Reshaping Commercial Operations for COVID-Era Launch Success

Healthcare professionals’ jobs have become more difficult during the COVID-19 pandemic. In-person interactions with patients continue to be disrupted, which makes diagnosis and treatment (including ensuring patient adherence) more challenging. At the same time, information about therapies has become more fragmented across informational channels as traditional sources of information (e.g., a pharma sales rep visit, an in-person speaker program) have increasingly given way to an array of digital engagements.

Pharma companies must respond to these challenges and, in everything they do from a commercial perspective, seek to serve HCPs and ultimately their patients. This customer-service role includes providing HCPs with the information they need (at the right time and in the right format) to make proper treatment decisions for their patients as well as helping HCPs keep patients compliant with therapies. In this more complex information exchange, companies must be careful not to overwhelm HCPs with too much information. Pharma companies also need to find ways to effectively communicate the value of their products in materials that HCPs increasingly access on their own without the guidance of a well-trained sales rep.

The bottom line is pharma companies must be more customer-centric in their commercial efforts. For optimal results, companies must put the building blocks of customer centricity in place pre-launch so they can identify the “first-best actions” needed to generate commercial results. When executed properly, a customer-centric commercial effort will generate more predictive, responsive, and timely promotional activities across channels and help pharma companies more effectively engage with HCPs. But, to create more customer-centric commercial efforts, a company should reassess its entire commercial operation and marry meaningful organizational change with a new, overarching technology infrastructure.

Commit to Fundamentals to Identify “First-best Actions”

In the 1986 film “Hoosiers,” the new high school basketball coach (played by Gene Hackman) prohibits his players from shooting or scrimmaging in their first practice together. Instead, he puts them through dribbling, passing, and defensive drills, forcing them to master the fundamentals before they even start to think about point scoring. The players grumble, but the tedious drills pay off in the end as the team becomes a well-conditioned, cohesive, and disciplined unit.

In the same way, pharma companies must commit to getting the fundamentals of data management right before they start to think about the exciting potential offered by flashy technologies and algorithms. This data management work is complex and time-consuming. But it’s essential that companies put in place a robust data management infrastructure pre-launch to facilitate insight-gathering, customer targeting, and sales reporting post-launch.

This pre-launch preparatory work is more involved than it has been historically. Most notably, companies must acquire, process, and organize more data pre-launch (e.g., open claims data, clinical trial data, prescription data, key opinion leader identification, etc.) to uncover customer preferences, set accurate goals, map patient treatment journeys, and identify the ideal “first-best actions,” instead of waiting to see what happens post-launch to identify “next-best actions.”

Data management fundamentals are widely known (if not widely executed), so we won’t belabor them here. But the main goal of this data management work should be to build an infrastructure that allows the company to create a single source of truth for stakeholders across the commercial organization. Foundational data management is an essential piece of the exciting enhancements that come next. Without it, a company’s attempts to create customer-centric commercial operations will crumble. Users won’t be able to trust the data they see, and the commercial effort will grow disjointed if all parties are not working off the same correct information in a coordinated fashion.

Enhance Insight-gathering with “Customer 360” Technology

The next step is to build on this data management foundation by implementing technology that overlays all commercial efforts and leverages artificial intelligence to speed insight gathering and uncover insights not easily attained via traditional approaches. In some cases, this technology will be a standalone platform. Sometimes it will be a bolt-on to a robust CRM system. The technology will look different for every company, but two key characteristics these “customer 360” technology systems should share are:

1. Automation capabilities: Artificial intelligence capabilities built into much of today’s advanced data management technology can facilitate automated insight gathering that can help pharma companies quickly flag, investigate, and correct potential commercial issues and identify and leverage opportunities. The key to facilitating automation is using the pre-launch data collection and analysis we discussed in the previous section to come up with the right assumptions (about dosing, patient duration, etc.). Include local knowledge from the field sales force in this data collection and assumption-building process. Reps have insights that don’t show up in the data. Incorporating these insights into the AI-based recommendation engine will ensure the company has a holistic understanding of customers and makes fully informed commercial decisions.

With the data and assumptions in place, a company can deploy AI-enabled technology to flag events that deviate from those assumptions. Be sure that these assumptions and the resulting recommendations align with the product label and strategy. For example, if a company has multiple products that are aimed at slightly different customer groups within a single therapy area, it must make sure that its AI-generated recommendations for one product don’t inappropriately cannibalize target customers for another of the company’s products.

Automation will look different depending on the company, its products, and therapy areas. In the launch phase for a therapy treating a chronic condition, for example, helping patients work through side effects of treatment and keeping them on therapy may be a top priority. Therefore, it’s important that prior to launch a company chart out predictions for compliance and deploy AI to flag discontinuations for the analytics team to assess. Numerous discontinuations could point to the HCP becoming a non-believer in the therapy, so the commercial team may have to act quickly to address brewing issues with the prescriber. Further, with a well-mapped patient journey, a company can use AI to flag deviations from that “normal” journey and address them with targeted commercial activity.

In addition to enabling quick responses to potential commercial challenges, AI-enabled technology can also help companies learn how different physicians treat patients and thereby better tailor promotional strategies. For example, by helping the team better understand a physician’s dosing preferences and treatment protocols, AI helps the team further tailor its promotional efforts to that physician’s unique needs and preferences. A pharma company’s primary commercial goal today should be to make their customers (both physicians and the patients they serve) better off. Anything they can to do to ensure their interactions with customers align with those customers’ preferences advances this cause.

2. Integration with other platforms: This “customer 360” technology platform must also integrate well with a company’s other technologies, such as CRM systems and business intelligence tools. To create a full “customer 360” technology capability, the data warehouse must feed seamlessly into reporting tools and dashboards to enable insight sharing and action (from commercial leadership through the field sales force). Users must be able to see, trust, and act on insights generated from the technology platform.

Appoint a Customer-centricity “Champion” to Drive Organizational Change

Embedding this “customer 360” technology into the commercial operation is a significant step toward creating more customer-centric commercial efforts. But it’s not enough by itself. A company must also alter its commercial organization to ensure they capitalize on its AI-generated insights. Breaking down siloes across the commercial organization is crucial. Sales, marketing, and digital teams must interact seamlessly, work off the same data playbook, and leverage a common set of technology tools.

We also recommend appointing a single “customer-centricity champion” whose top priority is to oversee this technological and organizational change. This individual will ideally interface with all the departments within the commercial operation and secure buy-in for these new customer-centric processes across teams. This individual will look different from company to company but, generally, they should be an executor with a strategic mindset who understands how these customer-centricity efforts advance the company’s overall business goals. This individual should also be someone with stature in the organization who has the public support of management.

Embrace Customer Centricity to Thrive in an Unstable Environment

The move toward customer centricity in the pharma industry is not new, but it has been accelerated by the COVID-19 pandemic. Pharma companies must accept they are in a customer-service business and commit wholeheartedly to serving their physician customers, and through them, the patients who rely on their therapies to improve their lives.

The three-pronged approach we’ve described—which starts with data management fundamentals, continues with the implementation of an overarching technology infrastructure, and ends with organizational change—will help a company embed customer centricity into its commercial operations right at launch.


You May Also Like

Demystifying the Complexities of Multi-Channel Marketing

‘UID Plague’ and the ‘Hydra Effect’ weaken multi-channel marketing with errors that propagate through ...

Lessons From The Presidential Election

The recent presidential election provided a fine example of how the analytics community can ...

Building Brands With Purpose

In our industry, we often use the regulatory environment as a layer of protection, ...