Pharmaceutical companies face an ongoing challenge: how to engage healthcare professionals (HCPs) in a way that is both meaningful and efficient. The modern life sciences landscape is defined by immense pressure. Patent cliffs loom, traditional blockbuster drugs are giving way to highly specialized therapeutics, and physicians are overwhelmed by digital noise. In this environment, generic outreach is not just ineffective; it is counterproductive.
Despite access to large volumes of data from CRM systems, prescription analytics, and digital marketing platforms, converting that information into actionable insights often remains difficult. As a result, engagement efforts can become broad and uninspired, missing the opportunity to personalize outreach and optimize the use of precious sales and marketing resources.
The concept of Next Best Action (NBA) offers a powerful way forward. By leveraging advanced analytics, artificial intelligence (AI), and scalable data platforms, NBA enables life sciences organizations to deliver timely, tailored recommendations that maximize the impact of every single HCP interaction. This blog explores how NBA helps solve common commercial challenges, and how modern platforms like Databricks can make this approach scalable, governable, and truly intelligent.
Solving HCP Engagement Challenges with NBA
In a data-rich but insight-poor environment, the life sciences commercial model faces multiple roadblocks to effective HCP engagement. Field teams are often navigating a complex web of priorities without a clear, data-driven map. These challenges can be summarized into five key areas:
1. Lack of Precise Targeting
Many sales teams struggle to identify which HCPs are most aligned with current sales strategies or therapeutic priorities. Reps may default to their “comfort zone” or focus only on known high prescribers, ignoring emerging physicians who have high potential. Without clear, dynamic prioritization, effort is spread too thin across broad segments, reducing the overall impact of outreach.
2. Ineffective Resource Allocation
Without the right analytics and measurement frameworks, sales and marketing resources are either underutilized or overextended. The cost of an in-person field rep visit is substantial. Is that visit being spent on the right physician, or could a lower cost email or virtual call have achieved the same, or better, outcome? Reps may spend too much time engaging low-potential accounts while high-value opportunities remain underserved.
3. One-Size-Fits-All Messaging
HCPs are not a monolith. They differ significantly in their preferences, prescribing behaviors, and engagement responsiveness. An academic researcher at a major hospital needs different information than a community-based physician managing a high volume of patients. Sending the same message to every stakeholder reduces relevance, erodes trust, and fails to create the differentiated value that drives real influence.
4. Identifying the Right Message
Even when the right HCPs are targeted, determining what to communicate can be complex. What is the most impactful message for a cardiologist who recently prescribed a competitor drug? The answer is probably not “our drug is better.” A more effective message might be, “Here is new clinical data on patient adherence for those switching from their current therapy.” The ability to tailor content to these specific nuances is critical for improving engagement quality.
5. Choosing the Best Channel and Timing
Engagement channel and timing are equally important. Some HCPs prefer short updates via email, while others may respond best to in-person calls or formal webinars. An NBA system must orchestrate a multi-channel journey. Similarly, knowing when physicians are available or most receptive, such as specific days or hours, can dramatically improve connection and response rates.
NBA addresses these challenges head-on. It combines predictive modeling, AI-driven personalization, and human feedback loops into a unified decision framework that guides commercial teams.
Next Best Action in Life Sciences: Key Use Cases
The value of a Next Best Action (NBA) framework becomes most evident when applied to real-world commercial operations in life sciences. By empowering sales teams and marketing functions with timely, data-driven recommendations, NBA enhances precision, personalization, and performance across the entire commercial ecosystem.
Empowering Sales Representatives
- Contacting the Right HCP: NBA helps sales representatives identify and prioritize the most relevant healthcare professionals (HCPs) based on prescribing behavior, territory potential, and engagement history. Instead of working from static call lists, reps receive dynamic prompts to meet or engage with high-prescribing or high-potential HCPs who are most aligned with the brand’s current objectives.
- Right Message to the Right HCP: Each HCP values different information. Some seek clinical trial data, while others respond better to real-world outcomes or patient adherence insights. NBA models analyze behavioral data, feedback, and preferences to suggest discussion topics that resonate most with each HCP. This ensures that every interaction is relevant, evidence-based, and impactful.
