The market research and insight's function are critical to the success of any brand in any industry. However, it is especially important in the pharmaceutical industry because of the complexity involved in both the products as well as the customer stakeholders in pharma.
Every pharmaceutical brand has many customers they need to target and better understand through market research:
Every customer stakeholder involved in the pharmaceutical ecosystem has its own behaviors, attitudes, decision-making processes, needs, demographics, etc. Pharmaceutical market research must study all major stakeholders and provide deep insight into each one to better inform brand marketing strategies and tactics targeted to each stakeholder. This multiplies the complexity of pharmaceutical market research manifold compared to other Industries.
Unlike most other industries, success in the pharmaceutical market often requires brands to drive significant behavior change in customers, which is a very challenging marketing task.
Changing customer behaviors requires a comprehensive understanding of current customer behaviors and the deep-seated drivers of decision-making that drive customers to behave the way they do. Sometimes, customer behaviors can even be irrational, leading to suboptimal care for patients and less-than-desirable health outcomes.
Pharmaceutical products take more than 10 years to launch from identification of the molecule to an FDA-approved brand in the marketplace. Pharmaceutical market research, therefore, aims to inform a very extended product launch planning cycle, supporting a diverse set of needs for the launch planning team.
Face-to-face qualitative interviews in pharma have gone through a major transformational phase in the last 5 years. During the COVID pandemic, face-to-face IDIs had mostly been eliminated, but now many brands are not only returning to IDIs, but some are also even preferring them due to the perceived higher quality of the feedback.
Artificial Intelligence is also transforming the way qualitative research findings are being analyzed and reported. Using generative AI, qualitative research companies can now summarize interview transcripts in seconds, extract quotes from each interview, conduct NLP text analytics, and even generate summary slides from every interview.
Since most pharma brands sit on a lot of qualitative research from the past already, AI is also facilitating insight mining of past/existing research to inform future marketing decisions more efficiently. As a result, brands are often looking at past research to get answers, before commissioning new qualitative market research due to budget constraints.
Quantitative research in the pharma market research industry has also gone through major changes over the last decade. Increasingly do-it-yourself survey platforms powered with AIbased survey writing are now being used to create surveys more efficiently and also improve respondent experience during the surveys. On the other hand, the quality of responses from quantitative research has become questionable as chatbots are being used to take fake surveys.
AI companies are also training machine learning models to serve as synthetic respondents that can be used to supplement samples in rare diseases or Global Studies while recruiting real respondents can be challenging.
Micro Surveys are short two to three-question surveys that 20 to 30 physicians can answer within a few minutes or hours. For many research needs, this modality of micro-survey research is ideally suited to get continuous feedback from your customer community and to iteratively get quick insights, test ideas, or even collect brand feedback.
While micro survey research has gotten traction in the pharmaceutical industry, actual utilization may fall short of industry expectations due to the complexity of DIY survey programming.
AI streamlines the micro-survey design and its analysis. With quicker insights and better physician engagement, AI personalizes by learning from past responses so that micro-surveys will be more efficient and matched to the industry's needs.
Secondary research tools enabled by AI are making it easier for Pharma brand marketers and insights professionals to conduct desk research quickly and cost-efficiently. In the past, most desk research was conducted by expensive consultants with subscriptions to research databases. Using generative AI, technology companies like Alpha Cents have created new secondary research platforms that can scour the entire web and collect data/information on disease States, brands, Pharma companies, and competitors.
Pharma’s use of UI/UX research has been increasing because of interactive vis aids, greater use of digital/social media channels, and development of patient-facing mobile apps.
However, that is little/no methodological innovation beyond A/B testing in how your UI/UX research works and the field continues to struggle with scalability constraints, i.e. being able to test many options in research.
Pharmaceutical market research was late in adopting Neuroscience techniques like fMRI, EEG, or even smartwatch-based Neuroscience research. Lately, the industry has embraced this new modality of research and is using it to uncover deep insights about the patient journey and also to get more System 1-based responses to marketing stimuli like mood boards, creative concepts, positioning statements, messages, DTC ads, etc. Since neuroscience research techniques are not easily scalable and are limited to small sample sizes of 20 to 30 respondents, it is ideally suited for specialty and rare diseases, where patient recruitment is challenging. The use of neuroscience techniques with HCPs is still Limited and not likely to grow in the future either.
