Social Listening: Adapting Customer and Competitive Intelligence to the Digital Era: An Abstract

Customers have increased their power toward the seller in B2B sales. One reason is that the buyer has access to more information. Moreover, it has been argued that customer collects, and makes decisions based on, this information before contacting a salesperson. The traditional listening model, relying on a physical meeting between the seller and the buyer, does not offer a solution in this new buying situation, where the customers collect information through digital media, and knows much more about the seller’s solution before any contact. Therefore, to continue to add value to the customer, the seller needs to listen to the customer through digital channels to understand which customers are looking for information about the seller’s solution and to collect information about the customer’s business.

The objective of this research is to present a theoretical framework of “Digital Sales Listening and Learning” (DSLL) and related research propositions. By looking at, among others, the salespeople’s digital exposure and the strength of their network, and how they are using their network to sense what potential customers are asking about their products, we argue that this will have an effect on selling-related knowledge.

DSLL proceeds the traditional sales listening model. Based on listening- and Connectivism learning theories, the model argues that sales listening can be used to collect information about prospects before the initial contact, making it possible for the salesperson to more fully understand the needs of the customers and thereby offer additional value propositions when in contact with prospects. It also makes it possible for salespeople, in an early phase, to detect prospects that are looking for information regarding possible solutions offered by the seller.

Our main contribution is a proposed extension of salesperson listening, by including how salespeople listen to their customers online, including proposed antecedents, consequences, and moderators of the DSLL model.