Biomedical Engineering Reference
In-Depth Information
(
D
/
R
)
=−
(
D
/
P
)
=
mD
,
( 18.1 )
S
E
E
E
where
D
S
denotes personal selling (detailing) spending;
D
E
is the detailing elasticity;
R d
enotes sales revenues;
P
E
represents the price elasticity, and
m
is the gross margin
(price less cost of goods sold expressed as a fraction of price). We apply this condi-
tion to obtain some normative implications.
What is the benchmark optimal detailing spend to sales ratio
? Following (18.1),
the meta-analytic estimate of detailing elasticity can be employed to answer this
question given some estimate of the mean price elasticity or the average gross mar-
gin percentage. Over the years, various reports have indicated that makers of patent-
protected, or single-source brand-name drugs typically enjoy gross margins greater
than 60 % (e.g., Scherer
2007
). In
Pharmaceutical Executive
'
s
Tenth Annual Audit,
Trombetta (
2011
) reports that the average gross margin of the top 23 publicly traded
drug companies (by sales revenue) for 2010 was about 72 % which is down from the
mean gross margin estimate of about 78 % for the top 20 pharma and biotech com-
panies in 2008 offered by analysts of Zacks Investment Group in March 2009.
4
Consider a typical monopoly drug today priced at a gross margin of 72 %. If the
maker of this drug is pricing optimally then (18.1) implies the corresponding price
elasticity is about −1.39. Employing this illustrative mean price elasticity estimate
of −1.39, and our overall bias-corrected estimate of 0.178 for detailing elasticity, the
D-S theorem condition implies the optimal percentage of detailing expenditures to
total revenues is about 13 %. In contrast, assuming the same price elasticity, the
bias-corrected estimate of short-term pharma detailing elasticity of about 0.227
derived from AMS (
2010
) implies a higher optimal detailing to sales percentage of
about 16 %. Thus, the difference of about 0.05 between the current and AMS (
2010
)
studies' meta-analytic estimates of detailing elasticity is signifi cant in that it implies
a signifi cant difference in the corresponding optimal detailing to sales percentages.
Naturally, the magnitude of this optimal ratio decreases as the detailing elasticity
decreases. Thus, given the fi xed estimate of price elasticity, our meta-analytic mean
estimate of detailing elasticity provides a benchmark detailing to sales ratio for
companies to assess the optimality of their overall detailing expenditure, i.e., it
serves as a “starting point for optimization” (Farley and Lehmann
1994
).
5
However, as stated at the outset, the industry has been signifi cantly cutting back
on detailing since 2007 (e.g., Berenson
2006
; Hensley
2007
). For example, the
5
In this elasticity-based analysis, “detailing spending” properly includes all costs that vary as the
units of detailing effort vary, e.g., reps' compensation, cost of transportation, preparation time, and
expenses but not necessarily the costs of area and regional managers, training etc. as in the CAM
Group measure (see Footnote 1). Thus, we expect the detailing spending measure will include a
few more cost items than the IMS measure but not as many as the CAM measure. A reasonable
compromise appears to be to use the SDI Promotional Audits measure of detailing spending as
utilized by the Congressional Budget Offi ce (Campbell
2009
). Unfortunately, we have been unsuc-
cessful so far in fi nding the precise defi nition of the SDI detailing spending metric and if and how
it differs from the metric used by IMS now that the latter has acquired SDI.
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