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

D. Rohaan*, E. Topan, C. G.M. Groothuis-Oudshoorn

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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.

Original languageEnglish
Article number115925
Number of pages13
JournalExpert systems with applications
Volume188
Early online date13 Oct 2021
DOIs
Publication statusE-pub ahead of print/First online - 13 Oct 2021

Keywords

  • B2B sales forecasting
  • Imbalanced data
  • Information Extraction
  • Natural Language Processing (NLP)
  • Prioritization on sales potential
  • Supervised machine learning

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