The rise of the next-generation launch room

Leading pharma companies are using their launch rooms not just to prepare for product launches, but as labs to experiment with design thinking, agile approaches, and advanced analytics.

A successful launch paves the way to long-term success for a drug or device, but pharma companies have only one shot at getting it right. Over the past few years, many industry leaders have set up launch situation rooms (LSRs) in which cross-functional teams with decision-making authority work together to adapt their launch plans to maximize market uptake. This approach has proved its worth in launch after launch, but it is now ripe for renewal.

In this article, we explore how changing times call for a new generation of LSRs. We diagnose the flaws in past LSR implementation and identify what best-in-class launch rooms look like today. Finally, we sketch out how a few top pharma companies are using their launch rooms as test beds for introducing advanced analytics, design thinking, and agile approaches to the wider organization.

What is a launch situation room for?

In the first few months of any launch, companies make hundreds of decisions. No matter how thorough their prelaunch planning has been, they will need to reconsider some of the decisions from time to time because of feedback from patients, healthcare providers, and payers or responses from competitors. An LSR provides a mechanism for making these adjustments quickly and effectively. It monitors feedback, accelerates decision making, and helps the wider organization become more agile in the market. 1


However, many LSRs were set up years ago in a world that looked very different from today’s (see sidebar, “What’s gone wrong with past launch situation rooms?”). Digitization and automation were in their infancy, and the pace of change in health systems was slower. Leaders based decisions on backward-looking key performance indicators and on monthly or quarterly activity milestones. Launch teams had an analytic- and process-oriented role, focused on consolidating data and raising alerts. A typical LSR looked like a control tower, with dozens of charts and traffic-light rating systems that relied on expert input. It operated through round after round of back-and-forth with external teams to generate insights, validate recommendations, and implement changes.

What is different about next-generation launch rooms?

Next-generation launch rooms represent a major advance on those of only a few years ago. Drawing on real-time data feeds and multiple, unstructured data sets, they apply advanced analytics to develop forward-looking insights. Empowered teams work outside the standard corporate structures, operate in sprints, and make quick decisions using real-time dashboards. Charts are few, intuitive to use, and logically connected. Perhaps the biggest difference between early LSRs and today’s best-in-class upgrades is the way the next-generation launch rooms use design thinking, agile working, and advanced analytics to create competitive advantage not only for the launch but for the business as a whole.

Design thinking

Putting the user experience at the heart of product development and launch is the governing principle of design thinking. This approach helps companies ask the right questions, understand what matters most, and reengineer tools and processes to meet the needs they have identified—and only those needs. Leading pharma companies are adopting design thinking to optimize the way that launch teams collaborate with the broader organizations and use market data to make quick decisions (Exhibit 1).

Design thinking can change the launch situation room.
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The basis of design thinking is a careful exploration of user needs, gained from interviews with a sample of users: in this case, LSR members, brand teams, and any other groups involved in launch decision making. Designers use this material to create personas that encapsulate the needs and characteristics of particular user groups and to design user journeys that cover all the stages in users’ experiences with a tool or product. These personas and journeys help define the functionalities of the launch dashboard and associated processes, which then undergo testing, refinement, and assembly into a coherent LSR design in cross-functional workshops.

Applying design thinking to create user-friendly systems and processes for launch teams can deliver tremendous impact. One example is using data-monitoring tools that present information in a coherent form that is easy to understand and interact with. One such tool enables users to track the capture of new patients at country level, anticipate how the patient-capture share might evolve by analyzing doctors’ mind-sets, and see recommendations for the best next move for individual field reps (Exhibit 2). Dashboards, charts, alerts, and navigation are self-explanatory, even for users with no prior knowledge or training. Update cycles and data freshness are clear at a glance. Teams using the tool can communicate easily via text- and video-based interfaces on tablets and smartphones, staying connected at a distance and on the move.

A good launch dashboard enables intuitive navigation from a few metrics at a global level down to individual accounts and provides a forward-looking view, informing decisions.
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But great design is more than skin deep. It also allows users to perform intuitively the tasks that previously required an experienced data analyst, such as querying variations in data, combining backward- and forward-looking key performance indicators, and visualizing trends on heat maps or location maps.

One pharma company launched a novel drug at the same time as a competitor launched a similar product based on a different technology. It faced the challenge of persuading healthcare professionals to embrace its technology rather than its rival’s. It tracked the effectiveness of its global messaging strategy by monitoring patient share in the first line and combining that retrospective analysis with a view of how healthcare professionals’ behaviors were evolving in real time. It used a heat map to understand which doctors preferred which technology. It also mined unstructured data—such as comments in health-management systems, app-based feedback from the field force, and keywords captured in virtual ad boards—to anticipate mind-set shifts.

The company used dashboards that showed patient share and behavioral assessments side by side. The dashboards also enabled users to drill down from an aggregate view to a one of individual doctors and to compare current views with views from a few months earlier. The tools and reports produced in the company’s LSR enabled global management to modify education initiatives, update messaging in real time, and push tactical recommendations to district sales managers to help them update engagement plans for healthcare professionals.

