AI-Based Dynamic Targeting and Segmentation for Pharma Sales and Marketing

The pharmaceutical industry stands at a pivotal moment, where the pursuit of growth and the imperative of cost optimization have become intertwined. Seasoned executives, accustomed to navigating the complexities of this landscape, are now confronted with a daunting challenge: how to strike a balance between these competing demands.

The traditional segmentation and targeting (S&T) strategies that have long underpinned strategic planning show signs of obsolescence. Relying heavily on historical data, these traditional methods need help to capture the nuanced, real-time dynamics of an increasingly digital and fast-paced healthcare ecosystem.

This disconnect between static strategies and the fluid realities of the market presents both a significant challenge and an unprecedented opportunity. Advanced analytics, particularly in AI-based dynamic behavioural segmentation, offers a solution to this conundrum. By leveraging the power of AI to predict and adapt to future individual prescribing behaviours, pharmaceutical companies can break free from the constraints of conventional S&T approaches. This seismic shift not only addresses the immediate pressures of cost optimization but also unlocks new pathways for growth, positioning forward-thinking organizations at the vanguard of innovation in HCP engagement and market responsiveness.

Current Targeting and Segmentation Process

The pharmaceutical industry’s current targeting and segmentation process is a complex, multi-step endeavour that has been the cornerstone of strategic planning for years.

It begins with the creation of a target list, typically generated from historical prescribing data, claims data, CRM data, channel data and demand data, which serves as the foundation for identifying high-value Healthcare Professionals (HCPs).

The next step involves segmenting these HCPs based on a multitude of data points, including historical prescribing, claims data, institution affiliation, personality profiles, and previous CRM data, and channel data amongst other things. While this approach has been effective in the past, its historical and static snapshot nature has become a significant liability in today’s fast-paced healthcare landscape.

Target lists are often generated biannually or quarterly, which means that by the time they are implemented, they may already be obsolete. Furthermore, the data used to inform these lists is largely historical prescription data, which, although useful, does not fully capture the nuances of dynamic changing HCP behaviour and preferences.

Newer engagement data, such as that generated from Non-Personal Promotion (NPP) channels and online data, is increasingly available but often underutilized. The drawbacks of the traditional approaches are numerous, but perhaps the most significant is its inability to adapt to unexpected external events, such as the COVID-19 pandemic, which highlighted the obvious drawback of leveraging mainly historical static data.

By failing to fully leverage the vast amounts of fast-paced dynamic data available, the current targeting and segmentation process leaves companies vulnerable to missed opportunities and stagnant growth. Ultimately, a more dynamic and responsive approach is needed, one that can harness the power of advanced analytics and artificial intelligence to deliver real-time insights and drive more effective engagement with HCPs.

Navigating the Future: Embracing Market Dynamism in Pharma

As the industry hurtles towards a more dynamic and responsive future, we must revolutionize our targeting and segmentation frameworks to keep pace. The road ahead demands a seismic shift from infrequent, static targeting to a more agile and adaptive and more real-time approach.

By leveraging advanced analytics and AI-driven insights, we can transition to a dynamic targeting framework that conducts weekly, daily or even real-time analysis, ensuring that our strategies remain aligned with the ever-changing needs of Healthcare Professionals (HCPs).

This new paradigm will incorporate a diverse array of data sources, including some of the previous data but also constant integration of HCPs dynamic online activity data, engagement data from Non-Personal Promotion (NPP) channels, dynamic up-to-date call activity data, up-to-date patient lab test data, and changing patient load data and much more, to create a more nuanced understanding of HCP behaviour. By integrating AI and ML algorithms, we can replace manual statistical modelling with more accurate predictions of future HCP prescribing and brand switching behaviour, enabling more precise targeting at the individual prescriber level.

This HCP-level targeting will supplant traditional segment-level approaches, allowing pharmaceutical companies to tailor their engagement strategies to the unique needs and preferences of each HCP, ultimately driving more effective relationships and better patient outcomes.

Benefits of AI-Based Segmentation

Predictive Insights for Future Growth

AI-based dynamic behavioural segmentation offers a significant advantage over traditional segmentation methods by providing predictive insights into future behaviours. By analysing a vast array of data and identifying patterns, AI models can predict how HCPs are likely to behave in the future with much higher accuracy, allowing pharmaceutical companies to make proactive adjustments to their sales and marketing strategies in real time (or near real-time) for stronger growth outcomes.

For example, AI can help identify HCPs who are at risk of switching to a competitor’s product, enabling companies to develop targeted retention strategies to maintain market share. Similarly, AI can predict which HCPs are most likely to adopt a new medication, allowing companies to prioritize their marketing efforts and optimize their launch strategies.

By leveraging AI-driven predictive insights, pharmaceutical companies can stay ahead of the competition, drive business growth, and improve patient outcomes.

Personalization

The advent of AI-based dynamic behavioural segmentation has revolutionized the way pharmaceutical companies engage with HCPs. By analysing vast amounts of data, AI algorithms can create uniquely personalized experiences that cater to the unique needs and preferences of individual HCPs. This level of personalization increases the relevance and engagement of marketing efforts, as HCPs are more likely to respond to messages that resonate with their specific interests and concerns.

