AUTHOR=Liu Sicong , Gong Chengzhu , Pan Kai 

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

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1077266

DOI=10.3389/fenrg.2023.1077266

ISSN=2296-598X

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