Using supervised machine learning for B2B sales forecasting: A case study of spare parts sales forecasting at an after-sales service provider

In this paper, we present a method to use advance demand information (ADI), taking the form of request for quotation (RFQ) data, in B2B sales forecasting. We apply supervised machine learning and Natural Language Processing techniques analyze learn from RFQs. test our approach case study at large after-sales service maintenance provider. After evaluation found that identifies ∼ 70% actual (recall) with precision rate 50%, which represents performance improvement slightly more than factor 2.5 over current labor-intensive manual process Our research contributes literature by giving step-by-step guidance on incorporating artificial intelligence forecasting revealing potential pitfalls along way. Furthermore, gives an indication can be expected when adopting into