CN113919865A - Network retail amount statistical method - Google Patents

Abstract

The invention provides a network retail amount statistical method, which belongs to the technical field of statistics and analysis, and comprises the steps of firstly determining a target of big data monitoring, determining a network retail platform and acquired contents which need to be acquired, then performing internet crawler acquisition, storing the acquired data into a database, then performing data processing on the acquired data, standardizing the data acquired by each platform according to a planned unified standard, performing 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 network retail amount data containing various dimensions and indexes.

Description

Network retail amount statistical method

Technical Field

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

Background

In recent years, with the rapid development of electronic commerce, online shopping has become a new form of consumption, and has gradually grown to a new power of economic growth. In order to reflect the development status of the emerging business state in time, the national statistical bureau establishes an online retail statistical system, enhances the utilization of network data such as electronic commerce and enterprise electronic transaction records, brings online retail into consumption statistics better, and publishes online retail amount data.

The current statistics department for the network retail amount mainly collects data by direct report of enterprises with larger scale and enterprise sampling with smaller scale, and other modes such as big data mining and E-commerce platform cooperation, so that the problems of accuracy, timeliness, representativeness of sampled data and the like of the direct report data of the enterprises are solved. If the big data technology is used for monitoring completely, other problems can be encountered, the platforms are various and have various quantities, all the platforms cannot be collected one by one, the quantity of commodities on the platforms is large, all the commodities cannot be collected by the existing technical capability, the actually collected data volume can be smaller than the actual data volume due to the influence of the reasons of collection time interval, reverse crawling, collection loss, processing rules and the like in the collection process, the complete situation collected every month is inconsistent, and the key problem of network retail statistics is how to calculate the overall network retail situation more accurately according to the collected data.

Disclosure of Invention

In order to solve the technical problems, the invention provides a network retail amount statistical method, which is used for timely, accurately and efficiently acquiring electronic commerce network retail data and mastering the development of the electronic commerce industry and enterprises in the local area.

The technical scheme of the invention is as follows:

a network retail amount statistical method is characterized in that,

firstly, determining a target of big data monitoring, determining a network retail platform and acquired contents which need to be acquired, then performing internet crawler acquisition, storing acquired data into a database, then performing data processing on the acquired data, standardizing the data acquired by each platform according to a planned unified standard, performing 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 network retail amount data containing various dimensions and indexes.

Further, in the above-mentioned case,

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 enterprise information acquisition of stores;

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 names, prices, monthly sales volumes, accumulated evaluation numbers and commodity parameter information;

further, in the above-mentioned case,

data acquisition and processing

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

for the condition of abnormal commodities, abnormal values need to be screened and processed; a merchandise listing and a store listing are formed for each platform.

Further, in the above-mentioned case,

statistics and estimation of data

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

The monitoring coverage was calculated as follows:

the current month sales is the current month monitoring sales/current month monitoring coverage

The monitoring coverage rate is a numerical value which is 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 detailed commodity table and the shop table which are unified in standard and processed with abnormal values by the monitoring coverage rate in the month to obtain the complete data of the platform in the month.

Setting the top 20% of platform sales as a head platform, obtaining full sales data of the industry (the full sales of the industry is the sales of the monitoring head platform/the market share of the head platform) according to the sales of the head platform and the market share of the platform monitored in the specific industry and the industry of network retail, respectively forming real object data 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 to which the platform belongs, the name of the platform, and dimensionality, commodity number, sales and sales indexes required by analysis.

The invention has the advantages that

The invention is mainly applied to statistics and analysis of the electronic commerce network retail amount, accurately acquires electronic commerce network retail data by means of a big data technology and a statistical analysis technology, and masters the development of the electronic commerce industry and enterprises in various regions. The monitoring coverage rate is calculated through a model formula, the whole sales volume of the platform is calculated according to the monitoring coverage rate, and the influence of difficult complete acquisition, acquisition loss and unstable acquisition completion condition of the platform on data is solved. By acquiring major industry platforms and calculating other platforms and non-major industry methods in the major industry, the problem that the platforms are various in type and quantity and cannot be acquired one by one is solved.

Drawings

FIG. 1 is a schematic workflow diagram of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention, and based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts belong to the scope of the present invention.

