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导读：这是一篇网络营销专业assignment范文，讨论了网络购物中的统计学。随着互联网的发展普及、国家拉动内需、刺激社会消费以及网络购物服务的深化，网络购物已经从一种新生事物迅速中成长为年轻人购物的重要方式，其时间灵活性、空间的无限延展性、价格的优惠、产品的丰富多彩吸引了越来越多的消费者，作为消费者在购物过程中可以用到大量的统计学知识。同时，随着网络购物的飞速发展，各家电商也在迅速发展壮大并进行着激烈的竞争，电商在建设网站提供产品、服务的过程中也用到大量的统计学知识。 Assignment题目：Statistics in network shopping In the national stimulating domestic demand, stimulate consumption under the background of society, the deepening of network shopping service led to more netizens realize daily consumption through online shopping, shopping website frequently promotional also inspired netizens new purchase demand, led to the network shopping user scale robust growth. The online transaction of goods and services is more abundant, driving the user network shopping frequency and the significant increase of the amount. According to the China Internet network information center, the number of shopping users reached 194 million, up 20.8 percent from the end of last year. In booming online shopping industry, electric business enterprise in the fierce competition, consumers in the process of online shopping also need to deal with a large number of heterogeneous information, this needs to have professional knowledge of statistics as auxiliary, this paper tries to analyze the network shopping process we use or you can use the statistical knowledge. Because the C2C network shopping taobao is a large one, this paper takes taobao's setting as an example to analyze the statistics in shopping. 在国家发展拉动内需、进而刺激消费的社会背景下，网络购物服务的深化导致更多的网民通过网络购物实现日常消费，购物网站频繁的推广也激发了网民新的购买需求，导致网络购物用户规模稳健增长。商品和服务的在线交易更加丰富，带动了用户网络购物的频率和金额的显著增加。根据中国互联网信息中心的数据，购物用户达到1.94亿，比去年年底增长了20.8%。在蓬勃发展的网络购物行业,电力企业在激烈的竞争中,消费者在网上购物的过程中还需要处理大量的异构信息,这需要有专业知识的统计数据作为辅助,本文试图分析网络购物过程中,我们使用或您可以使用统计知识。由于C2C网络购物淘宝是一个较大的网络购物平台，本文以淘宝为例分析了购物数据。 Consumers online shopping is first used in a large number of observation, because consumers will before hand shopping online browsing, watching the countless times, contrast, many times, a lot, and even after a long period of time to browse to watch to buy some for suitable products. Consumers in the process of online shopping, e-commerce sites with a mass of confused in commodities, statistical grouping methods will this lot of goods according to age and by sex, according to the price, according to region, according to the brand, and so on many marks for statistical grouping, consumers can easily find they need some kind of goods. At the same time, consumers can also see various classification groups in various stores, and classify the goods in detail. The methods of statistical grouping have saved consumers a lot of time and energy by avoiding the search for needle-like products in the haystack. Certain types of goods and even pictures of the same commodity prices are a far cry from, consumers usually cheap goods is not good but also want to clear cost-effective and network shopping scratching their biggest weakness is can't see, there's no way to compare these goods together fully. Consumers can sort through the products they need, rank them in descending order of sales, or rank them by store credit, etc., and refer to other consumers' choices. For example, according to the sales order, we find that the products with the largest turnover of this kind of products, which is the mode of mode, are the representative of consumers' purchasing choice results, and usually have credibility. And the price can be given a range to choose, to find the right commodity in their psychological price. Website not only can do sorting, consumers can also be in store all the goods according to sales, according to the price, in accordance with the collection sorting, through the analysis of hot seller in consumer can easily find the store, and to help their own choices. On the site after entering the shop, choose a certain product consumers can see the store customer satisfaction evaluation results, such as the baby is in conformity with description, the seller service attitude, sell the home to deliver goods speed score, this is the shop customer feedback after complete the network shopping satisfaction evaluation level analysis, is a large number of arithmetic average of the scores, can be more accurate reflection of previous customer satisfaction. By comparing these customer satisfaction levels with the industry average, consumers can better understand the location of the store's products in the same industry. Rate is high praise of customers account for the proportion of all customers, can store customer satisfaction reaction, but the relative index must be linked with absolute index analysis, such as a new open store only a few pen volume, even if well received 100% do not tell the full store credit is good. Taobao also calculate the seller's credit accumulated absolute index calculation, clinch a deal in a short period of time after get a high praise can accumulate points, it will be the seller won acclaim for summary and credit rating according to the score. This will enable consumers to have a full understanding of the store's trading credit. Consumers can also be on a particular product is