Download A Marketing Decision Support System For Pricing Piiarmaceutica

A Marketing
Decision
Support System
For Pricing
New
Piiarmaceutica
Products
By Sanjay K. Rao
Research-based pricing strategies heip enhance the launch of a new product
THE CONTEXT
More than 4,000 potential new pharmaceutical products are
in development at companies around the world. Whiie some are
in eariy stages of definition, others are well on their way to commercialization and launch Business wisdom dictates that early
assessment of a product concept s potential to generate acceptable investment returns is crucial when deciding how to allocate
further resources toward its development and commercialization On average, it's estimated that bringing a drug to market
can cost S400 million to S700 million Therefore, it's critical to
consider optima! pricing strategies when determining the viability of launching a new drug. Appropriate pricing over a drug's life
cycle can generate a healthy stream of revenue without denying
its potential benefits to patients.
For example, consider the following business situation,
which is typical of new pharmaceutical product launches:
The product is to be launched in a market dominated by a
single brand from an established manufacturer with considerable resources. The market is very satisfied with the
dominant market leader, especially because it has demonstrated immediate and sustained benefits for patients who
can at best hope for symptomatic relief. Scientifically substantiated clinical data on the new product indicates it is
comparable to the market leader in terms of some of the
most important market needs, such as efficacy in a variety
of typical patient diagnoses. Additionally, the new product
is less likely than the market leader to cause minor, but
undesirable, side effects. Like most new pharmaceutical
products, this product's success in the marketplace also
depends on its availability on a managed care plan formulary. Clearly, the chances of a potential sale are helped if a
patient is enrolled in a health care plan supporting the
drug. The market leader currently enjoys almost universal
support from most plans in the country. Because of the
existing "goodwill" and the lack of any significant, substantiated negative perception associated with the market
ieader, the odds of a typical plan replacing it with the new
product are not high. Neither is it likely a typical plan might
carry both drugs on its formulary, because they are similarly efficacious. About the only apparent avenue open to
encouraging inclusion is through discounting price relative
to the market leader.
The situation represents a challenge to the business
because several interrelated product, pricing, and marketing
strategy issues need to be addressed. The product's marketing
team needs to consider the following questions:
• How best to differentiate the product for maximum
acceptance?
• How best to use price as a differentiator to encourage
availability and trial?
• How best to use price in tandem with marketing and
sales force resources to encourage trial and switching
behaviors?
• How best to manage pricing strategy to account for timerelated phenomena, such as
- the product's diffusion among different customer
groups such as prescribers. patients, and managed care
organizations (MCOs)?
- market uncertainties, changes in the size of the potential market, and the introduction of other new products
in the future?
- competitive reactions, especially in terms of price, to
the new pro-liicfs introduction and marketing?
- optimizing cash flow/profitability over time?
E X E C U T I V E
S U M M A R Y
In an era of rising R&D costs, it's more important than ever
to develop viable pricing strategies for new pharmaceutical
products. Marketing-research functions at pharmaceutical
firms need to develop actionable pricing strategies that view
price as an important element of the marketing mix. along
with detailing and advertising. This article describes a
research-based Marketing Decision Support System (MDSS)
for pricing that can help develop pricing strategy for a new
pharmaceutical product nearing launch in the larger, more
realistic context of other marketing and sales-force decisions. Such a system can systematically investigate various
actions under the marketer's control via traditional and
newly emerging research and modeling methodologies.
Further, the system provides an evolving business framework in which to assess potential pricing, related actions,
and their consequences in terms of sales, share, and profits
over time.
THE MARKETING RESEARCH PROCESS
Astute pharmaceutical marketing research and consulting
groups are using "holistic" research, modeling, and system
approaches to answer such questions. The marketing research
and consulting group at ZS, for instance, has created integrated
Marketing Decision Support Systems (MDSS) for pricing and
other decisions for use by pharmaceutical brand marketing
teams in the United States and Europe,
The following features characterize such a system:
• The consistent use of a derived segmentation scheme at
every stage of the research and modeling process (rather
than treating the population as a whole)
ment. Putting these blocks in place helps create a synergistic working infrastructure designed to address key marketer objectives.
