Market business data acquisition and analysis method and system

Abstract

The invention mainly relates to the technical field of market business data analysis, and provides a market business data acquisition and analysis method and system in order to obtain a more accurate future market business data prediction result. The first target parameters comprise sales data, user behavior data and market data, and the second target parameters comprise user attribute data, product attribute data and market environment data; storing the first target parameter and the second target parameter to a block chain, and establishing a data preprocessing mechanism on the block chain to preprocess the first target parameter and the second target parameter; the key target parameters in the first target parameters and the second target parameters are determined based on the market business prediction purposes, the market business data analysis model is constructed based on the key target parameters corresponding to different market business prediction purposes, future market business data are predicted, and enterprises are helped to make more scientific and reasonable market decisions.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of market business data analysis, and in particular to a market business data collection and analysis method and system. Background Art

[0002] In today's business environment, market data collection and analysis has become a crucial tool for companies to formulate marketing strategies, develop new products, and understand market trends. Existing market data collection and analysis systems primarily include data collection, data processing, data analysis, and data visualization. While existing technologies have achieved certain successes in market data collection and analysis, they still face numerous drawbacks. The primary issue is that existing market data analysis methods typically analyze future related market data from a single source. However, actual market data is typically multi-dimensional and interrelated. Single-data prediction methods ignore the impact of interrelationships between related data on the prediction results, fail to reflect dynamic changes in the market environment, and exhibit limited prediction accuracy.

[0003] At the same time, existing technologies often suffer from incomplete data collection. Some key data may not be collected in a timely manner, leading to biased analysis results. For example, some companies may focus solely on online data, ignoring the importance of offline data; or focus solely on customer purchase data, ignoring customer browsing, searching, and other behavioral data. Incomplete data collection can affect the accuracy and completeness of analysis, and thus hinder the formulation of market optimization strategies. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a market business data collection and analysis method and system, the purpose of which is to obtain more accurate future market business data prediction results based on the correlation between market business data, and make more accurate future market analysis and decision-making.

[0005] The technical solution adopted by the present invention to solve the above technical problems is:

[0006] In one aspect, the present invention provides a method for collecting and analyzing market business data, the method comprising:

[0007] Step 1: Collect first target parameters and second target parameters, where the first target parameters include sales data, user behavior data, and market data, and the second target parameters include user attribute data, product attribute data, and market environment data;

[0008] Step 2: Save the first target parameter and the second target parameter to the blockchain, and establish a data preprocessing mechanism on the blockchain to preprocess the first target parameter and the second target parameter;

[0009] Step 3: Determine the key target parameter among the first target parameter and the second target parameter based on the market business forecast purpose;

[0010] Step 4: Build a market business data analysis model based on the key target parameters corresponding to different market business forecasting purposes to predict the corresponding future market business data.

[0011] Furthermore, the sales data includes sales revenue, sales volume, sales channels, return rate, and customer satisfaction; the user behavior data includes registration information, login frequency, browsing history, purchase history, and comment feedback; and the market data includes market research report data, competitor analysis data, and consumer survey data.

[0012] Furthermore, the user attribute data includes the user's age, gender, region, occupation, and income level; the product attribute data includes: product category, price, functional features, and after-sales service data; and the market environment data includes policies and regulations, economic environment, and social trends.

[0013] Furthermore, the first target parameter and the second target parameter are collected in the following ways: automatic collection using an API interface and crawler technology, and manual collection through questionnaires, telephone interviews, and field visits.

[0014] Furthermore, the pre-processing in step 2 includes:

[0015] Data cleaning, removing outliers in the first and second target parameters;

[0016] Data format conversion, converting data in different formats into a format that can be recognized by machine language;

[0017] Data integration: Merging data from different sources, formats, and characteristics into a unified data storage structure;

[0018] Data reduction: reducing the size, complexity, or redundancy of the first and second target parameters.

[0019] Furthermore, the method also includes outputting and displaying the future market business data predicted by the market business data analysis model in the form of a bar chart, a line chart, a pie chart, a scatter plot or a heat map.

[0020] Furthermore, the method also includes verifying the future market real business data based on the future market business data predicted by the market business data analysis model. If the difference between the predicted future market business data and the future market real business data exceeds a set threshold, the validity verification mechanism is triggered to verify the authenticity of the future market business data through the data generation time and data generation address.

[0021] Furthermore, in step 3, key target parameters corresponding to different market business forecasting purposes are determined based on cluster analysis, regression analysis or decision tree algorithm.

[0022] Furthermore, in step 4, a market business data analysis model is established based on time series analysis and machine learning algorithms.

