March 25, 2014
Dr. Venkatesh Shankar
Coleman Chair Professor in Marketing
Director of Research, Center for Retailing Studies Mays Business School, Texas A&M
Title: A Flexible Semiparametric Approach to Model, Measure and Improve Sales Agency Productivity
Abstract:
Numerous firms use third-party sales agencies to sell their products.
These firms are interested in identifying clusters of agencies with similar productivity and in making retention, reward, training, and sales forecasting decisions associated with the agencies. Because these firms can neither observe sales agencies’ efforts nor directly control them, it is a challenge to model or measure their productivity. Existing approaches or models primarily measure the productivity of own sales force, not of sales agencies. Even so, they make parametric assumptions in productivity estimation and in clustering of agencies, potentially misspecifying the sales response model and misestimating sales productivity. We propose a flexible nonparametric method–Multivariate Dirichlet Process (MVDP) model–that overcomes these potential problems and simultaneously classifies the sales agencies into groups with similar sales productivity parameters. Using simulation, we first show substantial gains in sales productivity parameter recovery for our model over alternative models. We then estimate our model on two product categories using two different datasets from two major Fortune 500 firms, one each from the goods and services industries in business-to-business (B2B) and business-to-consumer
(B2C) markets. We compare our model with alternative approaches and show that it yields more accurate descriptive results, enables improved sales prediction in holdout samples, offers a better grouping of similarly performing sales agencies, enables improved interpretation and forecasts, and leads to more useful training decisions than do benchmark models.
Keywords: Sales force management, productivity, multivariate statistics, B2B marketing, Dirichlet process.