Offline store data analysis method based on fuzzy theory

Abstract

The invention discloses an offline store data analysis method based on a fuzzy theory, and the method comprises the steps: data collection: collecting data analysis indexes of an offline store, including the sales state of an article and related data information, calculating the collected data information, obtaining accurate data, and synchronously updating the related data of the offline store in real time; and database establishment: transmitting currently obtained data related information into a database, constructing a data algorithm system, and perfecting and updating internal data information within specific time. According to the invention, through accurate analysis and comparison of specific operation conditions and related data of offline stores, corresponding analysis can be carried out through selling conditions of visitors and articles in the stores, accurate data differences can be obtained through independent comparison with database information, corresponding measures and policies are formulated according to the accurate data differences, and the service life of the offline stores is prolonged. Therefore, the operating condition of an offline store is improved, and the sales quota is increased while the expenditure cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of offline store data analysis, and specifically to an offline store data analysis method based on fuzzy theory. Background Art

[0002] New retail has had a strong impact on the real economy, especially the sales channels and sales models of physical stores, driving the digital transformation of the traditional retail industry, industry structure adjustment, and reshaping of the ecosystem. The digitization of physical stores mainly includes aspects such as marketing, products, supply chain, and personnel. Among them, the digitization of personnel mainly focuses on consumer behavior. However, store managers have a direct impact on the operating conditions of the store, and the digital transformation of human resources is a necessary measure for chain retail enterprises to conduct intelligent management of stores and improve store operating efficiency in the context of new retail.

[0003] The relevant operating information of offline stores is generally analyzed through the obtained data. When analyzing the data, it is impossible to accurately analyze based on the customer arrival time and the sales status of items, resulting in errors in the data analysis of offline stores and unable to effectively obtain accurate data. Summary of the Invention

[0004] The purpose of the present invention is to provide an offline store data analysis method based on fuzzy theory to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An offline store data analysis method based on fuzzy theory, and its analysis method includes the following steps: S1. Data collection: Collect the data analysis indicators of offline stores, including the sales status of items and relevant data information, calculate the collected data information to obtain accurate data, and synchronously and real-time update the relevant data of offline stores; S2. Database establishment: Transmit the current data-related information to the database, and construct a data algorithm system to improve and update the internal data information within a specific time; S3. Data analysis: Regularly read the information inside the database and analyze and compare it with the previous data information. The analyzed data includes the item sales status of offline stores, the customer arrival time for visiting the store, and the arrival time of different groups of people at the store; S4. Offline store management and decision-making: Based on the data and specific relevant information of each offline store, regularly transmit them to the database for comparison through big data, and the specific information of offline stores can be intuitively obtained, so as to analyze the obtained data, formulate relevant measures for improvement, and improve the operating conditions of offline stores accordingly.

[0006] Preferably, the relevant data of each offline store shall be recorded, a detailed analysis report on the sales situation of items shall be made, and the relevant data of each different item shall be collected.

[0007] Preferably, the internal database stores the corresponding data of each item in the offline store, including quantity, purchase channel and relevant storage date. By comparing the collected data information, the operation data of the corresponding offline store can be quickly obtained.

[0008] Preferably, the age of each customer arriving at the store shall be recorded, the purchasing power shall be analyzed through customers of different age groups, and the items purchased shall be recorded, so as to accurately obtain the consumption purchasing power of the corresponding age group.

[0009] Preferably, the data information in the database shall be updated regularly to adapt to new market changes and ensure the accuracy of data analysis.

[0010] Preferably, specific records shall be made on the sales status of items in the offline store and the position changes of internal employees. When analyzing data, it is also necessary to analyze according to the regional status and the purchasing power of the surrounding population.

[0011] Preferably, corresponding measures and policies shall be formulated for the obtained operation status of the offline store and the relevant data analysis results, so as to improve the operation status of the offline store, reduce the expenditure cost and increase the sales volume at the same time.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: Through precise analysis and comparison of the specific operation status and relevant data of the offline store, the present invention can conduct corresponding analysis through the in-store visitors and the sales status of items. By independently comparing with the database information, accurate data differences can be obtained, and corresponding measures and policies can be formulated accordingly, so as to improve the operation status of the offline store, reduce the expenditure cost and increase the sales volume at the same time. Detailed implementation manners

[0013] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0014] To achieve the above object, the present invention provides the following technical solutions: An offline store data analysis method based on fuzzy theory, and its analysis method includes the following steps: S1. Data collection: Collect data analysis indicators of offline stores, including the sales status of items and relevant data information, calculate the collected data information to obtain accurate data, and synchronously and real-time update the relevant data of offline stores; S2. Database establishment: Transmit the currently obtained data-related information into the database, and construct a data algorithm system to improve and update the internal data information within a specific time; S3. Data analysis: Regularly read the information inside the database and conduct analysis and comparison in combination with the previous data information. The analyzed data includes the item sales status of offline stores, the customer arrival time at the store, and the arrival time of different groups of people at the store; S4. Offline store management and decision-making: Based on the data and specific relevant information of each offline store, transmit them to the database regularly for comparison through big data, and the specific information of offline stores can be obtained intuitively, so as to analyze the obtained data, formulate relevant measures for improvement, and improve the operation status of offline stores accordingly.

