Classifying urban gas customers based on Kmeans++ algorithm and LGBM model
In order to help natural-gas sales companies classify their urban gas customers and manage customer relation as well as offer the customer-oriented personalized services or professional marketing,some characteristics data about con-sumption behaviors and motivations of the customers were gathered from these companies to perform customer clustering analysis.The Kmeans++ clustering algorithm and the trained LGBM classification model were presented to discriminate new customers and further classify them,forming a multi-dimensional customer classification model.Results show that(ⅰ)for classifying one certain sales company's urban gas customers which are great in quantity and consumption,assisted with the elbow method,the Kmeans++ model can be used to cluster and analyze the gathered consumption data on these cus-tomers.The clustering enjoys better effect,and behaviour characteristics are more evident in various customer base;and(ⅱ)with higher accuracy,the LGBM model can sort out new customers.And the classification is in good agreement with the characteristics of major customer categories obtained from the Kmeans++ clustering.
Urban gas customerCustomer classificationNatural gasDigital transformationCustomer clustering
NETL
NSTL
万方数据
