Operation data analysis method and system based on electronic commerce big data

Abstract

The invention discloses an operation data analysis method and system based on e-commerce big data, belongs to the technical field of e-commerce data analysis, and aims to solve the problems that large-scale data and rapidly changing real-time data are difficult to efficiently process and hidden associated information behind the data cannot be fully mined. Comprising a data acquisition step, a data preprocessing step, a data analysis step, a result interpretation and decision support step and a data visualization step. Through a real-time data acquisition and processing technology, an enterprise can acquire latest operation data almost without delay, so that market changes and user behaviors are quickly responded, manual intervention is reduced through automatic data acquisition, preprocessing and analysis processes, the risk of human errors is reduced, meanwhile, the data processing speed is increased, and the user experience is improved. Therefore, enterprises can obtain valuable insights more quickly, and the operation efficiency and the response speed are effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of e-commerce data analysis, and specifically relates to an operation data analysis method and system based on e-commerce big data. Background Art

[0002] E-commerce usually refers to a new business operation model in which, in the open network environment of the Internet, buyers and sellers conduct various business activities without meeting each other in a wide range of commercial and trade activities around the world, based on the client / server application method, to achieve consumers' online shopping, online transactions and online electronic payments between merchants, as well as various business activities, transaction activities, financial activities and related comprehensive service activities.

[0003] With the rapid development of e-commerce, e-commerce platforms have accumulated a large amount of valuable resources such as transaction data and user behavior data. How to effectively use this data for operational analysis, optimize marketing strategies, and improve user experience has become an important issue facing e-commerce companies. Existing technologies mostly rely on traditional data warehouse and business intelligence (DW / BI) technologies, which are difficult to efficiently process large-scale data and rapidly changing real-time data, and cannot fully explore the hidden related information behind the data.

[0004] Therefore, there is a need for an operational data analysis method and system based on e-commerce big data to solve the problems existing in the prior art that it is difficult to efficiently process large-scale data and rapidly changing real-time data, and cannot fully mine the hidden related information behind the data. Summary of the invention

[0005] The purpose of the present invention is to provide an operational data analysis method and system based on e-commerce big data to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: an operation data analysis method based on e-commerce big data, comprising the following steps:

[0007] S1. Data collection step: determine the data analysis target, identify and determine the data source, and use tools such as API interface, data capture technology or web crawlers to collect transaction data, user behavior data, product information and possible external data of the e-commerce platform from the data source in real time or regularly;

[0008] S2, data preprocessing step: clean the collected raw data, remove duplicate data, process missing values, correct erroneous data, convert the cleaned data according to a unified format and standard to form a standardized data set, and integrate the data from different data sources into a unified data set;

[0009] S3. Data analysis step: Extract features that are valuable to the analysis target from the integrated data set, select appropriate analysis methods according to the analysis target, including but not limited to descriptive statistical analysis, trend analysis, comparative analysis, association rule mining and cluster analysis, and use machine learning algorithms or statistical models to model and analyze the extracted features to discover hidden associations and patterns behind the data;

[0010] S4. Result interpretation and decision support step: Interpret the results obtained from the analysis, understand their business implications and influencing factors, and provide decision support based on the analysis results, including but not limited to optimizing operation strategies, adjusting product pricing, and improving user experience;

[0011] S5. Data visualization step: Use data visualization tools to display the analysis results in the form of charts, dashboards, etc., provide an intuitive and easy-to-understand data visualization interface, and support interactive analysis functions, allowing users to further explore and analyze the data.

[0012] It should be noted in the scheme that the data collection step in step S1 also includes an assessment of data quality to ensure that the collected data meets the analysis requirements.

[0013] It is further worth noting that the data preprocessing step in step S2 also includes data desensitization processing to protect user privacy and business secrets.

[0014] It should be further explained that the data analysis step in step S3 also includes verifying and evaluating the analysis results to ensure the accuracy and reliability of the analysis results.

[0015] The present invention also provides the following technical solution: an operation data analysis system based on e-commerce big data, including the above-mentioned operation data analysis method based on e-commerce big data, and the system also includes:

[0016] A data collection module, used to execute the data collection step of step S1 in the operation data analysis method based on e-commerce big data;

[0017] A data preprocessing module, used to perform the data preprocessing step of step S2 in the operation data analysis method based on e-commerce big data;

[0018] A data analysis module, used to perform the data analysis step of step S3 in the operation data analysis method based on e-commerce big data;

[0019] A result display module, used to perform the data visualization step of step S4 in the operation data analysis method based on e-commerce big data, and provide an interactive analysis function;

[0020] The decision support module is used to provide decision support based on the analysis results, including but not limited to optimizing operation strategies, adjusting product pricing, and improving user experience suggestions.

