This paper proposes and analyzes an integrated model of salesforce learning, product portfolio pricing and salesforce design. We consider a firm 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. The results obtained from this model reveal some important insights into the structure and size of optimal salesforces in environments characterized by learning and product complexity. Numerical experiments with the model indicate that salesforce size increases with salesforce productivity and decreases with salesforce costs (per representative), product production costs and consumer price sensitivity. We also find that learning plays a complex role in determining optimal salesforce staffing. In particular, when calculating the value of an additional salesperson, we identify a tradeoff between the incremental revenue due to increased throughput and the incremental decrease in utilization, learning, and productivity for the entire salesforce. We also examine the issue of specialization in salesforce design and discuss the conditions governing its optimality. The results highlight the value of conducting salesforce design with a full understanding of the related operational issues of experience-based learning and queueing phenomena.