The invention provides a network retail amount statistical 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 commodities, online catering, online tourism, online entertainment, online education, online traffic and the like, the retail platforms in each field are numerous, according to industrial research, the platform sales amount also accords with the twenty-eight principle, the head main platform comprises more than 90% of the sales amount in the field, and higher sample representativeness can be ensured by collecting the head platform in each field. 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 enterprise information acquisition of stores. The store information mainly includes information such as a store name, a business name, a store registration address, and a store opening time. The commodity information mainly comprises commodity names, prices, monthly sales volumes, accumulated evaluation numbers, commodity parameters and other information. And then, by summarizing the characteristics of each electronic commerce platform, a unified standard data structure suitable for each platform is extracted, and a foundation is made for business unified analysis.

The second is the collection and processing of data. The data of the network management shops and the data of the shop management commodities on the platforms, including shop ids, shop names, shop locations, commodity delivery places, scores, commodity names, prices, evaluations, sales volumes and the like are acquired by the internet crawler technology according to months and stored in a database. And then data processing is carried out, the data collected by each platform are standardized according to the unified standard of planning, and calculation of each index, such as sales, is carried out. For the case of abnormal goods, the abnormal value needs to be screened and processed. A merchandise listing and a store listing are formed for each platform.

Thirdly, statistics and estimation of data. The collection completion conditions of each platform in each month are different, the specific completion conditions are difficult to accurately evaluate, the collection data can be estimated through a plurality of indexes in the collected data, the commodity number and the commodity quantity in the current month of each platform are counted and compared with the data comparison month, the comparison month can select the month with the best data integrity condition or the average value condition of the nearly several data integrity months, and the data integrity condition in the current month is estimated according to the data integrity condition in the comparison month, namely the monitoring coverage rate. The specific formula is as follows:

the current month sales is the current month monitoring sales/current month monitoring coverage

The monitoring coverage rate is a numerical value which is 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 detailed commodity table and the shop table which are unified in standard and processed with abnormal values by the monitoring coverage rate in the month to obtain the complete data of the platform in the month. The method comprises the steps of obtaining full-scale sales data of the industry (the full-scale sales of the industry is the sales of the monitoring head platform/the market share of the head platform) according to sales of the head platform and the market share of the platform monitored in the specific industry and the industry of network retail, wherein different industries respectively form real object data and non-real object data, and due to the fact that the number of the non-real object industries is large, the industry outside monitoring is estimated according to the condition of industry scale research. And finally, summarizing the data to form a summary table, wherein the summary table comprises dimensions such as industries to which the platforms belong, platform names, dimensions required by analysis (such as commodity types, provinces, cities and counties) and the like, and indexes such as commodity number, sales volume and the like. Through the collected data summary table, the network retail integral data and the data conditions under different dimensions or cross dimensions can be conveniently obtained.

The above description is only a preferred embodiment of the present invention, and is only used to illustrate the technical solutions of the present invention, and not to limit the protection scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (8)

1. A network retail amount statistical method is characterized in that,

firstly, determining a target of big data monitoring, determining a network retail platform and acquired contents which need to be acquired, then performing internet crawler acquisition, storing acquired data into a database, then performing data processing on the acquired data, standardizing the data acquired by each platform according to a planned unified standard, performing 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 network retail amount data containing various dimensions and indexes.

2. The method of claim 1,

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 enterprise information acquisition of stores;

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 names, prices, monthly sales volumes, accumulated evaluation numbers and commodity parameter information.

3. The method of claim 1,

data acquisition and processing

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

for the condition of abnormal commodities, abnormal values need to be screened and processed; a merchandise listing and a store listing are formed for each platform.

4. The method of claim 3,

the data of the store and the data of the store operation goods include a store id, a store name, a store location, a goods delivery place, a score, a goods name, a price, an evaluation, and a sales volume.

5. The method of claim 1,

statistics and estimation of data

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

6. The method of claim 5,

the monitoring coverage was calculated as follows:

the current month sales is the current month monitoring sales/current month monitoring coverage

The monitoring coverage rate is a numerical value which is 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.

7. The method of claim 6,

and dividing the detailed commodity table and the shop table which are unified in standard and processed with abnormal values by the monitoring coverage rate in the month to obtain the complete data of the platform in the month.

8. The method of claim 6,

setting the top 20% of platform sales as a head platform, obtaining full sales data of the industry (the full sales of the industry is the sales of the monitoring head platform/the market share of the head platform) according to the sales of the head platform and the market share of the platform monitored in the specific industry and the industry of network retail, respectively forming real object data 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 to which the platform belongs, the name of the platform, and dimensionality, commodity number, sales and sales indexes required by analysis.

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