Product placement method, system, electronic device, and medium

Abstract

The embodiment of the application relates to the technical field of digital marketing, and provides a product putting method and system, an electronic device and a medium, the method comprising: collecting putting feedback data after target product putting based on an initial putting scheme, wherein the putting feedback data at least comprises market feedback data and target user behavior data; performing data analysis on the market feedback data and the target user behavior data to generate corresponding data analysis charts; determining a market demand trend of the target product based on the data analysis charts; and formulating a putting scheme again based on the market demand trend, and putting the product based on the formulated putting scheme again. Therefore, the limit conditions of putting quantity, specific crowd and putting rules can be flexibly coped with to provide an efficient solution, and more accurate putting and higher conversion rate are realized.

Description

Technical Field

[0001] This invention relates to the field of digital marketing technology, and in particular to a product delivery method, system, electronic device, and medium. Background Technology

[0002] With the continuous development of digital marketing, product recommendation systems have become an important tool for improving market conversion rates. However, existing technologies still lack sufficient research on optimizing recommendation order and managing ad delivery volume. Recommendation systems typically focus on optimizing recommendation algorithms, but lack systematic solutions for efficiently configuring recommendation pages, comprehensively considering boundary conditions such as ad delivery volume limitations, specific audience needs, and ad delivery rules. This results in poor performance of recommendation systems in practical applications, failing to fully realize their potential. In actual product deployment processes, existing technical solutions have significant shortcomings in the following aspects: Delivery Limitations: Existing technologies fail to effectively handle product delivery limitations, resulting in recommendation order that does not accurately reflect actual delivery capacity. For example, when delivery resources are limited, the system fails to intelligently adjust the recommendation order to optimize resource utilization.

[0003] Specific audience limitations: Traditional recommendation systems lack a precise understanding of the needs of specific groups, often resulting in recommendations that do not match the user's actual needs. This not only reduces user satisfaction but also affects product conversion rates.

[0004] Limitations of delivery rules: Existing technologies fail to effectively apply complex delivery rules, and the recommendation order and delivery strategy are difficult to adjust flexibly according to actual situations. For example, different time periods, regions, or user behavior patterns may require different recommendation strategies, but traditional systems cannot adapt to these dynamic changes.

[0005] The aforementioned shortcomings result in unsatisfactory performance of recommendation systems, preventing the full realization of the product's market potential. Existing recommendation systems lack flexibility and versatility, making it difficult to meet complex and ever-changing practical needs. Summary of the Invention

[0006] This invention provides a product delivery method, system, electronic device, and medium to address the shortcomings of existing technologies, such as limitations on delivery volume, delivery rules, and specific target groups. It enables flexible responses to complex restrictions, allowing for more precise product delivery and improved conversion rates.

[0007] This invention provides a product delivery method, comprising: Collect campaign feedback data after the target product is launched based on the initial campaign plan. The campaign feedback data includes at least market feedback data and target user behavior data. The market feedback data and target user behavior data are analyzed to generate corresponding data analysis charts; Based on the data analysis charts, the market demand trend of the target product is determined; Based on the aforementioned market demand trends, a new marketing plan was developed, and products were launched based on the revised plan.

[0008] In one possible implementation, the method further includes: Obtain the initial delivery plan for the target product input by the user, wherein the initial delivery plan includes at least the target product delivery volume, target users, delivery region, and delivery time; Based on the initial deployment plan, a product deployment instruction is sent to the market terminal devices in the target deployment area. The market terminal devices then identify the target users based on the target product deployment quantity and deployment time to complete the deployment of the target product.

[0009] In one possible implementation, the method further includes: The traffic conversion rate is determined based on the market feedback data, and the user click-through rate is determined based on the target user behavior data; Traffic maps and traffic trend charts are drawn based on the market feedback data, target user behavior data, traffic conversion rate, and user click-through rate.