[0021] As a preferred implementation, it also includes a data quality assessment module for assessing the data quality during the data collection process.

[0022] As a preferred implementation, the data preprocessing module also includes a data desensitization processing function to protect user privacy and business secrets.

[0023] As a preferred implementation, the data analysis module also includes result verification and evaluation functions to ensure the accuracy and reliability of the analysis results.

[0024] Compared with the prior art, the operation data analysis method and system based on e-commerce big data provided by the present invention have at least the following beneficial effects:

[0025] (1) Through real-time data collection and processing technology, enterprises can obtain the latest operational data with almost no delay, thereby quickly responding to market changes and user behavior. In addition, through automated data collection, preprocessing and analysis processes, human intervention is reduced, the risk of human error is reduced, and data processing speed is accelerated, allowing enterprises to obtain valuable insights more quickly and effectively improve operational efficiency and response speed.

[0026] (2) By utilizing big data technology and machine learning algorithms, the present invention can deeply explore the hidden connections and patterns behind the data and provide more accurate business analysis. Moreover, through comprehensive operational data analysis, enterprises can examine business conditions from multiple dimensions (such as user behavior, product sales, market trends, etc.) and obtain a more comprehensive perspective, thereby providing strong support for the enterprise's decision-making, helping enterprises make more scientific and reasonable decisions, and helping enterprises discover potential opportunities and problems, providing a more comprehensive reference for decision-making.

[0027] (3) Based on the in-depth analysis of user behavior data, the present invention can construct a personalized product recommendation model to provide users with product recommendations that are more in line with their interests and needs. By analyzing market trends and user needs, enterprises can more accurately grasp changes in user needs and thus adjust products and services in a timely manner to meet user expectations.

[0028] (4) Through comprehensive operational data analysis, enterprises can discover bottlenecks and waste in the operation process, thereby optimizing resource allocation and improving resource utilization efficiency. At the same time, through in-depth analysis of market trends and potential risks, enterprises can identify possible risk factors in advance and formulate corresponding response strategies.

[0029] (5) Through in-depth data analysis, enterprises can discover new market opportunities and user needs, thereby developing new products or services and expanding new business areas. At the same time, operational data analysis based on big data provides enterprises with rich data resources and insights, which helps stimulate the innovative thinking and creativity of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0031] Figure 1 This is a flow chart of an operational data analysis method based on e-commerce big data according to the present invention;

[0032] Figure 2 This is a block diagram of the operational data analysis system based on e-commerce big data of the present invention. DETAILED DESCRIPTION

[0033] The present invention will be further described below in conjunction with the embodiments.

[0034] See also Figure 1 The present invention provides an operation data analysis method based on e-commerce big data, comprising the following steps:

[0035] S1. Data collection step: determine the data analysis target, identify and determine the data source, and use tools such as API interface, data capture technology or web crawlers to collect transaction data, user behavior data, product information and possible external data of the e-commerce platform from the data source in real time or regularly;

[0036] First, clarify the goals and needs of data analysis, determine what types of data need to be collected, and then identify and determine the source of data collection, including transaction data, user behavior data, product information, etc. within the e-commerce platform, as well as external data that may be needed, such as market trends, competitor data, etc. Finally, use API interfaces, data capture technology, web crawlers and other tools to collect data from data sources in real time or regularly; through real-time data collection and processing technology, enterprises can obtain the latest operational data with almost no delay, thereby quickly responding to market changes and user behavior. This real-time nature ensures that enterprises can adjust their operational strategies in a timely manner and improve operational efficiency.

[0037] S2, data preprocessing step: clean the collected raw data, remove duplicate data, process missing values, correct erroneous data, convert the cleaned data according to a unified format and standard to form a standardized data set, and integrate the data from different data sources into a unified data set;

[0038] S3. Data analysis step: Extract features that are valuable to the analysis target from the integrated data set, select appropriate analysis methods according to the analysis target, including but not limited to descriptive statistical analysis, trend analysis, comparative analysis, association rule mining and cluster analysis, and use machine learning algorithms or statistical models to model and analyze the extracted features to discover hidden associations and patterns behind the data;

[0039] Based on in-depth analysis of user behavior data, the present invention can build a personalized product recommendation model to provide users with product recommendations that are more in line with their interests and needs. This personalized recommendation can enhance the user's shopping experience and increase user stickiness. Through the analysis of market trends and user needs, enterprises can more accurately grasp changes in user needs and adjust products and services in a timely manner to meet user expectations. This demand insight helps to improve user satisfaction and loyalty. Through comprehensive operational data analysis, enterprises can discover bottlenecks and waste in the operation process, thereby optimizing resource allocation and improving resource utilization efficiency. This optimization helps to reduce the company's operating costs.

