Sales representatives (reps) maximize sales profitability by selling goods and services. They visit retail stores within an assigned territory to fully exploit the sales potential for the products of their represented company. During their visits the sales reps record and optimize product placement, install advertising displays, check products for compliance, i.e. whether the store offers them to buy, as well as out-of stock situations, and talk to the stores’ managers to improve the product representation within the store. Regularly, sales reps have to schedule their store visits for the upcoming time frame with the goal to choose the “right” stores, which are supposed to increase the sales profitability as much as possible. Until now, this planning is done manually based on static and aggregated data provided by spreadsheets from the rep’s manager.
The vision of the Dynamic Tour Planning project is to incorporate as much and recent information as possible, even think about fine granular point-of sales data, to find out the optimal tour schedule. To achieve this, we automatize the planning process and exploit millions of data records storing information about past visits, point-of-sales, share-of shelf etc. Using a configurable algorithm, we are able to calculate an optimized schedule. The developed prototype is an extended calendar applications displaying past and planned visits, as well as all other events, e.g. team meetings and private appointments. Further it features the functionality to request an automatically calculated tour suggestion for the upcoming time frame. Based on the suggestion, the sales rep can adapt the schedule manually with knowledge which is not regarded by the system. In cases the sales representative makes changes that decrease the overall expected impact of his visits dramatically, the application notifies the sales rep so that he can double check his adjustments. Further, the prototype features a map view to inspect the planned tours geographically.