[0010] In one possible implementation, the method further includes: The traffic map and traffic trend chart are analyzed using multiple preset data analysis tools to obtain a market demand trend analysis report for the target product.

[0011] In one possible implementation, the method further includes: Compare the differences between the market feedback data and target user behavior data and the expected targets; Based on the market demand trend analysis report and the data discrepancies, a new launch plan for the target product was developed.

[0012] The present invention also provides a product delivery system, comprising the following modules: The data integration module is used to collect campaign feedback data after the target product is launched based on the initial launch plan. The campaign feedback data includes at least market feedback data and target user behavior data. The data analysis module is used to perform data analysis on the market feedback data and target user behavior data; The traffic map module is used to generate corresponding data analysis charts and visualize them based on the data analysis results provided by the data analysis module. The data analysis module is also used to determine the market demand trend of the target product based on the data analysis charts; The plan formulation module is used to revise the launch plan based on the market demand trend and launch the product based on the revised launch plan.

[0013] Optionally, the system further includes: The configuration management module is used to provide a user interface and obtain the initial deployment plan of the target product input by the user. Based on the initial deployment plan, it sends product deployment instructions to market terminal devices in the target deployment area. The market terminal devices identify target users according to the deployment volume and deployment time of the target product to complete the deployment of the target product. The product launch details module is used to monitor the product launch status and provide the data integration module with market feedback data and progress tracking data after the product launch. The analysis report module is used to analyze the data analysis charts based on multiple preset data analysis tools to obtain a market demand trend analysis report for the target product.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the product delivery method as described above.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the product delivery method as described above.

[0016] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the product delivery method as described above.

[0017] The product delivery method, system, electronic device, and medium provided by this invention collect delivery feedback data after a target product is delivered based on an initial delivery plan. This feedback data includes at least market feedback data and target user behavior data. The method then analyzes the market feedback data and target user behavior data to generate corresponding data analysis charts. Based on these charts, the method determines the market demand trend for the target product. Finally, it re-formulates the delivery plan based on this trend and delivers the product using the revised plan. Compared to traditional recommendation methods that fail to effectively address limitations in delivery volume, lack precise understanding of specific demographic needs, and struggle to flexibly adjust recommendations based on actual circumstances, this method comprehensively considers the limitations of delivery volume, specific demographics, and delivery rules, enabling flexible responses to complex needs and providing an efficient solution for more precise delivery and higher conversion rates. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is one of the flowcharts illustrating the product delivery method provided by the present invention.

[0020] Figure 2 This is the second flowchart illustrating the product delivery method provided by the present invention.

[0021] Figure 3 This is a schematic diagram of the product delivery system provided by the present invention.

[0022] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0025] Figure 1 This is one of the flowcharts illustrating the product delivery method provided by the present invention, such as... Figure 1 As shown, the method specifically includes: S11. Collect feedback data on the deployment of target products based on the initial deployment plan.

[0026] In this embodiment of the invention, combined with Figure 3The diagram illustrating the product delivery system architecture explains that users can first access the configuration management module by logging into the system. This module provides a user interface and retrieves the initial delivery plan for the target product from the user's input. Based on this plan, it sends delivery instructions to market terminal devices in the target delivery region. These terminal devices then identify target users based on the delivery volume and timing of the target product to complete the delivery. The target product can be digital marketing advertisements, etc. The initial delivery plan includes at least the target product delivery volume, target users, delivery region, and delivery time. The configuration management module provides an intuitive visual interface, allowing users to view real-time previews and ensure the configuration meets business requirements. It also provides intelligent prompts during the configuration process to promptly identify and correct potential errors. Users can dynamically adjust delivery strategies based on real-time data, such as adjusting the delivery volume or modifying the target user group.

