Predictive modeling and decision support using machine learning in business contexts
Artykuł w czasopiśmie
MNiSW
100
Lista 2024
| Status: | |
| Autorzy: | Bojanowska Agnieszka, Kulisz Monika |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 10 |
| Wolumen/Tom: | 19 |
| Strony: | 385 - 395 |
| Impact Factor: | 1,3 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus | BazTech |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 1 września 2025 |
| Abstrakty: | angielski |
| With the growing emphasis on data-driven decision making, artificial intelligence (AI) methods have become increasingly important in managerial practice. This study aims to develop and evaluate supervised machine learn- ing models for predicting customer brand loyalty and satisfaction based on selected behavioral, attitudinal, and programmatic attributes. This paper presents a lightweight decision support application that leverages machine learning techniques – specifically, artificial neural networks (ANN) and support vector machines (SVM) – to pre- dict key customer-related indicators: brand loyalty and satisfaction. The models were trained on behavioral and attitudinal inputs and achieved excellent predictive performance, with test accuracies reaching 100%. The novelty of this study lies in the deployment of these models within an intuitive graphical user interface (GUI), enabling real-time predictions by non-technical users. Unlike traditional approaches focused solely on algorithm develop- ment, this research demonstrates a practical implementation of computational intelligence for operational and tac- tical business decision-making. The tool supports managers in profiling customers, optimizing loyalt. |
