CN113919865B - Network retail sales statistics method - Google Patents

Abstract

The invention provides a network retail sales statistics method, which belongs to the technical field of statistics and analysis, and comprises the steps of firstly determining a big data monitoring target, definitely determining a network retail platform and collected content which need to be collected, then collecting an internet crawler, storing the collected data into a database, then carrying out data processing on the collected data, standardizing the collected data of each platform according to a planned unified standard, carrying out index calculation and abnormal data processing, and forming a commodity list and a shop list of each platform; and then summarizing the monitoring data, summarizing and counting the whole network retail data, and finally obtaining the network retail sales data containing various dimensions and indexes.

Description

Network retail sales statistics method

Technical Field

The invention relates to the technical field of statistics and analysis, in particular to a network retail sales statistics method.

Background

In recent years, with the rapid development of electronic commerce, online shopping has become a new consumer form and has also grown into a new effort for economic growth. In order to reflect the development status of the new state in time, the national statistical bureau establishes an online retail statistical system, strengthens the utilization of network data such as electronic commerce, enterprise electronic transaction records and the like, better incorporates online retail into consumption statistics, and distributes online retail amount data.

The current statistics department is mainly used for counting network retail sales, direct reporting of enterprises with more than a scale, sampling of enterprises with smaller scale, and other modes of large data mining, e-commerce platform cooperation and the like are adopted for collecting data, so that the problems of accuracy, timeliness, representativeness of sampled data and the like of the direct reporting data of the enterprises are solved. If the large data technology is used for monitoring, other problems are encountered, the platforms are various and various in number, all the platforms cannot be collected one by one, the number of commodities on the platforms is various, the prior art can not collect all the commodities, the actual collected data volume can be smaller than the actual data volume due to the influence of the reasons of collection time interval, anti-climbing, collection missing, processing rules and the like in the collection process, the complete situation collected every month is inconsistent, and how to calculate the whole network retail situation more accurately according to the collected data becomes a key problem of network retail statistics.

Disclosure of Invention

In order to solve the technical problems, the invention provides a network retail sales statistics method, which can timely, accurately and efficiently acquire electronic commerce network retail data and master the development of electronic commerce industry and enterprises in local areas.

The technical scheme of the invention is as follows:

a network retail sales statistics method is characterized in that,

firstly, determining a big data monitoring target, definitely acquiring a network retail platform and acquired content which need to be acquired, then acquiring an internet crawler, storing the acquired data into a database, then carrying out data processing on the acquired data, standardizing the acquired data of each platform according to a planned unified standard, calculating indexes and processing abnormal data to form a commodity list and a shop list of each platform; and then summarizing the monitoring data, summarizing and counting the whole network retail data, and finally obtaining the network retail sales data containing various dimensions and indexes.

Further, the method comprises the steps of,

determining targets for data monitoring

After the platform is determined, the acquisition content needs to be planned, and the acquisition content is mainly divided into 3 links: store information acquisition, commodity information acquisition and store affiliated enterprise information acquisition;

the store information mainly comprises information such as store names, enterprise names, store registration addresses, store opening time and the like;

the commodity information mainly comprises commodity name, price, month sales, accumulated evaluation number and commodity parameter information;

further, the method comprises the steps of,

data acquisition and processing

Acquiring data of network business shops and commodity data of the business shops on the platforms by an internet crawler technology according to month, and storing the data into a database; then data processing is carried out, data collected by each platform is standardized according to the planned unified standard, and calculation of each index is carried out;

for the case of abnormal commodity, screening and processing of abnormal value are needed; a commodity list and a store list for each platform are formed.

Further, the method comprises the steps of,

statistics and estimation of data

Counting the number of commodities in the same month and the number of sales commodities in each platform, comparing the commodity number with the data comparison month, selecting the month with the best data integrity condition or the average value condition of more than one data integrity month in the comparison month, and estimating the data integrity condition in the same month according to the data integrity condition of the comparison month, namely monitoring coverage rate.

The monitoring coverage was calculated as follows:

monthly sales = monthly sales/monthly coverage

The monitoring coverage rate is a numerical value larger than 0 and smaller than 1, and the weight in the monitoring coverage rate calculation formula can be adjusted according to the specific platform condition.

And dividing the unified standard and processed detail commodity list and shop list after abnormal value by the current month monitoring coverage rate to obtain the complete current month data of the platform.

Setting 20% of platform sales as a head platform, obtaining industry total sales data (industry total sales = monitoring head platform sales/head platform market share) according to the sales of the head platform and the market share of the platform monitored in the specific industry and industry of network retail, respectively forming real object and non-real object data by different industries, and finally summarizing the data to form a summary table, wherein the summary table comprises the industry, the platform name, the dimension required for analysis, the commodity number, the sales and the sales index.

The invention has the beneficial effects that

The invention is mainly applied to statistics and analysis of the e-commerce network retail sales, and accurately acquires the e-commerce network retail data by means of big data technology and statistical analysis technology, and grasps the development of e-commerce industry and enterprises in various areas. And calculating the monitoring coverage rate through a model formula, and calculating the integral sales of the platform according to the monitoring coverage rate, so as to solve the problem of influence on data caused by difficult complete acquisition, acquisition loss and unstable acquisition completion condition of the platform. The method solves the problems that the types and the number of the platforms are various and the platforms cannot be acquired one by collecting important platforms in the main industry and calculating other platforms and methods in non-important industries in the main industry.

Drawings

Fig. 1 is a schematic of the workflow of the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.

