CN115456679A - Analysis method, device and medium for network retail shop index system - Google Patents
Analysis method, device and medium for network retail shop index system Download PDFInfo
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- CN115456679A CN115456679A CN202211147284.8A CN202211147284A CN115456679A CN 115456679 A CN115456679 A CN 115456679A CN 202211147284 A CN202211147284 A CN 202211147284A CN 115456679 A CN115456679 A CN 115456679A
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Abstract
The invention relates to the technical field of big data analysis, and particularly provides an analysis method of an index system of a network retail store, which comprises the following steps: s1, collecting shop information of a network retail platform and related data of commodity transaction information in shops; s2, establishing a network retail shop index system; and S3, establishing a comprehensive evaluation index model of the shop by using a principal component analysis method. Compared with the prior art, the invention provides the total quantity change and the structure distribution condition of the network retail stores for related departments, helps industry associations and enterprises to master the store development trend, and provides data support for consumers to know the store ranking condition.
Description
Technical Field
The invention relates to the technical field of big data analysis, and particularly provides an analysis method, device and medium for an index system of a network retail store.
Background
The domestic consumption market still has extremely strong consumption toughness and purchase potential, and online consumption becomes an important engine for promoting growth in pulling. In order to measure the development condition of the network retail store, clarify the development target and direction of the store, establish a network retail store index system, perform comprehensive index evaluation on the network retail store, and help enterprises and consumers master the development condition of the store.
The commodity transaction information and the store information of the electronic commerce platform are acquired in a large scale by using a data acquisition technology, the overall development and the structural distribution condition of the network retail store cannot be comprehensively analyzed by the existing technology, various indexes of the network retail store cannot be directly acquired by acquisition, and the development condition of the store in each industry cannot be known in time. At present, no uniform network retail shop index system and comprehensive index evaluation exist in the market.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an analysis method of an index system of a network retail store, which has strong practicability.
The invention further aims to provide the analysis device of the network retail store index system, which is reasonable in design, safe and applicable.
It is a further technical task of the present invention to provide a computer readable medium.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an analysis method of a network retail store index system comprises the following steps:
s1, collecting shop information of a network retail platform and related data of commodity transaction information in shops;
s2, establishing a network retail shop index system;
and S3, establishing a comprehensive evaluation index model of the shop by using a principal component analysis method.
Further, in step S1, a crawler technology is used to collect basic information of the web retail platform store page and the commodity page, and the collected data is stored in a database.
Furthermore, preprocessing the acquired original data, supplementing or deleting null values of the acquired store information and commodity sales information, and representing all indexes of all store and commodity information into indexes in the same direction;
the standardization processing is to convert different order of magnitude indexes into the same order of magnitude indexes, convert unstructured data into structured data and finally carry out standardization processing on an industry structure and a region structure.
Further, in step S2, the retail store index system includes store operation time, store operation dimension, store network retail scale, number of on-shelf commodities, average store price, store distribution industry structure and region structure;
and analyzing the shop commodity transaction data within a certain time range, and establishing a network retail shop index system.
Further, the principal component analysis method comprises the steps of solving an eigenvalue and an eigenvector, measuring a correlation coefficient before each original index, establishing a correlation coefficient matrix, and solving the eigenvalue and the eigenvector according to the correlation coefficient matrix;
determining the number of the principal components, wherein the cumulative variance contribution rate of each principal component is required to be higher than 85%, the higher ones of the contribution rates are selected principal components, confirming the weight occupied by each principal component according to the selected variance contribution rate of each principal component, and calculating the comprehensive evaluation index of the shop according to the weight.
Further, in step S3, the operation time of the store comprehensive evaluation index model, the store operation dimension, the store network retail scale, the number of commodities on the store, the average store price, and the data of the store location are analyzed to perform data statistics, the data are formed into a matrix, a plurality of indexes are reduced into several main components by a principal component analysis method, the data are analyzed according to the fitting degree of the store comprehensive evaluation index model and the P value judgment of the correlation coefficient, and the store comprehensive evaluation index is generated.