Agile working

As product life cycles contract and innovation accelerates, being first to market can seal the fate of a new drug or device. Time without competition may now be as short as one to two years (Keytruda, Lynparza, and Mekinist all had two years until competition), whereas older products (like Avastin and Herceptin) enjoyed six to seven years on the market before entry of other compounds with similar modes of action, even if they weren’t in the same tumor type or line of therapy. 2 Faced with such rapid moves, companies need to grow quickly after launch and be ready to adapt their strategies if competitors enter while their products are still in the expansion phase.

Agile approaches can help companies meet this need (Exhibit 3). These approaches are characterized by collaborative work across functions, an iterative test-and-learn approach, rapid decision making, freedom from bureaucratic reporting structures and constraints, and shared ownership of—and accountability for—information, processes, and products. By adopting them, companies can reduce time to market, improve productivity, and cut costs.

Agile working can change the launch situation room.
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One pharma company was struggling with a product launch that was running 12 months behind schedule and beset by multiple organizational challenges, including duplicated work and a failure to align on what success would look like. In an effort to reboot the launch, it adopted agile methods, such as involving vice-president-level leaders in problem solving and validating features for the next product iteration on the spot. The company managed to reduce the time spent in meetings by 35 percent, freeing up launch leaders to spend 80 percent of their time on problem solving. Within three months, key deliverables were back on schedule.

We have seen pharma companies reap numerous benefits from introducing agile working in their LSRs. When companies streamline the information flows between field and global teams, they can quickly identify and resolve emerging issues. As teams learn through trial and error, they can rapidly fine-tune launch plans in response to market changes. Teams empowered to resolve issues as they arise via sprints experience a similar sense of fulfillment to those in start-ups. With less bureaucracy and fewer meetings, people spend their time more productively and enjoy higher job satisfaction.

Advanced analytics

Unlike sectors such as retail and telecom, the pharma industry has been slow to embrace the power of advanced analytics, artificial intelligence, and machine learning. Most pharma companies are still at an early stage of implementing commercial use cases at scale. But with the increasing penetration of electronic medical records (EMRs) and the emergence of prescriber data in markets beyond the United States, companies have never had a better opportunity to use data and analytics to accelerate launches and improve the returns on their commercial activities globally (Exhibit 4).

Advanced analytics can change the launch situation room.
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The most advanced LSRs take in data from new sources to help them better interpret the data they already have. For example, they use real-world data from EMRs and prescription records to complement their customer-relationship-management data to build a better picture of patient treatment. The insights they derive can be syndicated, refined, and validated with prescribers and other decision makers and then used to shed new light on data from traditional market research. They can house the wealth of data from all these sources in a data lake and make it available for analysis anywhere in the organization. A few leading pharma companies have had notable success in harnessing new technologies to build decision-support tools for use in an LSR setting.

One global company sought to understand the growth potential for a recently launched biologic in key EU markets and to improve its patient and prescriber targeting. It drew on longitudinal prescription data to understand the scope of the opportunity and combined it with EMR data to understand the reasons for moving patients to different treatments. At the same time, it developed an approach to identify priority patient and physician groups and capture regional differences. As a result, the company was able to target twice as many easy-to-capture physicians and patients as expected in the first two years after biologic launch.

A biopharma company turned to advanced analytics to boost its commercial effectiveness. To help it refine customer engagement and messaging, it built a new physician segmentation based on responsiveness to calls, with variations by channel and conversation topic. It then reallocated 30 to 40 percent of its calls to 110 prescribers who contributed 60 percent of new growth. In parallel, it applied artificial intelligence to enable regular refreshing of call allocation to focus on the physicians with the greatest potential.

Using a launch room as a test lab

Companies pioneering the new wave of launch rooms recognize that a launch has a faster metabolism than an organization’s other operations. They take advantage of this characteristic to boost innovation and change beyond the launch itself. The relatively confined launch-room environment enables teams to experiment with new approaches and techniques, learn from failures, and iterate to find workable solutions much more rapidly than they could during a full-scale organizational effort. An LSR can also act as a self-contained learning platform that enables a region or the whole company to build and test new capabilities before rolling them out more broadly.

To function in these ways, an LSR must be fed with real-time data, use forward-looking measures, and have full authority to adapt the launch strategy as needed. Once new approaches have proved their worth in the launch room, leaders can capitalize on the success of the launch by introducing them across the whole organization.

Setting up a launch room proved to be a wake-up call for one pharma company that had been lagging in IT and analytics capabilities. It struggled to consolidate data from market research with its internal sales data and financial reports. While building infrastructure to feed a new launch room, the company connected data streams to its field force for the first time, beginning in one country. A subsequent launch in that country achieved faster sales uptake than the company had ever experienced in that therapeutic area. Thanks to this success, the company quickly rolled out the solution to all its key markets. The innovation also acted as a catalyst, sparking ideas for other advanced-analytics use cases across the business.

Perhaps the best advice for a company without an LSR is to start building one at least a year before launch. Sketching out a preliminary version can happen in as little as a day via a workshop that brings together future users, agile coaches, designers, and advanced-analytics experts. The company can then develop that simple prototype in a modular way by gradually adding more layers upon proficiency in each new capability or subject area.

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