IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p11942-d921627.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/11942/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/11942/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Exenberger, Emil & Bucko, Jozef, 2020. "Analysis of Online Consumer Behavior - Design of CRISP-DM Process Model," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 12(3), September.
    2. Dong-Hui Jin & Hyun-Jung Kim, 2018. "Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    3. Gupta, Shaphali & Ramachandran, Divya, 2021. "Emerging Market Retail: Transitioning from a Product-Centric to a Customer-Centric Approach," Journal of Retailing, Elsevier, vol. 97(4), pages 597-620.
    4. Megawati Simanjuntak & Nadia E. Putri & Lilik N. Yuliati & Mohamad Fazli Sabri, 2020. "Enhancing customer retention using customer relationship management approach in car loan bussiness," Cogent Business & Management, Taylor & Francis Journals, vol. 7(1), pages 1738200-173, January.
    5. Sohrabpour, Vahid & Oghazi, Pejvak & Toorajipour, Reza & Nazarpour, Ali, 2021. "Export sales forecasting using artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    6. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    7. Ionuț NICA & Nora CHIRIȚĂ & Ștefan IONESCU, 2021. "Using of KPIs and Dashboard in the analysis of Nike company's performance management," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(1(626), S), pages 61-84, Spring.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bargoni, Augusto & Santoro, Gabriele & Messeni Petruzzelli, Antonio & Ferraris, Alberto, 2024. "Growth hacking: A critical review to clarify its meaning and guide its practical application," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    2. Naragain Phumchusri & Nichakan Phupaichitkun, 2024. "Sales prediction hybrid models for retails using promotional pricing strategy as a key demand driver," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(5), pages 461-480, October.
    3. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    4. Fildes, Robert & Kolassa, Stephan & Ma, Shaohui, 2022. "Post-script—Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1319-1324.
    5. Priyanka Meena & Praveen Sahu, 2021. "Customer Relationship Management Research from 2000 to 2020: An Academic Literature Review and Classification," Vision, , vol. 25(2), pages 136-158, June.
    6. Malobi Mukherjee & Jacob Wood, 2021. "Consolidating Unorganised Retail Businesses through Digital Platforms: Implications for Achieving the UN Sustainable Development Goals," Sustainability, MDPI, vol. 13(21), pages 1-28, October.
    7. Wellens, Arnoud P. & Boute, Robert N. & Udenio, Maximiliano, 2024. "Simplifying tree-based methods for retail sales forecasting with explanatory variables," European Journal of Operational Research, Elsevier, vol. 314(2), pages 523-539.
    8. Zirar, Araz & Ali, Syed Imran & Islam, Nazrul, 2023. "Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda," Technovation, Elsevier, vol. 124(C).
    9. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    10. Dugar, Anurag & Chamola, Pankaj, 2021. "Retailers with traits of consumer: Exploring the existence and antecedents of brand loyalty in small unorganized retailers," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    11. Abirami Raja Santhi & Padmakumar Muthuswamy, 2022. "Pandemic, War, Natural Calamities, and Sustainability: Industry 4.0 Technologies to Overcome Traditional and Contemporary Supply Chain Challenges," Logistics, MDPI, vol. 6(4), pages 1-32, November.
    12. Soheila Khajoui & Saeid Dehyadegari & Sayyed Abdolmajid Jalaee, 2023. "Forecasting exports in selected OECD countries and Iran using MLP Artificial Neural Network," Papers 2312.15535, arXiv.org.
    13. Mansoor, Mahnaz & Paul, Justin & Saeed, Abid & Cheah, Jun-Hwa, 2024. "When mass meets prestige: The impact of symbolic motivations, inspirations, and purchase intentions for Masstige products," Journal of Business Research, Elsevier, vol. 176(C).
    14. Escamilla, Rafael & Brosi, Prisca & Fransoo, Jan C. & Mora-Quiñones, Camilo & Mejia Argueta, Christopher, 2024. "Tax privacy concerns hamper digitization of the nanostore channel," Other publications TiSEM 8ead01df-f8bf-421a-911d-3, Tilburg University, School of Economics and Management.
    15. Shahzad Khalil & Mirza Ameen ul Haq, 2022. "Enhancing Retail Brand Equity through Consumption Value: The Mediating Effect of Brand Experience," iRASD Journal of Management, International Research Alliance for Sustainable Development (iRASD), vol. 4(1), pages 114-126, March.
    16. Sandberg, Erik & Oghazi, Pejvak & Chirumalla, Koteshwar & Patel, Pankaj C., 2022. "Interactive research framework in logistics and supply chain management: Bridging the academic research and practitioner gap," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    17. Hamid Ahaggach & Lylia Abrouk & Eric Lebon, 2024. "Systematic Mapping Study of Sales Forecasting: Methods, Trends, and Future Directions," Forecasting, MDPI, vol. 6(3), pages 1-31, July.
    18. Yi Liang & Chenyu Zhao & Min-Jae Lee, 2023. "Institutional Pressures on Sustainability and Green Performance: The Mediating Role of Digital Business Model Innovation," Sustainability, MDPI, vol. 15(19), pages 1-14, September.
    19. Vladimir V. Syropyatov & Igor A. Arenkov, 2024. "Impact of customer service on brand value and efficiency of a company’s marketing funnel," Upravlenets, Ural State University of Economics, vol. 15(2), pages 96-107, May.
    20. David Fehrenbach & Carolina Herrando & María José Martín-De Hoyos, 2024. "Co-Creation with AI in B2B Markets: A Systematic Literature Review," Sustainability, MDPI, vol. 16(18), pages 1-13, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11942-:d:921627. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.