1. Using supervised machine learning for B2B sales forecasting
- Author
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C.G.M. Groothuis-Oudshoorn, Engin Topan, D. Rohaan, Industrial Engineering & Business Information Systems, and Health Technology & Services Research
- Subjects
Service (business) ,Prioritization on sales potential ,Process (engineering) ,Computer science ,business.industry ,General Engineering ,Imbalanced data ,Natural Language Processing (NLP) ,Service provider ,computer.software_genre ,Machine learning ,B2B sales forecasting ,Computer Science Applications ,Test (assessment) ,Information extraction ,Request for quotation ,Artificial Intelligence ,Spare part ,Artificial intelligence ,Performance improvement ,Supervised machine learning ,business ,computer ,Information Extraction - Abstract
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 to analyze and learn from RFQs. We apply and test our approach in a case study at a large after-sales service and maintenance provider. After evaluation we found that our approach identifies ∼ 70% of actual sales (recall) with a precision rate of ∼ 50%, which represents a performance improvement of slightly more than a factor 2.5 over the current labor-intensive manual process at the service and maintenance provider. Our research contributes to literature by giving step-by-step guidance on incorporating artificial intelligence in B2B sales forecasting and revealing potential pitfalls along the way. Furthermore, our research gives an indication of the performance improvement that can be expected when adopting supervised machine learning into B2B sales forecasting.
- Published
- 2022