IDEAS home Printed from https://ideas.repec.org/a/spt/rmkjrc/v12y2025i1f12_1_6.html

Market Basket Analysis Using Apriori Algorithm: Identifying Consumer Purchase Patterns for Strategic Business Decisions

Author

Listed:
  • Cheng-Wen Lee
  • Ahmatang

Abstract

Market Basket Analysis (MBA) is a data mining technique used to discover association rules from transaction data, supporting the development of effective marketing strategies. This study applies the Apriori algorithm to transaction data from Maetala Café in Tarakan City during the period of October 2024 to January 2025. The Apriori algorithm efficiently identifies frequent itemsets and determines potential associations between purchased items based on minimum support and confidence thresholds. The data were processed using RStudio with the apriori algorithm, involving stages of data preprocessing, rule generation, and evaluation using support, confidence, and lift metrics. The results reveal that the strongest association rule is between Chicken Rice Salad and Mineral Water, with a support value of 4.11% and confidence of 68.13%, indicating a strong and consistent purchasing pattern. These findings suggest that consumers tend to purchase main dishes alongside mineral water as a complementary item. The identified association rules provide valuable insights for café managers in implementing cross-selling, designing bundled promotions, and optimizing product recommendations to increase sales performance.

Suggested Citation

  • Cheng-Wen Lee & Ahmatang, 2025. "Market Basket Analysis Using Apriori Algorithm: Identifying Consumer Purchase Patterns for Strategic Business Decisions," Journal of Risk & Control, SCIENPRESS Ltd, vol. 12(1), pages 1-6.
    • Handle: RePEc:spt:rmkjrc:v:12:y:2025:i:1:f:12_1_6
    as

    Download full text from publisher

    File URL: http://www.scienpress.com/Upload/JRC%2fVol%2012_1_6.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Neha Verma & Dheeraj Malhotra & Jatinder Singh, 2020. "Big data analytics for retail industry using MapReduce-Apriori framework," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(3), pages 424-442, July.
    Full references (including those not matched with items on IDEAS)

    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. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.
    2. An, Min Jeong & Jung, Seung Hwan & Lee, Dong Hee, 2025. "Demand forecasting in micro-fulfillment centers using association rule-based machine learning," International Journal of Production Economics, Elsevier, vol. 290(C).
    3. Mohamed Jasim, K., 2024. "Determinants of the continuance use of smart shopping carts: Findings from PLS-SEM and NCA," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spt:rmkjrc:v:12:y:2025:i:1:f:12_1_6. 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: Eleftherios Spyromitros-Xioufis (email available below). General contact details of provider: http://www.scienpress.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.