[0015] Embodiment 1: An offline store data analysis method based on fuzzy theory, and its analysis method includes the following steps: S1. Data collection: Collect data analysis indicators of offline stores, including the sales status of items and relevant data information, calculate the collected data information to obtain accurate data, and synchronously and real-time update the relevant data of offline stores. The relevant data of each offline store must be recorded, a detailed analysis report on the sales situation of items must be made, and the relevant data of each different item must be collected; S2. Database establishment: Transmit the currently obtained data-related information into the database. The internal storage of the database contains the corresponding data of each item in the offline store, including quantity, purchase channel, and relevant storage date. Compare the collected data information to quickly obtain the operation data of the corresponding offline store, and construct a data algorithm system to improve and update the internal data information within a specific time; S3. Data analysis: Regularly read the information inside the database and conduct analysis and comparison in combination with the previous data information. The analyzed data includes the item sales status of offline stores, the customer arrival time at the store, and the arrival time of different groups of people at the store; S4. Offline store management and decision-making: Based on the data and specific relevant information of each offline store, transmit them to the database regularly for comparison through big data, and the specific information of offline stores can be obtained intuitively, so as to analyze the obtained data, formulate relevant measures for improvement, and improve the operation status of offline stores accordingly.

[0016] Embodiment 2: Data analysis method for offline stores based on fuzzy theory, and the analysis method includes the following steps: S1. Data collection: Collect data analysis indicators of offline stores, including the sales status of items and relevant data information, calculate the collected data information to obtain accurate data, and synchronously update the relevant data of offline stores in real time; S2. Database establishment: Transmit the current data-related information to the database, and construct a data algorithm system to improve and update the internal data information within a specific time; S3. Data analysis: Regularly read the information in the database and analyze and compare it with the previous data information. The analyzed data includes the item sales status of offline stores, the customer arrival time at the store, and the arrival time of different groups of people. Record the age of each customer who arrives at the store, analyze the purchasing power through customers of different age groups, and record the items purchased to accurately obtain the consumption purchasing power of the corresponding age group; S4. Offline store management and decision-making: Based on the data and specific relevant information of each offline store, transmit them to the database regularly for comparison through big data, and regularly update the data information in the database to adapt to new market changes, ensure the accuracy of data analysis, and intuitively obtain the specific information of offline stores, so as to analyze the obtained data, formulate relevant measures for improvement, and improve the business conditions of offline stores accordingly.

[0017] Embodiment 3: Data analysis method for offline stores based on fuzzy theory, and the analysis method includes the following steps: S1. Data collection: Collect data analysis indicators of offline stores, including the sales status of items and relevant data information, calculate the collected data information to obtain accurate data, and synchronously update the relevant data of offline stores in real time; S2. Database establishment: Transmit the current data-related information to the database, and construct a data algorithm system to improve and update the internal data information within a specific time; S3. Data analysis: Regularly read the information in the database and analyze and compare it with the previous data information. The analyzed data includes the item sales status of offline stores, the customer arrival time at the store, and the arrival time of different groups of people. Make specific records of the item sales status of offline stores and the changes in the positions of internal employees. When analyzing data, it is also necessary to analyze according to the regional status and the purchasing power of the surrounding population; S4. Offline store management and decision-making: Based on the data and specific relevant information of each offline store, which are transmitted to the database regularly and compared through big data, the specific information of the offline store can be obtained intuitively, so as to analyze the obtained data, formulate relevant measures for improvement, and thus improve the operating conditions of the offline store, while reducing the expenditure cost and increasing the sales volume.

[0018] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Offline store data analysis method based on fuzzy theory, characterized by: The analysis method includes the following steps: S1. Data collection: Collect data analysis indicators of offline stores, including the sales status of items and related data information, calculate the collected data information to obtain accurate data, and update the relevant data of offline stores in real time; S2. Database establishment: transfer the currently acquired data-related information to the database, and construct a data algorithm system to improve and update the internal data information within a specific time; S3, Data Analysis: Regularly read the information in the database and analyze and compare it with previous data information. The analysis data includes the sales status of items in offline stores, customer visit time and store visit time of different groups of people; S4. Offline store management and decision-making: The data and specific related information of each offline store are regularly transmitted to the database for comparison through big data. The specific information of the offline stores can be obtained intuitively, so as to analyze the data and formulate relevant measures to improve it, thereby improving the operating conditions of the offline stores.

2. The offline store data analysis method based on fuzzy theory according to claim 1 is characterized by: The relevant data of each offline store must be recorded, a detailed analysis report must be made for the sales of the items, and relevant data for each different item must be collected.

3. The offline store data analysis method based on fuzzy theory according to claim 1 is characterized by: The database stores data corresponding to each item in offline stores, including quantity, purchase channel and relevant storage date. By comparing the collected data information, the operating data of the corresponding offline stores can be quickly obtained.

4. The offline store data analysis method based on fuzzy theory according to claim 1 is characterized by: The age of each customer who comes to the store is recorded, and the purchasing power is analyzed by customers of different age groups, and the purchased items are recorded, so that the consumption purchasing power of the corresponding age group can be accurately obtained.

5. The offline store data analysis method based on fuzzy theory according to claim 1 is characterized by: The data information within the database is regularly updated to adapt to new market changes and ensure the accuracy of data analysis.

6. The offline store data analysis method based on fuzzy theory according to claim 1 is characterized by: Specific records are made of the sales status of items in offline stores and the job changes of internal employees. When analyzing the data, it is also necessary to analyze based on the geographical status and purchasing power of the surrounding population.

7. The offline store data analysis method based on fuzzy theory according to claim 1 is characterized by: The obtained offline store operating status and related data analysis results are used to formulate corresponding measures and policies to improve the offline store operating conditions, thereby increasing sales while reducing expenditure costs.