Market volume analysis method based on Amazon e-commerce platform

Abstract

The invention relates to a market volume analysis method based on an Amazon e-commerce platform. The method comprises the following steps: step 1, data source access: cooperating with a third-party data source to obtain related data of the Amazon platform; step 2, data cleaning and preprocessing: cleaning the original data to remove repeated, wrong and invalid data; 3, data integration: integrating the cleaned and preprocessed data, and establishing a data warehouse or a data lake; 4, crawler capturing: capturing commodity information from the Amazon e-commerce platform, capturing a plurality of pages by adopting a multi-thread technology, designing an anti-crawler strategy, and setting a reasonable data updating frequency; step 5, data cleaning: removing invalid, wrong or incomplete data, and unifying data formats; and step 6, data analysis and insight: establishing a data analysis model to perform deep analysis on the market volume data, and generating operable suggestions and strategies for merchants by applying a natural language processing technology and a machine learning algorithm according to an analysis result.

Description

Technical Field

[0001] The present invention relates to the technical field of e-commerce data analysis, and particularly to a method for analyzing market share of voice based on the Amazon e-commerce platform, aiming to provide a more efficient, accurate and intelligent market analysis method for Amazon sellers, helping them better understand the market competition situation, optimize advertising strategies, and increase the market share and sales performance of products. Background Art

[0002] In today's globalized e-commerce market, Amazon, as one of the largest e-commerce platforms in the world, attracts numerous brands and merchants to participate in the competition. Share of Voice (SOV), as an important indicator to measure the influence of a brand or product in the market, is of crucial significance for merchants to formulate marketing strategies, optimize advertising, etc. Accurately calculating and analyzing the market share of voice can help merchants understand their own positions in the market, gain insights into competitors, and thus make more informed business decisions.

[0003] Currently, Amazon officially provides sellers with the products with the highest sales volume in a category based on the product category (Category) for analysis. However, sellers mainly conduct market analysis manually based on these data, and this traditional model has many problems and disadvantages:

[0004] Low data processing efficiency: With the continuous expansion of Amazon's platform business, the data volume has increased exponentially. Sellers manually collect and organize the data provided by the official, and the process is cumbersome and time-consuming, making it difficult to meet the needs of the rapid development of the business. For example, during the preparation stage of promotional activities such as "Black Friday" or "Cyber Monday", sellers need to quickly grasp the market dynamics and timely adjust product pricing and promotional strategies. However, the speed of manually processing a large amount of data far lags behind the rhythm of market changes, which may cause merchants to miss the best market opportunities and fail to gain the upper hand in the fierce competition.

[0005] Difficulty in ensuring data accuracy: Manual operations are extremely prone to data entry errors, data omissions, etc. When recording key information such as product sales volume, price, and number of reviews, even a slight human oversight may lead to data deviation. Once analyzing and making business decisions based on these incorrect data, it may cause serious economic losses to merchants. For example, wrongly estimating market demand and blindly increasing inventory, which ultimately leads to overstocking of goods, occupying a large amount of funds and affecting the capital turnover and profitability of the enterprise.

[0006] Limited analysis dimensions: Due to the dual limitations of manpower and time, it is very difficult to conduct multi-dimensional and in-depth market analysis manually. Usually, sellers can only perform simple statistics and analysis on basic data such as sales volume and rankings. For the complex data correlation analysis involved in market voice calculation, such as the internal relationship between the positive review rate and sales volume of products, and the specific impact of different advertising channels on market share, manual operation is almost impossible to complete. This limitation prevents merchants from comprehensively and deeply understanding the market competition situation and formulating targeted and competitive marketing strategies.

[0007] Lack of real-time and dynamic monitoring: The e-commerce market changes rapidly, and situations such as the strategic adjustments of competitors and the sudden launch of new products may occur at any time. However, manual analysis cannot achieve real-time monitoring and dynamic updating of market data. This results in sellers being unable to promptly capture these important information and adjust their marketing strategies in a timely manner, putting them in a passive position in the fierce market competition. For example, when a competitor suddenly launches a large-scale price discount activity, and the seller fails to notice it in time and take corresponding measures, it may lead to a sharp decline in the sales volume of their own products and a rapid erosion of market share.

