  While it is relatively easy to start an online advertising campaign,
obtaining a high Key Performance Indicator (KPI) can be challenging. A large
body of work on this subject has already been performed and platforms known as
DSPs are available on the market that deal with such an optimization. From the
advertiser's point of view, each DSP is a different black box, with its pros
and cons, that needs to be configured. In order to take advantage of the pros
of every DSP, advertisers are well-advised to use a combination of them when
setting up their campaigns. In this paper, we propose an algorithm for
advertisers to add an optimization layer on top of DSPs. The algorithm we
introduce, called SKOTT, maximizes the chosen KPI by optimally configuring the
DSPs and putting them in competition with each other. SKOTT is a highly
specialized iterative algorithm loosely based on gradient descent that is made
up of three independent sub-routines, each dealing with a different problem:
partitioning the budget, setting the desired average bid, and preventing
under-delivery. In particular, one of the novelties of our approach lies in our
taking the perspective of the advertisers rather than the DSPs. Synthetic
market data is used to evaluate the efficiency of SKOTT against other
state-of-the-art approaches adapted from similar problems. The results
illustrate the benefits of our proposals, which greatly outperforms the other
methods.
