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Accurately estimating demand, either for a new product or new product features, is essential for several critical business decisions. It drives Go/No Go decisions, the design of clinical trials, leveraging key and differentiating demand drivers into marketing and communication materials, and sales force sizing and organizations. Therefore, estimating demand is the most fundamental element of establishing the scope and size of an opportunity.
We have developed new product demand prediction for most therapeutic categories, in both rare disease and primary care. We can report with confidence that there are two keys to more accurately predicting new product demand.
We strongly advocate testing a full range of variables. Markets are complex and can’t easily be described using a few variables. To accurately predict new product demand, you need to replicate the market. This requires testing all relevant marketplace attributes in your research.
The good news is contemporary estimation methods do not limit what can be tested. So many of our clients believe methods confine or limit what can be tested in research on new product demand. The convention, they think, is they can only test 8 to 10 product attributes in a limited number of alternative product profiles. 20 years ago, this was the case. Things have changed! With the contemporary analytic methods, it is feasible to test the full range of variables that could impact product demand. In fact, now we concern ourselves with the real threat to a weak forecast. If we constrain the elements we test, we may fail to account for a factor that could dramatically alter your forecast.
For example, clients often want to just test the details of their product and avoid testing the potential competitive responses to keep things simple. But isn’t likely that an existing competitor will have an extensive counter-detailing initiative? What will happen when a competitive product goes generic? Or what if a competitive product receives an additional indication? Beyond competition, other marketplace factors will continue to evolve such as the standard of care and Payor practices.
However, just because we can test an almost unlimited number of variables, it doesn’t mean we should. At the end of the day, there is a real human being, a doctor, or a patient, at the receiving end of any survey we design. At Brabeuo, we maximize the number of variables we can test with robust contemporary methods and intuitive survey design. We employ graphic designers and world-class computer programmers to construct surveys that are easy to navigate, modeling our approach on familiar e-commerce sites. This way we minimize respondent fatigue and maximize the reliability of the responses.
To replicate what happens in real life, we focus on specific patient profiles, not broad patient types. Marketers like to think in broad patient types, whether they be age-related (Pediatric/Adult/Seniors) or disease-based (mild/moderate/severe). But that’s not how doctors think. In real life, doctors evaluate product trade-offs (i.e., efficacy, safety parameters, dosing regimens, etc.) for specific, individual patients. In new product demand forecasting, it is important to assess HCP perceptions on how a specific attribute would impact individual patients and/or caregivers. For example, what impact does an HCP think a monthly injection would have on patient acceptance versus an oral taken TID? To really answer that question, they have to think about an individual patient. With the advanced analytics Brabeuo employs, it is easy to systematically vary the details about a patient, much like we do about a product, to produce a highly detailed patient profile.
It’s also important to consider how patient preferences evolve over time. By the time your new drug is launched, will patients be thinking differently about efficacy, safety, side-effect trade-offs? Markets evolve. HIV patients were initially focused on the lifesaving properties of new drugs. However, as their life expectancies have grown, they are increasingly focused on long-term safety and convenience.
An added benefit to the more detailed and comprehensive Brabeuo approach is that it can produce a simulation tool that can account for the new events as they evolve over time and after launch. There is no need to do new research or create a new tool. Instead, you can just adjust the inputs into the existing Brabeuo model. The more comprehensive Brabeuo approach ends up saving clients time and money over the long run.