[0008] High labor costs: To complete manual market analysis work, sellers need to invest a large amount of manpower, including professional data collectors, experienced analysts, etc. The salary expenses of these personnel and the related training costs have greatly increased the operating costs of the enterprise. For small and medium-sized sellers, the high labor cost burden may seriously restrict the profitability and development potential of the enterprise, putting them at a disadvantage in the market competition.

[0009] In summary, there are many drawbacks to manually conducting Amazon market analysis, and there is an urgent need for a more efficient, accurate, and intelligent market analysis method to meet the rapid development needs of the e-commerce market. Summary of the Invention

[0010] In view of the deficiencies of the prior art, the purpose of the present invention is to provide a method for analyzing market voice based on the Amazon e-commerce platform, which is a comprehensive, accurate, and in-depth insight and application function Amazon market voice analysis method, solving the problems of incomplete data acquisition, inaccurate calculation logic, and lack of in-depth insight and application in the prior art, helping merchants better understand the market competition situation, optimize advertising strategies, and increase the market share and sales performance of products.

[0011] The above object of the present invention is achieved through the following technical solutions:

[0012] A method for analyzing market voice based on the Amazon e-commerce platform, comprising the following steps:

[0013] Step 1, Data source access: Collaborate with third-party data sources to obtain relevant data on the Amazon platform, including product information, search keywords, advertising data, and sales data. At the same time, obtain ABA search report data from the official Amazon interface;

[0014] Step 2, Data cleaning and preprocessing: Clean the original data, remove duplicate, incorrect, and invalid data, and perform standardization processing to unify the data format and units;

[0015] Step 3, Data integration: Integrate the cleaned and preprocessed data, establish a data warehouse or data lake, and achieve data association between different data sources;

[0016] Step 4, Crawler scraping: Develop an advanced web crawler to automatically scrape product information from the Amazon e-commerce platform, use multi-threaded technology to scrape multiple pages, process JavaScript dynamically generated content, design anti-crawler strategies, and set a reasonable data update frequency;

[0017] Step 5, Data cleaning: Remove invalid, incorrect, or incomplete data, and unify the data format, including deduplication, format standardization, outlier detection, and missing value handling;

[0018] Step 6, Data analysis and insights: Use data visualization tools to convert market volume data into intuitive charts and tables, establish a data analysis model to deeply analyze the market volume data, and use natural language processing technology and machine learning algorithms to generate actionable suggestions and strategies for merchants based on the analysis results.

[0019] As a further technical solution of the present invention: The method further includes a market volume calculation logic:

[0020] Total brand SOV: In the search results of a specified keyword, the number of search results with the ASIN being the specified brand is divided by the total number of search results, and then multiplied by 100%;

[0021] Brand natural position SOV: In the search results where the position type of the specified keyword is the natural position, the number of search results with the ASIN being the specified brand is divided by the total number of search results in the natural position, and then multiplied by 100%;

[0022] Brand advertising position SOV: In the search results where the position type of the specified keyword is the advertising position (SP + SB + SBV), the number of search results with the ASIN being the specified brand is divided by the total number of search results in the corresponding advertising position, and then multiplied by 100%;

[0023] ASIN total SOV: In the search results of a specified keyword, the number of search results with the specified ASIN code is divided by the total number of search results, and then multiplied by 100%;

[0024] ASIN Natural Position / Advertising Position / SP Advertising Position SOV: In the search results where the position type for the specified keyword is a certain position, the number of search results with the specified ASIN code is divided by the total number of search results for the corresponding position type, and then multiplied by 100%.

[0025] As a further technical solution of the present invention: In the data source access, the third-party data sources include industry data providers and social media data platforms to obtain more comprehensive market-related data.

[0026] As a further technical solution of the present invention: In the data cleaning and preprocessing, the standardization process also includes uniformly converting sales volume data in different units. For example, the sales volume of products with different packaging specifications is uniformly converted into the sales volume of a single product.

