A1 Journal article (refereed)
Augmenting machine learning with human insights : the model development for B2B personalization (2024)
Yaghtin, S., & Mero, J. (2024). Augmenting machine learning with human insights : the model development for B2B personalization. Journal of Business and Industrial Marketing, 39(6), 1192-1208. https://doi.org/10.1108/jbim-02-2023-0073
JYU authors or editors
Publication details
All authors or editors: Yaghtin, Shahrzad; Mero, Joel
Journal or series: Journal of Business and Industrial Marketing
ISSN: 0885-8624
eISSN: 2052-1189
Publication year: 2024
Publication date: 01/01/2024
Volume: 39
Issue number: 6
Pages range: 1192-1208
Publisher: Emerald
Publication country: United Kingdom
Publication language: English
DOI: https://doi.org/10.1108/jbim-02-2023-0073
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/92923
Abstract
Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.
Design/methodology/approach
The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.
Findings
The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.
Originality/value
This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.
Keywords: machine learning; corporate clients; business-to-business marketing; data systems
Free keywords: machine learning; B2B personalization; human-machine learning augmentation; personalized marketing; business customers; personalized information system
Contributing organizations
Ministry reporting: Yes
VIRTA submission year: 2024
Preliminary JUFO rating: 1
