Product putting method and system, electronic equipment and medium

Abstract

The embodiment of the invention relates to the technical field of digital marketing, and provides a product putting method and system, an electronic device and a medium, the method comprises the steps of collecting putting feedback data after target product putting based on an initial putting scheme, the putting feedback data at least comprising market feedback data and target user behavior data; performing data analysis on the market feedback data and the target user behavior data to generate a corresponding data analysis chart; determining a market demand trend of the target product based on the data analysis chart; and reformulating a delivery scheme based on the market demand trend, and performing product delivery based on the reformulated delivery scheme. Therefore, by comprehensively considering the limiting conditions of the delivery amount, the specific crowd and the delivery rule, complex requirements can be flexibly met, an efficient solution is provided, and more accurate delivery and a higher conversion rate are achieved.

Description

Technical Field

[0001] The present invention relates to the field of digital marketing technology, and in particular to a product delivery method, system, electronic equipment and medium. Background Art

[0002] With the continuous development of digital marketing, product recommendation systems have become an important tool for improving market conversion rates. However, existing technologies are still insufficient in optimizing recommendation order and managing delivery volume. Recommendation systems usually focus on optimizing recommendation algorithms, but lack systematic solutions for how to efficiently configure recommendation pages and comprehensively consider boundary conditions such as delivery volume restrictions, specific population needs, and delivery rules. This leads to poor results of recommendation systems in practical applications and their inability to fully realize their potential. In the actual product delivery process, existing technical solutions have obvious defects in the following aspects: Delivery volume limit: Existing technologies fail to effectively handle product delivery volume limits, resulting in the recommendation order not being able to accurately reflect actual delivery capacity. For example, when delivery resources are limited, the system fails to intelligently adjust the recommendation order to optimize resource usage.

[0003] Specific group restrictions: Traditional recommendation systems lack accurate understanding of the needs of specific groups, which often leads to mismatches between recommendation results and users’ 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 flexibly adjust according to actual conditions. 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 above defects make the recommendation system less effective and the market potential of the product not fully tapped. The existing recommendation system lacks flexibility and versatility and is difficult to meet the complex and changing actual needs. Summary of the invention

[0006] The present invention provides a product delivery method, system, electronic device and medium, which are used to solve the defects of delivery quantity limitation, delivery rule limitation and specific population limitation in the prior art, achieve flexible response to complex restriction requirements, and can provide more accurate product delivery and improve conversion rate.

[0007] The present invention provides a product delivery method, comprising: Collecting delivery feedback data after the target product is delivered based on the initial delivery plan, wherein the delivery feedback data at least includes market feedback data and target user behavior data; Perform data analysis on the market feedback data and target user behavior data to generate corresponding data analysis charts; Determine the market demand trend of the target product based on the data analysis charts; Redefine the placement plan based on the market demand trend, and conduct product placement based on the redefined placement plan.

[0008] In a possible implementation, the method further includes: Obtain the initial placement plan of the target product input by the user terminal, where the initial placement plan at least includes the target product placement quantity, target users, placement region, and placement time; Send a product placement instruction to the market terminal devices in the target placement region based on the initial placement plan, and identify the target users through the market terminal devices according to the target product placement quantity and placement time to complete the placement of the target product.

[0009] In a possible implementation, the method further includes: 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; Draw a traffic map and a traffic trend map based on the market feedback data, target user behavior data, traffic conversion rate, and user click-through rate.

[0010] In a possible implementation, the method further includes: Analyze the traffic map and traffic trend map based on a plurality of preset data analysis tools to obtain a market demand trend analysis report of the target product.

[0011] In a possible implementation, the method further includes: Compare the data differences between the market feedback data and target user behavior data and the expected goals; Redefine the placement plan of the target product based on the market demand trend analysis report and the data differences.

