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RFM analysis is a powerful tool for understanding customer value and behavior, enabling businesses to optimize marketing strategies, improve customer satisfaction, and increase business revenue through data-driven customer segmentation.
This article explains the fundamentals, principles, methodologies, and practical applications of user profiling, covering core concepts such as user characteristics, behavior, preferences, needs, and value, the data collection-to-model pipeline, common models like RFM, clustering, association rules, text mining, and how these insights enable personalized recommendation, precise marketing, brand management, service optimization, CRM, market research, and product innovation.
This article presents a data‑driven personalized red‑envelope marketing solution that cleans and selects features, builds consumption‑demand and red‑envelope‑sensitivity logistic regression models, and iteratively optimizes parameters to lower costs while boosting cross‑project new‑customer conversion rates.
At Ctrip’s TDAY event, senior VP Eric Ye explained how big‑data techniques such as cross‑screen processing, real‑time APIs, and predictive models enable personalized travel recommendations and dramatically improve call‑center efficiency, illustrating the commercial impact of data‑driven marketing.