[0027] Furthermore, after the target product is launched to the market, the launch details module monitors the product launch status, collecting market feedback data and progress tracking data from multiple data sources. A combination of real-time stream processing and batch processing is used to ensure the timeliness and accuracy of the data. After data collection, data processing is performed, including data cleaning, data fusion, and data storage, providing the data integration module with post-launch market feedback data and progress tracking data. The launch feedback data includes at least market feedback data and target user behavior data. Market feedback data may include, but is not limited to, product pageviews, conversion rates, product sales, and target audience information. Target user behavior data includes, but is not limited to, target user clicks, pageviews, conversions, purchases, and city tiers.

[0028] S12. Perform data analysis on the market feedback data and target user behavior data to generate corresponding data analysis charts.

[0029] Data analysis charts can include a variety of visualizations, such as traffic distribution maps: these maps show the geographical distribution of user traffic, and can use color shades, icon sizes, and other methods to represent the traffic volume in different regions.

[0030] Traffic trend chart: It can be a line chart, dotted line chart, bar chart or combination chart, etc. The embodiments of this invention are not specifically limited, and it is used to show the trend of traffic changes over time.

[0031] Heatmap: Displays a heatmap of user clicks, browsing, and other behaviors on a webpage or application interface to help identify the areas that users are most interested in.

[0032] Traffic funnel chart: Shows the traffic loss at each step from when a user enters the website to when they complete their target behavior (such as purchase or registration).

[0033] S13. Determine the market demand trend of the target product based on the data analysis charts.

[0034] The data analysis charts can be analyzed using the analysis report module based on multiple preset data analysis tools to obtain a market demand trend analysis report for the target product.

[0035] Cluster analysis can be used to divide data into different groups or clusters in order to discover common characteristics and behavioral patterns of user groups.

[0036] Classification and Prediction: Build classification models to classify users or predict their future behavior, such as predicting which users are likely to become paying users.

[0037] Association analysis: Discovering association rules between different data, such as which pages users visit before they are more likely to complete a purchase.

[0038] Use sophisticated analytical tools to analyze data and charts, such as statistical analysis: providing rich statistical analysis functions, such as the calculation of statistical quantities such as mean, variance, and standard deviation, as well as statistical methods such as hypothesis testing and analysis of variance.

[0039] Machine learning algorithms: Introduce machine learning algorithms, such as decision trees, random forests, and neural networks, to mine potential information and patterns in data.

[0040] Text Analysis: For scenarios involving text data, text analysis functions are provided, such as sentiment analysis and topic extraction.

[0041] S14. Based on the aforementioned market demand trends, a new launch plan is developed, and products are launched based on the new launch plan.

[0042] The campaign strategy can be redesigned based on the results of data mining and sophisticated analytics tools, such as adjusting the target regions, timing, and target users for ad campaigns. A / B testing capabilities are also available, allowing users to run multiple campaigns simultaneously and select the optimal one based on data analysis results.

[0043] Optionally, product features and user experience can be continuously optimized based on market feedback data and target user behavior data to improve user satisfaction and retention rates.

[0044] The product deployment method provided by this invention involves collecting deployment feedback data after a target product is deployed based on an initial deployment plan. This feedback data includes at least market feedback data and target user behavior data. The method then analyzes the market feedback data and target user behavior data to generate corresponding data analysis charts. Based on these charts, the method determines the market demand trend for the target product. Finally, it re-formulates the deployment plan based on this trend and deploys the product according to the revised plan. Compared to traditional recommendation methods that fail to effectively handle limitations on product deployment volume, lack precise understanding of specific demographic needs, and struggle to flexibly adjust recommendations based on actual circumstances, this method comprehensively considers the limitations of deployment volume, specific demographics, and deployment rules. It can flexibly address complex needs, providing an efficient solution for more precise deployment and higher conversion rates.

[0045] Figure 2 This is the second flowchart illustrating the product delivery method provided by the present invention, as shown below. Figure 2 As shown, the method specifically includes: S21. Obtain the initial deployment plan of the target product input by the user.

