SLING: A knowledge-based product selector

摘要

Wholesale industrial distribution is a mature yet highly competitive industry. Distributors seek competitive advantage in the immediacy and accuracy of their product recommendations and, hence, rely heavily on inside sales personnel. This paper describes a knowledge-based system called SLING that is used to enhance the sales effectiveness and efficiency of these inside sales people. In particular, since detailed product knowledge is often rare among novice sales people, a customer may be misinformed about a product and/or dissatisfied with the product and distributor. This situation often leads to lost sales (the customer calls a competitor with an order) or, at the very least, an upset customer who returns an incorrect product (resulting in wasted time for both the customer and the distributor). In fact, the second most common reason for lost sales in the lack of immediately available product knowledge. For example, when an inside salesperson says to a potential customer, “I will have to get back to you with that information,” he is inviting the customer to call on a competitor.In order to alleviate this problem, we have introduced a new technique from the field of Artificial Intelligence into the product marketing arena. Namely, we present a novel application of knowledge-based system technology to improving sales productivity for the industrial distributor. The SLING system aids inexperienced sales personnel in determining the appropriate industrial sling for a customer's specific lifting application.