[0012] The present invention also provides a product placement system, including the following modules: A data integration module for collecting placement feedback data after the target product is placed based on the initial placement plan, where the placement feedback data at least includes market feedback data and target user behavior data; A data analysis module for performing data analysis on the market feedback data and target user behavior data; A traffic map module for generating corresponding data analysis charts based on the data analysis results provided by the data analysis module and performing visual display; The data analysis module is also used to determine the market demand trend of the target product based on the data analysis chart; The plan formulation module is used to re-formulate the placement plan based on the market demand trend, and conduct product placement based on the re-formulated placement plan.

[0013] Optionally, the system further includes: The configuration management module is used to provide a user terminal interface and obtain the initial placement plan of the target product input by the user terminal. Based on the initial placement plan, it sends a product placement instruction to the market terminal device in the target placement area, and through the market terminal device, it identifies the target users according to the target product placement quantity and placement time to complete the placement of the target product; The placement details module is used to monitor the product placement situation and provide market feedback data and progress tracking data after product placement for the data integration module; The analysis report module is used to analyze the data analysis chart based on a plurality of preset data analysis tools to obtain an analysis report on the market demand trend of the target product.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the product placement method as described in any one of the above.

[0015] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the product placement method as described in any one of the above.

[0016] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the product placement method as described in any one of the above.

[0017] The product placement method, system, electronic device and medium provided by the present invention collect the placement feedback data after the target product is placed based on the initial placement plan. The placement feedback data at least includes market feedback data and target user behavior data; perform data analysis on the market feedback data and target user behavior data to generate a corresponding data analysis chart; determine the market demand trend of the target product based on the data analysis chart; re-formulate the placement plan based on the market demand trend, and conduct product placement based on the re-formulated placement plan. Compared with the traditional recommendation method, which fails to effectively handle the placement quantity limit of products, lacks accurate grasp of the needs of specific groups, and is difficult to flexibly adjust the recommendation plan according to the actual situation, the present solution can flexibly respond to complex needs by comprehensively considering the placement quantity, specific groups and placement rule constraints, provide an efficient solution, and achieve more accurate placement and higher conversion rates. Brief Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 is one of the schematic flowcharts of the product placement method provided by the present invention.

[0020] Figure 2 is the second of the schematic flowcharts of the product placement method provided by the present invention.

[0021] Figure 3 is the schematic architecture diagram of the product placement system provided by the present invention.

[0022] Figure 4 is the schematic structural diagram of the electronic device provided by the present invention. Detailed Embodiments

[0023] To make the objectives, technical solutions and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0024] For the convenience of understanding the embodiments of the present invention, the following will further explain with specific embodiments in conjunction with the drawings. The embodiments do not constitute a limitation to the embodiments of the present invention.

[0025] Figure 1 is one of the schematic flowcharts of the product placement method provided by the present invention. As Figure 1 shown, the method specifically includes: S11. Collect the placement feedback data after the target product is placed based on the initial placement plan.

[0026] In the embodiments of the present invention, in combination with Figure 3A schematic diagram of the product delivery system architecture shown will be described. First, users can enter the configuration management module by logging in to this product delivery system. The configuration management module provides a user interface and obtains the initial delivery plan of the target product input by the user. Based on the initial delivery plan, it sends a product delivery instruction to the market terminal devices in the target delivery region, and the target product is delivered through the market terminal devices identifying the target users according to the target product delivery volume and delivery time. Among them, the target product can be a digital marketing advertisement, etc. The initial delivery plan at least includes the target product delivery volume, target users, delivery region, delivery time, etc. The configuration management module provides an intuitive visual interface, and users can view real-time previews to ensure that the configuration meets business requirements. It can also provide an intelligent prompt function during the user configuration process to timely discover and correct possible incorrect configurations. Users are allowed to dynamically adjust the delivery strategy according to real-time data, such as adjusting the delivery volume, modifying the target user group, etc.

