The rapid development of the Internet,mobile and social media has brought many new online advertising and marketing channels to e-commerce enterprises.Under the background of integrated marketing and multi-channel online advertising,users may not only contact the online advertising channels once before purchase,and the channels contacted successively will affect consumers jointly but not independently.Therefore,in the effectiveness study of multi-channel online advertising,it is also necessary to consider the multi-channel combination effect of online advertising.The click-stream data of this study comes from the third party insurance agency website in Nanjing.After extracting,cleaning and transforming,the basic variables of this study are obtained.To explore the combinatorial effect of multi-channel online advertising,according to the choice set theory,related research hypotheses based on the classified combination effects of online advertising channels are proposed,and then the COX model is constructed.Based on the channel click path data of individual users in e-commerce enterprises,the relevant variables are extracted,and the COX model is verified by empirical analysis.Then the channel combination effect of the same channel,cross-channel and channel classification is analyzed.The results show that the combination of advertising channels from firm-initiated chan-nels to brand search of customer-initiated channels will have a positive combination effect on purchases,while the combination of advertising channels from customer-initiated channels to firm-initiated channels will have a negative impact on purchases.In addition,the order of channels that move from general-purpose search to brand-type search has a positive combination effect on purchases.The data are grouped by gender to explore the differences in advertising effects of users of different genders.The results can help enterprises establish their marketing mix of advertising channels and mitigate the cost of ineffective advertising channel mix.
Omni-channel retailing is a new model that combines online store and offline physical store.In the context of omni-channel retailing,retailers provide consumers with more options for return channels,including same-channel return mode(offline/online purchase,offline/online return)and cross-channel return mode(online store purchase,offline physical store return).For retailers,although the cross-channel return service can reduce the logistics cost of returns to an extent and increase the consumer traffic of offline physical store,the choice of return channels and the re-sale of returned products brings challenges to the inventory management.If the inventory is higher than consumer demand,it will bring the pressure of inventory holding cost to retailers.If inventory falls short of consumer demand,retailers will incur out-of-stock cost.Based on the analysis,the inventory decision under omni-channel retail considering returns is studied in two cases:same-channel returns and cross-channel returns.On the basis of considering consumer returns and the resale of returned products,the newsvendor model is used to construct inventory decision models in the case of same-channel returns and cross-channel returns aiming at maximizing retailers'profit,and the models are analyzed.The numerical analysis shows that,the proportion of online consumers using ROPS(reserve-online and pick-up-and-pay-in-store)to purchase when out of stock,the proportion of resale of returned products,the proportion of online returns,and the proportion of cross-channel returns have a significant impact on the inventory of the offline physical store and online store and profit.Specifically,under the cross-channel returns mode,the inventory decision quantity of offline physical store should be directly proportional to the proportion of online consumers using ROPS to purchase when out of stock or the proportion of online returns,and inversely proportional to the proportion of resale of returned products or the proportion of cross-channel returns;the inventory decision quantity of the online store should be directly proportional to the proportion of online consumers using ROPS mode to purchase when out of stock,the proportion of online returns or the proportion of resale of returned products,and inversely proportional to the proportion of cross-channel returns.The difference between the same-channel returns model and the cross-channel returns model is that the proportion of online returns or the proportion of resale of returned products has no significant influence on the inventory decision in the offline store.In particular,when the cross-selling profit is large,the profit of the retailers will increase with the proportion of cross-channel returns,but the profit of the retailers may decrease with the proportion of online returns.In terms of practical application,although there is still a certain gap between the model construction and the reality,the relevant conclusions can also provide a certain theoretical foundation and decision-making reference for the retail-ers'omni-channel retailing operation management.
It is of great significance to study the risk spillover effect between soybean futures markets in China and the United States under extreme circumstances.A dynamic model based on the average method and the method of the bureau of digit conditional value at risk are combined to study.The risk spillover effects of Chinese soybean futures market influenced by policy and economic environment change is studied.And it analyses the risk spillover effects influenced by spot market,downstream products of soybean,macro-economic variables,international market trading,etc.It is found that different policies and economic environments affected the soybean futures market risk spillover effects in different ways,such as reserve policy stability of China's soybean futures price fluctuations,the night trading system to increase the interaction of the agricultural product futures market of China and the United States to increase the degree of impact on the domestic market in the international market.At the same time,the study shows that each control variable has a different contribution to the change in the risk spillover effect of the Chinese and American agricultural futures markets in different periods.For example,soybean oil futures price and WTI crude oil price have a great influence on the risk spillover effect in the early stage of the night trading system.The impact of shipping indices,exchange rates and WTI crude oil prices was at a low level during the COVID-19 pandemic.
创业可以激发北方经济活力,解决南北方经济差异持续扩大的问题.但在缺乏全行业和中小微企业数据背景下,尚无研究对所有创业群体行为进行深入分析,导致区域发展不平衡问题始终缺乏一个稳妥且科学的应对方案.本文首度采用高频全量企业大数据,构建了我国277个城市的产业集聚指标,深入研究了集聚对南北方全量企业创业活动的影响,发现集聚能够促进城市创业.南方集聚边际效应要大于北方.这与集聚在南北方的作用机制有关.制度环境对集聚效应具有调节作用.在生产要素不断"南流"趋势下,北方地区的政府需要积极主动地创造合理的制度环境,引导企业家和劳动力"回流"集群,实现专业化协同下的产业发展与结构升级.本文是海量数据助力政府实现治理现代化和区域发展科学布局的一个重要尝试.本文结论还对推进大众创业万众创新和多渠道就业具有启发性意义.
When multi-individual learns from experience, the matching between information and analytic methods will affect the learning efficiency. The concepts of the first matching and the second matching between information and analytic methods are put forward in this paper, when multi-individual learns from experience, the analytic method needs to match the information under the learning goal, that is, both the first matching and the second matching are satisfied. In this paper, a multi-individual learning process mechanism model based on the matching between information and analytic methods is constructed. It is considered that under the constraints of the available resources, multi-individual can improve the first matching and second matching between information and analytic methods to achieve the desired results, by dynamically switching between the two ways of searching information based on analytic methods and searching analytic methods based on information, thus improving the learning efficiency. Some practical tools, such as Delphi technique and nomial group technique, are also listed. The process that multi-individual improves the matching of information and analytic methods to improve learning efficiency are paid attention to, and theoretical basis and practical suggestions are provided for improving learning efficiency.
