Biomedical Engineering Reference
In-Depth Information
with
æ
ö
æ
ç
ö
÷
(
)
ç
ç
÷
÷
Profit contribution Unit
*
*
wt pc qt
kin
()
=
-
()
e fx it.
+
e gx t
kin
g kin
Discounted marketing
multiplier
r
+
1-
ki
ki
ki
ë
ki * ()
û
ë
* ()
û
(19.20)
è
ø
sales
Saleselasticity
wrtmarketing
Growth elasticity
wrtmarketing
...
...
where w is an allocation weight, γ measures the marketing carryover, and all other
terms are defined as earlier. The star indicates that variables are in their optimum.
Fischer et al. ( 2011b ) note that this solution also holds under the assumption of
Nash competition. Optimal budgets, however, are likely to be different from
monopoly budgets since optimum values for sales and elasticities depend on com-
petitor equilibrium values.
Fischer et al. apply the approach to the budget allocation process at Bayer, a
global pharmaceutical and chemical firm. The authors note that solving the condi-
tions for optimal budgets in ( 19.19 ) requires a numerical optimization algorithm.
Because managers do not understand how a specific budget recommendation arises
from such a black box they do not accept it. For this reason, the authors develop an
easy-to-implement heuristic rule that can be used in a spreadsheet environment. The
heuristic integrates three types of information in form of an attraction rule to char-
acterize the relative attractiveness of a product in terms of its future profit genera-
tion potential:
-
The long-term effectiveness of marketing investments in the product
-
The profit contribution of the product
-
The product's growth expectations
Model implementation at Bayer resulted into a significant profit improvement
potential of more than 50 %. For the assumed 5-year planning horizon, that increase
was worth of more than EUR 500 million in incremental discounted cash flows.
19.5
Conclusions and Future Research
The pharmaceutical industry is a major industry. Pharmaceutical firms are among
the top marketing spenders across the world. Insofar, the industry offers the need
and the field for research on marketing spending that should contribute to our
knowledge. There is a solid ground of modeling approaches and empirical insights.
We have gained good knowledge about the responsiveness of pharmaceutical
demand towards traditional marketing activities such as detailing and professional
journal advertising, but also with respect to newer activities such as DTCA. We
have a good basic understanding of how to model the diffusion process of a new
drug. The double-log model, the mixed logit model, and the NBD model have
turned out to offer powerful modeling frameworks to describe market response in
terms of brand sales, brand choice, and prescription rates. Based on the achieved
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