Publication
Title
Strategic foresight of future B2B customer opportunities through machine learning
Author
Abstract
Within the strategic foresight literature, customer foresight still shows a low capability level. In practice, especially in business-to-business (B2B) industries, analyzing an entire customer base in terms of future customer potential is often done manually. Therefore, we present a single case study based on a quantitative customer-foresight project conducted by a manufacturing company. Along with a common data mining process, we highlight the application of machine learning algorithms on an entire customer database that consists of customer and product-related data. The overall benefit of our research is threefold. The major result is a prioritization of 2,300 worldwide customers according to their predicted technical affinity and suitability for a new machine control sensor. Thus, the company gains market knowledge, which addresses management functions such as product management. Furthermore, we describe the necessary requirements and steps for practitioners who realize a customer-foresight project. Finally, we provide a detailed catalogue of measures suitable for sales in order to approach the identified high-potential customers according to their individual needs and behaviour.
Language
English
Source (journal)
Technology Innovation Management Review
Publication
2018
ISSN
1927-0321
Volume/pages
8 :10 (2018) , p. 5-17
ISI
000450977400002
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 10.12.2018
Last edited 06.09.2021
To cite this reference