[0027] As a further technical solution of the present invention: In the crawler scraping, the multi-threaded technology adopts thread pool management, and dynamically adjusts the number of threads according to the server performance and network conditions to achieve the optimal scraping efficiency.

[0028] As a further technical solution of the present invention: In the data analysis and insight, in addition to Echarts and D3.js, the data visualization tool can also adopt the professional visualization software Tableau to provide richer visualization effects and interactive functions.

[0029] As a further technical solution of the present invention: In the data analysis and insight, the established data analysis model also includes a clustering analysis model, which is used to classify brands or products in the market so that merchants can better understand the market competition pattern.

[0030] As a further technical solution of the present invention: The method also includes an advertising optimization function. An advertising placement optimization system is established to connect with the Amazon advertising platform, and the advertising placement strategy is automatically adjusted according to the market volume analysis results and advertising optimization suggestions, such as adjusting keyword bids and advertising position placement budgets.

[0031] As a further technical solution of the present invention: The method also includes a competitor monitoring function. A competitor monitoring platform is built to collect the market volume data and advertising placement information of competitors in real time, conduct comparative analysis and provide a competitor analysis report, and set up an early warning mechanism to timely remind merchants when there are significant changes in the competitor's market volume.

[0032] As a further technical solution of the present invention: The method also includes a keyword management function. A keyword management module is developed to support merchants to add, delete, and block keywords. During the keyword addition process, a keyword recommendation function is provided to recommend potential high-quality keywords according to the market volume data and product relevance, and the keyword effect is monitored and analyzed in real time to provide keyword optimization suggestions.

[0033] In summary, the present invention includes at least one of the following beneficial technical effects:

[0034] The present invention discloses a method for analyzing market volume based on the Amazon e-commerce platform. Using this method, Amazon sellers can expect the following effects:

[0035] Advertising optimization: Establish an advertising placement optimization system, connect with the Amazon advertising platform, and automatically adjust the advertising placement strategy according to the market volume analysis results and advertising optimization suggestions. For example, when the system detects that the SOV of a certain keyword is high but the conversion rate is low, it automatically reduces the bid of that keyword; when it is found that the SOV of a certain brand in a specific advertising position is low, it automatically increases the advertising budget for that position. Through advertising optimization, sellers can improve the efficiency and effectiveness of advertising placement, reduce advertising costs, and increase the return on advertising investment.

[0036] Competitor monitoring: Build a competitor monitoring platform to collect real-time market volume data and advertising placement information of competitors. Through comparative analysis, provide a competitor analysis report for merchants, including the advantages and disadvantages of competitors, the characteristics and changing trends of advertising strategies, etc. At the same time, set up a warning mechanism to timely remind merchants when there are major changes in the market volume of competitors. Competitor monitoring helps sellers keep abreast of the dynamics of competitors, adjust their own market strategies, and maintain a competitive advantage.

[0037] Keyword management: Develop a keyword management module to support merchants in adding, deleting, blocking, etc. operations on keywords. During the keyword addition process, provide a keyword recommendation function, and recommend potential high-quality keywords for merchants based on market volume data and product relevance. At the same time, monitor and analyze the effects of keywords in real time, and provide keyword optimization suggestions for merchants. Keyword management can help sellers optimize the search exposure of products, and improve the click-through rate and conversion rate of products.

[0038] In summary, the present invention provides a powerful tool for Amazon sellers to improve operational efficiency, optimize market strategies, and ultimately drive sales growth and brand building. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow diagram of the business process in the present invention.

[0040] Figure 2 It is a flow diagram of the operation process in the present invention.

[0041] Figure 3 It is a flow diagram of competitor monitoring and product optimization in the present invention.

[0042] Figure 4 It is a statistical chart of SOV insight in the present invention.

