Author: Stephanie Armstrong, Associate Director, Beyond Blue’s CVRM team
Forecasting demand is never easy. Teams are often required to make major portfolio, investment, and launch decisions years before a therapy reaches the market, in a landscape that may look very different by the time it arrives.
The obesity market provides a powerful example of these challenges in action. New mechanisms, label expansions, combination approaches, oral therapies, and next-generation agents continue to reshape the competitive landscape. At the same time, treatment goals are evolving beyond weight loss alone, patient expectations are shifting, reimbursement remains uncertain, and healthcare systems are under growing pressure to manage demand. Adding further complexity, obesity is a highly heterogeneous condition, with diverse patient needs, treatment goals, and care pathways, making it difficult to predict treatment uptake and long-term demand.
Having supported numerous obesity demand and future landscape assessments, we have seen first-hand how traditional approaches can struggle to keep pace. The answer is not to abandon these approaches, but to design studies around uncertainty and the decisions clients need to make.
1. A market that refuses to stand still
Traditional demand studies are designed to estimate likely uptake under a defined set of market conditions. In obesity, those conditions can change materially before launch. Reimbursement expectations may shift, cardiometabolic outcomes may become more influential, and as higher-efficacy therapies enter the market, the threshold for what constitutes “good enough” weight loss may continue to rise.
Established methods such as conjoint analysis remain valuable for understanding stakeholder trade-offs, but obesity often requires a broader toolkit. Scenario-based approaches can help assess how demand changes as competition, access and treatment pathways evolve, rather than treating today’s assumptions as fixed.
The objective is therefore not to produce a single forecast, but to understand the range of plausible outcomes, helping clients assess how demand may vary under different market conditions and access assumptions.
2. There is no such thing as the “average” obesity patient
People seek obesity treatment for a wide range of reasons, with goals extending well beyond weight loss alone. While some are motivated primarily by weight reduction, others focus on improving mobility, reducing cardiometabolic risk, enhancing quality of life, or maintaining physical function. These motivations shape what treatment success means and which benefits are most compelling.
Yet clinical need alone does not determine demand. Confidence in treatment, social influence, affordability, and willingness to seek care can all influence whether an eligible patient starts and remains on therapy.
Forecasts also need to reflect where patients are in their treatment journey. The opportunity may come from people entering treatment for the first time, current users who are not achieving their goals, patients seeking a more convenient option, or those looking for a sustainable maintenance approach.
Rather than estimating uptake across one broad obesity population, stronger studies identify distinct patient segments, model demand within each, and then roll the findings up to the market level.
3. Weight loss is no longer the whole story
Today, the obesity treatment value equation extends far beyond weight loss alone. Lean mass preservation, quality of life, cardiometabolic outcomes, durability of response, treatment convenience and tolerability are all playing an increasingly important role in treatment decisions.
This raises a practical challenge: how do you accurately measure demand when stakeholders are evaluating products across a broader set of benefits? In obesity, the number of potentially relevant treatment outcomes can quickly exceed what a traditional conjoint exercise can realistically accommodate.
This is where a combined approach can be particularly valuable. Conjoint analysis remains highly effective for quantifying the trade-offs stakeholders are willing to make across a focused set of attributes. However, approaches such as Agile Scenario Assessment (ASA) (our proprietary technique) can complement this by evaluating the importance of additional attributes that may be impractical to include within the conjoint itself, whilst also assessing the importance of different attribute levels.
Together, these approaches can provide a more complete picture of value, helping researchers understand not only which outcomes matter most, but also how stakeholders trade off performance across them.
4. Demand is often driven as much by access as product value
In obesity, stated demand and real-world uptake can look very different.
A therapy may generate strong enthusiasm from physicians and patients, yet adoption can ultimately be determined by reimbursement criteria and out-of-pocket costs. In many cases, access has a greater impact on demand than differences in clinical performance.
This means that teams making portfolio, investment and launch decisions must do more than measure preference; they must understand how preferences translate into real-world demand. Scenario-based forecasting can quantify how demand may change under different reimbursement and access environments.
In obesity, commercial success is often determined as much by access as by product differentiation. The best product does not always win. The product with the broadest access often does.
5. The balance of HCP vs patient influence is changing
Demand studies in pharma have traditionally focused on physician decision-making, yet obesity challenges that approach more than many therapy areas.
Patients are more informed, engaged, and influential in treatment decisions than ever before. Direct-to-consumer awareness, social media and growing public discussion around obesity therapies are all contributing to greater patient involvement in treatment choice.
At the same time, we have seen first-hand how differently healthcare professionals and patients evaluate treatment value. Clinicians tend to focus on efficacy, safety and patient suitability, while patients often place greater importance on convenience, quality of life, the day-to-day experience, self-image, and overall wellbeing.
The balance of influence is unlikely to remain static. As the number of treatment options increases, healthcare professionals may play a stronger role in navigating increasingly complex choices, matching therapies to patient needs and managing trade-offs across efficacy, comorbidities, tolerability and long-term use. At the same time, informed and motivated patients will continue to shape whether treatment is sought, started and sustained.
Robust forecasts therefore need both perspectives. They should capture clinical recommendation alongside willingness to seek care, affordability, treatment initiation and persistence.
From forecast precision to decision confidence
Forecasting demand in obesity is no longer simply about estimating uptake for a new therapy. It requires understanding how patient needs, treatment goals, access conditions, and competitive dynamics may evolve over time.
The good news is that research methodologies are evolving too. Combining advanced demand techniques, scenario-based modeling, patient research, and sophisticated segmentation approaches enables teams to move beyond a single forecast to develop a richer understanding of future opportunity.
The most successful obesity demand studies do not claim to predict the future with certainty. Instead, they identify the assumptions that matter most, explore how demand may change under different market conditions, and help teams make more confident strategic decisions.
In a market defined by rapid change and uncertainty, the value of demand research lies not in producing a precise forecast, but in providing confidence in the decisions that follow.