- Right Engagement Channel: HCPs differ in how they prefer to engage. Some respond best to face-to-face meetings, while others prefer digital channels such as email or webinars. NBA learns from past engagement outcomes to recommend each HCP’s preferred channel of communication, improving both efficiency and engagement quality.
Together, these capabilities transform how field forces operate, helping them focus on quality over quantity, and turning every interaction into an opportunity for meaningful connection.
Precision Marketing
- Precise Targeting: Beyond field operations, NBA supports omnichannel marketing efforts by identifying high-value HCPs and stakeholders for non-personal promotions. Using insights from prescribing data, engagement patterns, and digital behavior, it enables marketing teams to direct personalized campaigns through emails, webinars, banner ads, and social media channels.
- Personalized Engagement: NBA ensures that the right message reaches the right stakeholder at the right moment. By integrating data from multiple sources, including CRM activity and real-time engagement analytics, the system tailors content to each recipient’s interests and needs. This approach strengthens brand relevance and drives higher response and conversion rates across digital channels.
- Agility and Real-Time Response: One of NBA’s greatest strengths is its agility. When new data becomes available, such as a competitor launch, a change in prescribing behavior, or updates in clinical guidelines, the system quickly recalibrates recommendations. This enables marketing and sales teams to pivot campaigns and messaging in real time, ensuring their strategies stay aligned with market dynamics.
Integrated Impact Across Commercial Functions
When deployed enterprise-wide, NBA bridges the traditional gap between sales and marketing by ensuring both functions operate from a shared intelligence layer.
- Sales teams execute informed, high-quality engagements driven by real-time insights.
- Marketing teams deliver synchronized, data-informed campaigns that complement field activities.
- Leadership teams gain visibility into which strategies and messages are delivering the best return on investment.
This alignment creates a unified, agile commercial engine that adapts continuously to evolving customer needs and market shifts.
Case Study #1: Scalable Next Best Action for US Pharma HCP Engagement
Challenge
A leading US-based pharmaceutical organization wanted to improve the efficiency and impact of its commercial operations by enabling smarter, data-driven HCP engagement. Despite having vast data resources and an experienced sales force, several challenges were limiting effectiveness:
- Strategic HCP Prioritization: The client needed to determine which healthcare professionals (HCPs) best aligned with their sales strategies and therapeutic priorities. The lack of dynamic targeting made it difficult for reps to focus on high-potential HCPs who could drive measurable business outcomes.
- Optimized Interaction Timing: Sales teams were uncertain about when to engage specific HCPs. Without an intelligent framework that considered both rep availability and HCP receptiveness, opportunities for meaningful engagement were often missed.
- Effective Pre-Interaction Briefing: Reps lacked easily accessible, data-driven insights ahead of HCP meetings. Preparing for discussions required navigating multiple reports, resulting in inconsistent preparation and reduced impact during interactions.
Solution Overview
Tiger Analytics implemented a Scalable Next Best Action (NBA) framework on Databricks, designed to prioritize and optimize HCP engagement using data-driven rules, AI-powered recommendations, and continuous learning.
- HCP Prioritization Rules: A robust rule-based engine was developed to rank HCPs based on prescribing behavior, patient demographics, and territory-level trends. These rules provided clear guidance to sales representatives, ensuring that engagement efforts focused on HCPs most relevant to current business objectives.
- Sales Rep Guidance: The solution integrated contextual insights directly into the sales workflow. Before each interaction, reps received concise, actionable information, such as HCP interests, preferred communication channels, and recent engagement history, allowing for more tailored and impactful conversations.
- Continuous Learning and Feedback Loop: To ensure continuous improvement, rep feedback was systematically collected and analyzed. This learning mechanism allowed the NBA models to refine HCP prioritization and engagement logic over time, improving accuracy and relevance with each iteration.
Solution Benefits
The deployment of Tiger Analytics’ NBA framework resulted in a measurable uplift in engagement efficiency and field force performance.
- Optimized Sales Targeting: The platform empowered the client’s sales teams to focus on the most valuable HCPs, directly aligning daily actions with overall sales goals and therapeutic priorities.
- Enhanced Sales Force Engagement: By providing accurate, context-rich insights, reps gained confidence in their outreach strategies. Field teams reported improved interactions and greater satisfaction with the quality of recommendations provided.
