Introduction

In an era where Healthcare Professionals (HCPs) engage with scientific advancements and medical developments across a vast and dynamic digital ecosystem, traditional segmentation methods are no longer sufficient.

The conventional approach to HCP profiling—relying primarily on prescribing patterns, internal CRM data, and basic demographic attributes—fails to capture the evolving complexity of today’s healthcare landscape.

This limitation is particularly evident when pharmaceutical companies launch new drugs or receive approval for additional indications. Expanding into new markets or specialties, such as targeting new specialities for a relatively new indication of a drug when you have been previously only targeting primary specialities, requires insights that internal data alone cannot provide.

Internal data reflects past relationships and existing specialties but offers little guidance when engaging with HCPs in uncharted segments. To achieve impactful engagement in these scenarios, pharmaceutical companies need a richer, more nuanced understanding of their target HCPs—an understanding that extends beyond historical behaviors to uncover their motivations, beliefs, and preferences.

In light of the technological transformation that has happened over the past couple of years with the onset of Large Language Models (LLMs), this paper highlights a modern approach to the HCP segmentation to highlight the concepts of beliefs, barriers and the confluence of these to optimize engagement strategies. It is a methodology that combines traditional internal metrics with the deep, contextual insights available from external data sources.

By analyzing HCP-authored opinion pieces, scholarly publications, conference commentary, and social media discussions, and leveraging LLMs to extract meaning from this data, we can build a comprehensive picture of HCPs’ attitudes, barriers to prescription, beliefs, and priorities. These insights are essential for developing engagement strategies tailored to new specialties and emerging markets, enabling more authentic and empathetic conversations with HCPs and ultimately contributing to better patient care and impactful therapeutic adoption.

Let’s dive into it: Headwinds indicating a change in the traditional segmentation approach

HCPs today are no longer passive recipients of information. They actively participate in global medical conversations, voicing opinions on therapies, challenging clinical guidelines, and debating real-world outcomes. Their contributions extend beyond traditional platforms, encompassing social media posts, blog entries, and podcasts.

This rich digital ecosystem offers a wealth of insights into their clinical philosophies and preferences—if one knows where to look.

For pharmaceutical companies, the stakes are higher when engaging with new specialties or launching drugs with expanded indications. Conventional methods like surveys or focus groups may provide some insights, but they are resource-intensive, slow, and difficult to scale. External data, on the other hand, provides a scalable, real-time alternative. By analyzing an HCP’s digital footprint—whether through their publications, forum discussions, or social media activity—companies can decode the “why” behind their behaviors. This has become more relevant and easier to extract with the onset of efficient methodologies to capture the publicly available data points on the HCP and use LLMs to synthesize them into structured data points. The emergence of efficient methodologies to capture publicly available data points on HCPs and synthesize them into structured data points using LLMs has made this more relevant and easier to extract.

This shift in perspective enables pharmaceutical companies to uncover motivations and barriers that influence HCP prescribing decisions. What excites them about new therapies? What concerns do they have about affordability or long-term safety?

Without these insights, segmentation efforts risk being incomplete and generic in this era.

Key Components of a New Segmentation Exercise:

Identifying Personas Behind the HCP

For years, segmentation centered around simplistic groupings, often categorizing HCPs by prescribing volume. While functional, this method reduced HCPs to static numbers, overlooking the nuanced factors that drive their choices. The shift to persona-driven segmentation marks a significant evolution, focusing on understanding why HCPs behave the way they do.

Personas represent archetypes that encapsulate an HCP’s attitudes, motivations, and decision-making style. Imagine the “Evidence-Driven Innovator”, an HCP eager to adopt therapies supported by robust clinical data. Contrast this with the “Cautious Skeptic”, who waits for long-term, real-world evidence before making changes to their prescribing patterns. Then there’s the “Cost-Conscious Advocate”, driven by affordability and access for patients.

By building personas, pharmaceutical companies can tailor their strategies to resonate deeply with HCPs. These personas go beyond prescribing behavior, combining internal metrics with external insights to create a multi-dimensional view of each HCP. Internally available data with the pharma companies may provide a foundational view but often lacks depth. Metrics like CRM interactions and prescribing trends highlight what an HCP does but fail to explain why—whether choices are influenced by clinical confidence, cost concerns, or hesitations about safety.

