The role of AI in the biopharmaceutical sector to date has focused mostly on optimising and accelerating R&D processes and on innovating clinical trials. Those activities belong to the pre-commercialisation stage of bringing a new therapy to market, and have historically absorbed the lion’s share of investment and attention in the industry. But with companies increasingly looking to AI to also support their commercialisation efforts, a new horizon is sliding into view: realising the technology’s potential to revolutionise biopharma’s marketing and sales processes.
Taking an asset to market is the culmination of what is usually a multi-year and multi-billion dollar/euro investment. The granting of a marketing authorisation, however, is not an assurance of success and often many challenges must be addressed to achieve a successful launch. One key factor that is critical to the success of a new medicine is having robust data and insights on which to base your launch strategy and make investment decisions. This could be information about unmet patient needs, treatment paradigms, prescription trends, influence maps in companies’ top accounts, share of scientific communication by segment, KOLs, digital opinion leaders (DOLs), and healthcare providers. In short, access to data is an opportunity for companies to gain competitive advantage before, during, and after product launch, and if there is one characteristic that neatly sums up AI capabilities, it is “expresswaying” access to and interpretation of large volumes of data.
The implication for the industry? Thanks to AI-enabled data analytics, medical affairs, sales, and marketing teams can gain deep commercial insights that not only measure the impact of existing market access and adoption strategies, but also serve as a guide on how to improve them by collecting, interlinking, and pinpointing the most relevant data and messaging approaches for each target stakeholder group. In particular, by gaining a deeper understanding of their KOLs and relationship networks in real time, medical affairs teams can craft highly tailored scientific communications, and by harnessing the power and speed of AI in the pre-launch phase, improve their understanding of a new therapy area at scale very quickly.
One of the most intuitive applications of AI in drug commercialisation is enabling high-precision profiling of HCPs, so that marketers can design customised outreach strategies instead of relying on a traditional one-size-fits-almost-all approach. AI technologies make this possible by micro-segmenting key stakeholder groups not only across therapy area, publishing output, or visibility – the traditional approach taken in audience segmentation. They also do so according to size of individual physicians’ professional networks and the degree centrality they occupy in them, communications preferences, availability, and location. Further, AI can search beyond companies’ existing customer data sets and unearth new cohorts of practitioners who may be open to considering novel treatments for some of their patients. The results can be applied to optimise engagement strategies, connect with would-be partners via their preferred communication channels, and ultimately increase market adoption rates.
In the same way that AI technologies can tap into public online databases and extract KOL and HCP-level data at a fraction of the time it would take a person to do it, they can garner patient-level data. For example, an AI algorithm trained to process natural language in the medical domain can harness data found in public forums or social media channels where patients share personal experiences of using a particular therapy or a drug. Those comments can then be cross-linked with physician-level observations shared in a similar social context, yielding multi-perspective insights from openly shared real-world evidence (RWE). Having such data enriches pharma companies’ understanding and monitoring of product performance across indication, region, physician specialty, type of patient (e.g. GP practice, hospital, specialty clinic), or type of payer (public vs. private). Lastly, AI-enabled social listening can also be leveraged to detect patterns in prescribing behaviour and to assess prevailing public sentiment towards new or existing medications, which are essential tools for running brand diagnostics.
On the regulatory and compliance front, AI can accelerate drug commercialisation by reducing the time and human resources that are invested into compliance review, allowing the latter to be shifted towards more demanding cognitive tasks that cannot be automated. AI-powered analyses can also help leaders decide how to deploy sales teams across a geographic region or therapy area for greatest return on investment, using insights from the micro-segmentation and social listening applications described above to determine what type of content and communications channel would be most impactful for each.
Last but not least, data-driven AI technologies can support drug commercialisation in the post-marketing or pharmacovigilance stage by compiling publicly shared data on undesirable side effects in real time. This can help companies conduct causality assessment on an ongoing basis and react faster to minimise damage to both patients’ health and the health of their own brand image.
Adoption challenges in Europe
Despite the competitive edge promised by embedding AI and machine learning-powered technologies into pharma´s commercial operations, the EU biopharma industry has been slow to adopt such technologies , according to a report by IQVIA published in 2018. Although the survey cited dates back to 2018 and company readiness to embrace AI has likely improved since then, it is an “open secret” in the industry that in comparison to the U.S. and some Asian countries, Europe is not moving as fast as it should and could.
