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360° Retail Business Analytics by Adopting Hybrid Machine Learning and a Business Intelligence Approach

Author

Listed:
  • Abdulmajeed Alqhatani

    (Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
    These authors contributed equally to this work.)

  • Muhammad Shoaib Ashraf

    (Department of Informatics and Systems, School of Systems and Technology, University of Management and Technology Lahore, Lahore 54770, Pakistan
    These authors contributed equally to this work.)

  • Javed Ferzund

    (Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan)

  • Ahmad Shaf

    (Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan)

  • Hamad Ali Abosaq

    (Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia)

  • Saifur Rahman

    (Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 11001, Saudi Arabia)

  • Muhammad Irfan

    (Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 11001, Saudi Arabia)

  • Samar M. Alqhtani

    (Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia)

Abstract

Business owners and managers need strategic information to plan and execute their decisions regarding business operations. They work in a cyclic plan of execution and evaluation. In order to run this cycle smoothly, they need a mechanism that should access the entire business performance. The sole purpose of this study is to assist them through applied research framework-based analysis to obtain effective results. The backbone of the purposed framework is a hybrid mechanism that comprises business intelligence (BI) and machine learning (ML) to support 360-degree organization-wide analysis. BI modeling gives descriptive and diagnostic analysis via interactive reports with quick ad hoc analysis which can be performed by executives and managers. ML modeling predicts the performance and highlights the potential customers, products, and time intervals. The whole mechanism is resource-efficient and automated once it binds with the operational data pipeline and presented results in a highly efficient manner. Data analysis is far more efficient when it is applied to the right data at the right time and presents the insights to the right stakeholders in a friendly, usable environment. The results are beneficial to viewing the past, current, and future performance with self-explanatory graphical interpretation. In the proposed system, a clear performance view is possible by utilizing the sales transaction data. By exploring the hidden patterns of sales facts, the impact of the business dimensions is evaluated and presented on a dynamically filtered dashboard.

Suggested Citation

  • Abdulmajeed Alqhatani & Muhammad Shoaib Ashraf & Javed Ferzund & Ahmad Shaf & Hamad Ali Abosaq & Saifur Rahman & Muhammad Irfan & Samar M. Alqhtani, 2022. "360° Retail Business Analytics by Adopting Hybrid Machine Learning and a Business Intelligence Approach," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11942-:d:921627
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    References listed on IDEAS

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    Cited by:

    1. Abeer Aljohani, 2023. "Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility," Sustainability, MDPI, vol. 15(20), pages 1-26, October.

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