An analysis device of a network retail shop index system comprises: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is used for calling the machine readable program to execute an analysis method of the network retail store index system.
A computer readable medium having stored thereon computer instructions which, when executed by a processor, cause the processor to perform a method of analyzing a cyber retail store index system.
Compared with the prior art, the analysis method, the device and the medium of the network retail shop index system have the following outstanding advantages:
the invention provides the total quantity change and the structure distribution condition of the network retail stores for related departments, helps industry associations and enterprises to master the store development trend, and provides data support for consumers to know the store ranking condition.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart schematic diagram of an analysis method of a network retail store index system.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments in order to better understand the technical solutions of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A preferred embodiment is given below:
as shown in fig. 1, the method for analyzing the index system of the retail store through the internet in this embodiment includes the following steps:
s1, collecting shop information of a network retail platform and related data of commodity transaction information in shops;
the crawler technology is utilized to collect basic information of the web retail platform shop page and the commodity page, including the shop name, the shop location, the shop owner and camp category, the commodity name, the commodity type, the commodity price, the commodity sales volume and the like, and the collected data are stored in the database.
The collected original data is preprocessed. The method mainly comprises abnormal value processing, trend processing and standardization processing. And supplementing or deleting null values of the acquired store information and commodity sales information, and representing all indexes of all stores and commodity information into indexes in the same direction, for example, the commodity information is converted into store information according to a network retail store index system. The standardization processing is to convert different order of magnitude indexes into the same order of magnitude indexes, convert unstructured data into structured data, and finally carry out standardization processing on an industry structure and a region structure so as to facilitate subsequent further analysis.
S2, establishing a network retail shop index system;
the index system comprises store operation time, store operation dimension, store network retail scale, store overhead commodity number, store average price, store distribution industry structure and region structure. And analyzing the shop commodity transaction data within a certain time range, and establishing a network retail shop index system.
The principal component analysis method comprises the steps of solving an eigenvalue and an eigenvector, measuring a correlation coefficient before each original index, establishing a correlation coefficient matrix, and solving the eigenvalue and the eigenvector according to the correlation coefficient matrix. Determining the number of principal components requires that the cumulative variance contribution of each principal component be higher than 85%, and the ones with higher contributions be selected principal components. And confirming the weight of each principal component according to the selected variance contribution rate of each principal component, and calculating the comprehensive evaluation index of the shop according to the weight.
S3, establishing a comprehensive shop evaluation index model by using a principal component analysis method;
the analysis model carries out data statistics on data such as store operation time, store operation dimensionality, store network retail scale, store on-shelf commodity number, store average price, store location and the like, the data form a matrix, a plurality of indexes are reduced into a plurality of main components by a main component analysis method, the data are analyzed according to the fitting degree of the model and the P value judgment of a correlation coefficient, and a store comprehensive evaluation index is generated. The index can also be split according to the industry and regional structure.
And finally obtaining a comprehensive evaluation index of the shop according to the shop name, the place of the shop, the enterprise of the shop operation subject, the near-half-year business amount of the shop, the main operation type of the shop, the time and the year of the shop, the commodity description score of the shop, the satisfaction score of the shop service and the logistics satisfaction score of the shop, the near-half-year operation trend of the shop, the satisfaction of the near-half-year commodity, the satisfaction of the service, the satisfaction of the logistics, the overall operation analysis and evaluation of the shop, the operation risk evaluation of the shop and the like. The method is also suitable for industry brand analysis, and the shop turnover can be replaced by the brand turnover.
Based on the above method, an analysis apparatus for an index system of a retail store on internet in the present embodiment includes: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor is used for calling the machine readable program to execute an analysis method of the network retail store index system.
A computer readable medium having stored thereon computer instructions which, when executed by a processor, cause the processor to perform a method of analyzing a cyber retail store index system.
