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

TL;DR

This paper unifies sales analytics and mathematical optimization to improve the planning of profitable customer visits for field sales, considering scoring methods and tour planning as interconnected problems.

Contribution

It formalizes the customer selection and tour planning as a multi-period team orienteering problem, integrating scoring methods with operational tour optimization.

Findings

The choice of scoring and planning methods depends on available data.

Aggregation level and information content significantly affect tour quality.

Focusing on data acquisition improves operational tour effectiveness.

Abstract

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.