Personal financial products, as an area of high concern for global financial consumers, are undergoing a transformation from traditional marketing to data driven precision marketing under the deep penetration of Internet technology breakthroughs and the digital economy. However, existing research has limitations such as insufficient systematic analysis of the marketing environment, lack of research on the integration and application of technological paths, and a lack of quantitative evaluation frameworks for practical cases. Traditional marketing models face severe challenges due to issues such as vague user profiles and low channel efficiency. This study is based on precision marketing theory and marketing 4P theory, using K-means clustering method and deep learning data mining technology to construct an RFMLT model (integrating five core variables: time proximity R, consumption frequency F, consumption amount M, total transaction duration L, and interaction frequency T, sorted by importance as R>F>M>L>T). Through clustering analysis, customers are divided into four categories: high growth, high value, active, and general customers, and further refined into four groups: "frequent inquiries but not purchases", "inquiries/collections but not purchases", "frequent searches but not purchases", and "special marketing customers". Targeted differentiated marketing strategies are designed from four aspects: product, price, channel, and promotion. Dimension proposes a precise marketing strategy guarantee system for personal financial products in the era of big data. The study emphasizes that precision marketing needs to rely on continuous data accumulation and empirical research to promote the transformation of financial institutions from "experience driven" to "data-driven", achieve personalized marketing content push, improve customer matching, marketing efficiency and long-term benefits, and promote the healthy development of customers and banks.

Yongqiang Ma. Research on Optimizing Precision Marketing Strategies for Personal Financial Products Driven by Big Data. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 89-96. https://doi.doi.org/10.3807/IJSSEM.2026.070110.

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