The invention provides a network retail sales statistics method, which mainly comprises three components:

the first is to determine the target of data monitoring. The network retail comprises a plurality of fields, the network retail comprises real objects and non-real objects, the non-real objects comprise virtual goods, online catering, online tourism, online entertainment, online education, online traffic and other fields, the retail platforms in each field are numerous, according to industry research, the sales of the platforms also accord with the two-eight principle, the main platform of the head comprises more than 90% of the sales of the field, and the head platform in each field can be collected to ensure higher sample representativeness. After the platform is determined, the acquisition content needs to be planned, and the acquisition content is mainly divided into 3 links: store information acquisition, commodity information acquisition and store affiliated enterprise information acquisition. The store information mainly includes information such as store name, business name, store registration address, and time of opening the store. The commodity information mainly comprises information such as commodity names, prices, monthly sales, accumulated evaluation numbers, commodity parameters and the like. And then, by summarizing the characteristics of each e-commerce platform, a unified standard data structure suitable for each platform is extracted, so that a foundation is made for unified analysis on business.

The second is the collection and processing of data. The data of network business shops and commodity business data of shops on the platforms are acquired by an internet crawler technology according to months, the data comprise shop ids, shop names, shop places, commodity delivery places, scores, commodity names, prices, evaluations, sales volumes and the like, and the data are stored in a database. And then data processing is carried out, data collected by each platform are standardized according to the planned unified standard, and calculation of each index, such as sales calculation, is carried out. In the case of an abnormal commodity, screening and processing of abnormal values are required. A commodity list and a store list for each platform are formed.

Third is the statistics and estimation of the data. The collection completion conditions of each month of each platform are different, the specific completion conditions are difficult to evaluate accurately, the evaluation can be performed through a plurality of indexes in the collected data, the number of commodities in the current month and the number of sales of each platform are counted and compared with the data comparison month, the month with the best data integrity condition or the average value condition of more complete months of the last data can be selected in the comparison month, and the data integrity condition of the current month is evaluated according to the data integrity condition of the comparison month, namely the monitoring coverage rate. The specific formula is as follows:

monthly sales = monthly sales/monthly coverage

The monitoring coverage rate is a numerical value larger than 0 and smaller than 1, and the weight in the monitoring coverage rate calculation formula can be adjusted according to the specific platform condition.

And dividing the unified standard and processed detail commodity list and shop list after abnormal value by the current month monitoring coverage rate to obtain the complete current month data of the platform. The full sales data of the industries are obtained according to the sales of the head platform and the market share of the platform monitored in the specific industries and industries of network retail (the full sales of the industries=the sales of the head platform/the market share of the head platform monitored), and different industries respectively form real object data and non-real object data. And finally, summarizing the data to form a summary table, wherein the summary table comprises indexes such as industries of platforms, names of the platforms, dimensions (such as commodity categories, provinces and counties) required for analysis, commodity numbers, sales and the like. Through the summary table of the collected data, the whole network retail data and the data conditions under different dimensions or cross dimensions can be conveniently obtained.

The foregoing description is only illustrative of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims (4)

1. A network retail sales statistics method is characterized in that,

firstly, determining a big data monitoring target, definitely acquiring a network retail platform and acquired content which need to be acquired, then acquiring an internet crawler, storing the acquired data into a database, then carrying out data processing on the acquired data, standardizing the acquired data of each platform according to a planned unified standard, calculating indexes and processing abnormal data to form a commodity list and a shop list of each platform; then summarizing the monitoring data, summarizing and counting the whole network retail data, and finally obtaining network retail sales data containing various dimensions and indexes;

statistics and estimation of data

Counting the number of commodities in the same month and the number of sales commodities in each platform, comparing the commodity number with the data comparison month, selecting the month with the best data integrity condition or the average value condition of more than one data integrity month in the comparison month, and estimating the data integrity condition in the same month according to the data integrity condition of the comparison month, namely monitoring coverage rate;

the monitoring coverage was calculated as follows:

monitoring coverage rate in the same month = ("A")

+/>

) Comparison month monitoring coverage

Monthly sales = monthly sales/monthly coverage

The monitoring coverage rate is a numerical value larger than 0 and smaller than 1, and the weight in the monitoring coverage rate calculation formula can be adjusted according to the specific platform condition;

dividing the unified standard and processed detail commodity list and shop list with the current month monitoring coverage rate to obtain complete current month data of the platform;

setting 20% of sales of the platform as a head platform, and obtaining full sales data of the industry according to the sales of the head platform and the market share of the platform monitored in the specific industry and industry of network retail, namely, the full sales of the industry = the sales of the monitoring head platform/the market share of the head platform; different industries respectively form real object data and non-real object data, and finally the data are summarized to form a summary table, wherein the summary table comprises industries of the platforms, names of the platforms, dimensions, commodity numbers, sales and sales indexes required for analysis.

2. The method of claim 1, wherein the step of determining the position of the substrate comprises,

determining targets for data monitoring

After the platform is determined, the acquisition content needs to be planned, and the acquisition content is divided into 3 links: store information acquisition, commodity information acquisition and store affiliated enterprise information acquisition;

store information includes store name, business name, store registration address, and store time information;

the commodity information includes commodity name, price, monthly sales, accumulated rating number, and commodity parameter information.

3. The method of claim 1, wherein the step of determining the position of the substrate comprises,

data acquisition and processing

Acquiring data of network business shops and commodity data of the business shops on the platforms by an internet crawler technology according to month, and storing the data into a database; then data processing is carried out, data collected by each platform is standardized according to the planned unified standard, and calculation of each index is carried out;

for the case of abnormal commodity, screening and processing of abnormal value are needed; a commodity list and a store list for each platform are formed.

4. The method of claim 3, wherein the step of,

store data and store business commodity data comprise store ids, store names, store locations, commodity delivery locations, scores, commodity names, prices, evaluations and sales volumes.

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