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Field sales forces play an important role in direct marketing, especially forcompanies offering complex products, services, or solutions in thebusiness-to-business context. A key task of sales representatives inoperational planning is to select the most promising customers to visit withinthe next days. On an operative horizon, a key task for sales representatives isto select the most promising customers to visit within the next days. Astrongly varying set of scoring methods predicting or approximating theexpected response exists for this customer selection phase. However, in thecase of field sales forces, the final customer selection is stronglyinterrelated to the tour planning decisions. To this end, we formalize variantsof the profitable sales representatives tour problem as a multi-period teamorienteering problem, thereby providing a unified view on the customer scoringand the tour planning phase. In an extensive computational study on real-worldinstances from the retail industry, we systematically examine the impact of theaggregation level and the content of information provided by a scoring methodand the sensitivity of the proposed models concerning prediction errors. Weshow that the selection of a customer scoring and tour planning variant dependson the available data. Furthermore, we work out where to put effort in the dataacquisition and scoring phase to get better operational tours.

Planning profitable tours for field sales forces: A unified view on sales analytics and mathematical optimization