The pharmaceutical industry is rapidly changing, and a new model for drug commercialization has emerged in the post-COVID pandemic environment. All the customer stakeholders that pharma serves (HCPs, NPPAs, patients, caregivers, payers,pharmacists, and more) are also evolving, forcing the industry to adapt quickly to marketplace changes.
The pharma insights function within large pharmaceutical companies is adjusting to the new digitally-driven commercialization model and shifting from a departmental silo approach to the integration of primary market research, data analytics, consulting, and other services needed to support better decision-making.
Since lead times for drug development and commercialization are very long, generating market insights and intelligence has historically never had the same urgency in the pharmaceutical industry as most other consumer-oriented industries. Launch planning for a new drug is stretched over 5-7 years and usually, there is sufficient lead time for market research and insights projects to be completed.
However, the old commercialization model is being replaced with a more nimble process, enabled by digital transformation that is accelerating the entire life science insights function. Pharma companies now have access to new sources of data and information, new technologies that can quickly extract valuable business insights from the data, and finally, efficient business workflows that enable more agile decisionmaking based on the new insights.
Over the past decade there has been significant innovation in survey software technology, leading to democratization of survey writing and even fieldwork. Brant teams can now create easy surveys themselves and get answers within hours, especially if they have first-party access to HCPs or patients. Novel survey platforms have also incorporated best-in-class data analysis and visualization tools, which means not only can the surveys be written and fielded quickly, but the insights are available instantaneously and presented in a more visually compelling way than ever before.
As the pharma commercial model continues to move towards specialty and rare diseases, there is a limited amount of data available on customer behaviors, and access to customers for primary market research is also constrained.
The latest developments in machine learning models have improved the ability of companies to generate synthetic data from small real-world data sets, whether they are from claims data, survey data, media consumption data, and more. As usual, synthetic data comes with many challenges, but the availability of synthetic data is transforming the way pharma performance insights are being discovered and delivered to brand teams.
With recent changes to privacy settings made by Google and other technology companies, there has been a recent shift in the pharma insights industry towards 1P (first-party) data. Many brand teams are looking to partner with companies that have built patient communities in respective disease states to access first-party data and insights from patients more efficiently.
Increasingly, pharmaceutical brands are launched into highly competitive disease states and their success depends on getting physicians and patients to change treatment behaviors - quickly and permanently.
Brand teams are therefore looking for pharma performance insights that go beyond the obvious and help uncover the hidden drivers and barriers of behavior change that are necessary for commercial success. The pharma insights community has been adopting behavioral science techniques, both in a primary market research setting, as well as in real-world experimentation through digital channels.
Pharmaceutical brand teams are adopting new tools and technologies that help them integrate disparate sources of data and information to identify the best set of pharma performance insights that can facilitate the most informed commercial decision-making.
Integrating information from a variety of sources not only helps accelerate the process of inside generation but also helps improve the quality of decision-making.
For example, life sciences insights teams already combine IQVIA or other prescription data with survey-based datasets for important commercial decisions like HCP targeting, segmentation, salesforce effectiveness, etc. More recently, even projects like patient journey mapping that had historically been mostly primary market research-based have now become integrated into nature because brands now combine patient claims data with patient chart audits, HCP/patient dialogue research, and even qualitative research to come up with a holistic patient journey.
Another example of integrated pharma performance insights is patient finding. Companies like Crossix, Deep Intent, Swoop, etc. are creating propensity models that integrate claims data, media consumption data, life sciences insights, psychographic/demographic data, etc. to find patients who are more likely to be diagnosed with a specialty disease state.
The integration of primary market research, secondary market research, syndicated market research, data analytics, and forecasting has also been implemented at an organizational level within pharma insights teams. In the 2020s, many leading Pharma companies have reorganized their business insights and intelligence functions and have consolidated roles and responsibilities across all research and analytics workstreams.
While in the past, there might have been different people handling market research, data analytics, and forecasting projects, now often only one team member is managing a slew of consultants and research providers, or an in-house business intelligence team offshored in other countries.