AI accelerates research into neuroscience by quick analysis of fMRI, EEG, and smartwatch data to advance deep insights from small samples. It allows gaining more intuitive insights into the response of the System 1 to marketing stimuli and helps the pharmaceutical campaign.
The use of emotion or affect sensing technologies is growing in pharmaceutical market research. Affect can be measured in humans using a variety of techniques, including micro facial expression analysis, voice tonal analysis, text analytics of spoken words, and biometric sensing of body temperature fluctuations.
Affect research techniques typically measure arousal (how much emotion) and valence (what type of emotion - positive, neutral, negative). Latest developments in affections in research have now allowed the mapping of specific emotions in respondents, either in response to ideas or everyday emotions experienced during the disease journey.
AI advances affect-sensing technologies by automating micro-expression, voice, and text analysis, allowing emotions to be mapped accurately. This speeds up insights concerning arousal and valence since pharma gets a much better sense of patient emotions as well as responses at multiple points during the disease journey.
Historically, pharmaceutical market research has utilized Verilogue-type services to capture actual physician-patient conversations from in-office visits and used them to extract actionable insights.
As more and more medical practices embrace generative AI, many new companies like Abridge have found innovative ways to capture millions of physician-patient conversations at scale. Currently, most of these companies are focused on HCP workflows Like patient note transcriptions, patient file updates, patient recommendations etc. but in the future, this technology can also be used for HCP/patient conversational research.
The pharmaceutical market research community has been using social listening technology for brand tracking, sentiment analysis, brand reputation defense, public relations signals, etc. With the growth of large patient communities in almost every disease state, patient social media conversations are more readily available through web scraping, but there has been little innovation in the analysis of social media content and its actionability beyond brand tracking.
An emerging use case of social listening technology is patients' findings in rare diseases where only 10,000s of patients exist and reaching them through mass marketing channels is not efficient.
Social listening, AI is an effective methodology for understanding massive conversations pertaining to the patient community that can easily help a lot in niche areas, say rare diseases. Pharma extracts actionable insights that are further than just brand tracking-precise engagement with harder-to-reach patient communities.
Pharmaceutical market research has used patient chart audit-based research methodology for decades despite some of the shortcomings of small sample size, data collection, anecdotal fallacy Etc. With the new Universal use of EHR-EMR systems, patient data is now available at scale and large language models can be used to extract better insights out of unstructured patient files instead of asking HCPs to explain their treatment decisions for a patient after looking at the file. Patient chart audit research is being transformed from stated to derived methodologies with the help of innovative companies like Clinakos, Komodo Health, and more.
Several trends will continue to shape the landscape of pharmaceutical market research over the next 5 to 10 years, ranging from highly strategic industry structural shifts.
Most pharmaceutical market research managers continue to face budget constraints every year and are looking for ways to get the same research (or sometimes even more) completed with a smaller budget and in a shorter period. Cost and speed have emerged as more important drivers of pharmaceutical market research projects and insights teams often have to make trade-off decisions between cost/speed vs. comprehensiveness/quality of the research.
There's a growing trend in pharmaceutical market research towards combining different modalities to obtain the best outcomes on every project. For example, combining qualitative IDIs with digital diaries, quantitative surveys, and text or other data analytics is now becoming more standard practice in pharmaceutical market research.
Market research used to be a services industry and market research vendors utilized a variety of software platforms to conduct research on behalf of their pharma clients. Over the last two decades, the industry has slowly shifted from services to software, and many new software companies have entered the market with do-it-yourself survey platforms that pharma brand teams can utilize directly. As a result, there is significant automation of lower-level pharmaceutical market research projects and activities, leading to lower costs and faster turnarounds for clients.
Generative AI will potentially transform pharmaceutical market research more than any other new tool or technology. Surveys will start using chatbot-like interfaces, synthetic respondents will be used to bolster sample sizes for rare disease and small country studies, qual IDIs will be transcribed, summarized, and analyzed with LLMs, quant surveys will be written by writing assistants, and dozens of other use cases will be possible.
Pharmaceutical market research is highly specialized and has historically been served by research vendors who only focus on the pharma industry. However, even within the landscape of specialized research vendors serving the pharmaceutical market research industry, there is a further and ongoing shift away from vendors who can implement any kind of pharma research vs. those who specialize in only specific types of research.
The pharmaceutical market research landscape has historically been extremely fragmented with hundreds of research vendors servicing an increasingly consolidated client market. In the last decade, however, there has been significant consolidation of research vendors in pharma, leading to industry roll-ups of large commercial pharma services companies like Syneos, Eversana, Inizio, Trinity, etc.
