  Field sales forces play an important role in direct marketing, especially for
companies offering complex products, services, or solutions in the
business-to-business context. A key task of sales representatives in
operational planning is to select the most promising customers to visit within
the next days. On an operative horizon, a key task for sales representatives is
to select the most promising customers to visit within the next days. A
strongly varying set of scoring methods predicting or approximating the
expected response exists for this customer selection phase. However, in the
case of field sales forces, the final customer selection is strongly
interrelated to the tour planning decisions. To this end, we formalize variants
of the profitable sales representatives tour problem as a multi-period team
orienteering problem, thereby providing a unified view on the customer scoring
and the tour planning phase. In an extensive computational study on real-world
instances from the retail industry, we systematically examine the impact of the
aggregation level and the content of information provided by a scoring method
and the sensitivity of the proposed models concerning prediction errors. We
show that the selection of a customer scoring and tour planning variant depends
on the available data. Furthermore, we work out where to put effort in the data
acquisition and scoring phase to get better operational tours.
