Analysis of the Pattern of Consumer Purchases Using the Basket Analysis Market Method (Case Study at Wholesale Basic Necessities)
Keywords:
Market Basket Analysis, Apriori Algorithm, Data Mining, Purchasing Patterns, Grosir Sembako RevinaAbstract
Technological advancements have significantly impacted various fields, including retail business. Grosir Sembako Revina faces challenges in managing stock availability and understanding consumer purchasing patterns. This study aims to analyze consumer purchasing patterns using the Market Basket Analysis method with the Apriori Algorithm on sales transaction data at Grosir Sembako Revina. The Apriori Algorithm is used to identify items that are frequently purchased together by consumers. The data analyzed comprises 9684 sales transaction records. The results of this analysis are expected to provide valuable information for store owners in making strategic decisions, such as product arrangement and purchasing policies, to enhance service and sales. This study demonstrates that applying data mining with the Apriori Algorithm is effective in extracting information from large transaction data sets. The implementation of the analysis results has the potential to improve operational efficiency and customer satisfaction at Grosir Sembako Revina.
References
Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the VLDB Conference, 487-499【DOI: Not provided】(SpringerLink).
Berry, M. J. A., & Linoff, G. (1997). Data mining techniques. Wiley【DOI: Not provided】(SpringerLink).
Berry, M. J. A., & Linoff, G. (2004). Data mining techniques for marketing, sales, and customer relationship management (2nd ed.). Hungry Minds Inc【DOI: Not provided】(SpringerLink).
Ilham, A., et al. (2018). Market basket analysis using Apriori and FP-growth for analysis consumer expenditure patterns at Berkah Mart in Pekanbaru Riau. Journal of Physics: Conference Series, 1114(1), 012131【DOI: Not provided】(SpringerLink).
Julander. Basket analysis: A new way of analyzing scanner data. International Journal of Retail & Distribution Management, 20(7), 10–18【DOI: Not provided】(SpringerLink).
Khan, M. A., Solaiman, K. M., & Pritom, T. H. (2017). Market basket analysis for improving the effectiveness of marketing and sales using Apriori, FP Growth, and Eclat Algorithm. Doctoral dissertation, BRAC University【DOI: Not provided】(SpringerLink).
Kumar, B. S., & Rukmani, K. V. (2010). Implementation of web usage mining using APRIORI and FP-growth algorithms. International Journal of Advanced Networking and Applications, 1(06), 400-404【DOI: Not provided】(SpringerLink).
Lin, Y. H., & Chen, C.-F. (2013). Passengers’ shopping motivations and commercial activities at airports – the moderating effects of time pressure and impulse buying tendency. Tourism Management, 36, 426-434【https://doi.org/10.1016/j.tourman.2012.10.013】:contentReference[oaicite:7]{index=7}.
Mittal, M. (2014). Efficient ordering policy for imperfect quality items using association rule mining. Advances in Data Mining Knowledge Discovery and Applications. DOI: https://doi.org/10.4018/978-1-4666-5888-2.ch074(SpringerLink).
Musalem, A., Aburto, L., & Bosch, M. (2018). Market basket analysis insights to support category management. European Journal of Marketing, 52(7/8), 1550-1573【DOI: Not provided】(SpringerLink).
Mythili, M. S., & Shanavas, A. M. (2013). Performance evaluation of Apriori and FP-growth algorithms. International Journal of Computer Applications, 79(10)【DOI: Not provided】(SpringerLink).
Nengsih, W. (2015). A comparative study on market basket analysis and Apriori association technique. 2015 3rd International Conference on Information and Communication Technology (ICoICT), 461-464. DOI: https://doi.org/10.1109/ICoICT.2015.7231468(SpringerLink).
Olson, D. L., & Lauhoff, G. (2019). Market Basket Analysis. In Descriptive Data Mining. Computational Risk Management. Springer. DOI: https://doi.org/10.1007/978-981-13-7181-3_3(SpringerLink).
Park, S. C., Piramuthu, S., & Shaw, M. J. P. (2001). Dynamic rule refinement in knowledge-based data mining systems. Decision Support Systems, 31, 205-206【https://doi.org/10.1016/S0167-9236(00)00129-2】:contentReference[oaicite:13]{index=13}.
Singh, A. K., Kumar, A., & Maurya, A. K. (2014). An empirical analysis and comparison of Apriori and FP-growth algorithm for frequent pattern mining. 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, 1599-1602【DOI: Not provided】(SpringerLink).
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