Consumer purchase analysis with a market basket analysis algorithm (case study: Alfamart)
Keywords:
market basket analysis, FP-Growth algorithm, purchase patterns, marketing strategies, customer satisfactionAbstract
This research applies market basket analysis with the FP-Growth algorithm to Alfamart, a retail company. The aim of this study is to identify significant patterns of co-purchases to enhance marketing strategies and customer satisfaction. The research methodology involves the collection and analysis of transactional data from Alfamart. The research findings indicate that the FP-Growth algorithm successfully identifies useful association rules for Alfamart. By understanding customer purchasing patterns, the company can improve their marketing strategies and create effective promotions. Additionally, the study tests the performance of the FP-Growth algorithm on transactional datasets, demonstrating good efficiency and accuracy in generating association rules. This research contributes to the development of market basket analysis with the FP-Growth algorithm in the context of Alfamart. The findings provide valuable insights for the company to understand customer purchasing behavior and make better marketing decisions. Using Fp-growth in value combinations A minimum of support 0.05 and a confidence rate of 0.8 can be found in six frequent combination of products purchased at Alfamart stores include beverage, snacks, cigarettes and plastic bags. It is anticipated that this research will advance the field of market basket analysis and offer benefits for business decision-making.
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