Salesforce Design with Experience-based Learning

This paper proposes and analyzes an integrated model of salesforce learning, product portfolio pricing and salesforce design. We consider a firm selling two products, with a pool of sales representatives that is split into separate salesforces, one for each product. The salesforce assigned to each product is faced with an independent stream of sales leads. The salesforce may also handle leads that overflow from other product salesforces. In addition, salespeople ‘learn by doing’ over their tenure on the job. In particular, the more time they spend selling a particular product, the more productive the sales effort. The objective of the firm is to maximize profits by optimizing the size of all salesforces as well as the prices of all products. Using data collected from the salesforce of a large manufacturer, we provide evidence for the link between experience and sales, and we demonstrate how parameters of the model may be estimated from real data. Numerical experiments using parameters derived from the data analysis indicate that the optimal salesforce size increases with both sales productivity and the learning rate, and decreases with salesforce costs (e.g., wage per representative), product production costs and consumer price sensitivity. We also find that worker learning can significantly dampen the effect of rising costs (or decreasing margins) on staffing levels. Finally, we examine the impact of learning on both the optimal salesforce structure (specialists vs. generalists) as well as the optimal routing of sales leads to sales representatives.