customer satisfaction analysis, clinch a deal the record and evaluation details can assist consumers know the goods sales basic situation and customer satisfaction. The analysis of shopping basket is a kind of technology to explore the relationship of purchase. It finds the intrinsic correlation between goods by analyzing the system of the purchase records of consumers. Shopping basket analysis can now be carried out: commodity configuration analysis, which products are easy to buy together; Customer demand analysis: customers' buying habits, buying time, purchasing location, etc.; Analysis of sales trend: study customers' purchasing trend, analyze seasonal buying patterns to determine the price reduction and clearance of goods; Improve products or develop new products. There is a large number of shopping basket analysis results in shopping, and when the consumption will add the item to the shopping cart, the website will remind "the users who bought this treasure also bought... ", this is goods configuration analysis, site or store after the basket analysis found that some goods are more likely to be purchased at the same time, so at this time to remind not only simplifies the search of consumers, also greatly increases the chances of these goods were purchased. In addition, e-commerce sites will also remind a series of "you might be interested in goods, this is a e-commerce sites to consumer buying patterns or after browsing for basket analysis to obtain useful information. In stores, shopping basket analysis has an important function is to organize sales, also is the match package, after the commodity basket analysis will easily be purchased at the same time of commodity packaging provides the certain preferential, are more likely to contribute to sales. E-commerce sites and shops will also according to the basket analysis for customers to buy time, time is at night, the network shopping to buy gold one box of diapers is about to run out of time will also receive warm reminder of the shop. The needs of the consumers of online shopping can collect directly by customer or evaluation opinions, than the traditional sales model can more convenient fast and accurately collect customer wishes, to improve the quality of the products or packaging, and to research and development of new products to be able to provide first-hand information. E-commerce sites recently by frequency of user access, access time, the average residence time and the number of average access page index evaluation website user loyalty, more precisely find loyal users, and try to keep them. From the trend of user loyalty, we find some users who may be losing, analyze the possible reasons for their loss, and try to retain the lost users. Compare the differences between loyal users and lost users in the index values, find out the reasons and optimize the performance of the website in these aspects. At present, the problems faced by shopping are not guaranteed by the quality of goods, and the delivery of logistics is not timely. E-commerce and stores can improve services for these problems. Consumers in the shopping process in contact with a lot of advertisements, e-commerce sites and stores to pay close attention to advertising effects will be, so advertising become an important content in the network shopping, advertising effectiveness evaluation in the process of application of statistical methods. Network advertising occupies an important position in the network shopping process. The evaluation of network advertising effectiveness can be carried out through a series of indexes such as click rate, performance growth rate, recovery rate, conversion rate and effective arrival rate. If click-through rate refers to the ratio of the number of clicks on an online AD to the number of times displayed, the relative index is one of the most direct and persuasive assessment indicators of online advertising. As to click on ads is likely to be those who are influenced by advertisements and form buying decision of the customer, or is interested in the advertising of products or services of potential customers, accurately identify these customers, and effective against their targeted advertising and promotional activities, can provide great help for the business. In this paper, the statistical methods commonly used in shopping are analyzed, and these statistical methods are very helpful to consumers and e-commerce enterprises. The future network shopping market will usher in a more standardized industry development environment. As e-commerce enterprises grow in scale, they also need to shift from extensive development to refined mining, and make great efforts to deepen the overall network shopping industry service level. CLP in this process, enterprises need to use scientific and modern knowledge of statistics, find out the root cause and find a reasonable way to solve the problem, customers can also through scientific statistical knowledge to help themselves to make the scientific shopping decisions, to jointly promote the health of the network shopping is booming. |
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