SEGiVIENTATION
An MDSS gains value by deriving segments driven by its
objectives. Experience shows that segments derived by productspecific research provide an evolving basis for an actionable
MDSS. Instead of working with a preconceived notion of what a
target segment is, derived segments provide a market-driven,
multidimensional view based on specialty, historical behavior,
attitudes, and intention specifically related to the new product.
Our experience with pharmaceutical markets has consistently
established the value of using market-driven data — collected in
thecontext of a product and its competitors — to help identify
target segments that are distinctly heterogeneous and multidimensional, especially in terms that help create reasonable tactics to reach and communicate with them.
In the specific situation outlined on page 23. the marketing
team and the researchers set up hypotheses for deriving potential segments based on the following:
• MD preferences for the market leader, as measured in
terms of volume, loyalty, and useof other related products
in the past
• Key MD attitudes of relevance such as the propensity to
take risk, sensitivities to price, openness to patient
requests, or tendency to prescribe drugs off label
• Affiliations to institutions of higher learning, access to
new clinical information, and level of education
Segments that rest on a hybrid of such obiective-driven constructs naturally link to outcomes predicted with an MDSS. For
example, it is understandable that switching behavior is more
common among more price-sensitive MDs: and if a new product
was launched with aggressive discounting, this segment would
adopt it faster.
• An actionable focus that provides all results at the population level through appropriate weighting/scaling methods
EXHIBIT 1
• A direct relationship between pricing, product, and marketing strategy inputs and aggregate outcomes such as
sales, market share, and profits
• An explicit relationship between top-line outcomes
and financial results, such as cash flows and Net Present
Value (NPV)
Further, the purpose of every MDSS is to provide knowledge
and help develop strategy through simulations. Therefore, wherever possible, all predicted estimates are stochastic in some
form, with a specified degree of uncertainty. The view is that an
MDSS. once set up. should be periodically refreshed with ongoing research.
In principle, an MDSS for pricing consists of a few, interlocking
building blocks shaped by marketing research, modeling, and iudg24 Winter 2000
Thecontext
Pharmaceutical
Firm
Patients
MCOs
Sett-evaluation
Lifestyles and opinions
Knowledge
Out-of-pocket expenses
Pharmaceutical firm's
Uniqueness ol drug
Benefit to patient
Costs
Availability
Prescribers
Clioices
Availabilily
Patient diagnoses
Price to tfie patient
Pharmaceutical tirm's
fi marketing and
t seiiing i m p a c t . ^ ^
EXHIBIT 2
The marketing research flow
Decision
modets
Segmentation
flows
X
r
Response
curves
r^g
Benctimarking
ftn»-t»sM'.
li
P'BtJictiore
Costs/ottier financial
assumptions
ESTIMATING MARKET FLOWS
An MDSS for pricing is more valuable if its segments can
change in size and composition with time. A demanding marketing team needs more than just point-in-tJme forecasts. For example, marketers would want to know how a pricing strategy executed over six months to a year would affect the top line for a
period of one to three years during and after execution. The task
of reliably predicting potential market benefits to reflect ongoing
market inflow and outflow depends on accurate and reliable esti•mates of market size and growth. For this reason, it's important
to consider the following issues:
• How many new MDs are likely to join the prescribing poo!
over the next four years?
• How many new patients would have been diagnosed for
the indicated disease over the same time?
• How many potential patients were prescribed the market
leader product over each of the past five years?
DECISION MODELS
A customer decision is the central driver of the MDSS for
pricing. Marketing researchers are generally driven by a desire to
understand the rationale of such a decision. The ability to realistically and accurately capture relevant customer decision-making
processes enhances the value of the MDSS as a decision support
tool. Not all customers are motivated similarly, nor is there any
reason to believe all customers shall behave similarly in a realistic market. Consequently, such systems are designed with the
following purposes in mind:
• To model various pricing-related decision-making processes, underlying product choice in the category
• To capture the interrelated dynamics of all key players relevant to a customer decision
• To provide links between models of price-related customer
decision-making processes and the marketer's
marketing/sales force strategies
For example, the business situation outlined in this article
presents a number of significant potential customer decisionmaking processes, including the following:
• A logical examination of clinical data in support of the new
product's claims and comparisons with the market leader
based on personal experience
• A trade-off between the new product's price to the patient
and its efficacy and safety profile
• An efficacy-to-price comparison between the new product
and the market leader
• How many patients would have been diagnosed for
another disease and then migrated to a different therapy?
