Estimating post-event seller productivity profiles in dynamic sales organizations

Abstract

In modern sales organizations, the salesforce is constantly in flux due to sellers retiring or leaving to take positions in other organizations, and due to new sellers being hired from universities, from other organizations, and transferring in from different divisions within the enterprise. The productivity of sellers in the time period after these human resource events is not the same as that of entrenched sellers. It takes time for new sellers to ramp up their productivity and the ramping up profile of productivity for new sellers of different categories varies. Also, sellers that attrit have a lasting effect on sales in the pipeline after they leave. Thus revenue projections and other business planning decisions cannot simply be made from the total headcount of sellers; post-event seller productivity profiles must also be taken into account. In this paper, we propose a regularized estimation technique using linear programming to minimize maximum residual error for determining seller productivity profiles from aggregate sales revenue and headcount data that can then be used in future planning. We demonstrate the estimation technique on real sales data from a global enterprise and compare results to productivity profiles elicited from salesforce leaders in the enterprise. © 2011 IEEE.