|
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
| |
|
DOI
|
|
|
|
10.22215/TIMREVIEW/1189
| |
|
Volume/pages
|
|
|
|
8
:10
(2018)
, p. 5-17
| |
|
ISI
|
|
|
|
000450977400002
| |
|
Full text (Publisher's DOI)
|
|
|
|
| |
|
Full text (open access)
|
|
|
|
| |
|