Realistically model market size in the life sciences sector
Why Aggregated Market Potential Is Rarely a Reliable Basis for Investment Decisions
Market Potential and Market Structure in the Life Sciences Sector
Why Epidemiological Data Alone Do Not Provide a Reliable Market Size
In many early business cases, market size is derived directly from epidemiological prevalence or incidence data. This approach often produces large TAM figures, but it offers only limited insight into a drug's actual market opportunity. The reason lies in the structure of pharmaceutical markets. Between the overall epidemiological population and the actual treated patient population, several structural filtering stages exist. These include diagnosis rates, therapy suitability, regulatory approval, reimbursement systems, and clinical guidelines.
In practice, this structure is often represented through so-called epidemiological funnel models. In these models, the potential patient population is gradually reduced from a broad epidemiological base to the population that can actually be treated.²
Only when these transitions are modeled systematically does a realistic picture of the addressable market emerge.
Methodological Structure of Robust Market Models
A well-founded market model in the life sciences sector is typically based on several interconnected analytical levels. Together, these analytical levels form the foundation of a robust market model.
Epidemiological Base Analysis
The starting point of any market analysis is epidemiological data on the prevalence or incidence of a disease. This data is often obtained from scientific literature, clinical registries, or global health databases. It should be noted that epidemiological data itself may be subject to uncertainty. Differences in diagnostic criteria, regional variations, and varying study designs can lead to significant deviations. A careful assessment of data quality is therefore an essential part of market modeling.
Diagnosis Rates and Patient Identification
Not all patients with a disease are actually diagnosed. Diagnosis rates can vary considerably depending on the indication, healthcare system, and available diagnostic capabilities. Particularly in rare diseases or complex clinical pictures, the diagnosed population may be significantly smaller than the actual prevalence. Realistic market modeling therefore takes into account both current diagnosis rates and potential changes resulting from improved diagnostics or screening programs.
Therapy Suitability and Access to Treatment
The next step is to analyze what proportion of diagnosed patients is actually suitable for a given therapy. Clinical criteria, disease stage, comorbidities, and existing treatment options can significantly reduce the potentially treatable population.
Practical factors also play a role. These include access to specialized centers, treatment complexity, and treatment guidelines, for example.
Pricing and Reimbursement Structure
A frequently underestimated factor in market analyses is the pricing and reimbursement logic of different healthcare systems.
Willingness to pay and actual reimbursement by healthcare systems differ significantly across regions. Institutions such as the National Institute for Health and Care Excellence (NICE) in the United Kingdom or the Institute for Quality and Efficiency in Health Care (IQWiG) in Germany systematically evaluate therapies with regard to their added benefit and cost-effectiveness.³ These assessments can have a substantial impact on pricing and market penetration of new therapies.
Competitive Dynamics and Pipeline Analysis
The market structure of an indication is shaped significantly by existing therapies as well as future pipeline products. A realistic market analysis therefore considers not only current competitors but also clinical programs under development.
Especially in therapeutic areas with intensive research activity, market structures can change considerably within just a few years. Pipeline analyses are therefore a central component of realistic market models.
Scenario Analyses and Areas of Uncertainty
Even when the addressable patient population has been modeled realistically, the future market penetration of a product remains subject to uncertainty. That is why scenario analyses are used in many market models. They incorporate different assumptions regarding market penetration, price development, or competition.
Such scenarios enable a more robust assessment of the economic viability of a development program.
Framework: Structure of a Robust Market Model in the Life Sciences Sector
A well-founded market analysis in the life sciences sector cannot be reduced to a single metric. Instead, it is based on several interconnected analytical building blocks.
Only the interaction of these factors enables a reliable assessment of the addressable market.
Market Size from an Investor Perspective
Market Analyses as Part of Risk Analysis
For venture capital investors, market size is a central component of risk analysis. Investors must assess whether a development program has the potential to generate sufficiently large value creation. A large TAM figure does signal scaling potential in principle, but it is meaningful only if the underlying market structure has been modeled realistically.¹
Investors typically analyze market models along several key questions.
1. Consistency Between Epidemiology and Target Population
A common review point is whether the epidemiological data is consistent with the defined target population.
2. Plausibility of Pricing Assumptions
Pricing assumptions are often benchmarked against existing therapy prices or comparator products.
3. Competitive Dynamics and Pipeline Development
Investors closely analyze which therapies are already on the market and which programs are in clinical development. Reports from organizations such as IQVIA regularly show that new therapeutic approaches can change the market structure of entire indication areas.⁴
4. Sensitivity of Key Assumptions
Investors also examine how strongly revenue forecasts change when key assumptions are adjusted. Sensitivity analyses show how robust a business case is in the face of uncertainty.
Typical Structural Errors in Market Models
In practice, similar structural weaknesses repeatedly appear in market analyses. A common mistake is to interpret overall epidemiological populations directly as the addressable market. This underestimates structural constraints such as diagnosis rates or therapy suitability.
Another issue is insufficient consideration of regional differences. Regulation, pricing, and access to therapies differ significantly between healthcare systems.
In addition, competitive dynamics are often underestimated. Pipeline analyses regularly show that several competing therapies are in clinical development at the same time.
Implications for Startups, Scaleups, and Investors
For startups and scaleups, a well-founded market analysis primarily provides strategic clarity. It helps prioritize development programs, define clinical strategies, and present investors with a realistic picture of the economic potential.
For investors, market analyses provide a basis for better assessing investment risks and allocating capital efficiently across a portfolio.
As a result, market size becomes an integral part of strategic decision-making processes in the life sciences sector.
Conclusion
Modeling market size is far more than an isolated metric in a business plan. Well-structured market models combine epidemiological data, clinical development strategy, pricing assumptions, and competitive dynamics into a consistent overall picture.
Only this integration enables a reliable assessment of the economic viability of a development program. Market size is therefore not a static value, but an expression of strategic clarity.
About Excellere LifeScience Consulting
Excellere LifeScience Consulting supports life sciences companies and investors in developing robust business cases, preparing financing rounds, and systematically assessing market and investment potential.
