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Business intelligence using machine learning algorithms

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Abstract

Business intelligence, as one of the branches of information technology, is increasingly considered by managers in today’s business world. In order to make better decisions about the business process, most business organizations are very willing to use intelligent systems. Intelligence refers to the ability to pursue a goal in the human way; therefore, it can be said that the more human-like a system is, the more intelligent it is. Through learning and gaining experience or acquiring new knowledge, the intelligent system can increase its knowledge. One of the main goals of implementing business intelligence in any organization is to create reports using variety of management dashboards for effective and critical decisions based on the organization’s key indicators. The present study aims to provide an efficient model for optimizing the products sales system in a pharmaceutical company using clustering methods and based on machine learning indicators and algorithms. The studied model uses RFM (Recency Frequency Monetary)-LRFM (Length Recency Frequency Monetary)-NLRFM (Number Length Recency Frequency Monetary) indices to utilize customer clustering algorithms. Also, the association rules method has been used in this study in order to show the relationship between the sold products, to analyze the customers’ shopping cart, and to offer to the customers based on the obtained rules. Finally, the results are reviewed with K-mean, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and Optics algorithms. According to the obtained results, the proposed model will provide the best results using the K-means algorithm.

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Notes

  1. Enterprise resource planning

  2. Customer relationship management

  3. density-based spatial clustering of applications with noise

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran

    Morteza Hamzehi & Soodeh Hosseini

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  1. Morteza Hamzehi
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  2. Soodeh Hosseini
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Correspondence to Soodeh Hosseini.

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Hamzehi, M., Hosseini, S. Business intelligence using machine learning algorithms. Multimed Tools Appl 81, 33233–33251 (2022). https://doi.org/10.1007/s11042-022-13132-3

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  • DOI: https://doi.org/10.1007/s11042-022-13132-3

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