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.
Highlights
In a business-to-business (B2B) environment, forecasting future de mand is quite crucial as the entire production and supply process de pends on these forecasts
A solution to analyzing such big unstruc tured data is natural language processing (NLP), which is a subfield of artificial intelligence that gives machines the ability to read, understand and derive meaning from human languages (Hirschberg & Manning, 2015)
In this paper we focus on B2B sales forecasting using advance or future demand information coming from customers, taking the form of requests for quotation (RFQs)
Summary
In a business-to-business (B2B) environment, forecasting future de mand is quite crucial as the entire production and supply process de pends on these forecasts. With the advancements in information technologies, companies possess ever more data with the potential to be mined for valuable insights and/or utilized for advanced analytics applications, e.g. machine learning (ML). The majority, an estimated 80–90% of big data is unstructured data (e.g. emails), which is growing faster than any other type of data. A solution to analyzing such big unstruc tured data is natural language processing (NLP), which is a subfield of artificial intelligence that gives machines the ability to read, understand and derive meaning from human languages (Hirschberg & Manning, 2015). NLP is said to have the ability to automate data extraction from large volumes of unstructured text (Li & Elliot, 2019)
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