Impact of Shipping Distance on Online Retailers' Sales:A Case Study of Maiyang on Tmall

Many studies have qualitatively explained that information and communication technology(ICT) has loosened the restrictions of distance and space on retailers' sales. Few empirical studies, however, have explored the impact of shipping distance on online retailers' sales. This study examined the Maiyang(M-Y) store on Tmall in China as a case study to investigate the relationship between shipping distance and sales. The results showed that sales volume in 2014 at the county level did not strictly obey the distance decay law. The shipped distance of high-priced commodities may not be much longer than that of low-priced commodities. Within the scope of investigation, the relationships between income, cost, and net profit curves do not follow central place theory. Goods have neither thresholds nor ranges. The key factor in the spatial discrepancy of sales is the size of market. The impact of shipping distance on sales is not as strong as that of traditional retailers in Information Era.

Identifying the Geography and Determinants of O2O Online Retailers in Megacity in Central China:A Case Study of Zhengzhou City

In recent years,O2O e-commerce,represented by online group-buying,has developed vigorously,which had significant impacts on urban commercial space.Zhengzhou City is a rising national central city in China,and its e-commerce development level is ahead,but relevant researches are rare.Therefore,the data of online retailers of Meituan.com was collected and combined with Baidu map and Baidu heat map data.Then,we adopted the methods such as spatial statistics and geodetector to explore the geography and determinants of O2O online retailers in Zhengzhou urban area.The main conclusions are 1)The spatial development of O2O online retailers is characterized by significant global high-value agglomeration.2)The agglomeration areas of different types of O2O online retailers are different.Most of them are concentrated in the old urban area within the Third Ring Road of Zhengzhou City,forming five comprehensive agglomeration areas.3)The areas with the high e-commerce development level are mainly concentrated in the northeast and southwest of the x-shaped region formed by the intersection of Lianyungang-Lanzhou and Beijing-Guangzhou railways.Erqi Square and Guomao 360 Plaza are at the highest development level,followed by Zhongyuan Wanda Plaza and Daxue Middle Road.The development level at other areas is relatively low.4)Zhengzhou’s O2O commercial pattern is highly dependent on physical business.The population distribution,especially the population distribution during the nightlife period,plays a vital role in its spatial development,followed by accessibility.The influences of physical distance are slightly larger than that of time cost,but the difference between them is little.In addition,travelling costs have the least impact.This paper could provide certain references for urban commercial planning.

Online Fashion Retailer Bonobos Selects Gerber Technology's YuniquePLM^(TM) Industry-Tailored Software to Support Rapid Growth and Fast-Paced Product Innovation

Tolland,Connecticut,USA-Gerber Technology,a business unit of Gerber Scientific, Inc.(NYSE:GRB) and a world leader in automated CAD/CAM and PLM solutions for the apparel and flexible materials industry,announces that fast-growing internet retailer Bonobos,Inc. has selected the YuniquePLM product

Explainable Artificial Intelligence Solution for Online Retail

Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for them.In the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the business.In this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one year.The main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the future.The permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the future.The performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC curve.RF model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree classifier.Hence,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying behaviour.Our research also reveals the importance of page value as a key feature for capturing online purchasing trends.This may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.