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.
Commercial teams nowadays are being asked to deliver sales pitches while swimming in an ocean of product, customer, and performance data. This is nowhere more challenging than in the highly regulated pharmaceutical industry, where promoting new treatments must be calibrated so as to grab the attention of busy clinicians with little spare time and convert it into business without resorting to hard selling, all while reaching the right stakeholders whose influence can multiply the effect of a sale. Deploying a Big Data-driven market research approach can be a powerful tool in orchestrating such a tightrope act.
Today, a dependence on labour-intensive manual research on the one hand and an overwhelming amount of raw data on the other can surpass the analytical capacity of even the most effective sales rep, leading to missed opportunities to leverage untapped insights.
This is where a Big Data-driven approach can help. A well-designed research methodology can breathe new life into lead generation by identifying with laser-like precision those clinicians that are most open to receiving sales pitches in a particular disease area. To generate such insights, it can automate the tracking of some clinician activities that tend to go unnoticed, the number of times a drug or therapeutic area is mentioned in a speech or presentation, or pre-print publishing in medRxiv and other non-peer reviewed repositories. The value of monitoring these activities is that it pinpoints in real time, sometimes serendipitously, the latest medical topics that circulate in the medical community before they make it to top journals. Having such customized knowledge at their fingertips can be extremely valuable for reps and marketers alike, as it provides them with a blueprint for personalizing their communication with individual clinicians. This increases the odds of securing precious “attention share” and, ultimately, converting attention into sales.
Another way Big Data can aid brand teams in refining customer value propositions is by providing insights into KOL feedback and scientific discussion around a given drug or disease area. Such insights, derived mostly from social listening via Twitter, Reddit, and other online platforms, allow pharma teams to filter marketing pitches on individual KOL criteria as expressed in more subtle and informal ways. Insights may include conversations about competitor products and activities that can help reps anticipate and “disarm” unhelpful comparisons, and they may even be geolocated for even higher customization.
To apply this level of granularity to their B2B and B2C operations, pharma companies need to invest in the necessary tools and capabilities. Opting for an external platform that collects, consolidates, and analyses such data can be a powerful, cost-effective solution.
However companies choose to go about it, the important thing is to let go of the “analogue mindset” and pivot to more data-rich, nuanced business intelligence tactics. The stakes of not doing so are simply too high: insufficiently targeted marketing, sales pitch overload for clinicians, burnout for sales team members, and reduced market share for the firm. Some organisations are fully aware of the benefits of leveraging Big Data in their marketing strategies, yet are failing in the implementation.
Healthscape, a software platform that transforms pharma-relevant data into actionable insights, has answers to these questions. By simplifying the process from data collection to analysis across multiple categories – clinical usage, social listening, competitor activity, events, and publications – it removes a major logistical challenge for marketing and sales professionals, as they no longer have to extract and triage this information manually. Its suite of customizable tools and algorithms slices and dices the data any which way that makes decision-making most efficient and coherent. In a nutshell, it helps commercial teams develop a more sophisticated understanding of HCPs and empowers them to focus on the highest-hanging fruit: building KOL loyalty and long-term sales potential.
So talk to us and let us show you a demo of how Healthscape can supercharge your business. We are sure it will delight you.