The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down.The general public has responded to call of the government to stay at home.Offline retail stores have been severely af...The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down.The general public has responded to call of the government to stay at home.Offline retail stores have been severely affected.Therefore,in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience,this study aims to utilize historical sales data for exploring,building sales prediction and recommendation models.A novel data science life-cycle and process model with Recency,Frequency,and Monetary(RFM)analysis method with the combination of various analytics algorithms are utilized in this study for sales prediction and product recommendation through user behavior analytics.RFM analysis method is utilized for segmenting customer levels in the company to identify the importance of each level.For the purchase prediction model,XGBoost and Random Forest machine learning algorithms are used to build prediction models and 5-fold Cross-Validation method is utilized to evaluate their.For the product recommendation model,the association rules theory and Apriori algorithm are used to complete basket analysis and recommend products according to the outcomes.Moreover,some suggestions are proposed for the marketing department according to the outcomes.Overall,the XGBoost model achieved better performance and better accuracy with F1-score around 0.789.The proposed recommendation model provides good recommendation results and sales combinations for improving sales and market responsiveness.Furthermore,it recommend specific products to new customers.This study offered a very practical and useful business transformation case that assists companies in similar situations to transform their business models.展开更多
Sale prediction plays a significant role in business management. By using support vector machine Regression （ε-SVR）, a method using to predict sale is illustrated. It takes historical data and current context data ...Sale prediction plays a significant role in business management. By using support vector machine Regression （ε-SVR）, a method using to predict sale is illustrated. It takes historical data and current context data as inputs and presents results, i.e. sale tendency in the future and the forecasting sales, according to the user＇s specification of accuracy and time cycles. Some practical data experiments and the comparative tests with other algorithms show the advantages of the proposed approach in computation time and correctness.展开更多
We study the problem of business model mining and prediction in the e-commerce context. Unlike most existing approaches where this is typically formulated as a regression problem or a time-series prediction problem, w...We study the problem of business model mining and prediction in the e-commerce context. Unlike most existing approaches where this is typically formulated as a regression problem or a time-series prediction problem, we take a different formulation to this problem by noting that these existing approaches fail to consider the potential relationships both among the consumers （consumer influence） and among the shops （competitions or collaborations）. Taking this observation into consideration, we propose a new method for e-commerce business model mining and prediction, called EBMM, which combines regression with community analysis. The challenge is that the links in the network are typically not directly observed, which is addressed by applying information diffusion theory through the consumer-shop network. Extensive evaluations using Alibaba Group e-commerce data demonstrate the promise and superiority of EBMM to the state-of-the-art methods in terms of business model mining and prediction.展开更多
基金This research is funded by the School of Computer Sciences,and Division of Research&Innovation,Universiti Sains Malaysia,Short Term Grant(304/PKOMP/6315435)granted to Pantea Keikhosrokiani.
文摘The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down.The general public has responded to call of the government to stay at home.Offline retail stores have been severely affected.Therefore,in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience,this study aims to utilize historical sales data for exploring,building sales prediction and recommendation models.A novel data science life-cycle and process model with Recency,Frequency,and Monetary(RFM)analysis method with the combination of various analytics algorithms are utilized in this study for sales prediction and product recommendation through user behavior analytics.RFM analysis method is utilized for segmenting customer levels in the company to identify the importance of each level.For the purchase prediction model,XGBoost and Random Forest machine learning algorithms are used to build prediction models and 5-fold Cross-Validation method is utilized to evaluate their.For the product recommendation model,the association rules theory and Apriori algorithm are used to complete basket analysis and recommend products according to the outcomes.Moreover,some suggestions are proposed for the marketing department according to the outcomes.Overall,the XGBoost model achieved better performance and better accuracy with F1-score around 0.789.The proposed recommendation model provides good recommendation results and sales combinations for improving sales and market responsiveness.Furthermore,it recommend specific products to new customers.This study offered a very practical and useful business transformation case that assists companies in similar situations to transform their business models.
基金This project was supported by the National Natural Science Foundation of China (60573159)the Natural Science Foundation of Guangdong Province (05200302).
文摘Sale prediction plays a significant role in business management. By using support vector machine Regression （ε-SVR）, a method using to predict sale is illustrated. It takes historical data and current context data as inputs and presents results, i.e. sale tendency in the future and the forecasting sales, according to the user＇s specification of accuracy and time cycles. Some practical data experiments and the comparative tests with other algorithms show the advantages of the proposed approach in computation time and correctness.
基金supported by the National Basic Research Program(973)of China(No.2012CB316400)Zhejiang University-Alibaba Financial Joint Lab,Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis,Chinathe US National Science Foundation(No.CCF-1017828)
文摘We study the problem of business model mining and prediction in the e-commerce context. Unlike most existing approaches where this is typically formulated as a regression problem or a time-series prediction problem, we take a different formulation to this problem by noting that these existing approaches fail to consider the potential relationships both among the consumers （consumer influence） and among the shops （competitions or collaborations）. Taking this observation into consideration, we propose a new method for e-commerce business model mining and prediction, called EBMM, which combines regression with community analysis. The challenge is that the links in the network are typically not directly observed, which is addressed by applying information diffusion theory through the consumer-shop network. Extensive evaluations using Alibaba Group e-commerce data demonstrate the promise and superiority of EBMM to the state-of-the-art methods in terms of business model mining and prediction.
