Five Steps to Commercialization Success and Common Challenges You Can Easily Avoid
Contributed Commentary by Janardan Prasad
August 17, 2020 | It’s no surprise that advancements in novel technologies and the advent of big data have propelled significant breakthroughs in pharmaceutical development. The last decade has not only seen an increase in new chemical and biological entities but also significantly more innovative medicines reaching the market. Looking to the future, we also expect to see an intensified focus on neglected diseases as new and emerging biopharma companies (EBP) drive new medicine launches. However, to get there, organizations reaching for “first-in-market” status or securing key market areas depends on their ability to swiftly commercialize these new technologies and therapeutics.
In addition to investment in R&D and other resources to support development, an effective commercialization strategy is also key to ensuring a successful launch. Pharmaceutical commercialization requires an integrated approach that is both forward-looking and comprehensive. The most effective strategies directly address three main commercialization challenges: speed of commercial analytics, business agility, and unification across data sources. Due to the data intensive nature of the biopharmaceutical industry, inefficient commercial analytics synthesis and utilization can potentially slow the journey to market.
Key Characteristics of Good Commercial Analytics
Pharmaceutical companies who have the ability to incorporate critical commercial analytics into a commercialization strategy can make all the difference between a swift product launch and costly delays. The following 5 steps address the challenges and solutions involved:
1. Self-serve your data needs: All companies hire domain experts of one type or another to supply a variety of skills, but they may or may not possess the technical skills needed.
The Challenge: When a domain expert is not technical, he/she depends on the engineering or IT team to support the majority of their data-related needs. Oftentimes, this dependency leads to delayed access of key data insights.
The Solution: Commonly referred to as “ad-hoc analysis” organizations are achieving better results by providing users with domain expertise so they can self-serve most of the data-related needs without the need for a third party. As a result, data insight access is expedited and business impact is achieved sooner.
2. Encourage cross-functional collaboration: Even in most well-connected development teams, data tends to exist in organizational silos meaning that information is only available to certain types of employees or departments.
The Challenge: Silos prevent productive sharing of information between all teammates in the commercialization process. For example, Sales might not have access to key information regarding supply, Operations to have access to invoices, and Finance to be kept in the dark regarding sales/support team discounts.
The Solution: By actively breaking down silos to connect data and software at the enterprise level, a unified view is created across organization teams. This cross-functional visibility improves overall organizational performance as Sales would know when and where product supply is strong and allocate more representatives while Marketing could conduct precise targeting based on the status of field interactions and competitive prescriptions.
3. Establish flexible Key Performance Indicators (KPI): There will always be some gap between planning and execution due to things such as inadequate planning or insufficient bandwidth/resources. More often, however, changing market conditions are responsible for generating these gaps making an otherwise solid game plan fail simply because it was too robust to change in the field.
The Challenge: Most companies stick to their original plan because the cost of switching to a new one is far too expensive.
The Solution: If Plan A is no longer viable, the company should be able to quickly switch to Plan B without any repercussions. Should both plans fail because market conditions have unexpectedly changed, then the team should be able to go back to the whiteboard and have the flexibility to draw up a new commercial strategy, complete with launch goals, and KPIs. If the underlying product is strong, business users will be able to deftly change the goals and KPIs as required.
4. Consider interchangeability of data and software vendors: Many companies finalize decisions on data and software vendors prior to building final product applications.
The Challenge: Once a data or software vendor is signed, it is often extremely difficult to switch to another vendor due to incurred risk and additional costs. Like leasing a car, vendors cannot be modified or readily replaced even if you are not satisfied with vendor performance.
The Solution: Switching or adding new vendors is made simple if the current system is already pre-connected with most of them. Thus, teams can make an easy switch to a more relevant vendor should the current vendor’s data or software not provide the expected business value. The common data model enables this possibility and makes onboarding very fast and easy.
5. Encourage team agility: For emerging pharma companies, nearly every drug launch is akin to running a startup. The team is passionate, full of great ideas and is excited to challenge the established, big industry players they consider the competition. But as startups have taught us, data and analytics have to be both strong and handled swiftly in order to capitalize on your promise.
The Challenge: Rapid and robust data analytics handling requires experienced data and engineering teams—which are not easy to build in the early stages of a product launch.
The Solution: Develop a commercial strategy as though you are a startup and don’t limit yourself to the status quo. If someone on the team has a better idea or hypothesis, don’t be afraid to explore it and discover any potential impacts early. If the idea works, roll it out to the entire organization. If it doesn’t, simply move on to the next idea. Encourage team feedback early and often. Act on team feedback quickly—even if it’s late in the launch process.
Pharmaceutical commercialization demands an integrated approach that is thoroughly comprehensive. Solid commercialization strategies incorporate data self-service and cross-functional collaboration across organizational silos to improve flow of analytic insights and business unification across data sources. Business agility is readily established by a forward-thinking commercialization strategy that utilizes flexible KPIs, interchangeable data and software vendors, and iterative feedback from team members early on.
By blending a well-crafted commercial analytics plan into a commercialization strategy, product launches can be achieved faster and more importantly speed the time it takes to get life-altering products to market.
Janardan Prasad is Chief Business Officer and Head of New Initiatives at Lore IO. He has over 16 years’ experience helping businesses efficiently use technology to build innovative products that solve real-world data challenges. He can be reached at firstname.lastname@example.org.