Systematic Literature Review: Customer Segmentation Analysis in the Retail Industry for Marketing Strategy Optimization
Keywords:
Systematic Literature Review, Segmentation, Association, K-Means, FP-Growth, RFMAbstract
Customer segmentation is a crucial strategy in the retail industry to enhance marketing effectiveness and understand customer needs. This study adopts a Systematic Literature Review (SLR) approach using the PRISMA framework to analyze research related to customer segmentation from 2019 to 2024. The objective of this study is to evaluate the most frequently used clustering models and association patterns and identify the models that deliver the best performance in supporting marketing strategies. The findings indicate that K-Means is the most commonly used clustering model and excels in generating accurate and efficient customer clusters. Meanwhile, FP-Growth proves to be the best association rule model due to its efficiency in handling large datasets and identifying relevant association rules. This study also identifies several research gaps, including the lack of integration between RFM, K-Means, and FP-Growth models, as well as the limited practical implementation of segmentation and association results in marketing strategies such as up-selling and cross-selling. As a solution, this study proposes an integrated model combining RFM, K-Means, and FP-Growth to provide deeper and more actionable insights in supporting data-driven marketing strategies. This model is expected to enhance customer retention, marketing effectiveness, and operational efficiency in the retail industry.
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