This limitation becomes especially pronounced when targeting new specialties or launching drugs in new markets, where historical engagement with HCPs may be minimal or non-existent. External data fills these gaps, offering insights into beliefs, preferences, and barriers through HCP-authored publications, social media activity, and conference commentary.

Belief Statements: Anchors of HCP Engagement

Belief statements encapsulate the core philosophies, values, and priorities that guide an HCP’s clinical decision-making. These insights reveal how HCPs view therapies, patient care, and innovation, offering a lens into their prescribing behavior and willingness to adopt new treatments.

Examples of belief statements include:

Belief statements serve as the anchor for targeted engagement, ensuring that communication is aligned with the HCP’s worldview and priorities.

Barriers: The Obstacles to Prescription

Barriers are the tangible or perceived challenges that prevent HCPs from prescribing a therapy. These hurdles can stem from clinical concerns, logistical constraints, or even philosophical differences. Addressing barriers is essential for enabling adoption and building trust.

Examples of common barriers that have been reported in industry-wide studies include:

Safety Concerns:

HCPs may hesitate to prescribe due to perceived risks or insufficient long- term data. Addressing this requires presenting robust safety profiles, real- world evidence, and peer endorsements.

Economic Constraints:

High costs or lack of insurance coverage can discourage prescribing. Highlighting affordability programs and cost-effectiveness models can alleviate this concern.

Knowledge Gaps:

Lack of familiarity with a therapy’s mechanism, dosage, or administration can create hesitancy. Educational initiatives such as webinars, hands-on training, or peer-to-peer discussions can bridge this gap.

Preference for Non-Pharmacological Solutions:

Some HCPs prioritize lifestyle interventions over medication. Positioning therapies as complementary to holistic care approaches can make them more appealing.

Clinical Skepticism:

HCPs requiring robust evidence may delay adoption until long- term data or real-world validation becomes available. Transparent communication of trial outcomes and ongoing studies is key to addressing this barrier.

Barriers often work in tandem with belief statements. For example, an HCP who values affordability (belief statement) may hesitate to prescribe due to limited patient access programs (barrier). Understanding and addressing this interplay allows companies to craft highly tailored engagement strategies. Identifying these dimensions on an HCP has always been a challenge at scale given that it needed a primary research study to be conducted. The emergence and rapid advancement of technologies surrounding LLMs has significantly streamlined and simplified the process of generating content of this nature.

Once we start combining these together into a meaningful mesh, a beautiful matrix emerges that can be understood by every rep trying to engage with HCPs.

Scaling Personalized Engagement: Consistent Frameworks, Localized Personas, and the Role of AI

A globally consistent segmentation framework is critical for maintaining strategic alignment across markets. It provides structure by grouping HCPs into personas such as early adopters, cautious skeptics, or cost-conscious advocates, ensuring a shared language and approach for engagement. However, while the core framework can remain consistent, the nuances of belief statements and barriers require localization to reflect market-specific realities.

For instance, a “Cost-Conscious Advocate” in one region may prioritize affordability due to high out-of-pocket expenses, whereas in another, the barrier may be logistical—such as access to diagnostic tools or healthcare infrastructure limitations. Similarly, a “Cautious Skeptic” in one country might focus on real-world evidence, while elsewhere, adherence to national guidelines or a preference for peer endorsements may drive their decisions. Localization tailors engagement to these specific needs, ensuring HCPs see relevance in every interaction.

The Role of LLMs in Scaling Personalization

Achieving this level of localization while maintaining global consistency requires sophisticated tools capable of analyzing vast amounts of data across markets. This is where LLMs and AI agents become indispensable. LLMs can process external data such as HCP-authored publications, conference discussions, blog posts, and social media activity, extracting nuanced insights that inform belief statements and barriers at a local level. For example, an LLM analyzing HCP contributions across different markets might reveal:

By automating the synthesis of this data, LLMs enable companies to scale personalization across multiple geographies without losing depth. The result is a consistent global framework adapted with localized nuances, ensuring that strategies resonate with HCPs regardless of their market context.

