Title
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Strategic foresight of future B2B customer opportunities through machine learning
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Author
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Abstract
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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. |
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Language
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English
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Source (journal)
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Technology Innovation Management Review. - -
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Publication
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2018
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ISSN
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1927-0321
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DOI
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10.22215/TIMREVIEW/1189
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Volume/pages
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8
:10
(2018)
, p. 5-17
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ISI
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000450977400002
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Full text (Publisher's DOI)
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Full text (open access)
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