Most of these roll-ups have also combined market research, consulting, analytics, and agency-like services to offer pharma brand teams.
The pharmaceutical market research community has been prioritizing storytelling when presenting results of market research studies to their marketing counterparts. There is a greater emphasis from all commercial brand teams to share insights from market research in a more actionable way that allows for faster and more efficient decision-making by the team. Market research reports are no longer limited to PPT decks and include advanced data visualization tools, cloud-based interactive dashboards, AI-generated images and videos, and more.
Primary pharmaceutical research directly gathers original data from sources through surveys, interviews, or focus groups. It gives accurate information about how patients behave, what they prefer, and what they experience as well as direct feedback from healthcare practitioners; hence, helping gain a deeper understanding of the patient's journey.
Secondary research is based on secondary information available in journals of science and market reports. It is helpful in trend and comparison of competitive pharmaceuticals.
Data analysis uses statistical evaluation, thematic coding, and trend spotting to extract actionable insights to help make data-driven decisions in the industry.
Even though pharmaceutical market research continues to evolve rapidly, there are still many pressing gaps for which pharma clients need more innovative solutions.
The demand for market research technology that can deliver faster and cheaper customer insights will continue to grow. However, brand teams will face difficult decisions about the trade-off between the quality of insights and the efficiency of their pharmaceutical market research projects.
At some point, if the market research dollars they are spending do not produce aha insights, no matter how efficient the spending is, it would be unproductive.
Increasingly, the success of pharma brands depends on changing HCP and patient behaviors, which is very challenging. Unless the market research they conduct helps identify the hidden drivers of customer behaviors and unspoken barriers to behavior change, all their marketing efforts can end up being informed by what customers say they do vs. what they actually do.
Pharmaceutical market research is often based on the Paradox of small numbers, which means very large business decisions could be based on market research with a very small group of customers. As the drug discovery model in the pharma industry continues to move towards rare, orphan, and specialty drugs, the paradox of small numbers will get even worse.
The industry needs better market research solutions to get deep insights that are predictive of the larger universe of customers in the real world even though they were derived from a small group of customers in market research.
Pharmaceutical brand teams are conducting research among the same physicians and patients using very similar market research techniques, eventually leading to a convergence of thinking in the industry. As a result, many brands within the same disease state end up with similar messaging and marketing campaigns with little/no differentiation, except brand familiarity created by the order of entry in the market. Pharmaceutical market research needs more innovation, akin to the type of innovation that has taken place in Pharma data analytics over the last decade.
All Pharmaceutical brands are facing budget pressure and when budgets are being cut, market research spending is often tapped first before marketing dollars are cut back.
Even when pharmaceutical market research budgets are cut, research demands are often not scaled back and insights teams are expected to stretch their research dollars and deliver the same insights even with a 20%-30% reduction in budgets.
With this pharmaceutical landscape on the move, 2024 will be a very critical year for pharmaceutical market research. Companies have to march forward in an environment that sees rising operational costs, strict regulatory scrutiny, and the need for innovative strategies. Pharmaceutical market research will focus on the following major predictions to stay ahead in the game and create meaningful value for patients and investors.
AI and analytics will be deployed to speed up drug development, improve clinical trials, and optimize patient engagement by cutting operational timelines and cost.
Mergers and acquisitions will play a huge role in transformation as the firms will have to redesign portfolios and improve scale depending on market dynamics.
R&D investments will be steered toward "white space" opportunities with a focus on breakthrough treatments that address unmet patient needs with limited competition.
The Pharma companies would start taking stringent cost management policies in order to ensure alignment of spending with the value creation and optimum utilisation of resources in a rising cost environment.
Companies will be on their toes while navigating a constantly changing regulatory environment by keeping stakeholders' trust and demonstrating compliance with the new norms.
The pharmaceutical market research function has gone through significant changes over the last 10 years and continues to evolve rapidly, keeping market researchers in the industry on their toes. Pharma companies that will adapt to these changes on a predictive basis, i.e. getting ahead of the curve, will be better positioned to course correct as automation technology, AI and other disruptive forces change the nature of how pharmaceutical market research will work in the future. Companies that will integrate market research and insights throughout the entire organization, expanding the scope beyond the commercial teams to include clinical, drug discovery, medical affairs, HEOR, etc. will be able to leverage pharmaceutical market research more effectively and efficiently.
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Newristics
01 Feb 2024
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