[0046] In this embodiment of the invention, combined with Figure 3 The diagram illustrating the product delivery system architecture is shown below. First, users can log in to the product delivery system to access the configuration management module. The configuration management module provides a user interface and obtains the initial delivery plan for the target product input by the user. The target product can be digital marketing advertisements, etc. The initial delivery plan includes at least the target product delivery volume, target users, delivery region, and delivery time.

[0047] S22. Based on the initial deployment plan, a product deployment instruction is sent to the market terminal devices in the target deployment area. The market terminal devices identify target users according to the target product deployment quantity and deployment time to complete the deployment of the target product.

[0048] Based on the initial deployment plan, product deployment instructions are sent to market terminal devices in the target deployment area. These market terminal devices then identify target users and complete the deployment of the target product according to the deployment volume and time. The configuration management module provides an intuitive visual interface, allowing users to view real-time previews and ensure that the configuration meets business needs. It also provides intelligent prompts during the user configuration process to promptly identify and correct potential configuration errors. Users can dynamically adjust deployment strategies based on real-time data, such as adjusting the deployment volume and modifying the target user group.

[0049] S23. Collect feedback data on the deployment of target products based on the initial deployment plan.

[0050] After the target product is launched, the launch details module monitors its performance, collecting market feedback and progress tracking data from multiple data sources. A combination of real-time streaming and batch processing is employed to ensure data timeliness and accuracy. Data processing follows, including data cleaning, fusion, and storage, providing the data integration module with post-launch market feedback and progress tracking data. The launch feedback data includes at least market feedback and target user behavior data. Market feedback data may include, but is not limited to, product views, conversion rates, sales volume, and target audience information. Target user behavior data includes, but is not limited to, target user clicks, views, conversions, purchases, and city tiers.

[0051] S24. Determine the traffic conversion rate based on the market feedback data, and determine the user click-through rate based on the target user behavior data.

[0052] S25. Draw a traffic map and a traffic trend chart based on the market feedback data, target user behavior data, traffic conversion rate and user click rate.

[0053] Traffic conversion rate refers to the proportion of website visitors who take the desired action (such as purchasing, registering, downloading, etc.); click-through rate reflects a user's interest in clicking on a specific element (such as a link, button, or advertisement) within a website or application.

[0054] For example, traffic maps show how users move from one page or feature to another, as well as key nodes and conversions throughout the process. The mapping steps include: identifying traffic nodes: recognizing key user behavior points within the website or application, such as the homepage, product detail page, shopping cart, checkout page, etc.; connecting traffic paths: mapping the user flow paths between these nodes based on user behavior data; and labeling conversion rates: labeling the corresponding traffic conversion rate at each node or path to visually demonstrate the conversion situation.

[0055] Traffic trend charts display traffic data that changes over time, helping to analyze the reasons and trends behind these changes. The steps involved in creating a chart include: selecting a time range: determining the period to be analyzed, such as daily, weekly, or monthly; collecting traffic data: gathering key data such as total visits, clicks, and conversion rates based on the time range; and creating charts: using charting tools (such as Excel, Tableau, Power BI, etc.) to create traffic trend charts, including trends in total visits, clicks, and conversion rates.

[0056] S26. Analyze the traffic map and traffic trend map based on multiple preset data analysis tools to obtain a market demand trend analysis report for the target product.

[0057] Trend Analysis: By combining market feedback data and user behavior data, analyze the reasons behind traffic trends, such as holiday promotions, new product launches, and marketing activities.

[0058] The data analysis charts can be analyzed using the analysis report module based on multiple preset data analysis tools to obtain a market demand trend analysis report for the target product.

[0059] Cluster analysis can be used to divide data into different groups or clusters in order to discover common characteristics and behavioral patterns of user groups.

[0060] Classification and Prediction: Build classification models to classify users or predict their future behavior, such as predicting which users are likely to become paying users.

[0061] Association analysis: Discovering association rules between different data, such as which pages users visit before they are more likely to make a purchase.