• A decision based on a combination of risk aversion, sensitivity to sales, representative detailing, patient request for
drug, and the availability of samples
• Realistically, how many potential and current patients are
fair game for the new product?
• A decision largely driven by peer influences and messages
that provided clinical data in an easy-to-understand format
• How can prIce-to-patient marketing affect all of the above?
A variety of secondary data sources such as UN reports, disease-support groups, medical associations, and internal company memos can help provide estimates. In addition, a quick
research project that surveys relevant target groups and develops
accurate counts of the required metrics can provide useful product- and category-tailored information. Setting up the MDSS on
the basis of such flow estimates creates a solid foundation for
making projections. Model-based results are almost always relative and limited by the choice of sample, panel size, variables
studied, or the context in which the data were collected and
assembled. Adjusting and projecting such results within the realistic framework provided by external estimates of market size,
product potential and market flows only adds to their validity.
Further, MD decisions of any kind are also contingent on
the new product's availability on formulary in the patient's
plan. Therefore, it is aiso important to model MCO processes
leading up to a decision to carry the drug. For example, it is
necessary to estimate the likelihood of formulary inclusion
under varying circumstances characterizing the new product
(i.e.. its clinical data, acquisition costs, discounting, indications, and the price of the new product to the MCOl. Often, the
specific nature of a new product and its competition dictates
that the MDSS consider the possibility of obtaining a limited
type of formulary Inclusion - such as one where inclusion and
prescribing would be contingent on specific requests from
potential prescribers. Such MCO-level decision-making processes are then necessarily linked lor "folded in') into the MD
model of a decision to prescribe.
2S
Some pharmaceutical products - such as the one described
in the first section - also are supported by direct-to-consumer
(DTC) marketing activity, whereby the marketer seeks to develop
a direct relationship with the patient and motivate certain behaviors such as visiting an MD for a potential diagnosis or requesting a prescription for the marketer s drug. A patient's decision to
do these things can positively affect a prescribing decision, especially among segments of MDs who may be swayed by non-diagnostic patient considerations, such as feedback on other therapies and ability of the patient to socialize freely despite
symptoms. Consequently, researchers would want to estimate a
decision model for patients as well, especially as it might influence an MD's decision to prescribe. As with the MCO model, the
modeler would likely need to appropriately "fold in" the model of
patient behavior into the MD model. Marketing modelers can do
this by estimating the magnitude and intensity of patient
requests for a drug as a result of relevant patient-level marketing
activities, and subsequently re-calibrating the MD level model
with this information.
MDSS researchers can go about collecting information on
product-specific decision processes by setting up appropriate
market research experiments soliciting key inputs and decisions
in contexts that approximate potential market situations. For
example, a pricing MDSS such as the one used for the new product described in this article would do the following:
• Model customer trade-off decisions, including those
involving price, via choice-based hybrid conjoint experiments designed to key in on the hypotheses under scrutiny
• Model decisions characterized by latent attitudes (i.e., risk
aversion or price sensitivity! and intervening, composite
constructs {i.e., satisfaction with the market leader) via
data collected as part of a latent class-measurement
experiment leading up to Path Analysis/LISREL estimation
processes
• Mode! decisions that link marketing/sales force activities
to potential behavior by an appropriate analysis of primary
research data, company-level information on historical
marketing/sales force activity, and resultant behavior as
captured by secondary data sources, as well as through
self-stated reactions obtained from affected customers
• Model decisions likely to be influenced by messages
(i.e., sales aids, DTC content) using bundle optimization
techniques combined with models of cause and effect as
necessary
Once estimated, such decision-making processes need to be
aggregated up to an actionable unit of measurement, such as an
objective-driven integrated segment. Often, the researcher's
choice of model specification will inherently dictate the aggregation process. For instance, a researcher may choose to develop
segment-specific choice functions directly, after ascertaining the
incremental benefit of estimating individual level functions.