[0023] On the other hand, the present invention also provides a market business data collection and analysis system, the system comprising: a data collection module, a pre-processing module, an analysis module, a market business data prediction module;

[0024] The data acquisition module is used to collect first target parameters and second target parameters, the first target parameters including sales data, user behavior data and market data, and the second target parameters including user attribute data, product attribute data, market business data analysis model establishment module and market environment data;

[0025] The preprocessing module is used to preprocess the collected first target parameter and second target parameter;

[0026] The data analysis module is used to determine key target parameters corresponding to different market business forecast purposes from the first target parameters and the second target parameters;

[0027] The market business forecasting module is used to forecast corresponding future market business data according to key target parameters corresponding to different market business forecasting purposes.

[0028] The beneficial effects of the present invention are: a market business data collection and analysis method and system described in the present invention comprehensively collects basic business indicator parameters including sales data, user behavior data and market data as the first target parameters, and also collects refined dimension parameters such as user attribute data, product attribute data and market environment data as the second target parameters, establishes a full-link parameter indicator covering market business data, and extracts key target parameters for predicting different market business parameters from the target parameters covering different key areas, establishes a market business data analysis model based on the key target parameters to predict future market business data, helps enterprises quickly understand market conditions and user needs, and thus make more scientific and reasonable market decisions, which helps enterprises maintain their competitive advantage in the fierce market competition. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of a market business data collection and analysis method described in the present invention. DETAILED DESCRIPTION

[0030] The market business data collection and analysis method of the present invention is as follows Figure 1 As shown, including:

[0031] 1. Collect target parameters.

[0032] Target parameters are divided into first and second target parameters. The first target parameter is the basic data for market business forecasting, mainly including sales data, user behavior data, and market data. Specifically, sales data includes sales volume, sales channels, return rate, and customer satisfaction. User behavior data includes registration information, login frequency, browsing history, purchase history, and review feedback. Market data includes market research report data, competitor analysis data, and consumer survey data. The second target parameter is the refined data for market business forecasting, mainly including user attribute data, product attribute data, and market environment data. Specifically, user attribute data includes age, gender, region, occupation, and income level. Product attribute data includes product category, price, functional features, and after-sales service. The market environment data includes policies and regulations, economic environment, and social trends.

[0033] The first target parameters and the second target parameters include obtaining corresponding parameters from CRM systems, ERP systems, social media platforms, and third-party market research institutions through both automatic and manual collection. Automatic collection includes grabbing corresponding data from web pages through API interfaces, data crawlers, etc., and manual collection includes questionnaires, telephone interviews, and field visits.

[0034] 2. Store the collected target parameters on the blockchain.

[0035] Blockchain has the characteristics of decentralization, immutability, and high transparency, which can ensure the authenticity and security of data. At the same time, the data on the blockchain can be easily traced and verified, improving the credibility of the data. Therefore, the collected first target parameters and second target parameters are stored on the blockchain, and a data index is established on the blockchain to facilitate subsequent data retrieval and query.

[0036] A data cleaning and integration mechanism is established on the blockchain to pre-process the collected first and second target parameters, addressing issues such as data duplication, missing data, and anomalies, and preparing for subsequent analysis. This pre-processing specifically includes: data cleaning to remove outlier data; data format conversion to unify data formats and convert units; data integration to integrate data from different sources into a unified framework; data filling to fill in missing data; and data reduction to reduce the size, complexity, or redundancy of the first and second target parameters.

[0037] 3. Obtain key target parameters.

[0038] Utilize statistical software and data mining tools, analyze the first target parameter and the second target parameter based on machine learning algorithms such as cluster analysis, regression analysis, and decision tree, analyze the degree of correlation between each parameter in the first target parameter and the second target parameter and different market business forecast purposes, and select the parameters with a correlation degree higher than the set value from the first target parameter and the second target parameter as the key target parameter for the corresponding market business forecast purpose.

[0039] The screening process of key target parameters can be carried out outside the blockchain. After obtaining the key target parameters outside the blockchain, the key target parameters are stored back on the blockchain for subsequent verification and traceability.

[0040] 4. Build a market business data analysis model.

[0041] Using time series analysis and machine learning algorithms (such as LSTM and ARIMA), market data analysis models are established. Historical data on key target parameters for various market data points is analyzed to predict the likely future values of specific market data points. The predicted market data is stored on-chain to ensure data security and prevent data leakage or tampering. Based on the predicted future market data, potential market opportunities, user segmentation strategies, and product improvement directions are analyzed. The analysis results are presented in intuitive charts and reports, facilitating quick understanding and decision-making by management. A feedback mechanism is also established to apply the analysis results to actual business operations, providing specific improvement suggestions and optimization strategies, such as adjusting marketing strategies, optimizing product portfolios, and enhancing user experience.