The above embodiments are only specific ones of the present invention, and the scope of the present invention includes but is not limited to the above embodiments, and any suitable changes or substitutions that can be made by one of ordinary skill in the art and in accordance with the method, apparatus and medium claims for analyzing the index system of the retail store over internet according to the present invention shall fall within the scope of the present invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. An analysis method of a network retail shop index system is characterized by comprising the following steps:
s1, collecting shop information of a network retail platform and related data of commodity transaction information in shops;
s2, establishing a network retail shop index system;
and S3, establishing a comprehensive evaluation index model of the shop by using a principal component analysis method.
2. The analysis method of the network retail store index system according to claim 1, characterized in that in step S1, a crawler technology is used to collect basic information of a network retail platform store page and a commodity page, and the collected data is stored in a database.
3. The analysis method of the network retail store index system according to claim 2, characterized in that the collected original data is preprocessed, the null values of the collected store information and the commodity sales information are supplemented or deleted, and all indexes of all store and commodity information are expressed as indexes in the same direction;
the standardization processing is to convert different order of magnitude indexes into the same order of magnitude indexes, convert unstructured data into structured data and finally carry out standardization processing on an industry structure and a region structure.
4. The method for analyzing the network retail store index system according to claim 3, wherein in step S2, the retail store index system comprises store operation time, store operation dimension, store network retail scale, store on-shelf commodity number, store average price, store distribution industry structure and region structure;
and analyzing the shop commodity transaction data within a certain time range, and establishing a network retail shop index system.
5. The analysis method of the index system of the network retail shop according to claim 4, characterized in that the principal component analysis method comprises the steps of solving eigenvalues and eigenvectors, measuring correlation coefficients before each original index, establishing a correlation coefficient matrix, and solving the eigenvalues and the eigenvectors according to the correlation coefficient matrix;
determining the number of the principal components, wherein the cumulative variance contribution rate of each principal component is required to be higher than 85%, the high-contribution rate ones are selected principal components, determining the weight of each principal component according to the selected variance contribution rate of each principal component, and calculating according to the weight to obtain the comprehensive evaluation index of the shop.
6. The method as claimed in claim 5, wherein in step S3, the data statistics is performed by analyzing the operation time of the store comprehensive evaluation index model, the store operation dimensions, the store network retail scale, the number of commodities on the store, the average store price and the data of the store location, the data is formed into a matrix, a plurality of indexes are reduced into several main components by a principal component analysis method, and the data is analyzed to generate the store comprehensive evaluation index by judging the fitting degree of the store comprehensive evaluation index model and the P value of the correlation coefficient.
7. An analysis device for an index system of a network retail shop, comprising: at least one memory and at least one processor;
the at least one memory to store a machine readable program;
the at least one processor, configured to invoke the machine readable program, to perform the method of any of claims 1 to 6.
8. A computer readable medium having stored thereon computer instructions which, when executed by a processor, cause the processor to perform the method of any of claims 1 to 6.
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| CN202211147284.8A CN115456679A (en) | 2022-09-19 | 2022-09-19 | Analysis method, device and medium for network retail shop index system |
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| CN202211147284.8A CN115456679A (en) | 2022-09-19 | 2022-09-19 | Analysis method, device and medium for network retail shop index system |
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| JP2013029883A (en) * | 2011-07-26 | 2013-02-07 | Giken Shoji International Co Ltd | Shop profiling system |
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2022
- 2022-09-19 CN CN202211147284.8A patent/CN115456679A/en active Pending
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| JP2013029883A (en) * | 2011-07-26 | 2013-02-07 | Giken Shoji International Co Ltd | Shop profiling system |
| CN108960927A (en) * | 2018-07-12 | 2018-12-07 | 山东汇贸电子口岸有限公司 | A kind of e-tailing development index system based on web crawlers and economic statistics |
| CN112734255A (en) * | 2021-01-15 | 2021-04-30 | 永辉云金科技有限公司 | Enterprise competitiveness analysis method |
| CN112990973A (en) * | 2021-03-22 | 2021-06-18 | 山东顺能网络科技有限公司 | Online shop portrait construction method and system |
| CN112966962A (en) * | 2021-03-24 | 2021-06-15 | 山东浪潮云服务信息科技有限公司 | Electric business and enterprise evaluation method |
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Application publication date: 20221209 |