[0040] S4. Result interpretation and decision support step: Interpret the results obtained from the analysis, understand their business implications and influencing factors, and provide decision support based on the analysis results, including but not limited to optimizing operation strategies, adjusting product pricing, and improving user experience;

[0041] Through in-depth analysis of market trends and potential risks, enterprises can identify possible risk factors in advance and formulate corresponding response strategies. This risk warning and response mechanism helps to reduce the operational risks of enterprises and ensure their stable development.

[0042] S5. Data visualization step: Use data visualization tools to display the analysis results in the form of charts, dashboards, etc., provide an intuitive and easy-to-understand data visualization interface, and support interactive analysis functions, allowing users to further explore and analyze the data.

[0043] Furthermore, it is worth specifying that the data collection step in step S1 also includes evaluating the data quality to ensure that the collected data meets the analysis requirements.

[0044] Furthermore, it is worth specifying that the data preprocessing step in step S2 also includes data desensitization processing to protect user privacy and business secrets.

[0045] Data desensitization refers to the processing of sensitive data to remove or replace the sensitive parts of the data without changing the data attributes and business meaning, in order to protect personal privacy and data security. Its main purpose is to prevent sensitive data from being accessed, leaked or abused without authorization, and to ensure the legal use and effective protection of data.

[0046] Furthermore, it is worth specifying that the data analysis step in step S3 also includes verifying and evaluating the analysis results to ensure the accuracy and reliability of the analysis results.

[0047] See also Figure 2 The present invention also provides an operation data analysis system based on e-commerce big data, including the above-mentioned operation data analysis method based on e-commerce big data, and the system also includes:

[0048] The data collection module is used to execute the step S1 data collection step in the operation data analysis method based on e-commerce big data, deploy data collection agents on different data sources of the e-commerce platform, collect transaction records, user browsing records, search keywords and other data in real time, and use the storage service of the cloud computing platform to store the collected data safely and efficiently;

[0049] A data preprocessing module is used to execute the data preprocessing step of step S2 in the operational data analysis method based on e-commerce big data, perform data repair or data cleaning on the collected original data, remove duplicate, missing, and erroneous data, obtain secondary data, integrate the secondary data into a preset standardized data model, perform standardized processing, and ensure the consistency and comparability of the data;

[0050] A data analysis module is used to execute the data analysis step S3 in the operation data analysis method based on e-commerce big data, and use the preset big data technology and machine learning algorithm to conduct in-depth analysis and mining of standardized data to extract hidden association information from the data. The machine learning algorithms used include but are not limited to linear regression models, cluster analysis models, text sentiment analysis models, random forest algorithms, and association rule mining algorithms;

[0051] A result display module is used to perform the data visualization step of step S4 in the operation data analysis method based on e-commerce big data, and provide an interactive analysis function, using data visualization technology to generate an intuitive and easy-to-understand data visualization interface based on the analysis results, so that enterprise decision makers can quickly grasp the operation status;

[0052] The decision support module is used to provide decision support based on the analysis results, including but not limited to optimizing operational strategies, adjusting product pricing, and improving user experience suggestions. It provides decision support functions based on the analysis results, such as automatically generating reports and providing optimization suggestions. The decision support layer converts the analysis results into actionable decision suggestions to help companies optimize operational strategies and improve business results.

[0053] Furthermore, it is worth specifying that it also includes a data quality assessment module for assessing the data quality during the data collection process.

[0054] Furthermore, it is worth specifying that the data preprocessing module also includes a data desensitization processing function to protect user privacy and business secrets.

[0055] Furthermore, it is worth specifying that the data analysis module also includes result verification and evaluation functions to ensure the accuracy and reliability of the analysis results.