[0042] When a future time period is reached, the difference between the corresponding future market business data forecast and the actual market data is verified. When the difference exceeds the set threshold, the validity verification mechanism is triggered. The validity verification mechanism mainly includes the verification of the data source time and address. By checking whether the market business data is generated within a reasonable time range and whether it comes from a trusted data source address, it is determined whether the market business data is generated in a centralized manner. If there are any abnormalities in the data, further investigation and verification will be carried out.

[0043] The market business data collection and analysis system of the present invention includes a data collection module, a pre-processing module, an analysis module, and a market business data prediction module;

[0044] The data acquisition module is used to collect first target parameters and second target parameters, the first target parameters including sales data, user behavior data and market data, and the second target parameters including user attribute data, product attribute data, market business data analysis model establishment module and market environment data;

[0045] The preprocessing module is used to preprocess the collected first target parameter and second target parameter;

[0046] The data analysis module is used to determine key target parameters corresponding to different market business forecast purposes from the first target parameters and the second target parameters;

[0047] The market business forecasting module is used to forecast corresponding future market business data according to key target parameters corresponding to different market business forecasting purposes.

Claims

1. A method for collecting and analyzing market business data, characterized in that: The method comprises: Step 1: Collect first target parameters and second target parameters, where the first target parameters include sales data, user behavior data, and market data, and the second target parameters include user attribute data, product attribute data, and market environment data; Step 2: Save the first target parameter and the second target parameter to the blockchain, and establish a data preprocessing mechanism on the blockchain to preprocess the first target parameter and the second target parameter; Step 3: Determine the key target parameter among the first target parameter and the second target parameter based on the market business forecast purpose; Step 4: Build a market business data analysis model based on the key target parameters corresponding to different market business forecasting purposes to predict the corresponding future market business data.

2. A market business data collection and analysis method according to claim 1, characterized in that: The sales data includes sales revenue, sales volume, sales channels, return rate, and customer satisfaction; the user behavior data includes user shopping registration information, login frequency, browsing history, purchase history, and comment feedback; and the market data includes market research report data, competitor analysis data, and consumer survey data.

3. A market business data collection and analysis method according to claim 1, characterized in that: The user attribute data includes user age, gender, region, occupation, and income level; the product attribute data includes product category, price, functional features, and after-sales service data; and the market environment data includes policies and regulations, economic environment, and social trends.

4. A method for collecting and analyzing market business data according to claim 1, characterized in that: Methods for collecting the first target parameter and the second target parameter include: automatic collection using an API interface and crawler technology, and manual collection through questionnaires, telephone interviews, and field visits.

5. The method for collecting and analyzing market business data according to claim 1, characterized in that: The pre-processing in step 2 includes: Data cleaning, removing outliers in the first and second target parameters; Data filling, filling in missing data; Data format conversion, converting data in different formats into a format that can be recognized by machine language; Data integration: Merging data from different sources, formats, and characteristics into a unified data storage structure; Data reduction: reducing the size, complexity, or redundancy of the first and second target parameters.

6. A method for collecting and analyzing market business data according to claim 1, characterized in that: The method further includes outputting and displaying the future market business data predicted by the market business data analysis model in the form of a bar chart, a line chart, a pie chart, a scatter plot, or a heat map.

7. A method for collecting and analyzing market business data according to claim 1, characterized in that: The method also includes verifying the future market real business data based on the future market business data predicted by the market business data analysis model. If the difference between the predicted future market business data and the future market real business data exceeds a set threshold, the validity verification mechanism is triggered to verify the authenticity of the future market business data through the data generation time and data generation address.

8. The method for collecting and analyzing market business data according to claim 1, characterized in that: In step 3, the key target parameters corresponding to different market business forecasting purposes are determined based on cluster analysis, regression analysis or decision tree algorithm.

9. The method for collecting and analyzing market business data according to claim 1, characterized in that: In step 4, a market business data analysis model is established based on time series analysis and machine learning algorithms.

10. A market business data collection and analysis system, used to implement a market business data collection and analysis method according to any one of claims 1 to 9, characterized in that: The system includes: a data acquisition module, a pre-processing module, an analysis module, and a market business data prediction module; The data acquisition module is used to collect first target parameters and second target parameters, the first target parameters including sales data, user behavior data and market data, and the second target parameters including user attribute data, product attribute data, market business data analysis model establishment module and market environment data; The preprocessing module is used to preprocess the collected first target parameter and second target parameter; The data analysis module is used to determine key target parameters corresponding to different market business forecast purposes from the first target parameters and the second target parameters; The market business forecasting module is used to forecast corresponding future market business data according to key target parameters corresponding to different market business forecasting purposes.

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