基于时空大数据的铁路货运客户画像构建与精准营销策略研究OA

With the deepening reform of railway freight transportation and increasingly fierce competition in the logistics market,the multi-dimensional construction of customer profiles is of great significance for the in-depth analysis of customer demands and the implementation of precise marketing.Based on spatio-temporal big data and the RFM model,this paper constructed customer profiles for railway freight,revealed the spatio-temporal evolution patterns of freight demands and the hierarchical characteristics of customer value,and provided theoretical and practical bases for precise marketing and capacity resource optimization in the railway freight market.Multi-source data such as railway freight waybills and freight car booking data were integrated;data cleaning and feature engineering were conducted on the data of the past three years to construct basic labels including customer industry attributes,transportation categories,and average transportation distance.The RFM model was introduced to quantify customer value from three dimensions:recent delivery time(Recency),delivery frequency(Frequency),and average daily revenue(Monetary);meanwhile,the spatio-temporal clustering algorithm was used to identify logistics agglomeration characteristics,and seasonal index decomposition was adopted to mine the seasonal fluctuation patterns of freight transportation for different customers.The customer profiles constructed based on the RFM model can accurately distinguish typical customer groups,including strategic customers(cornerstone type),core customers(stable type),potential customers(growth type),and observational customers(opportunistic type).Among them,strategic customers present the characteristics of high delivery frequency and high revenue contribution,while observational customers are significantly affected by regional industrial cycles and present obvious seasonal fluctuation characteristics.This paper verified the effectiveness of combining multi-source spatio-temporal data with the RFM model in deepening the understanding of railway freight customers,carrying out precise market analysis,and gaining customer demand insight,which can provide key decision support for customizing marketing plans,optimizing freight product design,and accurately allocating capacity.