Real-Time Insights for Dynamic Engagement

One of the most transformative capabilities of AI-powered platforms is their ability to deliver real- time insights. Traditionally, collecting, cleaning, and analyzing data for segmentation was a time-intensive process, making it difficult to act on emerging trends or new information. Today, real-time AI workflows enable pharmaceutical companies to ingest and evaluate external data as it becomes available, ensuring that HCP engagement strategies remain dynamic and responsive.

Imagine an HCP in Germany publishing a blog post expressing skepticism about a new drug’s safety profile. A real-time insight platform powered by LLMs could immediately flag this sentiment, enabling the sales or medical team to adapt their messaging with robust safety data or peer-reviewed endorsements. Similarly, if an HCP in Indonesia mentions logistical challenges in a social media post, the system could prompt adjustments to digital content or tailor a field rep’s conversation to address these barriers.

For field reps managing dozens of HCPs, real-time updates can also serve as powerful preparation tools. A rep might receive daily notifications summarizing an HCP’s recent activities, such as conference participation or new publications, providing timely conversation starters. Simultaneously, omnichannel platforms can dynamically adjust digital content—suppressing irrelevant messaging or emphasizing new data—to maintain alignment with the HCP’s current persona and interests.

Scaling Personalization with AI: The Unified Takeaway

The integration of LLMs and real-time AI workflows bridges the gap between global consistency and local personalization. Companies can maintain a consistent framework to categorize HCPs globally while adapting belief statements, barriers, and engagement strategies to reflect local realities.

This hybrid approach enables:

In essence, LLMs and AI agents empower pharmaceutical companies to achieve the best of both worlds: global consistency in approach and scalable personalization by country. This unified strategy ensures that HCP engagement is not only impactful but also sustainable in a highly competitive and dynamic environment. By embracing these technologies, companies can foster authentic connections with HCPs, address barriers effectively, and drive better patient outcomes.

How to get started? A tactical framework

While blending external and internal data offers a powerful way to understand HCP behaviors and beliefs, ensuring accuracy and reliability is equally critical.

Primary research—such as interviews, focus groups, or short surveys—can serve as a valuable validation step. This combination of data-driven segmentation and first-hand insights ensures that resulting clusters truly reflect the real-world perspectives of the HCPs they represent.

Data Ingestion & Harmonization

Key Data Sources: Combine internal data (CRM interactions, prescribing trends) with external data (HCP publications, social media, conference commentary).

Standardization: Normalize internal and external data formats for seamless integration. Use LLMs to extract insights from unstructured text (e.g., opinion pieces, PDFs).

Identity Resolution

HCP Matching: Align internal records with external data using unique identifiers (e.g., NPI numbers) and name-affiliation matching.

Compliance: Validate data accuracy, remove duplicates, and ensure alignment with privacy regulations (e.g., HIPAA, GDPR).

Unified Profile Creation

Algorithm Selection: Use clustering techniques (e.g., K-Means, Hierarchical) to create HCP segments.

Persona Development: Define personas based on clusters, e.g., Evidence-Driven Innovators, Cost-Conscious Advocates, Cautious Skeptics.

Belief and Barrier Mapping: Align belief statements and barriers with each persona.

Feature Engineering & LLM-Based Extraction

Internal Attributes: Analyze prescribing behaviors, engagement history, and content preferences.

External Attributes: Use LLMs to extract sentiment, key themes, and barriers from external data, converting outputs into features (e.g., “Sentiment Score: Affordability = High”).

Clustering & Segmentation

Algorithm Selection: Use clustering techniques (e.g., K-Means, Hierarchical) to create HCP segments.

Persona Development: Define personas based on clusters, e.g., Evidence-Driven Innovators, Cost-Conscious Advocates, Cautious Skeptics.

Belief and Barrier Mapping: Align belief statements and barriers with each persona.

Content Personalization & Deployment

Veeva PromoMats Integration:

Tag content with persona labels, belief statements, and barriers.

• Example: “Cost-Conscious Advocates – Affordability Programs,” “Cautious Skeptics – Real-World Evidence.”

Persona-Specific Content:

• Evidence-Driven Innovators: Highlight clinical trial data.

