Integration of Machine Learning Insights into Organizational Learning: A Case of B2B Sales Forecasting

Marko Bohanec (), Mirjana Kljajić Borštnar () and Marko Robnik-Šikonja ()
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Marko Bohanec: Salvirt ltd.
Mirjana Kljajić Borštnar: University of Maribor
Marko Robnik-Šikonja: University of Ljubljana

A chapter in Blurring the Boundaries Through Digital Innovation, 2016, pp 71-85 from Springer

Abstract: Abstract Business-to-Business (B2B) sales forecasting can be described as a decision-making process, which is based on past data (internal and external), formalized rules, subjective judgment, and tacit organizational knowledge. Its consequences are measured in profit and loss. The research focus of this paper is aimed to narrow the gap between planned and realized performance, introducing a novel approach based on machine learning techniques. Preliminary results of machine learning model performance are presented, with focus on distilled visualizations that create powerful, yet human comprehensible and actionable insights, enabling positive climate for reflection and contributing to continuous organizational learning.

Keywords: B2B sales modeling; Machine learning; Visual data mining; Organizational learning; Forecasting error reduction; Knowledge engineering (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-319-38974-5_7

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DOI: 10.1007/978-3-319-38974-5_7

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