Biopharmaceutical Commercial Models of the (Very Near) Future: Implications for People, Data and Organizational Change

With less dependence on face-to-face physician/sales rep interactions, the evolution to new commercial models will require individuals to think holistically about the relationship with the physician, take on new roles and responsibilities and hone new skills—including the use of new analytical and reporting tools to maximize the effectiveness of each interaction.

The “orchestrator rep.” In commercial models of the future there are fewer traditional sales reps, and the next generation of reps are not out in the field selling on their own but function more like the hub of a wheel. This “orchestrator rep” collaborates with and is supported by other roles on the customer-facing team and they are skilled at pinpointing customer needs and identifying the resources available to meet them. They are adept at selecting from an array of new team members, solutions and digital channels. They know when to push and when to pull to provide “just in time” services, when and where and how the customer needs and prefers them. They are also skilled at engaging customers the way they want to be engaged. They know whether HCPs prefer face-to-face or peer-to-peer interactions, for example, and what their preferences for digital/remote engagement are (e.g., email programs, self-guided portals, etc.).

Focused on key accounts. Increasing provider consolidation, complexity of administering new therapies and the increased burden of government reporting requirements mandate a steppedup focus on key accounts and organized customers. Depending on the local market, these could be large employers, ACOs, IDNs, HCP groups or combinations of these in the U.S., or country, regional, commissioning group or hospital-level teams in other countries. Importantly, all of these organizations operate within distinct, increasingly complex regional and local markets, requiring customer-facing teams to be skilled at local market analysis and strategy, able to identify and understand the key decision-makers and influencers throughout the local ecosystem.

In addition, organized customers are interested in tailoring interventions to their particular patient populations, again emphasizing the need for field teams to understand local market conditions and how to resource each market accordingly. Evidence needs can vary widely. Consequently, field teams need to:

• Develop a deeper understanding of a drug’s population health benefits, particularly when it comes to chronic conditions like cardiovascular disease, diabetes and respiratory illnesses; 
• Have greater fluency with health economics data as a key factor in the value equation; and
• Think of themselves as “full-service” providers, able to package therapeutic area, above-thebrand and product-specific programs and services tailored to an organized customer’s needs (e.g., disease management and decision-making tools for HCPs, self-management and adherence support for patients, coordination of care programs, reimbursement support, joint research efforts, etc.)—all with the goal of improving outcomes. 

Training will need to include optimal segmentation techniques and better understanding of the branded and unbranded organizational tools, messages and programs that can be designed for—and even with—customers in areas such as data utilization, outcomes research, patient engagement, adherence, coordination of care, reimbursement and decision support. The shift from one-directional pushing of key messages toward more needs-based, strategic partnering can be facilitated by creating tool kits that provide partnership and co-creation opportunities, supported by case studies to help sales teams better understand customers’ needs and relationship-building strategies.

R&D has already begun using big data, artificial intelligence and predictive analytics to change the way drugs are being developed—using them to sift through vast amounts of genetic, metabolic and clinical information to identify patterns and biological underpinnings of disease, and to identify medications likely to work in specific patient populations, while quickly steering companies away from drugs likely to fail.

Accomplishing a similar digital transformation on the commercial side is taking more time, during which numerous tech companies are rapidly ramping up their efforts to collect, organize and access healthcare data in new ways. There are many examples of digital tools being used outside of biopharmaceutical companies to transform our approach to healthcare. Some are figuring out how to derive meaning and insight from the raw sensor data captured on smartphones, which when combined with health-related claims data, is giving us a preview into how health outcomes can be steadily advanced through the combination of digital insights plus effective interventions. Improvements in natural language processing (NLP) are helping to tackle the challenges of managing vast amounts of paper-based data. The companies advancing these innovations are rapidly outpacing pharmaceutical companies in their ability to capture deep insights into the therapies and protocols that can best manage chronic diseases such as diabetes, hypertension, hyperlipidemia and other costly conditions. Pharmaceutical companies could and should be doing more to increase value by using the big data at their disposal to design accessible user experiences and help HCPs and patients make better treatment and disease management decisions.

However, the reality is that, just like people in many biopharmaceutical companies, “big data” still largely exists in discrete, disconnected packages and functional silos across the clinicalcommercial spectrum and external to the company. Data that “lives” this way can never tell the whole story. New organization structures and technology enhancements are needed that are capable of harmonizing all this data, deriving business-relevant insights from it and acting on it in real time. Furthermore, data generated and owned by the company needs to be seamlessly harmonized with external data sources, both public and proprietary.

For commercial teams, this means improving the flow and integration of data from throughout the organization (Fig. 2) to support agility in:

• “Always on” analytics, including machine learning and workflow integration, to generate real-time customer profiles and information differential resourcing;
• Precision targeting, to dynamically allocate resources across customers and channels based on insights;
• Customer journeys, to more consistently integrate drivers, barriers, needs and pain points into strategic solutions;
• Advanced engagement, to more rapidly incorporate lessons learned to enhance multichannel engagement and content, tailor to customer needs and preferences, and integrate customer-facing roles accordingly; and
• Integrated deployment, to adopt measurement results and provide stakeholders with “Next Best Actions.”

Some pharmaceutical companies are starting to experiment with various frameworks to support these new modes of behavior, skill sets and data flow, but none have fully succeeded just yet. In particular, leveraging clinical experience to improve the chances of launch success remains an opportunity for most biopharmaceutical companies. Crucial insights into prescriber behavior, payer needs, competitive dynamics and patient profiles often remain with clinical teams, failing to be integrated into downstream adoption planning. Potential data and opportunities to integrate clinical data into launch planning include:

• Frequency, consistency and quality of enrollment, and other development-derived evidence (e.g., patient, key opinion leader and site/investigator insights);
• Patient and provider targeting lists from trial data (where permissible by local regulation, e.g., General Data Protection Regulation);
• Key brand distribution, dispensing and support service needs from trial experience;
• Real-world data/evidence analysis on economic story can be integrated into launch plan to optimize payer access and adoption; and
• Availability of reporting/collection and impact assessment of patient-reported outcomes can be brought into customer engagement strategies at launch.

The new commercial model of the (near) future is different in so many ways from what commercial teams have been used to that change on this scale can seem like an overwhelming proposition. More challenging still, it requires remodeling the current organization while still “living” in it and running the business. How do you get from the current status quo to “options creating” commercial potential? And how do you prepare for challenges along the way?

Change of this magnitude usually doesn’t happen by decree. It can help to approach it as an iterative process that can take place as a series of opportunistic builds rather than large-scale, all-at-once transformation (Fig. 3). To start:

Develop your vision. Change begins with envisioning where you want to be in five years and, importantly, knowing what foundation needs to be laid over the next two years to get there:

• What “crawl, walk, run” progression of capabilities needs to be nurtured to maturity?
• Identify current best practices that can be extended and gaps that need to be filled.
• Identify the information and insights needed and how to generate them or find them within the larger organization. What tools for doing this can you give customer-facing roles?

Test and learn. Consider starting incrementally—opportunistically—by identifying “test and learn” opportunities for pilot projects with key accounts and/or other stakeholders, e.g.:

• Assess critical engagement needs of patients and providers, and identify brand and indication-level opportunities for education, lifestyle, digital and/or advocacy.
• Create and execute cross-functional action plans for local market pilots with key customers (e.g., organized customers) or other stakeholders (e.g., academic centers, hospitals) involving sales, marketing, market access and/or medical affairs.
• Experiment with new roles (e.g., scientific advisers, patient navigators, etc.), tools and solutions.