A combinatorial model for natural gas industrial customer value portrait based on value assessment and clustering algorithm

Frequent geopolitical events have reduced the stability of natural gas supply and caused drastic price fluctuations, which poses a new challenge to consumer market. To improve anti-risk ability industrial market, this study constructs customer value portrait framework discern based on different types behavioral features emerging trends Specifically, we rediscover composition customers establish set indicators reflect in dimensions with mixed data types. Then, visualizable classification model has been established by combining Gower’s dissimilarity coefficient PAM clustering algorithm. ensure accuracy results, optimal number clusters is determined gap statistics elbow point, average silhouette method used detect effect as well misclassified sample identification. verify applicability model, certain amount from large state-owned oil enterprise for application analysis effectively divided into three groups, demand-serving, demand-potential, demand-incentive, according their characteristics features. The results indicate that proposed can reasonably better characterize customers’ feature data, provide technical support big smart marketing.