- Improved Commercial Effectiveness: With unified data, governed workflows, and continuous feedback integration, the solution established a repeatable model for scalable, high-impact HCP engagement across brands and geographies.
Results
- 15% increase in HCP response rate, driven by more personalized and timely engagements.
- Optimized Sales Targeting, aligning rep activities with strategic objectives for maximum business impact.
- Enhanced Sales Force Engagement, as sales reps provided strong feedback on the contextual insights that improved their effectiveness.
Scalable “Next Best Action” Solution on Databricks
To operationalize NBA effectively, pharma organizations need more than just a model; they need a robust, scalable, and secure data foundation. Databricks provides a unified data and AI platform that supports this entire lifecycle. It handles data integration, machine learning, governance, and feedback mechanisms, all within a single environment.
A typical NBA solution built on Databricks includes several key components:
Unified Pharma Data and Governance
The foundation begins with a pre-built ingestion framework that seamlessly integrates data from fragmented pharma systems. For too long, sales data has lived in a CRM like Veeva, marketing data in Salesforce Marketing Cloud (SFMC), and claims data in a separate database. This siloed approach makes a holistic view impossible.
The solution starts by consolidating these diverse inputs:
- Call activity and promotions data from CRM platforms.
- Demand and prescription data from sources such as IQVIA’s Xponent or DDD datasets.
- HCP and HCO master data from sources like Tealium.
By consolidating these inputs into a unified data model on the Lakehouse, the organization achieves a single source of truth across all commercial functions.
Unified Data Lakehouse
Tools like Fivetran and AWS Glue enable the ingestion of structured and unstructured data from multiple sources into a centralized lakehouse. This not only simplifies integration but ensures scalability as new data sources, like telehealth platform logs or patient support program data, emerge. The lakehouse serves as the backbone for analytics, modeling, and governance, ensuring insights are consistent and accessible.
Rule-Based Engine
Before applying complex AI, a rules-based engine, built on Databricks Delta and SQL workflows, operationalizes core engagement logic. This layer acts as the essential “guardrail” for compliance and business strategy. For example, it manages constraints such as:
- Avoiding repeated messages to the same HCP.
- Monitoring touchpoint frequency to prevent over-engagement and digital fatigue.
- Ensuring adherence to call plans and regulatory compliance guidelines.
This layer serves as the first line of intelligence, defining the baseline engagement policies before AI models fine-tune the recommendations.
AI/ML Segmentation and Suggestion Engine
This is the intelligent core of the NBA system. Using MosaicAI and MLflow, advanced AI and ML models segment HCPs, identify behavior patterns, and prioritize outreach opportunities. These are not just static segments. The models are dynamic, using techniques like:
- Propensity Models: Predicting the likelihood an HCP will prescribe a specific drug.
- Channel Affinity Models: Determining if an HCP prefers email, virtual calls, or in-person visits.
- Content Resonance Models: Identifying which clinical data or marketing message will be most impactful.
MLflow manages the entire lifecycle of these models, from experimentation to production, ensuring they can be updated and retrained easily.
Rep Feedback Integration
An essential part of an effective NBA framework is the ability to learn continuously. The system is not a black box that dictates actions. Through structured feedback collection, such as in-app responses or qualitative notes from reps, insights are fed back into the model to refine its accuracy.
This feedback is crucial. A rep might reject a suggestion and note, “HCP is focused on a different patient population.” This qualitative data is gold for retraining the models, creating a self-improving system that becomes more attuned to field realities.
Centralized and Secure Governance
With Unity Catalog, Databricks provides centralized governance and lineage tracking across all data and models. This is non-negotiable in the life sciences industry. It ensures compliance with pharma data regulations, maintains data integrity, and facilitates secure sharing across functions like sales, marketing, medical affairs, and analytics. Teams can confidently build and use data assets knowing there is a full audit trail.
Gen AI-Powered Summarization and Recommendations
Generative AI further enhances the platform, turning raw information into actionable intelligence.
- Summarization: It can automatically summarize long, qualitative call notes from a rep’s visit and extract structured insights, like “HCP raised objection about cost.”
- Content Generation: It can produce a context-aware draft For example, it can generate a starting-point email to an HCP that references their last conversation and highlights the specific clinical data they requested.
