Decision Support Systems in Marketing

Abstract
The practical application of decision support systems in marketing is still in its infancy,
even though academic research has been recommending the use of such systems
for years. This is largely due to the lack of a single, generally applicable decision
support system. The specific requirements and problems of management are
too dissimilar to make the development of one universal decision support system
feasible. However, improved PC performance, enhanced market survey methods
such as conjoint measurement, and further developed programming tools permit
the varying requirements to be fairly flexibly met. The following article describes
state-of-the-art know-how regarding the practical utilization of such systems, and
addresses the preconditions for employing them.We have included many examples
gathered from our own experience with numerous applications.Introduction
Most industries have experienced a proliferation of products and competitors during
the last 20 years, a development which can be expected to continue. A valid
forecast of the impact of product and price measures will be even more important,
but also more difficult. In other areas like engineering, environment and training
the application of decision support systems has been already practiced. The objective
is always the same: To replicate complex interdependencies in a computer
model as realistically and, at the same time, as simply as possible. Doing so helps
forecast the impact of important decisions, minimize risk and reduce product development
time and cost. For example, semiconductor chips and processors are
designed, developed and tested today almost exclusively with computers. Flight
simulators used to train pilots can simulate a wide variety of situations without endangering
pilots or passengers. The automobile industry has simulators in which
a vehicle’s road handling characteristics can be simulated under various weather
conditions and in crash testing, resulting in reduced research and development
costs.
In contrast, the practical application of decision support systems inmarketing is
still in its infancy, even though academic research has been recommending the use
of such systems for years (Leeflang et al. 2000, Little 1979, Nikolaos and Siskos
2002). This is all the more remarkable given the large uncertainty under which
marketing departments attempt to optimally adjust products and pricing to customer
needs within the environment of an ever increasing competition and growing
complexity of markets and competitive frameworks. Judging from our own experiencedecision support systems deliver great value in supporting such adjustments.
With their help, the effects of product or pricing measures can be forecast prior to
their implementation which significantly reduces uncertainty.
Objectives of decision support systems
Instinct, experience and intuition alone are not sufficient for objectively evaluating
the bottom line impact of such important matters as the launch, modification
or elimination of a product, product bundling or price differentiation measures.
That is where the value of decision support systems lies (Dolan and Simon 1996);
they provide decision makers with a tool which allows market reactions to various
courses of action to be objectively tested prior to implementation, at low cost and
without risk (Lilien et al. 1992). In this context, decision support systems can be
used for planning and developing marking measure as well as to answer strategic
questions. Indeed, a well designed decision support system can and ideally should
be used for all product and pricing decisions made during the entire product life
cycle. For example, a German mobile telecommunication company used such a decision
support system to test the effects of new pricing structures and to evaluate
quarterly if price adjustments are necessary.