How PeakData can help
PeakData’s Healthscape software can support organisations that are ready to deploy AI for drug commercialisation. It comprises a set of tools that perform real-time web wide market intelligence based on publicly available, decision-shaping conversations about drug uses and indications, real-world outcomes, and patient and provider-generated insights across social media, peer-reviewed publications, preprint repositories, press releases, conference agendas, and other formal and informal sources. This comprehensive search is complemented by rigorous and nuanced analysis of the findings, according to client-defined variables and characteristics, to obtain a panoramic view of all the open data and social signals generated around a drug that is about to go to market or is already being commercialised. As an added value, Healthscape’s capabilities are governed by a clear strategy for sourcing the data such complex AI searches require.
If you are interested in learning more about Healthscape and how it can make AI a reality in your company´s marketing and sales strategies, we invite you to schedule a demo.
Influencer ecosystems are continually evolving in response to changing consumer preferences and the pharmaceutical sector is no exception. On one hand, patients and their doctors are looking beyond traditional Key Opinion Leaders (KOLs) for guidance on therapies and treatments and want to hear from alternative sources, such as Patient Opinion Leaders (POLs), whose feedback resonates on both personal and real-world evidence level. On the other hand, as a growing number of people seek information online, they discover a parallel universe of Digital Opinion Leaders (DOLs), who tend to have a large followership and are more approachable. We wrote about this expanding universe of Opinion Leaders (OLs) in a previous blog.
Despite these shifts, many pharma companies cling to the idea that KOLs represent the best, safest, most authentic marketing partners. Yet as audiences are increasingly more diverse, better informed, and often skeptical of authority figures who speak only to their “filter bubble” of similarly credentialed physicians, KOLs are really just one of several types of sources healthcare professionals look to for advice and information.
Not only that, but the term “KOL” itself has begun to lose its luster. As far back as 2015, 62% of medical professionals and 56% of pharmaceutical executives believed that it should be replaced, according to a survey of nearly 400 respondents from both Europe and the U.S. On the physician side, many respondents felt that it is often used too loosely or attached to people who do not warrant that title or the trustworthiness implied by it. On the pharma side, many others signaled that their companies were phasing out use of the term because of its unflattering connotations and it being seen as “marketing speak.” It is reasonable to assume that in the five years since that survey was conducted, the “KOL” terminology has hardly gained back much support.
In the context of this less-than-enthusiastic adoption of the “KOL” concept, it is obvious why alternatives such as DOLs and POLs have been gaining currency. But what makes the most novel among them – Connected Opinion Leaders (COLs) – stand apart from the rest and what is their value to pharma companies?
Although COLs´ profiles seem similar to others, their unique feature is that they bring together the best of the KOL, DOL, and POL worlds. They are not only digitally savvy, relatable, and with an active presence across multiple content channels, but are also sought-after experts, published authors, and conference speakers. And it is precisely their panoramic view of the industry from both a professional and a communications perspective make them a strategic partner to pharma marketers who want to deliver sleek messaging to an engaged professional network.
To build a relationship with COLs, the industry must expand the prism through which it searches for them. Instead of relying only on traditional metrics, such as impact factor of journals they publish in or conferences they present at, it should seek to glean insights on factors such as who else is in their networks or what publicly communicated research projects they participate in. Pharma marketers should then triage those traditional metrics with social stats such as the frequency of their online activity, number of followers, geographic reach, likes, and reposts/retweets, to name a few. Once identified, to then engage them in a sustainable, authentic partnership, pharma marketers should consider offering them membership on advisory boards, consultancies, or content creation on blogs or proprietary websites, where their expertise, communication and social skills can truly shine. At PeakData, our software platform HealthScape™ can help your company develop an effective strategy for identifying COLs with the highest potential and relevancy for your business. For example, our algorithms and comparison tools let you sift through data on healthcare professionals labeled according to multiple features, select the ones that meet your essential criteria in terms of therapeutic area or publications, and then triage the results against relevant social metrics to understand each person´s reach and digital footprint. Give us a chance to show you a new, smarter way of choosing who you entrust with your marketing message – we are sure our solution will surprise, inspire, and delight you.