UTILISING ARTIFICIAL INTELLIGENCE TO INVESTIGATE THE RELEVANCE OF CUSTOMER BENEFITS

Adrian Braumandl, Alex Ponnraj, BRÃœCKEL Julian, Katharina Bause () and Albert Albers ()
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Adrian Braumandl: Karlsruhe Institute of Technology, IPEK - Institute of Product Engineering, Kaiserstraße 10, 76131 Karlsruhe, Germany
Alex Ponnraj: Karlsruhe Institute of Technology, IPEK - Institute of Product Engineering, Kaiserstraße 10, 76131 Karlsruhe, Germany
BRÜCKEL Julian: Karlsruhe Institute of Technology, IPEK - Institute of Product Engineering, Kaiserstraße 10, 76131 Karlsruhe, Germany
Katharina Bause: Karlsruhe Institute of Technology, IPEK - Institute of Product Engineering, Kaiserstraße 10, 76131 Karlsruhe, Germany
Albert Albers: Karlsruhe Institute of Technology, IPEK - Institute of Product Engineering, Kaiserstraße 10, 76131 Karlsruhe, Germany

International Journal of Innovation Management (ijim), 2023, vol. 27, issue 05, 1-16

Abstract: To support market success, it is important to identify customer needs, and the relation between customer needs and customer purchasing behaviour. This paper provides an overview over existing, already established approaches to determine the relevance of customer benefits. Then, an approach utilising artificial neural networks to correlate the attributes of battery electric vehicles and their sales performance is presented. This approach is discussed in relation to needs expressed by customers in surveys as well as typical user behaviour of passenger cars. It seems that, for example, charging speed of electric vehicles is more important than operational range despite customers regularly expressing operational range as their greatest concern. The presented approach can be integrated into the reference process for developing product profiles and can be coupled with drive system optimisation methods, to consider sales performance alongside vehicle performance, efficiency and costs in the early stage of product generation engineering.

Keywords: Artificial intelligence; battery electric vehicle; customer benefits; customer needs; innovation; neural network; product development; product profile; sales prediction (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S1363919623400066

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