• Cost-Conscious Advocates: Focus on affordability and patient access.

• Cautious Skeptics: Share real-world evidence and safety profiles.

Omnichannel Execution

Email Campaigns: Automate persona-specific email outreach.

Field Reps: Provide engagement guides tailored to each persona, suggesting relevant messaging and materials.

Digital Channels: Personalize portals and webinars to reflect HCP preferences.

Veeva CRM: HCP Engagement

Dynamic Profiles: Enrich Veeva profiles with personas, beliefs, and barriers for tailored engagements.

Suggested Actions: Use LLM-driven insights to recommend next-best actions (e.g., targeted materials, follow-ups).

Validation & Feedback Loops

Primary Research: Use surveys or interviews to validate persona accuracy and refine belief and barrier assumptions.

Real-Time Updates: Incorporate rep feedback, prescribing trends, and digital engagement metrics to iteratively improve profiles and content.

Performance Metrics

Engagement KPIs: Track content interaction rates, call outcomes, and HCP satisfaction.

Conversion Metrics: Measure adoption rates and prescribing changes post-engagement. By implementing this tactical framework, companies can create a scalable, precise, and actionable segmentation strategy that directly improves HCP engagement and patient outcomes.

Maintaining Credibility: Validation and Continuous Refinement

Segmentation is only as valuable as its alignment with reality. Hence, validation plays a crucial role. Scalable, lightweight micro-surveys embedded in digital communications can confirm whether a given cluster truly values, say, real-world data summaries over randomized trial results.

Similarly, A/B testing for targeted content can reveal if a message crafted around patient adherence resonates more deeply with a segment identified as “Adherence Champions.” Field teams offer another layer of validation. Medical Science Liaisons (MSLs) and sales representatives can provide rapid feedback on how well the attitudinal factors inferred from external data match their in-field experiences. Moreover, long-term prescribing changes, advisory board participation, and evolving social media sentiment all serve as signals that can refine clusters over time. As language patterns shift—new therapies, new acronyms, new evidence paradigms—the LLM models themselves can be fine-tuned periodically. This ensures that evolving terminologies or emerging therapeutic areas are properly accounted for, maintaining the relevancy of sentiment and theme extraction.

Scaling for Tomorrow: Adaptation, Compliance, and Ethics

Pharmaceutical environments are perpetually evolving. Today’s rising therapy classes may be replaced tomorrow. Platforms that are hotbeds of professional discourse now may yield to new forums in a year’s time. Scalability and adaptability are therefore fundamental.

The data architecture behind this segmentation framework should be modular, allowing new data feeds to be integrated seamlessly and LLM models to be upgraded as language and science progress. Compliance and ethical considerations must remain paramount. This framework should adhere to all relevant privacy regulations (HIPAA, GDPR) and data governance policies. The extraction of sentiment and themes from publicly available information must be done responsibly, ensuring HCPs’ rights and confidentiality are respected. Ultimately, the goal is not to manipulate HCPs but to better understand their perspectives to facilitate informed, value-added engagements.

Beyond Segmentation: Unlocking Downstream Outcomes

Segmentation lays the groundwork for a cascade of tangible benefits across the entire HCP engagement lifecycle. By understanding not just who HCPs are but why they behave the way they do, pharmaceutical companies gain a powerful lever for influencing everything from educational initiatives to real-time personalization.

Below are some of the key downstream outcomes that become achievable once a persona-driven segmentation strategy is in place, along with additional considerations for automating “next best action” recommendations and integrating with CRM platforms like Veeva to manage promotional materials seamlessly.

Targeted Education & Next Best Action

By defining belief-driven personas, companies can deliver the most relevant educational content for each HCP’s mindset. Integrating next-best-action models ensures that materials like safety data or cost analyses are automatically recommended when an HCP’s behavior or sentiments shift. This blend of personalization and real-time AI fosters deeper trust and knowledge transfer.

Enhanced Omnichannel Strategies

A persona-based approach fuels tailored campaigns across channels, from webinars for digital adopters to budget analyses for cost-focused HCPs. Integrations with platforms like Veeva PromoMats ensure content is both compliant and immediately accessible for distribution. Messages remain aligned with each HCP’s evolving beliefs and barriers.

