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By Marite Talbergs, Senior Vice President, GfK Strategic Marketing,
and Doug Willson, Ph.D., Senior Vice President,
Marketing Science, GfK Strategic Marketing
Conducted during the product commercialization
phase prior to launch of a new product, pricing research in the pharmaceutical
industry is both an art and a science. In the U.S. pharmaceutical market,
managed care makes early decisions on formulary placement of the product,
assessing price, product characteristics and other factors. Managed care
in effect sets the amount the covered patient will pay in terms of co-pay,
but physicians still choose (or choose not) to prescribe the brand for
the patients, and the patients still choose (or choose not) to fulfill
the prescription.
Good pricing research is therefore complex because the actions of multiple
stakeholders directly impact a new product’s uptake curve. Statistically
solid forecasts can be built based upon data collected from interviews
among physicians, patients and managed care, yet success in correctly
pricing a new product depends upon both using input from the right people
as well as putting the pieces of the puzzle together and analyzing the
result appropriately.
One quantitative approach to pricing and demand forecasting is to take
into account input from each customer stakeholder group, examine each
group separately, and then model a “united” forecast.
In this approach, as each stakeholder group has a slightly different perspective
in brand choice, each survey is tailored to the respondent group’s
role in the process. The core interview typically includes a choice model
exercise designed to tease out likely actions given various hypothetical
market scenarios (scenarios may include new or existing competitors, various
price levels for your product and for the competitor, therapeutic benefits,
promotion, etc.). Each customer stakeholder group has a specific input
into the model, and each makes a different kind of decision about the
new drug entering the market. The model is shown below:
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For example, let’s
look at a hypothetical new analgesic “Sansapain,” which is
about to be launched. Sansapain is expected to be indicated for chronic
pain associated with osteoarthritis. It will be competing with a broad
list of NSAIDs and will primarily be prescribed by Rheumatologists and
Primary Care Physicians. To identify optimal pricing and forecast demand,
we need to understand the following basic elements from each respondent
type:
- Managed Care (Pharmacy and Medical Directors):
Given Sansapain’s profile, indications and possible price points,
where will managed care place Sansapain on formulary? Will it have
restrictions?
- Physicians (Rheumatologists and Primary Care):
Given Sansapain’s profile, indications and possible co-pay (or
price) levels/restrictions, how often will physicians prescribe the
product and to which types of patients?
- Patients (osteoarthritis sufferers): If their physician
prescribes Sansapain and given various co-pay (or price) levels, what
proportion of patients will fill the prescription?
Once the information is collected, the starting point for this modeling
approach is to create separate demand curves for each stakeholder group.
The first step in the integration process is to convert "formulary
demand" curves into market share or usage demand curves within the
MCO sector. Formulary penetration (i.e., a projection of lives covered
at various tiers) is therefore one of the intermediate outputs of this
forecast. In the next step, expected physician prescribing is modeled
given the drug’s formulary status. The final step is to add the
effect of patient acceptance (given various co-pay levels) of the prescription.
The combined forecast, therefore, brings together perspectives of each
group, weighted appropriately and projected to the population. This forecast
can be converted to units sold, revenue, net revenue or other appropriate
metric. Optimal price is determined to be the point at which revenue (or
net revenue or profit or units sold) is maximized.
An important product of the research is a market simulator that allows
the user to investigate the impact of changes in prices and other inputs
on market share, total revenue and/or profit. Other elements (for example,
effect of DTC, speed of uptake and so on) can also be overlaid onto the
forecast for a fuller understanding of the product’s likely demand.
The simulator can be used to examine alternative pricing strategies and
investigate the sensitivity of results to other assumptions in the forecast
(e.g., number of competitors and their prices). Examples of common strategic
pricing issues include:
- Pricing for Products With Multiple Indications
– It is often not viable to market a drug at different prices
for different indications (e.g., an oncology indication versus a nephrology
indication). Which sequence of indications and what price will maximize
revenues? Launching in a higher-priced indication may secure a higher
price, but demand may then be limited in subsequent markets.
- Optimal Competitive Pricing – How will demand
for your product be influenced by the pricing of new competitors?
Can your product command a price premium above competitors in the
marketplace? How will demand for your new product be influenced as
existing branded products become available as lower-priced generics?
- Portfolio Pricing – Manufacturers are increasingly
marketing different drugs in the same category. Line extensions involving
changes in dosing and formulation can also represent important sources
of revenue after launch. What pricing strategy will maximize revenue
for the franchise as a whole?
In each situation, payers, physicians and patients will have different
decision criteria and each will be influenced, to some degree, by the
decisions of the others. Understanding the drivers of choice and the role
of price for each stakeholder, and how they interact to generate an integrated
market forecast, is crucial for developing a winning pricing strategy.
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