[0056] In summary: through real-time data collection and processing technology, enterprises can obtain the latest operational data with almost no delay, so as to quickly respond to market changes and user behavior, and through automated data collection, preprocessing and analysis processes, human intervention is reduced, the risk of human error is reduced, and the data processing speed is accelerated, so that enterprises can obtain valuable insights more quickly and effectively improve operational efficiency and response speed; using big data technology and machine learning algorithms, the present invention can deeply explore the hidden associations and patterns behind the data, provide more accurate business analysis, and through comprehensive operational data analysis, enterprises can examine business conditions from multiple dimensions (such as user behavior, product sales, market trends, etc.) and obtain a more comprehensive perspective, thereby providing strong support for the company's decision-making, helping enterprises make more scientific and reasonable decisions, and helping enterprises discover potential opportunities and problems, providing more support for decision-making. Add comprehensive reference; Based on the in-depth analysis of user behavior data, the present invention can build a personalized product recommendation model to provide users with product recommendations that are more in line with their interests and needs. Through the analysis of market trends and user needs, enterprises can more accurately grasp the changes in user needs, thereby adjusting products and services in a timely manner to meet user expectations; through comprehensive operational data analysis, enterprises can discover bottlenecks and waste in the operation process, thereby optimizing resource allocation and improving resource utilization efficiency. At the same time, through in-depth analysis of market trends and potential risks, enterprises can identify possible risk factors in advance and formulate corresponding response strategies; through in-depth data analysis, enterprises can discover new market opportunities and user needs, thereby developing new products or services and expanding new business areas. At the same time, operational data analysis based on big data provides enterprises with rich data resources and insight capabilities, which helps to stimulate the innovative thinking and creativity of enterprises;

[0057] It has brought significant value and competitiveness to enterprises by improving operational efficiency, optimizing decision-making, enhancing user experience, reducing operating costs and risks, and promoting business growth and innovation.

[0058] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0059] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. An operational data analysis method based on e-commerce big data, characterized by: The following steps are involved: S1. Data collection step: determine the data analysis target, identify and determine the data source, and use tools such as API interface, data capture technology or web crawlers to collect transaction data, user behavior data, product information and possible external data of the e-commerce platform from the data source in real time or regularly; S2, data preprocessing step: clean the collected raw data, remove duplicate data, process missing values, correct erroneous data, convert the cleaned data according to a unified format and standard to form a standardized data set, and integrate the data from different data sources into a unified data set; S3. Data analysis step: Extract features that are valuable to the analysis target from the integrated data set, select appropriate analysis methods according to the analysis target, including but not limited to descriptive statistical analysis, trend analysis, comparative analysis, association rule mining and cluster analysis, and use machine learning algorithms or statistical models to model and analyze the extracted features to discover hidden associations and patterns behind the data; S4. Result interpretation and decision support step: Interpret the results obtained from the analysis, understand their business implications and influencing factors, and provide decision support based on the analysis results, including but not limited to optimizing operation strategies, adjusting product pricing, and improving user experience; S5. Data visualization step: Use data visualization tools to display the analysis results in the form of charts, dashboards, etc., provide an intuitive and easy-to-understand data visualization interface, and support interactive analysis functions, allowing users to further explore and analyze the data.

2. The operational data analysis method based on e-commerce big data according to claim 1 is characterized by: The data collection step in step S1 also includes evaluating the data quality to ensure that the collected data meets the analysis requirements.

3. The operational data analysis method based on e-commerce big data according to claim 1 is characterized by: The data preprocessing step described in step S2 also includes data desensitization processing to protect user privacy and business secrets.

4. The operational data analysis method based on e-commerce big data according to claim 1 is characterized by: The data analysis step in step S3 also includes verifying and evaluating the analysis results to ensure the accuracy and reliability of the analysis results.

5. An operation data analysis system based on e-commerce big data, comprising an operation data analysis method based on e-commerce big data according to any one of claims 1 to 4, characterized in that: The system further comprises: A data collection module, used to execute the data collection step of step S1 in the operation data analysis method based on e-commerce big data; A data preprocessing module, used to perform the data preprocessing step of step S2 in the operation data analysis method based on e-commerce big data; A data analysis module, used to perform the data analysis step of step S3 in the operation data analysis method based on e-commerce big data; A result display module, used to perform the data visualization step of step S4 in the operation data analysis method based on e-commerce big data, and provide an interactive analysis function; The decision support module is used to provide decision support based on the analysis results, including but not limited to optimizing operation strategies, adjusting product pricing, and improving user experience suggestions.

6. The operational data analysis system based on e-commerce big data according to claim 5 is characterized by: It also includes a data quality assessment module for assessing data quality during the data collection process.

7. The operational data analysis system based on e-commerce big data according to claim 5 is characterized by: The data preprocessing module also includes a data desensitization processing function to protect user privacy and business secrets.

8. The operational data analysis system based on e-commerce big data according to claim 5 is characterized by: The data analysis module also includes result verification and evaluation functions to ensure the accuracy and reliability of the analysis results.

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