Pharmaceuticals & Biotech

Life sciences companies should embrace next-generation call planning: Dynamic targeting

Dec. 20, 2020 | Article | 8-minute read

Life sciences companies should embrace next-generation call planning: Dynamic targeting

Over the past 10 to 15 years, the life sciences industry changed significantly. Pharma commercial teams could access more information, allowing them to become more agile, instantaneous and data-driven. Even with such changes, one must wonder why so many companies have kept the same call planning process since the late-1990s.


Initially, smart reps, referenced in the graphic below illustrating the transformation of an evolving sales force, operated based exclusively on their own knowledge and relationships. Life sciences companies first gained access to reliable prescriber-level data in the mid-1990s. The industry then quickly developed ways to optimize its approach to sales force targeting and planning. It used new—but limited and not very timely—resources such as monthly prescription data and customer master processes. While these resources gave companies good insight into the best ways to allocate the field’s scarce capacity, it was only enough to do so infrequently. Plus, by the time these plans became published, the data used to develop these insights reflected outdated information. All of these factors point to an outdated call planning process and the need for a change.

Over the years, as data and analytical capabilities grew, many more near-real-time data sources and new analytical approaches became available. This gave the sales force more access to greater insights, enabling them to significantly increase their effectiveness. Where reps once operated exclusively on their own experiences and relationships, they became empowered reps armed with greater information about customers, as well as alerts and suggestions that helped them further develop a meaningful relationship.


Today, life sciences companies can quickly access far more evolved master data management systems that offer a 360-degree view of patients and prescribers in a timelier fashion. They are powered by granular data sources, including claims, electronic health records, deep provider profiles and customer omnichannel activity.


As a result, pharma companies have the data, analytics and technology to rapidly update field activity in response to changing markets. This approach of dynamic targeting is a more proactive and customer-centric process that evolved from the reactive call planning process. It enables pharma companies to create significant competitive advantages and increase sales even in the most challenging times, such as a global pandemic. 

“With traditional call planning, the gap between market change and company action is far too great for pharma companies to maintain a competitive edge.”

Dynamic targeting brings engagement

Market changes occur quickly, and life sciences companies want the ability to follow suit. When they don’t react in tandem with real-time changes, they miss opportunities and are left unprepared for the future.


In the past, reps might not reach out to a new doctor in their territory for months; with dynamic targeting, they can reach out on day one. The same is true of other scenarios, like regulatory changes or the release of a competitor’s drug. And it’s this same delay in action that has left many companies struggling during the COVID-19 pandemic and scrambling to catch up to market conditions.


With traditional call planning, the gap between market change and company action is far too great for pharma companies to maintain a competitive edge. By comparison, an AI-assisted rep is empowered to have more meaningful engagements with technologies such as artificial intelligence (AI) and machine learning (ML) accessing of-the-moment data and providing timely insights and next-best actions. This empowers reps to call on the right healthcare professionals and offer the best information at a time when they are most receptive. According to ZS, companies embracing this change can experience a 4 to 8% sales lift. 

Fear of change holds companies back

The idea of adopting any new technology or process on a companywide scale can be daunting. Whether there is an inherent distrust of unfamiliar technology or a desire to maintain the status quo, some life sciences companies have delayed the adoption of dynamic targeting and continue to use traditional call planning, making it difficult to stay competitive in an ever-changing marketplace.


It’s understandable, as people find it easier to embrace the familiar. Adopting dynamic targeting would require reps to be retrained. Both brand and sales operations managers would need to develop a new cadence as their inputs become immediately field-ready, rather than once a quarter. Additionally, data and analytics teams would be called upon to ensure their data is clean enough for such frequent updates.


While these changes seem daunting, these struggles happen whenever a new technology comes along in business and daily life. For example, society made the leap from physical maps to GPS. When first introduced, the technology seemed new and unfamiliar. Many weren’t sure it would be as accurate and get people to destinations on time. But people soon found the real-time information about traffic patterns, accident information and road closures indispensable. Now, using GPS is a commonality. Similarly, many companies transitioned from using expensive data warehouses to Amazon Web Services. In even more basic terms, many retail companies switched from brick-and-mortar to digital stores. The reality is that while some are reluctant to change, it is a necessity to stay competitive.

Making the move to dynamic targeting

Fortunately, the switch to dynamic targeting is not an all-or-nothing proposition. Reps do not need to abandon local knowledge, and there should still be cross-functional collaboration between reps, leadership and brand leaders to align priorities. While life sciences companies can leverage existing analytical approaches and their already-built data foundation to set up the right prioritization and allocation of effort, it’s the automation and field tool set that will require some work.


To achieve constant reallocation, as in the case of GPS, companies will need an analytical engine that evaluates the data and generates guidance, acting as a user-friendly tool to guide the reps. Brand segments, resource allocation, traditional call planning and field feedback all inform this engine. Ultimately, dynamic targeting actively adapts to market conditions, as well as the rep’s local knowledge, and can be made available to the field on the platform of their choice.


After implementation, dynamic targeting strengthens call-plan adherence and overall execution by offering a 360-degree view of customers, integrating their profile, promotion, patient dynamics and payer access.


In order to transition to dynamic targeting, pharma companies should take into account these five considerations.

  1. Manage internal change: As a company transitions, they must consider how to manage organizational changes to roles, processes and capabilities. They should be prepared to help reps make the leap to agile territory management, while sales ops and brand leadership shift to updating call plans continuously, rather than in big quarterly or semesterly projects.
  2. Integrate new data gradually: There may be concerns about the data integration process, as well as the quality of the data. Begin by training reps to use and rely on just a few of their usual data sources and insights. As this information is vetted and the process becomes second nature, companies can gradually expand their insights to include additional sources and analytical sophistication of insights before moving to omnichannel coordination. This allows training and verification to happen at a comfortable pace.
  3. Increase insights and analytics capabilities: Reps integrating new information can start with rule-based insights. As with new data sources, they can begin slowly and expand as they become more comfortable. Once they begin to trust the data, reps can explore additional capabilities such as AI-enabled predictive and prescriptive insights. This allows them to focus on person-to-person interactions, while the system balances opportunities and offers next-best actions for any situation. To reach the full potential of AI assistance, next-best actions and similar insights are required to optimize not only the sequence of customer interactions, but also suggest timing, content and drive the customer forward along their journey.
  4. Embrace user experience: Advances in technology, data and AI that assist your organization both in the field and at headquarters may seem daunting, especially for non-analytical users. User experience will be important for adoption. For example, reps will struggle to adopt a cluttered and overly complex system, so it’s a good idea to implement a focused dynamic targeting app based on in-depth user research to create a seamless user experience that can easily help reps plan and act. According to ZS, this could mean the difference between a 40% insights adoption rate with traditional technology, compared to a 70 to 80% rate with the help of a specialized tool.
  5. Progress to full customer-centric execution: Ultimately, implementing these changes is just one piece of the puzzle. To create the missing link between planning and execution, and operate as a true omnichannel rep, a pharma organization must orchestrate automated, next-best actions for any scenario. This will enable them to move beyond traditional sales tactics and transform a pharma company into a true integrated, customer-centric sales and marketing organization. 
It’s important for life sciences companies to pursue change and innovation in their approach to sales and commercial execution. Those companies that continue to maintain the status quo and utilize traditional call planning processes risk missing opportunities and falling behind. Just like the many processes and approaches that changed with the advent of better data and analytics, it’s time for call planning to change as well, and become continuous, rich, just-in-time dynamic targeting.

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