For instance, AI can help identify HCPs who are more likely to prescribe a particular medication allowing pharmaceutical companies to tailor their messaging and educational content accordingly. By leveraging AI-driven personalization, pharmaceutical companies can build stronger relationships with HCPs, drive better patient outcomes, and ultimately improve their bottom line.

Efficiency

One of the most significant benefits of AI-based dynamic behavioural segmentation is its ability to automate the segmentation process, saving pharmaceutical companies a substantial amount of time and resources. Traditional segmentation methods often rely on manual data analysis and subjective decision-making, which can be time-consuming and prone to errors.

AI algorithms, on the other hand, can quickly and accurately analyse vast amounts of data, identifying patterns and trends that may not be apparent to human analysts. By automating the targeting and segmentation process, pharmaceutical companies can free up resources to focus on more strategic initiatives, such as developing targeted marketing campaigns and educational programs. Moreover, AI-driven segmentation can help reduce the administrative burden associated with managing large datasets, allowing companies to allocate more resources to high-value activities that drive business growth.

AI and ML for Dynamic Segmentation and Targeting

In the ever-evolving landscape of pharmaceutical marketing, dynamic segmentation and targeting have emerged as crucial strategies for fostering meaningful relationships with HCPs. By harnessing the power of AI and ML, pharmaceutical companies can develop a more nuanced understanding of HCP behaviour, preferences, and needs. This AI/ML-driven approach enables the identification of prescription propensity with higher accuracy, while frequent updates ensure that segmentation is continuously refreshed to reflect changing market conditions.

Dynamic attributes play a crucial role in model development, incorporating not only all the traditional data types such as sales data (TRx, NRx, NBRx, and sales per call), claims data (claims count, market potential, and switching behaviour), non-personal promotion (NPP) data (click-through rates, bounce rates, and conversion rates), call activity data (number of face-to-face and remote interactions), and demographic information (physician specialty, age, and region) but also by adding dynamic near real-time behavioural data by individual physician from real-world data (hospital data, clinical study data, website data, search domain data, treatment pattern data, medical diagnostics data, lab test data and more). These changing dynamic real-time data enable the model to predict HCPs’ future movement among segments, facilitating more targeted sales and marketing activities.

To further enhance dynamic targeting and personalized content, AI/ML-powered next-best-action (NBA) automation can be implemented. These engines identify the most effective communication channels and timings for engaging HCPs and automate the personalized delivery of the right messages, in the right channels at the right time for individual HCPs. Reinforcement learning is incorporated to continuously learn from interactions, adapting strategies to optimize engagement based on past performance. This holistic approach ensures that pharmaceutical companies remain agile, responsive, and effective in their HCP engagement efforts, ultimately driving superior patient outcomes and business success. 

Implementing AI-Driven Dynamic Segmentation: A Step-by-Step Approach

Step 1: Data Collection

Implementing AI-based dynamic behavioural segmentation begins with robust data collection. Pharmaceutical companies must gather comprehensive data from a variety of sources to create a holistic view of each Healthcare Professional (HCP). Digital interactions, such as website visits, doctor forum discussions, journal article engagement, email opens and length of engagement, content downloads, and individual physician from real-world data (hospital data, clinical study data, website data, search domain data, treatment pattern data, medical diagnostics data, lab test data and more). as an example of just some of the data sources available to be analysed alongside all the usual traditional data (prescription data, claims data, CRM data, personality profile data etc), and provides valuable insights into HCPs’ dynamic changing online behaviour and interests. Social media content and engagement metrics as well as sentiment analysis offer additional layers of understanding, revealing HCPs’ opinions and perceptions about various treatments and communication channels.

Furthermore, Customer Relationship Management (CRM) systems are still a treasure trove of both historical and current data, detailing past interactions, discussions and prescribing patterns. By integrating data from these diverse sources, pharmaceutical companies can build a rich and dynamic profile of each HCP, laying the groundwork for highly targeted and effective engagement strategies. This comprehensive approach not only ensures that marketing efforts are data-driven and relevant but also positions companies to adapt swiftly to evolving market conditions and HCP preferences.

Step 2: Leveraging AI Algorithms to target and create segments

The second crucial step in implementing AI-driven segmentation is leveraging machine learning and NLP algorithms to process and analyse the comprehensive data collected from various sources.

One common technique is cluster analysis, which groups Healthcare Professionals (HCPs) based on similar behaviours, preferences, and interests. Unsupervised clustering techniques, such as K-Means and DBSCAN, can be used to identify clusters and profile them into business-relevant segments. This approach enables companies to identify distinct segments and tailor their marketing efforts to resonate with each group. These clustering approaches have been employed for a long time but adding in the dynamic behavioural data adds a nuance that was not previously captured.

By leveraging ML for predictive analytics, we can forecast future actions and needs by analysing all the data and identifying trends. Machine learning algorithms can also be used to identify high-value current and future HCPs. Supervised learning models are often used to predict HCP switching behaviour and dynamic cluster movement, leveraging algorithm types such as Logistic Regression, Random Forests, and Deep Learning to ensure optimal accuracy and speed.