Perhaps the researcher may decide to use LISREL, which would
26 Winter 2000
imply that the model be estimated only "in aggregate," utilizing
all avaiiabie degrees of freedom.
In some situations, the modeler would likely combine the
outputs from a variety of modeling methods to derive a realistic
estimate. For instance, it is conceivable that results of a bundle
optimization routine that measures reach and return from a
given message set will be used in conjunction with an estimate
of share of choice to derive the number of MDs likely to prescribe
a new product.
RESPONSE CURVES AND CALIBRATION
As the marketing researcher creates realistic and representative models of decision-making processes, two key needs arise:
• The need to obtain estimates of market response to aggregate phenomena such as
-
Order of entry of the product in the market and its correlates (ex. pricesl
Spending levels for various marketing instruments,
such as DTC campaigns, journal advertising, or sales
force size
• The need to calibrate all models to the population to provide realistic, working representation of the market and its
forces
Clearly, such needs are vital even in the process of assessing
decision-making processes at the customer level. As an anonymous reviewer has also stated, these variables are likely to create
"significant inertia" at the individual prescriber level. However,
they are not directly considered as such for conceptual and pragmatic reasons. For example, while an MD is certainly expected to
make explicit trade-offs between efficacy and safety of a new
product in comparison with existing products, he or she is relatively less likely to consider the product's order of entry in the
market as a key factor at that time. By their very nature, constructs
such as order of entry in the market and spending levels are likely
to influence market acceptance in the aggregate, not so much at
the level of a customer decision, where product descriptors and
information are key. Also, and perhaps as a consequence, existing
modeling methods and data that seek to estimate the effect of
such phenomena are usually formulated at the aggregate level. By
definition, such constructs are multi-product, meaning the effect
of an activity on a product's performance is tied to the potential
effect on the competitor's product and marketing/sales force reactions. As such, hard data that enable model calibration needs to
span both the product and its competitors. Given the proprietary
nature of such information — for example, advertising and promotional spending levels, sales force sizes, detailing and sampling activities — these data are available only In aggregated,
audited forms. For such reasons, researchers need to model these
phenomena separately and in aggregate.
Where a new product has still not been launched, such as in
the situation described on page 23, it is only possible to make
reasonable conclusions about such effects from the vantage provided by analogues (i.e., products deemed sufficiently similar to
mance over a reasonable time horizon extending from the launch
date into the future Clearly, once launched, the new product would
necessarily have to evolve in response to its customers. The process of trial, experience, and adoption would need to be facilitated
and managed in the new product's interest. For instance, heavy discount pricing at launch might result in a more rapid diffusion and
faster fulfillment of expected potential, Additionally, depending
upon the markets perceptions of the product's differentiating factors interacting with price (such as its purported efficacy/tolerability), expected potential may even be surpassed or remain unmet.
the new product in as many ways as possible) As an example, we
once created a hypothetical product that represented weighted
effects of competing products in terms of historical performance.
The weighting was based on how similar the new product was to
potential competitors.
Various data sources provide historical, aggregated audit
level data on the marketing and sales force activities that support
products, especially in the pharmaceutical industry. These data
revolve around a unit of time, such as a month or a quarter going
back as far as the launch of the first product in the category, and
are open to analyses via traditional econometric models. Often,
the working hypotheses postulate considerable correlation
between causes and effects. For example, increases In journal
spending favoring one product this month may be effective after a
]ag of a month, and, as a result, a competitor may have upped
ii.>urnal spending as well, leading to a new, interdependent
dynamic. As such, the search for a valid representation of the phenomena involves setting up systems of simultaneous equations
with the ability to accommodate correlations in the error terms.
The exact type of model specification will of course vary with the
characteristics of the data being modeled. It is up to the modeler
to assemble, examine, and model the data collected for his/her
objectives in this context. For example, we have found seemingly
unrelated regression specifications and estimations to work well
with aggregated audit data with an inherent characteristic of considerable lag between cause (such as journal spending dollars)
and effect (such as change in market share or sales).
