Consumer Purchase Pattern Analysis Using FP-Growth Algorithm-Indomaret Store (Case Study: Market Basket Analysis)
Keywords:
Market-based analysis, consumer, corporate strategy developmentAbstract
The rapid development of the business world and economic changes has led to the creation of various products that cater to customer needs. The objective of this research is to analyze consumer purchasing patterns using the FP-Growth algorithm at Indomaret stores. Increasing competition in the market has driven companies to pay more attention to consumer behavior in purchasing goods and services. The retail industry, particularly related to Alfamart and Indomaret, is one of the rapidly growing sectors in the world today. Transaction data from Indomaret stores serves as the primary data source for this research. Information is collected from purchase receipts and grouped based on the purchase date. The FP-Growth algorithm is used to identify the combinations of products that are most frequently purchased by customers. The research findings indicate that a high minimum confidence value of 0.7 or 70% is often used as a threshold to validate a rule. This means that in 70% of cases, the rule is proven to be correct. Meanwhile, the minimum support value used is 0.2 or 20% as a common threshold. A lower minimum support value allows for flexibility in capturing itemsets that may occur unexpectedly or infrequently but are still significant. This study provides insights into consumer purchasing patterns at Indomaret stores and its relevance to the FP-Growth algorithm. The implication of this research highlights the importance of understanding consumer behavior to effectively compete in a competitive market. By utilizing data analysis techniques such as the FP-Growth algorithm, companies can optimize their marketing strategies and offer products that better suit customer needs.
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