Research by Puneet Manchanda, Peter E. Rossi, and Pradeep K. Chintagunta
New research provides a way for pharmaceutical companies
to measure the effectiveness of direct sales calls on individual
physicians.
A marketing manager's toolkit is composed of the four P's:
price, promotion (advertising and direct selling), place (distribution),
and product. Taken together, these four P's-the "marketing
mix variables"-compose the marketing strategy that companies
use to market their product to different customer segments.
Each variable acts as a lever that can be used to increase
sales. Managers make "optimal" marketing mix decisions
based on the extent to which sales increase in response to
changes in each of the marketing mix activities.
Academics and marketing managers have long been interested
in understanding how the four P's work. Historically, researchers
and practitioners have relied upon statistical models, called
"sales response models," to quantify the effect
of these variables and come up with the best mix. These models
are required to estimate the sensitivity of sales to a change
in a marketing mix variable-e.g., how much do sales of a product
increase if the price is reduced by 10 percent?
In a new study, "Response Modeling with Non-Random Marketing
Mix Variables," University of Chicago Graduate School
of Business professors Puneet Manchanda, Peter E. Rossi, and
Pradeep K. Chintagunta go beyond the standard sales response
model to determine the effect of different marketing mix variables.
Their model looks at the effect of the marketing mix instruments
on individual customers rather than at the aggregate level.
They apply their modeling approach to the problem of gauging
the effectiveness of direct sales calls to induce the greater
prescribing of drugs by individual physicians.
The marketing mix variables for pharmaceutical companies
include: direct sales calls; advertising directly to consumers;
advertising in medical journals; and advertising at various
physician meetings.
Of these variables, pharmaceutical companies spend the most
money on direct sales calls, with more than $6 billion spent
in 2000. In a typical scenario, a physician attends brief
individual meetings with more than a dozen salespeople from
different pharmaceutical companies on a given day, all of
whom will bring samples and tout their company's latest drugs.
For pharmaceutical companies, it is important to figure out
the increase in the number of new prescriptions written for
the particular drug as a result of a direct sales call to
that physician.
"Pharmaceutical companies spend nearly three times
as much money on direct sales calls than on advertising to
consumers," says Chintagunta. "That's why it is
important to measure how well pharmaceutical sales force efforts
are being allocated."
The authors find that pharmaceutical companies are not efficiently
allocating sales force efforts for at least 50 percent of
the physicians in the study. High-volume physicians, those
who write more prescriptions, receive far more direct sales
calls than low-volume physicians, seemingly without regard
to the physician's responsiveness to these visits. It appears
that unresponsive, but high-volume, physicians receive the
most direct sales calls.
The Physician as Individual
Manchanda, Rossi, and Chintagunta used data from a major
U.S. pharmaceutical firm that provided information on physician
prescription writing behavior and sales force efforts for
a drug in a mature product category. The "mature"
classification indicates that the category is not expanding,
and therefore total sales levels for the drug are not trending
up or down. The drug is part of an important therapeutic category
with an estimated 19 million patients in the United States.
The data included a monthly record of how many prescriptions
each physician wrote for the drug from June 1999 to June 2001.
From internal firm records, the authors accounted for the
number of direct sales calls made to each physician per month,
as well as the number of free samples distributed to each
physician.
From a total sample of more than 112,000 physicians, the
authors chose a random sample of 1,000 physicians for the
study. On average, physicians in this sample wrote five new
prescriptions for the drug per month, and received two direct
sales calls and six drug samples per month.
In a typical sales response model, the aim is to find out
how changes in a marketing mix variable affect sales. In such
analyses, the marketing mix variable is taken as a "given."
However, in many industries, the marketing mix variable is
set strategically based on prior information about the company's
consumers.
In this study, the authors were told by the pharmaceutical
company that they make more calls to physicians who write
more prescriptions. The pharmaceutical company producing the
drug does not set direct sales call levels randomly, and instead
contracts with a consulting firm to help optimize the allocation
of their national sales force. The consulting firm recognizes
that targets for direct sales calls should ultimately be set
at the physician level. The consulting firm and the pharmaceutical
firm managers cooperate to set direct sales call targets on
an annual basis.