The knowledge of a new product's potential performance
(over time) and underlying marketing and sales force rationale
is often used to make resource allocation more realistic and
market driven. For the new product discussed in this article, the
market—satisfied as it is with the market leader—probably
would take a cautious view, and adopt it gradually, rather than
quickly as typified in a steep growth curve. Such caution would
likely lead to a potential market niche, whereby—as a virtue of
clinical data supporting its superior side effects profile—it would
increasingly be seen as a safer version of the market leader Only
over time, and with a judicious use of cost-related negotiations,
would it be possible for this product to overcome barriers that
block its immediate placement on formulary lists either alongside or, preferably, instead of the market leader
For considerations such as these, an MDSS for pricing is
well-served by a calibrated model of how the new product is
likely to diffuse in the market over time. A well-planned functional representation, duly specified for potential causes of
adoption—such as thought-leader influences, information dissemination at medical conferences, word-of-mouth exchanges
with peer prescribers, and sales force activity as manifested
through detailing and sampling levels—can usually be tested,
estimated, and calibrated for each situation. In the absence of
realistic, historical data capturing such time-dependent phenomena in the category of interest, it is often possible to work
from data on analogue products. In addition, the literature on
DIFFUSION OF INNOVATION MODELS
All ot the research processes and models outlined thus far
seek to measure the new product's acceptance at a point in time,
usually at launch, while significant market and marketing forces
play in favor or against it. Once modeled and calibrated, such estimates are but a good starting point for initial budgeting and
resource-allocation decisions. A marketer, however, would likely
need to know more, especially when considering potential perfor-
EXHIBIT 3
Research elements: Decision models and response curves
Product profile
Competition
Future launches
Managed care
organizations
Efficacy atid safety data
Side effects
Curretil and potential
indicatiotis
Dosing/forms
Cotnpliatice
Potential prices
Launch dates
Order of entry
Ottier descriptors
A wide variety of product and marketing/sales force
strategy inputs are incorporated into tfie pricing MDSS
Ihrough decision models and response curves.
Current formulary content
Potential acquisition costs
Patient/physician
demand considerations
Types of formulary restrictions
Size
Current and planned
investments in
• Detailing and sampling
• Journal spending
• Prof, education pfog.
• Sales/serv. program
• Other product/competitive
information on
resource spending
Marketing eftort
Sales force effort
Degree of control
markelintiresearrfi 27
the modeling of the diffusion process in marketing and sociological contexts is rich with situations, corresponding model formulations and estimates of various effects. A systematic review of
such information almost always leads to sound working assumptions and proxies that can be used for a specific new application.
Once formulated and calibrated, a diffusion model of the
potential time-related adoption of the new product can be embedded in the pricing MDSS for enabling predictions of performance
over time. Cross-sectional estimates of performance at launch can
be projected into the future on the basis of such a mode!, in addition, because most of the predictions were made contingent on
marketer action (such as pricing, advertising spending, product
descriptor information, or sales force detailing activities), it is easier to make alternative time-based forecasts conditional on a variety of control variables and their combinations.
HANDLING UNCERTAINTIES
The process of forecasting is fraught with uncertainty. As
new clinical data become available, a new pharmaceutical product concept is susceptible to changes in description. The way the
market is introduced to the product constantly changes with
market situations. A competitor may mount a preemptive campaign to blunt the potential launch of the new product, emphasizing features that were until now the preferred domain of our
product. The market leader, for example, might consider launching a line extension that is perceived to deliver all the benefits of
its predecessor with the added benefit of a new and more convenient dosing formulation. Perhaps it may commission a small-
EXHIBIT 4
envelopes have provided ranges with good face validity, the
% of product X
change in market
share due to
% ot product X
change in market
share due to
Product Product Product Product
A
B
C
D
Winter 2000
The marketing researcher, accordingly, works to capture and
model such uncertainties through decision-modeling experiments that represent systematically varying product, market, and
customer scenarios, as well as by using model specifications that
provide working estimates of error at each step of the modeling
process. In addition, various statistical and non-parametric
methods exist to estimate error in the predictions made by such
a system. For example, a statistician can explicitly consider the
theoretical distributions underlying every model in the system
and their linkages to develop estimates of the net, compounded
error in the final predictions. Alternatively, non-parametric, datadriven methods such as Monte Carlo simulations can help
develop an envelope of predictions. Depending on the sensitivi-'
ties of the input conditions, the magnitude of these envelopes
can help measure the range of possible predictions. While such
System outputs: Interaction between pricing and other marketing mix strategies
Product X
• Increases price 7%
in 1Q 2000
• Increases detailing 10%
in 20 2000
• Continues DTC program as is
28
scale clinical study that specifically keys on safety-related issues,
aiming to identify and highlight situations and segments of
patients where its deficiencies on safety are irrelevant. The possibility that a new product from a third manufacturer enters the
fray two years into the future—with a positioning platform that
can only be reasonably guessed at—may need to be considered.