[0043] Figure 5 This is a schematic diagram of data statistics after SOV insight in the present invention. Detailed implementation manners

[0044] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application; obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0045] Embodiment 1:

[0046] Refer to Figures 1-5 , a method for analyzing market volume based on the Amazon e-commerce platform disclosed in the present invention, includes the following steps:

[0047] Step 1, data source access: Cooperate with a third-party data source to obtain relevant data of the Amazon platform, including product information, search keywords, advertising placement data, and sales data, and at the same time obtain ABA search report data from the official Amazon interface;

[0048] Step 2, data cleaning and preprocessing: Clean the original data, remove duplicate, incorrect, and invalid data, and perform standardization processing to unify the data format and unit;

[0049] Step 3, data integration: Integrate the cleaned and preprocessed data, establish a data warehouse or data lake, and realize data association between different data sources;

[0050] Step 4, crawler scraping: Develop an advanced web crawler to automatically scrape product information from the Amazon e-commerce platform, use multi-threaded technology to scrape multiple pages, process JavaScript dynamically generated content, design an anti-crawler strategy, and set a reasonable data update frequency;

[0051] Step 5, data cleaning: Remove invalid, incorrect, or incomplete data, and unify the data format, including duplicate removal, format standardization, outlier detection, and missing value processing;

[0052] Step 6, data analysis and insight: Use data visualization tools to convert market volume data into intuitive charts and tables, establish a data analysis model to deeply analyze market volume data, and use natural language processing technology and machine learning algorithms to generate actionable suggestions and strategies for merchants based on the analysis results.

[0053] In this embodiment, the method of the present invention further includes a market volume calculation logic:

[0054] Total Brand SOV: In the search results of specified keywords, the number of search results with the ASIN of the specified brand is divided by the total number of search results, and then multiplied by 100%;

[0055] Brand Organic SOV: In the search results where the position type of the specified keyword is organic, the number of search results with the ASIN of the specified brand is divided by the total number of organic search results, and then multiplied by 100%;

[0056] Brand Ad SOV: In the search results where the position type of the specified keyword is ad positions (SP + SB + SBV), the number of search results with the ASIN of the specified brand is divided by the total number of search results for the corresponding ad positions, and then multiplied by 100%;

[0057] Total ASIN SOV: In the search results of specified keywords, the number of search results with the specified ASIN code is divided by the total number of search results, and then multiplied by 100%;

[0058] ASIN Organic / Ad / SP Ad SOV: In the search results where the position type of the specified keyword is a certain position, the number of search results with the specified ASIN code is divided by the total number of search results for the corresponding position type, and then multiplied by 100%.

[0059] During data source access, third-party data sources include industry data providers and social media data platforms to obtain more comprehensive market-related data.

[0060] During data cleaning and preprocessing, the standardization process also includes unified conversion of sales volume data in different units. For example, the sales volume of products with different packaging specifications is unified into the sales volume of a single product.

[0061] During web crawler scraping, the multi-threaded technology uses thread pool management to dynamically adjust the number of threads according to server performance and network conditions to achieve optimal scraping efficiency.

[0062] During data analysis and insights, in addition to Echarts and D3.js, data visualization tools can also use professional visualization software such as Tableau to provide richer visualization effects and interactive functions.

[0063] During data analysis and insights, the established data analysis model also includes a clustering analysis model, which is used to classify brands or products in the market so that merchants can better understand the market competition pattern.

[0064] The method of the present invention also includes an advertising optimization function. An advertising placement optimization system is established to connect with the Amazon advertising platform, and the advertising placement strategy is automatically adjusted according to the market volume analysis results and advertising optimization suggestions, such as adjusting keyword bids and advertising placement budgets.

[0065] The method of the present invention further includes a competitive product monitoring function, which builds a competitive product monitoring platform to collect real-time market volume data and advertising placement information of competitors, conducts comparative analysis and provides a competitive product analysis report, and sets up an early warning mechanism to timely remind merchants when significant changes occur in the market volume of competitive products.

[0066] The method of the present invention further includes a keyword management function, which develops a keyword management module to support merchants in adding, deleting, and blocking keywords. During the keyword addition process, a keyword recommendation function is provided to recommend potential high-quality keywords based on market volume data and product relevance, and to monitor and analyze the keyword effects in real time, providing keyword optimization suggestions.

