Digital Marketing Technology Based on Electricity User Portrait and K-means Clustering Algorithm

Rui Fan, Zhixin Jing, Dehua Guo
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Abstract

With the further opening of the electricity market, it is urgent to provide better service for electricity users. The digital marketing technology based on electricity user portrait is proposed in this paper, which provides differentiated services for electricity users and achieves accurate marketing. Five indexes, i.e., daily electricity consumption, daily valley-to-peak, seasonal fluctuation, electricity arrears rate, and electricity price sensitivity, are extracted to realize a multi-dimensional electricity user portrait. The K-means algorithm is applied to obtain these typical electricity user portraits, which is the cluster center. According to the characteristics of typical electricity user portraits, differentiated marketing strategies are proposed. Then, through calculating the Euclidean distance between the data of electricity user and the cluster center, its portrait result and optimal service are quickly obtained, realizing marketing digitization. Finally, a numerical case is analyzed to verify the availability and superiority of the proposed method.
基于电商用户画像和 K-means 聚类算法的数字营销技术
随着电力市场的进一步开放,为电力用户提供更好的服务迫在眉睫。本文提出了基于电力用户画像的数字营销技术,为电力用户提供差异化服务,实现精准营销。提取日用电量、日谷峰值、季节波动、欠费率、电价敏感度五个指标,实现多维度的电力用户画像。应用 K-means 算法获得这些典型电力用户画像,并以此为聚类中心。根据典型电力用户画像的特征,提出差异化营销策略。然后,通过计算电力用户数据与聚类中心的欧氏距离,快速得到其画像结果和最优服务,实现营销数字化。最后,通过实例分析验证了所提方法的可用性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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