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Sheila Li is a Manager in the Consulting & Analytics team with seven years of experience in healthcare consulting, asset valuation and forecasting. Her expertise lies in developing asset evaluation frameworks and building a variety of forecasting models, including but not limited to demand forecasts and NPV models.
In this series, Citeline and Evaluate consulting team members discuss some of the key challenges that face the companies they work with across the pharma space. Learn more about how Sheila Li, Manager, Consulting & Analytics, uses quantitative analysis to develop asset evaluation frameworks for clients.
I am a manager on the Consulting & Analytics team with over seven years of experience in healthcare consulting. My primary focus is asset valuation and forecasting, where I specialize in developing and designing customized evaluation frameworks and building Excel-based models such as demand forecast, Net Present Value (NPV) for early stage, pre-launch, and post-launch assets.
In my day-to-day work, I manage or support projects, break down tasks, synthesize multiple workstreams, and triangulate diverse datasets to ensure everything flows logically into a cohesive model that meets project objectives.
I’m passionate about forecasting because it uses modeling and data mining to deliver real, integrated business value and establish a robust quantitative foundation for our clients’ decision-making process.
My forecasting philosophy is grounded in two key elements. First, forecasting isn’t just a math exercise. It blends both art and science. Second, the most robust forecasts take a holistic view, integrating structural models, expert judgment, and data-driven insights.
I have worked with pharmaceutical industry clients across companies of varying sizes, including CFOs, commercial strategy leads, and senior directors in pricing strategy or global clinical operations and planning.
Our Norstella proprietary data is the core asset we leverage almost every day, and I’m still learning some of the newer datasets/services.
For any forecasting or quantitative analysis, the cornerstone is data, so having more data allows us to better leverage our resources and strengthen our insights. However, the challenge is identifying which datasets are the best fit for a given project and most aligned with the client’s objectives.
Another key part of the work is learning how to map different datasets, since each one is built for a specific domain and purpose. Helping clients understand these nuances is an important part of building robustness in the forecast. This process often involves validating across multiple sources and helping clients understand both the limitations and strengths of each dataset, and how to leverage the results to support more informed and robust decision-making.
I can share two examples that show how we use our datasets in practice and the real business impact they create for our clients.
The first example is an in-depth global market size and opportunity assessment for a top global pharmaceutical company. The client sought strategic forecasting to understand potential market penetration for their pipeline products, with an emphasis on quantifying IRA impacts. We leveraged multiple Norstella data sources — including Evaluate Pharma consensus forecasts, Datamonitor Healthcare, Pharmaprojects, and clinical trial datasets. The results were used to compare against internal forecasts and guide key business decisions on pipeline prioritization. We adopted a similar framework in a follow-on project with the same client, and the deliverables were shared more broadly across their internal teams.
The second example is a project that leveraged our clinical trial datasets to evaluate competitiveness across existing trial sites for a global pharmaceutical company. We delivered detailed reports and a data model enabling the client to identify, assess, and quantify competition at current oncology trial sites, supporting their strategy to achieve full enrollment. The feedback from the client was very positive.
Challenges naturally arise, and they serve as valuable discussion points with clients. For instance, we must align on core questions, ensuring everyone is clear on what we’re actually trying to answer, whether that’s market size, penetration, revenue, risk, scenario planning, etc.
Before a project gets underway, we must agree on defining the appropriate forecasting approach, whether it’s a high-level model, a bottom-up demand build, an analog-based approach, or a detailed scenario framework.
It’s also important for us to reconcile forecast differences, helping clients understand why our numbers differ from their internal forecast which may be down to methodology, scope, data, or assumptions.
Uncertainty and risk are inherent in forecasting, so it’s my job to transparently communicate caveats, such as data limitations (especially for early assets), and how to resolve them.
Challenges remain once the project is completed. I owe it to clients to explain complex outputs clearly, translating technical modeling logic into simple, intuitive explanations that resonate with non-technical stakeholders.
The Citeline and Evaluate Consulting & Analytics teams offers bespoke strategic solutions derived from their industry-leading products, along with the combined resources of parent company Norstella. These insights are curated by a global team of industry experts to address unique commercial challenges and help deliver lifesaving treatments to patients faster.
Get in touch with our team to explore the right solutions for your business.