[0062] Use sophisticated analytical tools to analyze data and charts, such as statistical analysis: providing rich statistical analysis functions, such as the calculation of statistical quantities such as mean, variance, and standard deviation, as well as statistical methods such as hypothesis testing and analysis of variance.

[0063] Machine learning algorithms: Introduce machine learning algorithms, such as decision trees, random forests, and neural networks, to mine potential information and patterns in data.

[0064] Text Analysis: For data containing text, text analysis functions are provided, such as sentiment analysis and topic extraction. This generates a market demand trend analysis report for the target product.

[0065] S27. Compare the differences between the market feedback data and target user behavior data and the expected target.

[0066] S28. Based on the market demand trend analysis report and the data discrepancies, revise the launch plan for the target product.

[0067] In the product launch and operation process, comparing market feedback data, target user behavior data, and expected goals is a crucial step. This step aims to reveal the discrepancies between actual market performance and expectations, providing data support for subsequent strategy adjustments. The following are exemplary methods for comparing this data: Define clear expected goals: Expected goals typically include key performance indicators (KPIs) such as product sales, user growth, market share, and brand awareness. These goals should be reasonably set before product launch based on factors such as market research, historical data, competitor analysis, and internal strategic planning.

[0068] Collect market feedback data, which can also cover user satisfaction with the product, feature evaluation, price acceptance, and brand impression. Analyze target user behavior data, obtained through website analytics tools, app data, and social media monitoring. Analyze user access paths, dwell time, click-through rates, conversion rates, and purchasing behavior to understand users' actual product usage and preferences.

[0069] Furthermore, compare the data discrepancies: Compare market feedback data and target user behavior data with the expected goals point by point. Identify data discrepancies, including which aspects exceeded expectations (e.g., high user satisfaction, strong purchase intention) and which aspects failed to meet expectations (e.g., low feature evaluation, insufficient conversion rate). Analyze the reasons for the data discrepancies, which may include market changes, competitor strategies, changes in user needs, product defects, etc.

[0070] Furthermore, the launch plan for the target products was revised based on the market demand trend analysis report and data discrepancies.

[0071] After identifying the data discrepancies and their causes, the next step is to revise the target product launch plan based on the market demand trend analysis report and the data discrepancies. The following are the steps for developing the new plan: Understanding Market Demand Trends: A market demand trend analysis report should cover market growth trends, changes in consumer preferences, competitor activities, and technological development trends. It should also analyze the impact of market demand trends on product launch strategies by considering data discrepancies.

[0072] Adjust product strategy: Based on market demand trends and data discrepancies, adjust product features, design, pricing, and other strategies. Prioritize resolving user feedback issues to enhance product competitiveness.

[0073] Optimize marketing strategies: Adjust marketing channels, promotion methods, and promotional activities based on target user behavior data and market demand trends. Strengthen interaction with target users to enhance brand awareness and user loyalty.

[0074] Develop an implementation plan: clearly define the implementation steps, timeline, and division of responsibilities for the new solution. Ensure that team members have a clear understanding and consensus on the new solution.

[0075] Monitoring and Evaluation: Continuously monitor market feedback and user behavior data during the implementation of the new solution. Regularly evaluate the effectiveness of the new solution and adjust strategies in a timely manner to respond to market changes.

[0076] The above-mentioned new product launch plan can be an example provided by the system. During the application process, users can also make adjustments according to the actual situation.

[0077] The product delivery method provided by this invention involves collecting delivery feedback data after a target product is delivered based on an initial delivery plan. This feedback data includes at least market feedback data and target user behavior data. The method then analyzes the market feedback data and target user behavior data to generate corresponding data analysis charts. Based on these charts, the method determines the market demand trend for the target product. Finally, it re-formulates the delivery plan based on this trend and delivers the product using the re-formulated plan. This method improves delivery effectiveness in terms of accuracy through refined constraints and optimized configuration methods. The system effectively manages product delivery volume, specific target audiences, and delivery rules to achieve optimal delivery results. Regarding optimization, the system comprehensively considers various constraints, significantly optimizing the strategy and improving market conversion rates and user satisfaction. The system can dynamically adjust the strategy according to actual conditions to ensure maximum delivery effectiveness. In terms of user experience, it provides an intuitive visual configuration interface, allowing users to easily set and adjust push strategies. The user interface is user-friendly, supporting quick learning and efficient operation.

