With the advent of one-to-one marketing media, e.g.
targeted direct mail or internet marketing, the opportunities to
develop targeted marketing campaigns are enhanced in such a way
that it is now both organizationally and economically feasible to
profitably support a substantially larger number of marketing
segments. However, the problem of what segments to distinguish,
and what actions to take towards the different segments increases
substantially in such an environment. A systematic analytic
procedure optimizing both steps would be very welcome.In this study, we present a joint optimization approach addressing
two issues: (1) the segmentation of customers into homogeneous
groups of customers, (2) determining the optimal policy (i.e.,
what action to take from a set of available actions) towards each
segment. We implement this joint optimization framework in a
direct-mail setting for a charitable organization. Many previous
studies in this area highlighted the importance
of the following variables: R(ecency), F(requency), and M(onetary
value). We use these variables to segment customers. In a second
step, we determine which marketing policy is optimal using markov
decision processes, following similar previous applications.
The attractiveness of this stochastic
dynamic programming procedure is based on the long-run
maximization of expected average profit. Our contribution lies in
the combination of both steps into one optimization framework to
obtain an optimal allocation of marketing expenditures. Moreover,
we control segment stability and policy performance by a bootstrap
procedure. Our framework is illustrated by a real-life
application. The results show that the proposed model outperforms
a CHAID segmentation