[0027] Furthermore, after the target product is launched into the market, the delivery details module monitors the product delivery situation, collects market feedback data and progress tracking data from multiple data sources, and adopts a method combining real-time stream processing and batch processing to ensure the timeliness and accuracy of the data. After collecting the data, data processing can be carried out, including steps such as data cleaning, data fusion, and data storage, to provide the market feedback data and progress tracking data after product delivery for the data integration module. Among them, the delivery feedback data at least includes market feedback data and target user behavior data. The market feedback data can include, but is not limited to, product view volume, view conversion rate, product sales volume, and product sales population information, etc. The target user behavior data includes, but is not limited to, the click volume, view volume, conversion volume, purchase volume, and city level of the target users.

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

[0029] The data analysis charts can include diverse visual charts. For example, a traffic distribution map: It shows the geographical distribution of user traffic through a map, and different ways such as the depth of color and the size of icons can be used to represent the traffic size in different regions.

[0030] A traffic trend chart: It can be a line chart, a dot-line chart, a bar chart, or a combined chart, etc. The embodiments of the present invention do not make specific limitations, and it is used to show the trend of traffic changing over time, etc.

[0031] A heat map: It shows the heat map of user clicks, views, etc. on a web page or application interface to help identify the areas that users are most concerned about.

[0032] A traffic funnel chart: It shows the traffic loss situation at each step during the process of users from entering the website to completing the target behavior (such as purchasing, registering).

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

[0034] The analysis report module can analyze the data analysis chart based on multiple preset data analysis tools to obtain an analysis report on the market demand trend of the target product.

[0035] Cluster analysis can be adopted: dividing the data into different groups or clusters to discover the common characteristics and behavior patterns of user groups.

[0036] Classification and prediction: constructing a classification model to classify users or predict their future behaviors, such as predicting which users are likely to convert into paying users.

[0037] Association analysis: discovering the association rules between different data, such as which page advertisements users are more likely to complete purchase behaviors after visiting.

[0038] Use complex analysis tools to analyze the data analysis chart. For example, statistical analysis: providing rich statistical analysis functions, such as calculating 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: introducing machine learning algorithms, such as decision trees, random forests, neural networks, etc., to mine potential information and patterns in the data.

[0040] Text analysis: for cases containing text data, providing text analysis functions, such as sentiment analysis and topic extraction.

[0041] S14. Re-formulate the placement plan based on the market demand trend, and conduct product placement based on the re-formulated placement plan.

[0042] Re-formulate the placement plan based on the results of data mining and complex analysis tools, such as adjusting the target area, placement time, target users, etc. of the advertisement placement. An A / B testing function can also be provided, allowing users to run multiple placement plans simultaneously and select the optimal plan based on the data analysis results.

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

[0044] The product placement method provided by the present invention collects placement feedback data after the target product is placed based on the initial placement plan. The placement feedback data at least includes market feedback data and target user behavior data; analyzes the market feedback data and target user behavior data to generate corresponding data analysis charts; determines the market demand trend of the target product based on the data analysis charts; re-formulates the placement plan based on the market demand trend, and conducts product placement based on the re-formulated placement plan. Compared with the traditional recommendation method that fails to effectively handle the placement quantity limit of products, lacks accurate grasp of the needs of specific populations, and is difficult to flexibly adjust the recommendation plan according to the actual situation, this solution can flexibly respond to complex needs by comprehensively considering the limiting conditions of placement quantity, specific populations, and placement rules, provide an efficient solution, and achieve more accurate placement and higher conversion rates.

[0045] Figure 2 It is the second flowchart of the product placement method provided by the present invention. As Figure 2 shown, the method specifically includes: S21. Obtain the initial placement plan of the target product input by the user terminal.

[0046] In the embodiment of the present invention, it is described in combination with Figure 3 the schematic diagram of the product placement system architecture shown. First, the user can enter the configuration management module by logging in to the product placement system. The configuration management module provides a user terminal interface and obtains the initial placement plan of the target product input by the user terminal. Among them, the target product can be a digital marketing advertisement, etc. The initial placement plan at least includes the target product placement quantity, target users, placement region, placement time, etc.

[0047] S22. Send a product placement instruction to the market terminal device in the target placement region based on the initial placement plan, and identify the target users through the market terminal device according to the target product placement quantity and placement time to complete the placement of the target product.