[0078] The product delivery system provided by the present invention is described below. The product delivery system described below can be referred to in correspondence with the product delivery method described above.

[0079] Figure 3 This is a schematic diagram of the architecture of the product delivery system provided by the present invention, specifically including: Data integration module 301 is used to collect deployment feedback data after the target product is deployed based on the initial deployment plan. The deployment feedback data includes at least market feedback data and target user behavior data. Data analysis module 302 is used to perform data analysis on the market feedback data and target user behavior data; The traffic map module 303 is used to generate corresponding data analysis charts and visualize them based on the data analysis results provided by the data analysis module. The data analysis module 302 is also used to determine the market demand trend of the target product based on the data analysis charts; The scheme formulation module 304 is used to reformulate the launch plan based on the market demand trend and launch the product based on the reformulated launch plan.

[0080] Optionally, the product delivery system provided by the present invention further includes: The configuration management module 305 is used to provide a user interface and obtain the initial deployment plan of the target product input by the user, send product deployment instructions to market terminal devices in the target deployment area based on the initial deployment plan, and complete the deployment of the target product by identifying target users through the market terminal devices according to the target product deployment volume and deployment time. The product launch details module 306 is used to monitor the product launch status and provide the data integration module with market feedback data and progress tracking data after the product launch. The analysis report module 307 is used to analyze the data analysis charts based on multiple preset data analysis tools to obtain a market demand trend analysis report for the target product.

[0081] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include a processor 810, a communications interface 820, a memory 830, and a communication bus 840, wherein the processor 810, communications interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a product launch method. This method includes: collecting launch feedback data after launching a target product based on an initial launch plan, the launch feedback data including at least market feedback data and target user behavior data; performing data analysis on the market feedback data and target user behavior data to generate corresponding data analysis charts; determining the market demand trend of the target product based on the data analysis charts; re-formulating the launch plan based on the market demand trend; and launching the product based on the re-formulated launch plan.

[0082] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0083] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the product deployment method provided by the above methods. The method includes: collecting deployment feedback data after deploying a target product based on an initial deployment plan, wherein the deployment feedback data includes at least market feedback data and target user behavior data; performing data analysis on the market feedback data and target user behavior data to generate corresponding data analysis charts; determining the market demand trend of the target product based on the data analysis charts; re-formulating the deployment plan based on the market demand trend; and deploying the product based on the re-formulated deployment plan.

[0084] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the product launch method provided by the above methods. The method includes: collecting launch feedback data after launching a target product based on an initial launch plan, the launch feedback data including at least market feedback data and target user behavior data; performing data analysis on the market feedback data and target user behavior data to generate corresponding data analysis charts; determining the market demand trend of the target product based on the data analysis charts; re-formulating the launch plan based on the market demand trend; and launching the product based on the re-formulated launch plan.

