Retail banking is shifting from scale-driven customer acquisition to refined management of existing customers. In this context, accurate customer value identification directly affects the efficiency of marketing resource allocation. To address the static nature of the traditional RFM model and the sensitivity of basic clustering algorithms to high-dimensional redundancy and extreme transaction fluctuations, this study uses an open-source credit card customer dataset from Kaggle. A customer value segmentation framework is developed by integrating Local Outlier Factor, principal component analysis, and K-Means++. Analysis of variance and post-hoc multiple comparison tests are further used for reverse validation. The results show that, based on 8950 initial samples and 18 original dimensions, 17 effective features were retained after removing the customer identifier. Local Outlier Factor identified and removed 269 abnormal samples when the neighborhood parameter was set to 20, accounting for 3.01% of the initial samples. Principal component analysis extracted 12 principal components, with a cumulative explained variance ratio of 96.53%. K-Means++ divided 8681 normal customers into four segments: the dormant long-tail segment, the basic engagement segment, the active growth segment, and the high-value behavioral segment. Further validation shows that the four segments differ significantly in activity, transaction frequency, and transaction amount. All pairwise post-hoc comparisons also reached statistical significance. These findings indicate that the proposed framework can identify interpretable customer value boundaries in high-dimensional, long-tailed, and noisy financial transaction data. The framework also provides a quantitative basis for precision marketing, resource allocation, and customer lifetime value management in retail banking.

Jiaqing Zhang. Research on Value Stratification and Precision Marketing of Retail Customers in Banks Based on Clustering Algorithm. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 1: 167-177. https://doi.org/10.38007/IJBMET.2026.070119.

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