Full-text search for competitive insights and better opinion leader selection

Identifying the right medical experts to include in your expert engagement strategy is difficult. Partly because of the exceptional volume of new research data being generated. In addition, digitalization makes more information readily available but at the same time drives the production of increasingly diverse and unstructured data, making it difficult to transform it into actionable insights.

In this post, we will focus on one important dimension in opinion leader mapping and prioritization - industry collaborations and competitive intelligence. As those who follow us closely know, the Monocl platform is built from scratch to capture diverse and siloed data and converts it into holistic 360-degree profiles of stakeholders. It already offers insights into industry collaborations through the monitoring of clinical trials, industry payments and news articles. Last week, however, we added yet another dimension with the introduction of full-text search capabilities for articles. Here we want to briefly show you how insights from full-text articles may be applied to gain competitive intelligence insights.

Real-life use case: re-evaluation of existing portfolio of opinion leaders

Pharmaceutical Inc. had just received clearance to proceed with the development of a monoclonal antibody thought to be a promising treatment of Non-Hodgkin Lymphoma. As a part of their pre-launch activities, they executed a thorough competitive analysis and crafted a differentiated messaging strategy and positioning of its compound. An important part of Pharmaceuticals Inc’s launch strategy was to craft and execute on an opinion leader engagement plan.

The company had been active in the field for a number of years and had established relationships with opinion leaders in the space. Luckily, Pharmaceutical Inc. was using a central repository to track all the opinion leaders that the company had collaborated with throughout the years. At the time, they had ongoing engagements with 30 opinion leaders - both national and international - in the immuno-oncology space. As a part of their engagement strategy they were performing a re-evaluation of all existing engagements by examining their portfolio along the following parameters:

1. Conflicting engagements: IO is a crowded space and Pharmaceutical Inc. had 3 key competitors in this space - Pharma A, Pharma B, Pharma C. As a results of the extensive competitive analysis that was executed, the company had developed a differentiated product positioning with a clear value proposition in relation to its three competing products. Therefore, the company wanted to perform a mapping of all potential conflicting interests the opinion leaders could have, to ensure they are not involved with the key competitors, broadcasting double/contradictory messages to their peers. Engagement with any of its three key competitors is regarded as grounds for terminating certain collaborative efforts.

2. Scope of influence: Pharmaceutical Inc. used Monocl in combination with secondary data to do an updated assessment of each opinion leader’s scope of influence by looking at:

  • Scientific influence through analysis of publications output and impact, level of international collaborations
  • Recent speaking engagements
  • Recent clinical trial involvement
  • Current engagements in societies, editorial boards, patient groups, regulatory functions, working groups
  • Received grant payment

To address the conflicting engagements, Pharma Inc. started off by looking at payment data, which shows no results. Now, let’s search for corporate ties with any of the three main competitors using Monocl’s recently released full-text search capabilities.

A full-text search means unleashing Monocl’s computational power to screen all sections in a publication for specific words, including the Acknowledgement- and Competing Interest section where conflicting engagements with your competitors will be mentioned. This opens up a completely new use case. Now, by searching for the names of three main competitors it becomes evident that of the three experts - Expert A and Expert B - have been engaging with the key competitor, Pharma A.

Two of the experts in Pharmaceuticals Inc's existing portfolio have in some capacity been involved with competitor Pharma A.

When we go into the individual profiles we can drill down into each publication and trial in which Pharma A has been mentioned. By doing so, we can unveil very interesting data points:

  • Expert A recently published a paper in which it was disclosed in the Competing interest section that Expert A is acting as speaker on behalf of Pharma A and has given lectures at educational symposiums for Pharma B. Expert A was deemed unsuitable for conference and speaking engagements as he was promoting competing products in parallel, which could dilute the product message/brand equity and credibility.
By drilling into the details of the search results it becomes evident that Expert A is is working closely with competitor Pharma A.
  • Expert B published a paper 3 weeks ago in which Pharma A was state as a partial supporter of the work in the papers acknowledgement section and it was stated that Expert B is currently receiving Advisory Board fees from Pharma A. In addition, Expert B has been involved in 13 trials that in some capacity is related to Pharma A.

With the above insights, Pharmaceutical Inc. decided to exclude the two experts from their engagement plan. Instead, they sought to replace the above experts with up-and-coming rising stars with no previous corporate engagement.

I hope you enjoyed this brief use case in which we try to showcase how to make the most our of this recent update. Hopefully, this gives you an idea of the type of value that a more data-driven mapping and prioritization strategy can bring to your team. Stay tuned and subscribe to our newsletter for more updates on how you can make data work for and enable you to make unbiased, smarter decisions.