Allocating direct sales calls based on the volume of prescriptions
written assumes that the marginal effect of sales calls on
the probability of prescribing the company's drug is the same
for all physicians.
"Pharmaceutical companies have been making direct sales
calls to these physicians for a long time, and have some notion
as to which physicians are responsive," says Chintagunta.
"As a consequence, the way they set the sales force effort
must be a function of their knowledge of this responsiveness,
which adds another layer of complexity to the response model.
You have to take this knowledge into account to get the appropriate
estimate of the effect of direct sales calls on prescription
writing."
Sales Response Modeling
Marketing is increasingly focused on the individual consumer,
and as a result there has been an increase in the use of a
statistical method referred to as Bayesian hierarchical modeling,
which helps predict individual consumer behavior in response
to changes in marketing strategies.
"For a particular physician, what is the impact of direct
sales calls on his or her prescription behavior?" asks
Chintagunta. "That level of understanding is made possible
by Bayesian hierarchical methods which help researchers drill
down to individual analyses."
The model developed by the authors is based on two equations:
1) a response equation, which calculates the level of prescription
writing as driven by direct sales calls; and 2) a direct sales
call equation, which builds on the parameters of the first
equation and uses the base level of prescriptions and the
responsiveness of the physician to sales force efforts as
a foundation for evaluating the direct sales call level itself.
The use of responsiveness to determine the level of calls
is a modeling innovation proposed by the authors. Thus, the
authors' conditional response model allows them to differentiate
between physicians who are big or small prescribers as well
as those who are or are not responsive to direct sales calls.
The authors find that the least responsive physicians receive
almost twice as many direct sales calls as the most responsive.
"Physicians who are actually very responsive to sales
calls are being visited far less frequently than they should
be," says Chintagunta. "If the company transferred
their sales force effort away from the least responsive physicians
to the most responsive physicians, the company would likely
see higher sales for the drug."
The authors find an excessive amount of sales calls were
made to the top 20 percent of prescription writers regardless
of their responsiveness to direct sales calls. However, the
authors do note that the most frequently visited physicians
also may receive many sales calls from competing drug companies,
which may lower their responsiveness to the average direct
sales call. Managers may not have access to data on competing
sales calls when making their sales force allocation decisions.
To increase the number of prescriptions written for the drug,
direct sales calls should be focused on physicians who are
most responsive to the visits, rather than those who write
the most prescriptions.
Optimal allocation means making sure those two elements,
direct sales calls and responsiveness, line up. The authors
find that the two elements did not line up for about 50 percent
of the physicians that the firm targeted. This implies that
there is significant potential for improvement in the sales
efforts of pharmaceutical firms.
Chintagunta suggests that the most efficient allocation of
sales force efforts would be as follows: "Take two physicians:
one is very responsive to direct sales calls, the other is
not responsive at all. The company should send its salesperson
to the physician who actually responds to these direct sales
calls by writing more prescriptions. Otherwise, the company
will be wasting its resources."
Applications for Other Industries
While physician data is available to most pharmaceutical
firms, it is difficult to implement these models because of
the technology and skill required to use advanced statistics
to calculate individual level response. As is the case with
most research, there is likely to be a time lag between new
findings and industry practice.
However, as technology advances, it will become easier for
marketing managers to perform these calculations and implement
these response models.
"If a company can understand at the individual level
how consumers respond to a promotion, then they can use that
information to tailor their marketing programs," says
Chintagunta.
"Furthermore," adds Manchanda, "our approach
can be applied to any industry where there is the potential
to strategically set marketing mix variables. For example,
advertising in different markets may be set based on how big
the market is and how responsive each market is to the advertising.
Our approach can be used to determine whether an advertising
expenditure is the optimal amount and how the allocation of
resources can be improved."
Most importantly, these methods can create a better understanding
of the efficiency and effectiveness of marketing activities,
which can help companies obtain a higher return on their marketing
investment.
Puneet Manchanda is associate professor of marketing at the University of Chicago Graduate School of Business. Peter E. Rossi is Joseph T. Lewis Professor of Marketing and Statistics at the University of Chicago Graduate School of Business. Pradeep K. Chintagunta is Robert Law Professor of Marketing at the University of Chicago Graduate School of Business.