[0067] This technical solution mainly includes data acquisition and integration, market volume calculation logic, and specific key aspects, as follows:

[0068] Data acquisition and integration:

[0069] Data source access: By cooperating with third-party data sources, relevant data of the Amazon platform is obtained, including but not limited to product information, search keywords, advertising placement data, sales data, etc. At the same time, it supports obtaining data such as ABA search reports from the interfaces provided by Amazon official to ensure the accuracy and timeliness of the data. The third-party data sources can include industry data providers, social media data platforms, etc. to obtain more comprehensive market-related data.

[0070] Data cleaning and preprocessing: The obtained raw data is cleaned to remove duplicate, incorrect, and invalid data. The data is standardized to unify the data format and unit for subsequent analysis and calculation. For example, unify the product price data from different sources to US dollar pricing, and unify the date format to the standard year-month-day format. The standardization process can also include unified conversion of sales volume data in different units. For example, unify the sales volume of products with different packaging specifications to the sales volume of a single product.

[0071] Data integration: The cleaned and preprocessed data is integrated to establish a data warehouse or data lake for unified management and analysis. Through data integration, data association between different data sources is achieved. For example, associate product information with corresponding search keywords and advertising placement data to provide comprehensive data support for the calculation and analysis of market volume.

[0072] Market volume calculation logic:

[0073] Total Brand SOV: In the search results of specified keywords, the number of search results with the ASIN of the specified brand is divided by the total number of search results, and then multiplied by 100%, that is, Total Brand SOV = (Number of search results of the specified brand / Total number of search results) × 100%. For example, in the search results of the keyword "sports shoes", a certain brand's products appeared 100 times, and the total number of search results was 1000 times, then the total SOV of this brand for this keyword is 10%.

[0074] Natural Brand SOV: In the search results where the position type of the specified keyword is the natural position, the number of search results with the ASIN of the specified brand is divided by the total number of search results in the natural position, and then multiplied by 100%, that is, Natural Brand SOV = (Number of search results of the specified brand in the natural position / Total number of search results in the natural position) × 100%. The natural position refers to the product display position in the search results that is not an advertising display position.

[0075] Advertising Brand SOV: In the search results where the position type of the specified keyword is the advertising position (i.e., SP + SB + SBV), the number of search results with the ASIN of the specified brand is divided by the total number of search results in the corresponding advertising position, and then multiplied by 100%, that is, Advertising Brand SOV = (Number of search results of the specified brand in the advertising position / Total number of search results in the advertising position) × 100%. Among them, SP (Sponsored Products) refers to product promotion ads, SB (Sponsored Brands) refers to brand promotion ads, and SBV (Sponsored Brands Video) refers to brand promotion video ads.

[0076] Total ASIN SOV: In the search results of specified keywords, the number of search results with the specified ASIN code is divided by the total number of search results, and then multiplied by 100%, that is, Total ASIN SOV = (Number of search results of the specified ASIN / Total number of search results) × 100%. By calculating the Total ASIN SOV, we can understand the market volume performance of a specific product.

[0077] ASIN Natural / Advertising / SP Advertising SOV: In the search results where the position type of the specified keyword is a certain position, the number of search results with the specified ASIN code is divided by the total number of search results in the corresponding position type, and then multiplied by 100%, that is, ASIN SOV of a certain position = (Number of search results of the specified ASIN in a certain position / Total number of search results in a certain position) × 100%. For example, ASIN Natural SOV = (Number of search results of the specified ASIN in the natural position / Total number of search results in the natural position) × 100%, and so on, the SOV of the ASIN in different advertising positions can be calculated.

[0078] Sub - key aspects:

[0079] This technical solution can be broken down into the following four key aspects:

[0080] 1. Crawler scraping:

[0081] Function description: Crawler scraping is the data collection stage of the entire technical solution. Develop advanced web crawlers to automatically scrape product information from the Amazon e-commerce platform, including but not limited to product search results, detail page content, user reviews, price changes, sales rankings, etc.

[0082] Technical key points:

[0083] Multi-threaded scraping: To improve efficiency, the crawler uses multi-threaded technology to scrape multiple pages simultaneously. The multi-threaded technology can adopt thread pool management to dynamically adjust the number of threads according to the server performance and network conditions to achieve the optimal scraping efficiency.

