How to Embed AI into Healthcare Market Research for Greater Results

Market research in healthcare has already greatly benefited from the digital revolution, and artificial intelligence (AI) is already being used to reshape market research, from recruitment and questionnaire design to data organization and analysis. In addition, AI-powered disruptors are dismantling all areas of the value chain in market research, taking revenue away from incumbent healthcare market research agencies.

To survive, agencies and market research teams need to do more than simply graft AI solutions onto existing processes. Instead, market research must be reimagined and reorganized in light of these solutions and built into a cohesive and coherent approach. Only by embedding AI into healthcare market research and understanding how their own market is changing can businesses hope to maintain relevance in the years to come.

Challenges facing healthcare market research teams

Market researchers working in the healthcare and pharmaceutical industries face a lot of pressure to perform. Many brand decisions are made based on insights from market research, and if those decisions don’t pay off, the blame often lands at the feet of market research and business insight teams.

There are a number of challenges facing such teams.

● Despite technological advancements, respondent recruitment remains relatively slow and is a key limiting factor. Identifying, targeting, and communicating with segments is a lengthy and imprecise process.
● Data gleaned from traditional market research is static, representing a single snapshot in time and space; between observation and delivery, markets can shift dramatically. This makes it difficult to predict and respond to changes in healthcare providers (HCP) and patient behavior and market dynamics.
● The emotional aspects of responses have also been historically very difficult to identify, organize, and analyze. Thus, researchers miss out on key customer information and are unable to understand the emotional and psychological levers involved in decision-making, especially as they differ between segments.
● Finally, despite—or perhaps due to—an unprecedented abundance of data, market researchers still struggle with linking results from one piece of research to the next. Similarly, quantitative and qualitative research has tended to remain distinct, thus multiplying time and money spent on recruitment, communication, organization, and analysis.

Overall, these problems erode confidence in market researchers’ findings and greatly limit what insights can be made.

Applications, use-cases, and impact

By leveraging AI, market research companies and embedded teams can make better use of consumer data and ultimately provide a higher-quality service to stakeholders. Here are just some of the ways AI is being used successfully to better understand healthcare and pharmaceutical markets.

Recruitment

As already mentioned, respondent recruitment remains a key complication in market research. It takes time and money, and as the first step in market research, is a primary limiting factor. A variety of AI tools now exist, however, to help optimize this process.

Phrasee, for example, is an AI-powered SaaS platform that enables businesses to find the most effective marketing language to meet their goals. The app generates dozens of on-brand messages based on a few simple questions about your campaign, sends out tester messages to a small portion of your audience, then gauges engagement and decides on the best language to use moving forward, increasing participation rates dramatically.

Primary market research

Primary market research, which refers to qualitative and quantitative research conducted via surveys and questionnaires, has remained relatively unchanged in scope and complexity even after the digital revolution. But AI is now being used to reshape how it’s conducted in a variety of ways.

For example, Revuze is an AI platform that collects unstructured data from surveys, reviews, and other user-generated content. It then intelligently classifies and organizes it to identify topics that matter most to consumers, building a unique taxonomy for each without the need for human input.

In a similar vein, TAWNY.ai is leveraging AI to understand the emotional impact of campaigns via affective facial analysis, allowing for scalable, deeper, and more easily quantifiable emotional insights from respondents. Similar AI solutions exist for detecting emotion in voice and text (from surveys or focus groups, for example).

A variety of AI companies also offer powerful solutions for social listening, which goes much deeper than social monitoring by leveraging natural language processing and sentiment analysis to understand the conversations people are having in real-time about a particular topic, product, or service.

Remesh.ai represents a revolutionary new approach to combining qualitative and quantitative research by allowing for qualitative insights at great scale. Remesh’s platform enables a single moderator to engage with and analyze the responses from up to 1,000 participants in real time. From open-ended responses and voting exercises, the Remesh AI analyzes and organizes responses, revealing common themes and the spread of opinion.

Finally, Qualtrics is a popular and powerful AI platform that enables companies to scan responses to understand the impact of questions and campaign content on respondents. It can then make suggestions on how to improve questions to maximize response rate and quality.

Secondary research

Secondary research refers to the use of data collected and organized by outside sources, including government agencies, peer-reviewed journals, newspapers, and other publications. We can also include here archived in-house research from previous campaigns. There is an enormous amount of data available to market researchers today—so much so that no human team can hope to ingest and analyze it all.

Fortunately, a variety of AI solutions exist. In a previous article, we introduced the idea of internal chatbots which can answer questions about data posed in a natural way, such as, “What is the key unmet need uncovered in this patient segment?” This is one technique Eularis is already using to help healthcare and pharmaceutical companies make better use of their data.

Strativo is another AI platform helping businesses get more out of their secondary research. According to an article in Forbes, 95% of content available to businesses for decision-making is unstructured, spread about in PDFs and various other file formats that defy simple indexing and analysis. Strativo enables businesses to unite secondary research sources in a single database and offers AI-powered search solutions for what is essentially a “Netflix for enterprise market research.”

Signal.ai provides a similar solution, leveraging a library of 5+ million news, blog, broadcast, and regulatory documents, plus natural language processing, to understand shifting public and regulatory sentiment regarding products, ingredients, treatments, and more.

Delivering insights

The final stage in market research, and arguably the most important, is delivering insights to stakeholders in a comprehensive, cohesive manner. Known as “storytelling,” this means building a narrative that allows decision-makers to understand the insights, their context, and how they should be applied.

AI is impacting delivery in a big way, too. Lexio by Narrative Science, for example, uses artificial intelligence to automatically generate data-based stories in a human-readable format, offering genuine insights from any number of business sources. A large number of such tools exists today, making it easier for market researchers to craft data-driven, persuasive, actionable insight stories.

The Future of AI in healthcare market research

Artificial intelligence is advancing at a rapid rate. Market researchers need to start preparing now for the tools of the future and planning a roadmap for how to integrate key elements in the new ecosystem. These include:

● Data. New forms of data are being created all the time. Soon, this will include deeper emotional information from customers and consumers. Wearables, cameras, social media content, and device data from smartphones and tablets will come together to paint a fuller picture of customers, which will need to be leveraged at every step of the market research process.

● Intelligent analysis. AI is becoming more adept at not just organizing but also intelligently analyzing and evaluating data in ways that more and more closely approximate human interpretations. Elements of this exist already, but it will become increasingly important for market researchers to understand how these analyses are generated, what human biases influence them, and how they can be leveraged to benefit stakeholders.

● Always-on insights. A holy grail of market research, continuous, spontaneous conversations with a wide audience may one day replace questionnaires and focus groups. Advanced conversational AI, like that offered by industry leader Amelia.ai, will enable market research teams to engage with thousands of individuals in a human, conversational, interactive fashion. As AI learns to respond to human comments, evaluate their affective and cognitive components, and ask follow-up questions, researchers will have access to a longitudinal, ongoing picture of the market, rather than a static one.

● Real-time decisions. As a result of these changes, market research teams will need to adapt their data collection, organization, and presentation for real-time decision-making. The benefit is greater agility and the ability to respond to shifting markets as they evolve. This is one area where a truly cohesive and integrated AI approach will shine, as real-time information from always-on insights and intelligent analysis needs to work seamlessly with decision-making software (and humans).