Perhaps the brand team responsible for marketing our product
would like to examine the potential impact of a range of realistic
pricing options in the light of such potential competitive reactions. Because an MDSS is designed to be sensitive to such product and market uncertainties, its value as a tool to hang our
strategic hats on is enhanced.
Product X
• Plans to increase aquisition costs by 4% in 2002
• Will hold detailing at current levels
• Will stop DTC in 2001
• Two new competitors are scheduled to hit
the market in 2000
EXHIBIT 5
System outputs: Interaction between pricing and other marketing mix efforts
35
£
e
30
Product X, #1
25
Product X, #4
20
Product Z
(existing)
CB
•S
15
E
10
Q.
1999
2001
2003
2005
2007
2009
Product X pioliies * 1 and 14 vary in terms ol etficacy and
safety information, dosing fonns, and compliance reQuiremenls.
question of whether one would have obtained tighter, more precise estimates with the statistical approach remains open.
trial costs, and other sources such as capital expenses/royalty/
milestone payments etc.
FINANCIAL IMPLICATIONS
CONCLUSION
Devetoping and commercializing a new pharmaceutical
product requires considerable capital costs for various prelaunch activities such as R&D, revamping (or hiring) sales forces,
or retooling manufacturing processes. These costs can run into
several billions of dollars, so the chances that even a potential
blockbuster will provide reasonably healthy profits from the year
of launch are questionable and need to be investigated systematically. Accordingly, a Pricing MDSS incorporates a cash-flow
statement that explicitly links top-line marketing predictions to
the bottom line, tvloreover, a cash-flow statement sensitive to the
set of inputs managed by the marketer is a useful measure of
evaluating the pros and cons of intended marketing strategy. On
many occasions, bottom-tine estimates can become a handy tool
in negotiating better resource allocations or suggesting the need
for seeking marketing partners and/or licensing opportunities,
The innards of an MDSS for pricing can be built with specific reference to the launch of a new pharmaceutical product in
a market where pricing is a key part of potential strategy. Such a
system is useful for developing and commercializing new pharmaceutical entities in an environment with escalating R&D
costs that need to be recovered to best benefit the manufacturer, the patient, and the provider Such a system can also be
duly adapted to many new product-launch scenarios in other
industries with little loss in generality. The fundamental principles guiding the creation of such a system remain the same:
developing a working system of models to represent a market,
the dynamic of potential customers in It, and using the system
to simulate processes of potential action and reaction. #
Lastly, an MDSS cash-flow statement is designed to provide
a working framework for resource-allocation discussions about
the new product inside the firm among marketing and financial
managers, Various marketing/sales force expenditure options
that previously were evaluated in isolation can now be simultaneously and systematically simulated for financial return and
assessed for feasibility. For example, it is invaluable to ascertain
the profits accrued as a result of a range of potential prices and
acquisition costs after factoring in all relevant cost centers such
as marketing/sales force/promotion expenditures. R&D/c!inical
Sanjay K. Rao is a manager at ZS Associates in Princeton, N.|,
He has consulted on, analyzed, and managed strategic-marketing research and consulting projects for Fortune 500 companies
for more than 14 years. Over the past six years, he has focused
his abilities exclusively on the pharmaceutical industry, consulting with the top firms in the United States and Europe on strategic-marketing design and execution projects. He can be reached
at [email protected].