A decision support system for B2B CRM Systems using belief rules

The complexity of business environment often forces sales representative to take decisions using intuition and/or subjective models created through experience instead of data driven decisions. However, data driven decisions generate better revenue. In this work, we present a methodology to create a Decision Support System (DSS) for Business to Business (B2B) sales that generates recommendations for actions to follow and actions to avoid to increase the chances of successfully closing a deal. At the core of our proposed DSS, is a three-step methodology relying on a particular approach of using belief rules. In the first step we generate, based on available data on previous won/lost deals, some forecasting models. As the best performing models are of black box type, the second step aims to extract if-then conventional rules that are converted, in the last step, into belief rules. Rather than using an attribute value probability matching to activate a belief rule, we propose a cost of activation that takes into account the current state of the active deal and the cost of transitioning the active deal's attributes into a new state that could activate a belief rule. Furthermore, we propose and analyze two methods of calculating the cost of transition related to the effort the sales representative should dispose to move the current deal into the new state. The threestep approach was used to create a DSS for a real world B2B complex sales Customer Relationship Management Systems and the results are discussed and included in this paper.