- Natural Language Querying: It can empower reps to ask questions like, “Show me Dr. Chen’s prescribing history for the last six months,” and get an immediate, clear answer.
This combination of deterministic rules, predictive intelligence, and generative insights transforms how sales and marketing teams operate. They can finally shift from reactive, calendar-based planning to proactive, intelligence-driven engagement.
Business Impact
Implementing a well-orchestrated NBA framework on a unified platform yields significant, measurable business benefits:
- Improved HCP Targeting: The system moves beyond static lists. AI-driven segmentation helps focus attention on the highest-potential accounts and emerging influencers, ensuring better alignment between commercial strategy and field execution.
- Personalized Engagement: By recommending the right message and content, teams can build deeper, trust-based relationships. When HCPs receive relevant information that addresses their specific clinical questions, they are more receptive, strengthening brand loyalty.
- Optimized Resource Utilization: Dynamic reprioritization ensures that rep time and marketing spend are directed where they have the most impact. This enables true commercial mix optimization, shifting budget from low-performing channels to high-performing ones in near real time.
- Increased ROI: More precise and effective engagement leads directly to higher prescription lift and faster brand adoption. This accelerates market share growth and maximizes the return on commercial investment.
- Scalability and Governance: A centralized architecture supports enterprise-wide adoption with the right compliance and auditability controls. New brands, teams, or regions can be onboarded quickly, all while maintaining strict regulatory adherence.
In practice, a well-designed NBA system transforms the daily workflow of a field representative. Instead of starting the day with a static list of calls, reps begin with a curated, prioritized plan.
Derived Business Impacts and Technical Capabilities of Next Best Action in Life Sciences
The adoption of a Next Best Action (NBA) framework redefines how life sciences organizations approach commercial excellence. By combining advanced analytics, AI, and scalable data platforms, NBA helps teams orchestrate more precise and impactful engagements with healthcare professionals (HCPs). Beyond improving day-to-day decision-making, the approach drives measurable improvements across sales, marketing, and overall business performance.
Derived Business Impacts
- Optimized and Effective Engagements: NBA enables field teams and marketers to connect with HCPs more intelligently, ensuring that every touchpoint is meaningful. Through data-driven prioritization and AI-powered recommendations, engagement strategies become more targeted, relevant, and timely.
- Higher Engagement and Conversion: Personalized outreach directly improves engagement metrics. Reps can tailor communications based on individual preferences and behaviors, leading to increased response rates and stronger conversion outcomes across key therapeutic areas.
- Optimized Sales Targeting: AI-driven segmentation allows the organization to identify and prioritize HCPs who best align with brand objectives and therapeutic goals. By directing resources toward high-value opportunities, the sales team achieves better alignment between strategy and execution.
- Scalable and Consistent Execution: A standardized NBA framework supports enterprise-wide consistency. Configurable rules and harmonized data inputs help replicate successful engagement models across geographies and brands, ensuring faster go-to-market rollouts and repeatable success.
- Enhanced Field Force Effectiveness: Reps receive context-rich recommendations, such as the most relevant talking points, preferred engagement channels, and optimal interaction timing. This boosts rep confidence, improves conversation quality, and strengthens the rep-HCP relationship. Feedback captured from the field further refines these recommendations, ensuring continuous model improvement.
- Investment Optimization: With real-time insights into what works and where, leadership teams can make smarter allocation decisions. NBA analytics reveal the highest-ROI channels, territories, and accounts, enabling data-backed reallocation of sales and marketing budgets.
Next Best Action: Technical Capabilities
Implementing an effective NBA solution requires a robust technical foundation that integrates data, AI, governance, and user experience. On Databricks, this is achieved through a set of interconnected components designed for scalability, automation, and insight generation.
Scalable Data Ingestion and Unification on Delta Lake
At the heart of the architecture lies Delta Lake, which serves as the unified, high-performance data foundation. The system ingests data from diverse internal and external sources, such as CRM systems, marketing automation platforms, prescription feeds, and HCP databases, and unifies it into a reliable and consistent structure. This single source of truth ensures that downstream analytics and models have access to complete, accurate, and up-to-date information.
