Author Identifier (ORCID)
Ferry Jie: https://orcid.org/0000-0002-6287-8471
Abstract
Omnichannel distribution integrates multiple sales channels into a single management platform to enhance customer experience. However, the complexity of consumer behavior data across channels often prevents business owners from effectively analyzing and predicting sales. This study proposes a sales prediction model in omnichannel distribution systems based on consumer behavior using a collaborative approach that integrates process mining and autoregressive integrated moving average with exogenous variables (ARIMAX). Event log data from an omnichannel service provider were used to extract consumer activity patterns, which were then analyzed through process discovery algorithms to identify dominant behavioral processes. The resulting behavioral indicators served as exogenous variables in the ARIMAX model for sales forecasting. The experimental results show that combining consumer behavioral data with ARIMAX improves prediction accuracy, achieving a mean absolute percentage error (MAPE) of 2.5% after logarithmic transformation. The findings demonstrate that consumer behavior significantly contributes to improving sales prediction accuracy, providing valuable insights for business decision-making in omnichannel environments.
Keywords
ARIMAX, consumer behavior, omnichannel, process discovery, sales prediction
Document Type
Journal Article
Date of Publication
1-1-2026
Volume
2026
Issue
1
Publication Title
Applied Computational Intelligence and Soft Computing
Publisher
Wiley
School
School of Business and Law
Funders
Universitas Diponegoro / LPPM of Universitas PGRI Yogyakarta
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Tridalestari, F. A., Mustafid, M., Jie, F., & Prasetyo, H. N. (2026). Sales prediction in omnichannel distribution systems based on consumer behavior using process mining and ARIMAX. Applied Computational Intelligence and Soft Computing, 2026. https://doi.org/10.1155/acis/6319448