By anticipating HCPs’ future behaviours, companies can proactively adjust their strategies to meet evolving needs and stay ahead of the competition.

By leveraging these advanced analytics techniques, pharmaceutical companies can unlock the full potential of AI-driven segmentation and drive meaningful engagement with HCPs, ultimately improving patient outcomes and business results.

Step 3: Ensure Evolving Dynamic Segments

The third critical step in implementing AI-driven segmentation is creating dynamic segments that evolve in real-time as new data becomes available. By leveraging the insights generated from machine learning algorithms, pharmaceutical companies can develop highly targeted and effective segments that reflect the ever-changing needs and preferences of Healthcare Professionals (HCPs).

A key factor to consider when creating these segments is engagement frequency, which reveals how often HCPs interact with a company’s channels, such as website visits, email opens, or social media engagement. This information enables companies to identify and prioritize high-value HCPs who demonstrate a strong interest in their products or services.

Another crucial factor is content preferences, which highlights the types of content HCPs consume most, such as clinical trials, research studies, journal articles, doctor forums, email, social media, podcasts or other content resources. By understanding these preferences, companies can tailor their content strategies to meet the unique needs of each segment, increasing the relevance and impact of their communications. This, in turn, enables companies to drive meaningful engagement, build strong relationships, and ultimately improve patient outcomes.

Step 4: Tailoring Strategies

The next pivotal step in implementing AI-driven segmentation is tailoring sales and marketing strategies to resonate with each distinct Healthcare Professional (HCP) segment. By leveraging the rich insights generated from dynamic segmentation, pharmaceutical companies can craft targeted marketing campaigns that speak directly to the unique interests, needs, and preferences of each group.

A crucial aspect of this approach is developing customized content that aligns with the specific interests of each segment. This may involve creating articles, videos, and infographics that address the most pressing concerns and topics of relevance to each group, such as emerging treatment options, clinical trial updates, or some other educational resources.

Furthermore, personalized communication is essential for fostering meaningful engagement with HCPs. By harnessing the power of AI-driven tools, companies can automate personalized email marketing campaigns that address each HCP by name, reference their specific interests, and provide tailored content recommendations. This not only enhances the relevance and impact of communications but also streamlines the marketing process, enabling companies to reach a larger audience with greater efficiency.

Additionally, AI-driven tools can also be used to optimize marketing channels, predict HCP engagement, and measure campaign effectiveness, allowing companies to refine their strategies and allocate resources more effectively. By combining precision-crafted content with personalized communication, pharmaceutical companies can establish a deeper connection with HCPs, build trust, and ultimately drive better patient outcomes.

Step 5: Continuous Evaluation

The final, yet indispensable, step in implementing AI-driven segmentation is continuous evaluation and adjustment. It could be that new data sources have not been included that need to be added, or some other aspect that needs adjustment. Pharmaceutical companies must regularly assess the effectiveness of their segmentation strategies to ensure they remain aligned with the dynamic needs and preferences of individual HCPs. Key performance indicators (KPIs) such as level of prescribing (where available), engagement rates as well as numerous other metrics provide means for evaluating success. By diligently tracking KPIs, companies can gain a clear understanding of what’s working and what needs improvement, enabling data-driven adjustments that enhance overall efficiency.
Additionally, incorporating feedback loops from HCPs is crucial for refining segments. Surveys, market research, and other data collection offer invaluable insights into HCPs’ evolving needs and preferences, allowing for nuanced adjustments that keep segmentation strategies relevant and impactful. This iterative process of evaluation and refinement not only ensures that marketing efforts remain effective but also builds trust and fosters stronger relationships with HCPs.
By maintaining a commitment to continuous evaluation, pharmaceutical companies can stay ahead of market trends, adapt to changing conditions, and drive superior patient outcomes and business results. This proactive approach ensures that investing in AI-driven segmentation translates into sustained growth and competitive advantage.

Conclusion

The application of AI-based real-time dynamic behavioural segmentation of HCPs marks a significant paradigm shift in pharmaceutical marketing, enabling companies to transcend traditional segmentation methods and foster meaningful, personalized relationships with their target audience.

By harnessing the power of AI-driven analytics, machine learning algorithms, and real-time data insights, pharmaceutical companies can create highly nuanced and dynamic HCP segments that reflect the complexities of individual behaviours, preferences, and needs. This, in turn, empowers marketers to craft targeted engagement strategies that resonate with HCPs on a deeper level, driving improved brand awareness, increased loyalty, and ultimately, better patient outcomes.

As the pharmaceutical industry continues to evolve, embracing AI-based dynamic behavioural segmentation will be crucial for companies seeking to stay ahead of the curve, differentiate themselves in a crowded market, and make a lasting impact on the lives of HCPs and patients alike. By investing in AI-driven technologies and cultivating a culture of data-driven decision-making, pharmaceutical companies can unlock the full potential of HCP engagement, revolutionize their marketing approaches, and shape the future of healthcare.

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