Modeling Research Of Customers' Selecting Behavior On The Online Retailing Market

In recent years,accompany with the continuous improvement of internet penetration and the rapid development of third-party logistics,the online retailing market has a rapid rise.The scale of online shopping have a significantly enhance,online shopping has become an important channel for consumer to spend,and still has much potential for development in the future.Scholars have been have meticulous and depth research on the process and patterns of consumers,buying behavior.But there have few probability models that can accurately predict the consumers' selecting behavior.Therefore,in order to know the relationships between the goods' probability of being selected and its rank and location in the selecting sequence,this paper establishes the probability models of the consumers'behavior to explain consumers,selection.This paper is based on Bernoulli probability model,establishes a basic and a threshold setting probability model which can calculate the selection probability for each good.And then make simulations for these models,comparative and analysis the simulation results and theoretical values.At the same time,take into account the online trading platform allow some goods have a fixed position in the selecting sequence,establishes probability models and makes comparison to the basic selecting model.After compared the various models,it found that when consumers choose randomly,the goods' probabilities of being selected are not related to goods'location in the selecting sequence,only have a relevant to its rank,showing a geometric distribution;When the selecting sequence exists threshold,the goods'probabilities of being selected have a relevant to its location,and its distribution is similar to a power-law distribution;When fixed the goods' position on the front of the selecting sequence,compared to choose randomly,the goods' probabilities of being selected increase significantly,and the increase in value is directly related to their ranks,the forward ones have more obvious increase.If the first location of the selecting sequence is fixed,consider the goods' probability of being selected which is fixed on the second position,compare to the randomly selecting sequence,find that there is little increase when the total goods are very few,but there was no significant change when choose from a large amount of goods.Take all models into consideration,it found that when the market have a large?number of similar goods,the probability of being selected only have a relevant to its rank and location in the selecting sequence.Thus,in order to improve sales of the online goods,the businesses should choose to sell those goods which have a higher rank in the market or try to advance the goods' position in the selecting sequence,but the second method is only applicable to the goods which have a relatively forward rank.The greatest contribution of this study is to establish Customers' buying behavior probability models with the online retailing market's characteristics,which can obtain the quantitative relationship between the goods' probabilities of being selected and their rank and location in the selecting sequence.Those models will provide a theoretical method for accurate sales forecasting,businesses can have a basic to develop their marketing strategies,they also can provide a reference for the service strategies' development of online retailing trade platform.