[0085] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A product delivery method, characterized in that, include: Collect campaign feedback data after the target product is launched based on the initial campaign plan. The campaign feedback data includes at least market feedback data and target user behavior data. The market feedback data and target user behavior data are analyzed to determine the traffic conversion rate based on the market feedback data and the user click-through rate based on the target user behavior data. Based on the market feedback data, target user behavior data, traffic conversion rate, and user click-through rate, a traffic map and a traffic trend chart are drawn. The traffic map is used to display the flow path of users between different pages or functional points and the traffic conversion rate of corresponding nodes and paths. The traffic trend chart is used to display traffic data that changes over time. The traffic map drawing process includes: determining traffic nodes and identifying key user behavior points within a website or application; connecting traffic paths and drawing user flow paths between these nodes based on the target user behavior data; and marking the corresponding traffic conversion rate on each node and path; the traffic trend chart includes total visit trends, click trends, and conversion rate trends. The traffic map and traffic trend chart are analyzed using multiple preset data analysis tools. Market feedback data and user behavior data are combined to analyze the reasons behind the traffic trend and determine the market demand trend of the target product. The preset multiple data analysis tools include correlation analysis and / or machine learning algorithms, which are used to jointly analyze the traffic map and traffic trend map, discover the correlation rules of traffic conversion between different pages, and identify the reasons affecting traffic changes by combining market feedback data and user behavior data. Based on the aforementioned market demand trends, a new marketing plan was developed, and products were launched based on the revised plan.

2. The method according to claim 1, characterized in that, Before collecting the feedback data on the target product after its deployment based on the initial deployment plan, the process includes: Obtain the initial delivery plan for the target product input by the user, wherein the initial delivery plan includes at least the target product delivery volume, target users, delivery region, and delivery time; Based on the initial deployment plan, a product deployment instruction is sent to the market terminal devices in the target deployment area. The market terminal devices then identify the target users based on the target product deployment quantity and deployment time to complete the deployment of the target product.

3. The method according to claim 1, characterized in that, The process of revising the marketing plan based on the market demand trend and launching the product based on the revised plan includes: Compare the differences between the market feedback data and target user behavior data and the expected targets; Based on the market demand trend analysis report and the discrepancies in the data, a new launch plan for the target product was developed.

4. A product delivery system, characterized in that, include: The data integration module is used to collect campaign feedback data after the target product is launched based on the initial launch plan. The campaign feedback data includes at least market feedback data and target user behavior data. The data analysis module is used to perform data analysis on the market feedback data and target user behavior data, determine the traffic conversion rate based on the market feedback data, and determine the user click-through rate based on the target user behavior data; Based on the market feedback data, target user behavior data, traffic conversion rate, and user click-through rate, a traffic map and a traffic trend chart are generated. The traffic map displays the user flow path between different pages or functionalities and the corresponding traffic conversion rate at each node and path. The traffic trend chart displays traffic data changing over time. Generating the traffic map includes: identifying traffic nodes and key user behavior points within the website or application; connecting traffic paths and drawing the user flow path between these nodes based on the target user behavior data; and marking the corresponding traffic conversion rate at each node and path. The traffic trend chart includes trends in total visits, clicks, and conversion rates. The traffic map module is used to generate corresponding data analysis charts and visualize them based on the data analysis results provided by the data analysis module. The data analysis module is also used to analyze the traffic map and traffic trend map based on multiple preset data analysis tools, and to analyze the reasons behind the traffic trend by combining market feedback data and user behavior data, so as to determine the market demand trend of the target product; wherein, the multiple preset data analysis tools include correlation analysis and / or machine learning algorithms, which are used to jointly analyze the traffic map and traffic trend map, discover the correlation rules of traffic conversion between different pages, and identify the reasons affecting traffic changes by combining market feedback data and user behavior data; The plan formulation module is used to revise the launch plan based on the market demand trend and launch the product based on the revised launch plan.

5. The system according to claim 4, characterized in that, The system also includes: The configuration management module is used to provide a user interface and obtain the initial deployment plan of the target product input by the user. Based on the initial deployment plan, it sends product deployment instructions to market terminal devices in the target deployment area. The market terminal devices identify target users according to the deployment volume and deployment time of the target product to complete the deployment of the target product. The product launch details module is used to monitor the product launch status and provide the data integration module with market feedback data and progress tracking data after the product launch. The analysis report module is used to analyze the data analysis charts based on multiple preset data analysis tools to obtain a market demand trend analysis report for the target product.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the product delivery method as described in any one of claims 1 to 3.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the product delivery method as described in any one of claims 1 to 3.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the product delivery method as described in any one of claims 1 to 3.

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