that define a potential KOL’s degree of influence and reach, and it can be used to predict the best paths to approach a KOL. Put differently, besides streamlining the process of identifying the KOLs, AI can provide actionable insights on the best way to overcome potential communication challenges. It can suggest the most efficient avenues to get closer to a given expert by understanding their specialties, preferences and future career developments. (Also read: How Can AI Help in Personality Prediction?)
Can AI Substitute Human Analysts?
We all knew it would come to this, didn’t we?
Whenever the AI topic is addressed in any form or shape, the main question on everyone’s mind is whether the newest ML-powered tools will render human professionals obsolete or largely unnecessary. The answer is more or less always the same—and in the KOL mapping industry things are no different.
In a nutshell: No; humans are still needed. AI can ease some of the most menial and repetitive tasks, but even the smartest tools cannot be left unattended. (Also read: Will Robots Take Your Job? It Depends.)
Current algorithms are good at creating long lists of names by merging information scraped from the public internet. They can be smart at finding connections, identifying a relevant KOL’s activities and even building a reasonably believable profile of that KOL’s position and affiliations. However, they all but lack the ability to describe the thing about the KOL that really matters, from a client’s perspective: the human being behind the name.
Who is this specific KOL? What’s their sphere of influence? What are their skills, their preferences, their specialties? And—more importantly—what’s the best way to approach them for marketing purposes?
Only a skilled human analyst with has a good degree of industry experience can discard all the noise from data, remove potentially predatory activities, find the influencers that matter, build their profiles and tune the list of names to the client’s specific needs. Simon Rosenberg, CEO of Scout—one of London’s most prominent KOL mapping agencies—put it thusly:
“Algorithms are a good guide, but left to their own devices, they are like asking the children to redesign the living room. AI is always a good starting point and gets you quickly down the road, but you have tidy up after. You can’t clean your house just by turning a vacuum cleaner on and leaving it on the floor—there has to be some physical work involved to get a shine.”
The KOL mapping industry might look like a small niche. But given the size and scope of the health care and pharmaceutical market, it isn’t.
Countless brilliant minds need to be identified every day before industry liaisons can connect with them. AI and ML tools have proved game-changers in this vertical, which was one of the earliest and leading adopters of these technologies. (Also read: Top 20 AI Use Cases: Artificial Intelligence in Healthcare.)