Stronger Collaboration

Identifying the right HCPs for advisory boards or clinical trials becomes easier when you know who values innovation or cost-effectiveness. Co-creation of studies and peer-to-peer education further solidifies partnerships. Well-matched collaborations amplify a therapy’s credibility and accelerate its adoption curve.

Optimized Resources

Segmentation pinpoints high-value HCPs and indicates exactly which content resonates most—be it trial data or patient support details. Teams can align sales and MSL efforts more effectively, focusing on the areas that spark adoption. Streamlined resource allocation cuts unnecessary touchpoints and strengthens overall impact.

Real-Time Adaptation

AI workflows monitor changes in HCP sentiment—whether through conference feedback or social media updates—and update segment profiles on the fly. This triggers the immediate recalibration of content, channel, or next-best-action recommendations. Agile responses prevent outdated messaging and reinforce credibility.

Improved Patient Outcomes

Fitting the right therapy to an HCP’s clinical philosophy encourages informed prescribing decisions. Focused engagement supports earlier adoption of beneficial treatments or cost-conscious solutions where needed. The result is elevated care quality, reduced treatment delays, and better long-term patient well-being.

Empathy and Precision: A Framework for the Future Belief-driven segmentation, powered by real-time AI and localized nuances, adapts effortlessly to new therapeutic areas or regulatory shifts. Regular updates to LLM models and CRM systems, including next-best- action tools, keep engagement strategies fresh and meaningful. By championing both empathy and precision, companies evolve from transactional touchpoints to genuine healthcare partnerships.

Conclusion: Unlocking the Full Potential of Modern HCP Engagement

The paradigm of HCP engagement is shifting from basic categorizations to deeper, persona- driven strategies, enabling pharmaceutical companies to truly understand and serve the medical community.

By incorporating external signals—such as opinion pieces, publications, conference insights, and digital activity—and leveraging LLM-driven analysis, companies can uncover the motivations and beliefs behind HCP behaviors. This precision fuels nuanced segmentation, transforming unstructured data into actionable insights.

Persona-Driven Segmentation: An Example

Imagine a pharmaceutical company preparing to launch a novel oncology therapy for advanced-stage breast cancer. Traditional segmentation might focus on high- volume prescribers, but a deeper persona-driven approach reveals three key segments within the target oncologist group:

Clinical Trial Enthusiasts: Oncologists who seek the latest treatments backed by robust clinical trials.

Belief: “Innovation drives better outcomes for my patients.”

Barrier: They are concerned about the lack of real-world data and want reassurance about the therapy’s long-term safety.

For Clinical Trial Enthusiasts, the company organizes KOL-led webinars to discuss the latest clinical trial outcomes and ongoing real-world studies. Detailed safety profiles and exploratory data from early adopters are shared to address their concerns.

Cost-Conscious Pragmatists: Oncologists focused on balancing affordability and access for their patients.

Belief: “Affordability is essential to delivering effective cancer care.”

Barrier: High treatment costs and limited patient access programs make them hesitant to recommend the drug.

For Cost-Conscious Advocates, tailored communication emphasizes patient assistance programs, cost-effectiveness models, and potential reimbursement pathways. Case studies from similar socioeconomic settings highlight affordability solutions already in place.

Cautious Validators: Oncologists who prefer established treatments until real-world validation is available.

Belief: “Real-world data matters more than clinical trial results.”

Barrier: They need peer-reviewed, long-term studies to feel confident about prescribing the therapy.

For Cautious Validators, engagement focuses on providing early real-world evidence through observational studies and peer-reviewed case reports. Field teams highlight endorsements from trusted peers and KOLs, building confidence in the therapy’s efficacy and safety over time.

By leveraging LLMs to analyze oncologists’ publications, conference participation, and online discussions, the company stays updated on evolving beliefs and barriers. This ensures that engagement strategies are continuously refined, addressing real-world concerns and delivering value-driven, practical support to HCPs. This grounded approach not only drives adoption but also builds trust and ensures better patient outcomes.

Case Study: Leveraging the power of the web combined with LLMs to create ‘touchless’ segmentation

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