Explaining machine learning models in sales predictions

A complexity of business dynamics often forces decision-makers to make decisions based on subjective mental models, reflecting their experience. However, research has shown that companies perform better when they apply data-driven decision-making. This creates an incentive to introduce intelligent, data based decision models, which are comprehensive and support the interactive evaluation of decision options necessary for the business environment. Recently, a new general explanation methodology has been proposed, which supports the explanation of state-of-the-art black-box prediction models. Uniform explanations are generated on the level of model/individual instance and support what-if analysis. We present a novel use of this methodology inside an intelligent system in a real-world case of business-to-business (B2B) sales forecasting, a complex task frequently done judgmentally. Users can validate their assumptions with the presented explanations and test their hypotheses using the presented what-if parallel graph representation. The results demonstrate effectiveness and usability of the methodology. A significant advantage of the presented method is the possibility to evaluate seller's actions and to outline general recommendations in sales strategy. This flexibility of the approach and easy-to-follow explanations are suitable for many different applications. Our well-documented real-world case shows how to solve a decision support problem, namely that the best performing black-box models are inaccessible to human interaction and analysis. This could extend the use of the intelligent systems to areas where they were so far neglected due to their insistence on comprehensible models. A separation of the machine learning model selection from model explanation is another significant benefit for expert and intelligent systems. Explanations unconnected to a particular prediction model positively influence acceptance of new and complex models in the business environment through their easy assessment and switching. (C) 2016 Elsevier Ltd. All rights reserved.