While the use of machine learning models and AI is already widespread in the former analytics function, pharma performance insights are slowly warming up to the adoption of ML/AI. Most large pharma insights teams now have task forces dedicated to creating an action plan for the adoption of AI in market research and insights. A wide range of alternatives are being considered and different companies are using different approaches based on their internal legal and ethical guidelines regarding the use of AI:
With the integration of Large Language Models into search engines, the hallucination effect of generative AI has been significantly reduced and is opening widespread adoption of AI-assisted search technology within enterprises.
Most pharma insights teams already own a lot of market research/data and AI-assisted search technology is being deployed for them to access the information/data in-house more easily and in a more usable way. Since LLMs can be fine-tuned on proprietary data sets, most Pharma teams are fine-tuning their Enterprise search engines on pharma-specific market research and data, thereby improving the accuracy of search results.
Generative AI has led to the rapid adoption of chatbots in not only customer support but also in market research and insights. Conversational AI platforms combined with knowledge graph technology can absorb a brand’s past market research/data as inputs and start providing answers to business questions through an easy-to-interact chatbot. Conversational AI platforms for insights allow brand team members to “talk naturally” to an AI analyst who can generate text and visual/graphic answers.
Pharma insights teams are experimenting with a wide range of use cases for AI to be integrated into their market research projects - either through the vendors, they are partnering with or implemented directly in-house.
Examples include AI-based survey writing, AI summarization of qualitative interviews, AI-generated data tables, charts, and summary decks, AI Improved stimuli for market research, AI-based team collaboration platforms for generating research inputs and sharing insights, and much more!
As part of broader changes taking place in the pharma commercial model, pharma insights are also evolving rapidly and are serving both as a supporter of what commercial teams need and as a driver of what the market needs.
The pharma industry is becoming more agile and Digital transformation efforts implemented over the last decade have started materializing in faster drug development and commercialization. As a result, pharma insights are also rapidly becoming more agile and are both supporting as well as enabling more efficient commercialization of drugs. In the past, launch teams had three to four years to prepare for a launch and the 15 to 20 foundational launch research and data analytics projects were stretched out over a three-year time frame. Now it is not uncommon for launch teams to start only 18 months before the launch date and produce the launch playbook in a very short period.
Continuous experimentation/optimization
- In the past, pharma marketing teams would launch new messaging or marketing campaigns and wait for a refresh for 12 to 18 months, typically aligning with a market event like the release of new Phase IV marketing data or an LCM initiative. In today's omnichannel pharmaceutical industry, marketing and pharma insights teams have to constantly optimize and fine-tune their marketing campaigns which also requires continuous exploration of new customer insights that can inform campaign refreshes. As a result, pharma insights are evolving from a traditional working model of one-time discrete projects to ongoing continuous experimentation-based research workflows.
The future of pharma insights will continue to be evolutionary and best-in-class pharma insights teams will likely have to make difficult organizational and investment decisions to stay ahead of the industry shifts.
- Most pharma insights teams will move toward an organizational model of fewer people being asked to do more and will need to coordinate with a globally distributed team. Primary market research, life sciences insights, data analytics, and consulting companies already have offshore teams to support their pharma insights clients.
However, lately, all pharma companies are facing extreme cost-cutting pressure, and many have brought some of the business insights and intelligence functions in-house and have created their own offshore teams in India, Taiwan, Ukraine, etc.
Within a pharmaceutical company, drug discovery, clinical trials, and even Medical Affairs teams have been the earliest adopters of AI. Commercial teams are now ramping up on AI adoption and pharma insights teams are probably the last ones within commercial to leverage AI broadly in business intelligence. While life science insights might be late to the AI party, it is likely to lead the way in the future and establish itself as a best-in-class user of AI within the organization.
As pharma insights teams are asked to deliver faster/cheaper/better results even though their budgets keep getting reduced every year, there will be a shift away from longer, turnkey primary market research projects and teams will look to solutions that can provide “quick and dirty” answers with data and information that is already available behind their firewall.
Pharmaceutical companies are being challenged to become more patient-centric while simultaneously launching drugs quickly and more efficiently than ever before. Speak to Market is no longer a competitive advantage for pharmaceutical brands and every disease state is hyper-competitive now, even those that are orphan or rare disease States.
Pharma insights teams are expanding beyond primary market research to more digital and agile sources of customer insights, information, and intelligence.
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Newristics
01 Feb 2024
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