Selling in the pharmaceutical industry has always been competitive. In a market where influence and decisions around treatment, prescription, and reimbursement are drowning in complexity, any edge can quickly become critical to driving sales growth.

Understanding a healthcare provider (HCP), and the context around them, is essential to prioritizing engagement. Typically, this prioritization is done in depth once or twice a year, to produce a traditional segmentation and targeting (S&T) list that directs the sales team. With many HCPs changing target segments within 6 months, the traditional, infrequent S&T refresh most pharma companies undertake essentially produces a static target list that does not reflect the dynamics of the market beyond this snapshot in time. Yet, conditions in the market are never static.

HCPs might start preferring one treatment over another because of the types of patients they’re seeing or because a colleague they trust has shared their experience with them. Conversely, they may receive guidance to prescribe a competitor or have a negative experience. Finally, we all know some HCP treatment decisions are heavily influenced by their interactions with the industry whereas others are virtually insensitive to any investment, and this too changes over time. All of these represent missed opportunities to grow and/or defend a brand’s position in the market that can be tracked by, and even predicted, from data.

By extracting these insights from data, and converting them into value-driving sales execution, dynamic segmentation of HCPs can leverage the investment many organizations have made in acquiring or generating data as well as promoting their treatments, while simultaneously benefiting the patients they serve.

What is dynamic segmentation?

Dynamic segmentation involves analyzing and utilizing data to identify patterns in HCP behavior and preferences. This approach enables pharmaceutical companies to predict the right time to engage with these HCPs and determine the most effective ways to engage with them. By doing so, sales teams can prioritize the opportunities for engagement that are most likely to result in a sale, thus maximizing their success.

The first step is to gather data on HCPs. This data can include their prescribing patterns, engagements across channels, preferences, and other relevant information. Once the data collection is established and automated, it can be analyzed using machine learning algorithms, which identify patterns in HCP behavior that can be used to predict the scale of an opportunity (e.g., number of NBRx for a treatment) in response to engagement (e.g., number of calls + rep-driven emails within a certain period of time). The predictions can be generated from the personal and non-personal engagements with HCPs, along with contextual information around their work settings or patient make-up, if these data improve the quality or precision of predictions.

Utilizing dynamic segmentation in this way allows sales teams to prioritize their efforts, focusing on the HCPs who are most likely to respond positively to their messaging and most likely to increase sales. This is what a true data-driven approach to targeting looks like and how pharmaceutical companies can maximize their return on investment and ensure sales efforts are as effective as possible. This is the edge that drives commercial success for a pharma brand over its competitors.

IQVIA has developed an approach underpinned by a platform which automatically ranks each HCP, including those not typically in the customer universe, and recommends the best sequence of engagements to use over the next few weeks. This approach starts with the patient, represented by every script, test, and diagnosis. We take historical patient level data and utilize it to predict which HCPs are most likely to diagnose and prescribe for a particular therapy area. From there, we layer in access context when relevant, additional marketing data, and digital information. This allows the development of a targeted list of customers or accounts ranked by their potential to increase writing of a particular pharmaceutical product in response to calls and other engagements. We refresh this segmentation weekly, or daily if needed, to ensure sales reps get the latest, most accurate, and most relevant insights into which HCPs to engage with and through what channel. Most importantly, this is based on valuable, predictive insights, rather than old, stale data.

The problem with static call plans for sales teams

For many years, pharmaceutical sales teams have relied on static call plans to guide their outreach efforts. This approach fails to account for HCPs who move from one area to another, or whose opinion or work conditions change, and are starting to prescribe more heavily in a particular area. Additionally, static segmentation does not consider external factors such as changes in market trends, competitive landscape, or even the impact of disruptive events like a pandemic.

Historically, the amount of data and time to analyze it made it impractical (or even impossible) to refresh these plans more than 3-4 times per year, and often this happened once a year or less. With the types and volumes of data tracking behavior and market conditions, and the availability of technology to automate and improve the analytics required to do this, there is no reason to be stuck in static planning.

Benefits of dynamic segmentation for sales teams

Unlike static segmentation, which only allows for HCP targeting once or twice a year, dynamic segmentation is a continuous process that enables sales teams to adapt their approach to the changing needs of the market and prescribers.

Dynamic segmentation provides several benefits to pharmaceutical sales teams, including:

• Agile approach to highest-potential HCPs: Dynamic segmentation updates the prioritized customer list weekly. This enables sales teams to identify and target HCPs who are most likely to maximize prescriptions if engaged with that week (or soon after). This agile approach ensures the sales rep is empowered to make intelligent data-driven decisions continuously as they’re presented with regularly updated predictions.
• Increased sales and improved ROI: Through the segmentation of the highest-potential HCPs, sales teams can increase their performance by fine-tuning the effort they dedicate to maxed-out customers versus growing the potential of others or finding new ones altogether.
• Competitive advantage in the pharmaceutical industry: Companies that adopt dynamic segmentation are more likely to gain a competitive edge in the pharmaceutical industry. By leveraging real-time data and predictive analytics, they can stay ahead of market trends and adjust their approach in the time frame that can impact sales opportunities.

Key factors for successful dynamic segmentation

To achieve success in dynamic segmentation, pharmaceutical companies require high-quality data, a trusted partner, and predictive analytics. These factors help companies identify high-potential healthcare professionals, anticipate their needs, and adjust their approach in real-time. By prioritizing these elements, pharmaceutical companies can drive sustained sales growth and stay ahead of the competition.

So, what do you need to get dynamic segmentation right?

• High-quality data: High-quality data is the foundation of dynamic segmentation. Accurate and up-to-date data enables companies to make informed decisions about which HCPs to target and which channels and messages to use, and when. Without high-quality data, companies may miss opportunities to engage with the highest-potential targets or waste resources on low-potential or promotion-refractory targets. It is better to leverage high-quality call and sales data, than to immediately try to include every datum of digital interaction.
• Actionable insights: The best performing sales representatives use as much data and analysis as possible to prioritize their sales approach and guide their actions. But drowning them in more and more data can exhaust and distract, and for the less analytically minded sales reps, it is even less useful. Dynamic segmentation requires that you be able to turn high volumes of high-quality data into insights that are relevant to decision making, and can be communicated to the field in simple to understand, simple to follow suggestions. Similarly, these insights can drive automated engagement in non-personal channels like emails, banner ads, and other communications.
• Machine Learning (ML): With all the excitement around AI/ML, it is easy to gloss over one of the obvious advantages of ML for dynamic segmentation: automation. The other major advantage is the ability to include data types that can be highly predictive even before you understand how they might relate to the behavior or needs of a customer. By using machine learning, organizations can make accurate and data-driven predictions about HCP behavior that improve over time. This means the faster you are successful with a dynamic segmentation approach, the bigger your edge is and the more value you accumulate over time. Machine learning ensures that the actionable insights extracted from data are always up-to-date and that their quality improves.

Conclusion

Dynamic segmentation is a powerful approach for pharmaceutical sales teams looking to improve their segmentation and boost their sales performance. By leveraging data and predictive analytics, companies can identify high-potential HCPs and adjust their approach in real time to stay ahead of competitors and market trends, and drive long-term growth and success.