[0084] Dynamic content processing: It can handle content dynamically generated by JavaScript to ensure data integrity.

[0085] Anti-crawler strategy: Design strategies to circumvent Amazon's anti-crawler mechanism to ensure the continuity and stability of data scraping.

[0086] Data update frequency: Set a reasonable data update frequency to ensure the timeliness of information.

[0087] 2. Data cleaning:

[0088] Function description: Data cleaning is a step to ensure data quality. It involves removing invalid, incorrect, or incomplete data, as well as unifying data formats to lay a solid foundation for subsequent analysis and modeling.

[0089] Technical key points:

[0090] Duplicate removal: Identify and delete duplicate data entries.

[0091] Format standardization: Convert data from different sources into a unified format.

[0092] Outlier detection: Identify and handle outliers or extreme points.

[0093] Missing value handling: Determine the strategy for filling or deleting missing data.

[0094] 3. Data analysis and insights:

[0095] Function description: Data analysis and insights is to use the cleaned data to build models that can analyze market trends, consumer behavior, and product performance.

[0096] Technical key points: Use data visualization tools such as Echarts, D3.js, etc. to convert the calculated market volume data into intuitive charts and tables. During the visualization process, pay attention to the interactivity and user experience of the charts. For example, implement functions such as zooming in and out of the charts, data filtering, and displaying detailed information on mouse hovering. In addition to Echarts and D3.js, professional visualization software such as Tableau can also be used as data visualization tools to provide richer visualization effects and interactive functions.

[0097] 4. Establish data analysis models such as time series analysis models, regression analysis models, clustering analysis models, etc. to deeply analyze the market volume data. For example, predict the future change trend of brand SOV through the time series analysis model, find out the key factors affecting SOV through the regression analysis model, and classify brands or products in the market through the clustering analysis model so that merchants can better understand the market competition pattern.

[0098] According to the data analysis results, use natural language processing technology and machine learning algorithms to generate actionable suggestions and strategies for merchants. For example, train an advertising optimization model through machine learning algorithms, and recommend the best advertising placement plan for merchants based on market volume data and advertising placement effect data.

[0099] The implementation principle of the present invention is: The present invention discloses a method for analyzing market volume based on the Amazon e-commerce platform, aiming to solve the problems existing in the existing manual market analysis methods, such as low data processing efficiency, difficult to guarantee accuracy, limited analysis dimensions, lack of real-time and dynamic monitoring, and high labor costs. The technical solution constructs the core content through aspects such as data acquisition and integration, market volume calculation logic, etc., and is subdivided into key links such as crawler scraping, data cleaning, data analysis and insight. In the crawler scraping stage, an advanced web crawler is used to automatically collect product information, and multi-threaded technology is adopted to process dynamic content and avoid anti-crawler mechanisms; in the data cleaning stage, invalid data is removed, the format is unified, and outliers and missing values are processed; in the data analysis and insight stage, visualization tools and data analysis models are used to deeply analyze the market volume data and generate actionable suggestions. Using this patented technology, functions such as advertising optimization, competitor monitoring, and keyword management can be realized, providing powerful tools for Amazon sellers, improving operation efficiency, optimizing market strategies, and promoting sales growth and brand development.

[0100] The embodiments of this specific implementation manner are all preferred embodiments of the present invention, and do not limit the protection scope of the present invention accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for analyzing market volume based on the Amazon e-commerce platform, characterized in that It includes the following steps: Step 1, data source access: Cooperate with third-party data sources to obtain relevant data on the Amazon platform, including product information, search keywords, advertising placement data, and sales data. At the same time, obtain ABA search report data from the official Amazon interface; Step 2, data cleaning and preprocessing: Clean the original data, remove duplicate, incorrect, and invalid data, and perform standardization processing to unify the data format and unit; Step 3, data integration: Integrate the cleaned and preprocessed data, build a data warehouse or data lake, and realize data association between different data sources; Step 4, crawler scraping: Develop an advanced web crawler to automatically scrape product information from the Amazon e-commerce platform. Use multi-threaded technology to scrape multiple pages, process JavaScript dynamically generated content, design anti-crawler strategies, and set a reasonable data update frequency; Step 5, data cleaning: Remove invalid, incorrect, or incomplete data, and unify the data format, including deduplication, format standardization, outlier detection, and missing value handling; Step 6, data analysis and insights: Use data visualization tools to convert market volume data into intuitive charts and tables. Build a data analysis model to deeply analyze the market volume data. According to the analysis results, use natural language processing technology and machine learning algorithms to generate actionable suggestions and strategies for merchants.

