Chang Da, Tianyi Zhang, Guohan Ma, Jingfeng Wang, Ruochen Liu
{"title":"Evaluation Method of Efficient Power Marketing Strategy Based on Multi-dimensional Clustering Algorithm","authors":"Chang Da, Tianyi Zhang, Guohan Ma, Jingfeng Wang, Ruochen Liu","doi":"10.13052/spee1048-5236.4233","DOIUrl":null,"url":null,"abstract":"In the power system, power marketing is a very important work. How to evaluate whether the currently used power marketing strategy is efficient and useful has always been the most important concern of the power system. This paper proposes an efficient electricity marketing strategy evaluation method based on multi-dimensional clustering algorithm, aiming to evaluate whether a new policy has an incentive effect on enterprises through the means of big data. First, we use big data to group motivated users, and then analyze the effect of personalized marketing and key marketing on target customer groups. Based on the existing marketing methods, the influence of different target groups is analyzed, and finally the feasibility of the proposed method is verified through two experiments. In the A and B companies selected in Experiment 1, the average errors of the predicted value and the actual value are 1.4% and 1.2%, respectively. For the 12 industries in the C area selected in experiment 2, the average error between the predicted value and the actual value is 0.55%. Both experiments show that the method proposed in this paper has certain applicability. The method in this paper can be applied to the marketing process of the power supply company, and the corresponding promotion strategy is put forward, which is helpful to improve the management level of the power supply enterprise and increase the economic income of the power supply enterprise.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Strategic Planning for Energy and the Environment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.13052/spee1048-5236.4233","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0
Abstract
In the power system, power marketing is a very important work. How to evaluate whether the currently used power marketing strategy is efficient and useful has always been the most important concern of the power system. This paper proposes an efficient electricity marketing strategy evaluation method based on multi-dimensional clustering algorithm, aiming to evaluate whether a new policy has an incentive effect on enterprises through the means of big data. First, we use big data to group motivated users, and then analyze the effect of personalized marketing and key marketing on target customer groups. Based on the existing marketing methods, the influence of different target groups is analyzed, and finally the feasibility of the proposed method is verified through two experiments. In the A and B companies selected in Experiment 1, the average errors of the predicted value and the actual value are 1.4% and 1.2%, respectively. For the 12 industries in the C area selected in experiment 2, the average error between the predicted value and the actual value is 0.55%. Both experiments show that the method proposed in this paper has certain applicability. The method in this paper can be applied to the marketing process of the power supply company, and the corresponding promotion strategy is put forward, which is helpful to improve the management level of the power supply enterprise and increase the economic income of the power supply enterprise.