{"title":"Do Sales Promotions Affect Dynamic Changes in Sales Outcomes: Estimation of Dynamic State of Product Sales","authors":"Yuta Kaneko, K. Yada","doi":"10.1109/APWCONCSE.2017.00012","DOIUrl":null,"url":null,"abstract":"In recent years, the consumer tastes have become complicated and store managers are increasingly required to execute multiple promotions and increase sales. In this research, we introduce a dynamic Bayesian model that applies a Cauchy distribution to the estimation of the dynamic state of a product sales trend. The time evolution of the sales data trend reflects customer purchase behavior, and shows a characteristic variation for each brand. We show that the proposed model can better explain the dynamic change of sales by comparison with a regression model. We compare the benchmark and proposed models based on variance, fitness, and prediction accuracy. We show that detecting change-points lurking in time series data of sales leads to important management improvement suggestions.","PeriodicalId":215519,"journal":{"name":"2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APWCONCSE.2017.00012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In recent years, the consumer tastes have become complicated and store managers are increasingly required to execute multiple promotions and increase sales. In this research, we introduce a dynamic Bayesian model that applies a Cauchy distribution to the estimation of the dynamic state of a product sales trend. The time evolution of the sales data trend reflects customer purchase behavior, and shows a characteristic variation for each brand. We show that the proposed model can better explain the dynamic change of sales by comparison with a regression model. We compare the benchmark and proposed models based on variance, fitness, and prediction accuracy. We show that detecting change-points lurking in time series data of sales leads to important management improvement suggestions.