AI Engine
The AI engine leverages Databricks’ advanced machine learning capabilities to power segmentation, scoring, and recommendation processes. Using MLflow, the solution manages and governs the end-to-end lifecycle of machine learning assets: from data preprocessing and model training to testing, deployment, and monitoring. This automation ensures continuous optimization and compliance while supporting transparency across all AI workflows.
Through MosaicAI, models are trained to understand and predict HCP behaviors, prescribing tendencies, and engagement preferences. This allows the system to dynamically generate personalized recommendations that evolve as new data and feedback are introduced.
Dynamic Triggers and Controls
The NBA recommendation system operates using dynamic triggers that respond to defined metric thresholds and behavioral cues. For instance, if an HCP’s engagement score drops or a competitor’s product usage spikes, the system can trigger a tailored action, such as sending updated efficacy data or scheduling a field visit.
To ensure controlled and efficient messaging, suppression rules prevent over-communication, while frequency controls maintain a balanced cadence. The combination of Delta Lake’s performance and MosaicAI’s intelligence ensures that these processes are both scalable and adaptive to real-world market changes.
User-Friendly Application
The solution is built with usability in mind. A modern, intuitive user interface allows commercial teams to manage engagement rules, triggers, and performance dashboards without requiring deep technical knowledge. The interface is powered by Databricks SQL Warehouses, which provide fast and reliable access to curated data for analysis and visualization.
Designed for modern deployment, the application can be hosted natively within Databricks Apps, enabling seamless integration with other enterprise systems such as Veeva CRM or Salesforce.
A governed feedback loop, supported by Unity Catalog and Delta Sharing, allows field reps to share real-time input on recommendation quality, message relevance, and outcome effectiveness. These insights are fed back into the model, driving ongoing refinement and higher accuracy in future recommendations.
Use Cases and Personas
- Field Sales Representatives: Reps receive daily, prioritized action plans that suggest which HCPs to contact, what message to share, and through which channel. This ensures that their outreach aligns with both corporate strategy and individual HCP preferences. Over time, their feedback helps fine-tune the model, making recommendations even more personalized.
- Marketing Teams: Marketers use NBA insights to craft more precise segmentation strategies and develop content that resonates with target audiences.Instead of relying on broad campaigns, they can deploy hyper-personalized communication tailored to each HCP’s unique interests and behaviors.
- Sales Managers and Regional Leaders: Managers gain access to real-time dashboards that track engagement performance across territories and brands. This visibility supports better coaching, resource planning, and strategic adjustments, ensuring that teams remain aligned with overall business goals.
- Data Science and Analytics Teams: With access to unified, high-quality data, analytics teams can experiment, validate, and deploy models quickly using Databricks’ collaborative environment. Automated MLOps pipelines ensure governance and reliability, reducing time to insight.
- Executive Leadership: Leaders can make more confident investment and strategic decisions by understanding which HCPs, channels, and messages deliver the greatest ROI. NBA insights provide a quantifiable view of what drives success, supporting long-term planning and performance tracking.
Bringing It All Together
Next Best Action transforms the traditional commercial model in life sciences by merging data science, AI, and human expertise into a continuous decision-making ecosystem. It equips every stakeholder, from sales reps to executives, with actionable intelligence that guides their daily choices.
By harnessing Databricks’ unified data and AI capabilities, organizations can scale NBA across brands and regions while maintaining compliance and governance. The result is a more responsive, data-driven commercial engine that not only improves engagement outcomes but also drives measurable growth and efficiency across the enterprise.
In essence, the power of NBA lies in its simplicity: enabling the right action, for the right HCP, at the right time, every single time.
The Road Ahead
As commercial models evolve, the demand for intelligence-driven engagement will only grow. Next Best Action is not just an analytics capability; it represents a fundamental cultural and operational shift toward precision, personalization, and continuous learning.
Platforms like Databricks make this possible at scale, connecting data, models, governance, and AI in one environment. By combining traditional data science with the power of generative AI, life sciences organizations can create more adaptive, human-centered engagement strategies that truly resonate with HCPs.
Ultimately, the goal is simple yet transformative: enable every representative to make the right decision, at the right time, for the right HCP, every single day. Because when HCPs are better informed, they can make better treatment decisions. And that leads to the one outcome that matters most: improving the lives of patients.