2. The method for analyzing market volume based on the Amazon e-commerce platform according to claim 1, wherein The method further includes a market volume calculation logic: Total brand SOV: In the search results of a specified keyword, the number of search results with the ASIN of the specified brand is divided by the total number of search results, and then multiplied by 100%; Brand natural position SOV: In the search results where the position type of the specified keyword is the natural position, the number of search results with the ASIN of the specified brand is divided by the total number of search results in the natural position, and then multiplied by 100%; Brand advertising position SOV: In the search results where the position type of the specified keyword is the advertising position (SP + SB + SBV), the number of search results with the ASIN of the specified brand is divided by the total number of search results in the corresponding advertising position, and then multiplied by 100%; Total ASIN SOV: In the search results of a specified keyword, the number of search results with the specified ASIN code is divided by the total number of search results, and then multiplied by 100%; ASIN natural position / advertising position / SP advertising position SOV: In the search results where the position type of the specified keyword is a certain position, the number of search results with the specified ASIN code is divided by the total number of search results in the corresponding position type, and then multiplied by 100%.

3. A method for analyzing market volume based on the Amazon e-commerce platform according to claim 1, characterized in that, In the data source access, the third-party data sources include industry data providers and social media data platforms to obtain more comprehensive market-related data.

4. A method for analyzing market voice volume based on the Amazon e-commerce platform according to claim 1, characterized in that, In the data cleaning and preprocessing, the standardization processing further includes unified conversion of sales volume data in different units. For example, the sales volume of products with different packaging specifications is unified and converted into the sales volume of a single product.

5. A method for analyzing market volume based on the Amazon e-commerce platform according to claim 1, wherein, In the crawler scraping, the multi-threaded technology is managed by a thread pool, and the number of threads is dynamically adjusted according to the server performance and network conditions to achieve the optimal scraping efficiency.

6. The method for analyzing market volume based on the Amazon e-commerce platform according to claim 1, wherein In the data analysis and insights, in addition to Echarts and D3.js, the data visualization tool can also adopt the professional visualization software Tableau to provide richer visualization effects and interactive functions.

7. A method for analyzing market volume based on the Amazon e-commerce platform according to claim 1, characterized in that, In the data analysis and insights, the established data analysis model also includes a clustering analysis model, which is used to classify brands or products in the market so that merchants can better understand the market competition pattern.

8. A method for analyzing market volume based on the Amazon e-commerce platform according to claim 1, characterized in that The method also includes an advertising optimization function, which establishes an advertising placement optimization system to connect with the Amazon advertising platform, and automatically adjusts the advertising placement strategy according to the market volume analysis results and advertising optimization suggestions, such as adjusting keyword bids and advertising placement budgets.

9. A method for analyzing market volume based on the Amazon e-commerce platform according to claim 1, characterized in that The method also includes a competitor monitoring function, which builds a competitor monitoring platform to collect the market volume data and advertising placement information of competitors in real time, conducts comparative analysis and provides a competitor analysis report, and sets up an early warning mechanism to timely remind merchants when significant changes occur in the competitor's market volume.

10. A method for analyzing market volume based on the Amazon e-commerce platform according to claim 1, characterized in that, The method also includes a keyword management function, which develops a keyword management module to support merchants to add, delete, and block keywords. During the keyword addition process, a keyword recommendation function is provided to recommend potential high-quality keywords based on market volume data and product relevance, and to monitor and analyze the keyword effects in real time, providing keyword optimization suggestions.

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