[0048] Send a product placement instruction to the market terminal device in the target placement region based on the initial placement plan, and identify the target users through the market terminal device according to the target product placement quantity and placement time to complete the placement of the target product. The configuration management module provides an intuitive visual interface, and the user can view the real-time preview to ensure that the configuration meets the business requirements. It can also provide an intelligent prompt function during the user configuration process to timely discover and correct possible incorrect configurations. Allow the user to dynamically adjust the placement strategy according to real-time data, such as adjusting the placement quantity, modifying the target user population, etc.

[0049] S23. Collect the placement feedback data after the target product is placed based on the initial placement plan.

[0050] After the target product is launched into the market, monitor the product launch situation through the launch details module, collect market feedback data and progress tracking data from multiple data sources, and adopt a method that combines real-time stream processing and batch processing to ensure the timeliness and accuracy of the data. After collecting the data, data processing can be carried out, including steps such as data cleaning, data fusion, and data storage, to provide the market feedback data and progress tracking data after the product launch for the data integration module. Among them, the launch feedback data includes at least market feedback data and target user behavior data. The market feedback data can include, but is not limited to, product view volume, view conversion rate, product sales volume, and product sales population information, etc. The target user behavior data includes, but is not limited to, the click volume, view volume, conversion volume, purchase volume, and city level of the target users, etc.

[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 graph based on the market feedback data, target user behavior data, traffic conversion rate, and user click-through rate.

[0053] The traffic conversion rate refers to the proportion of website visitors who take the desired actions (such as purchase, registration, download, etc.); the user click-through rate reflects the user's click interest in a specific element (such as a link, button, advertisement) within the website or application.

[0054] For example, the traffic map shows how users flow from one page or function point to another page or function point, as well as the key nodes and conversion situations in the whole process. The drawing steps include determining traffic nodes: identifying the key behavior points of users within the website or application, such as the home page, product details page, shopping cart, settlement page, etc. Connecting traffic paths: Drawing the flow paths of users between these nodes according to the user behavior data. Marking the conversion rate: Marking the corresponding traffic conversion rate on each node or path to visually display the conversion situation.

[0055] The traffic trend graph shows the traffic data changing over time, helping to analyze the reasons and trends of traffic changes. The drawing steps include selecting a time range: determining the time period to be analyzed, such as daily, weekly, monthly, etc. Collecting traffic data: Collecting key data such as total access volume, click volume, conversion rate, etc. according to the time range. Drawing a graph: Using graph tools (such as Excel, Tableau, Power BI, etc.) to draw the traffic trend graph, including the total access volume trend, click volume trend, conversion rate trend, etc.

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

[0057] Analyze trends: Combine market feedback data and user behavior data to analyze the reasons behind traffic trends, such as holiday promotions, new product launches, marketing campaigns, etc.

[0058] The market demand trend analysis report of the target product can be obtained by analyzing the data analysis chart through the analysis report module based on multiple preset data analysis tools.

[0059] Cluster analysis can be adopted: Divide the data into different groups or clusters to discover the common characteristics and behavior patterns of user groups.

[0060] Classification and prediction: Build a classification model to classify users or predict their future behavior, such as predicting which users are likely to convert into paying users.

[0061] Association analysis: Discover the association rules between different data, such as which page advertisements users are more likely to complete the purchase behavior after visiting.

[0062] Use complex analysis tools to analyze the data analysis chart. For example, statistical analysis: Provide rich statistical analysis functions, such as the calculation of statistics 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, neural networks, etc., to mine potential information and patterns in the data.

[0064] Text analysis: For cases containing text data, provide text analysis functions, such as sentiment analysis, topic extraction, etc. Obtain the market demand trend analysis report of the target product.

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

[0066] S28. Based on the market demand trend analysis report and the data differences, re-formulate the placement plan of the target product.

[0067] In the process of product market placement and operation, comparing the data differences between market feedback data, target user behavior data and expected goals is a crucial step. This step aims to reveal the deviation between the actual market performance and the expectation, and provide data support for subsequent strategy adjustment. The following are exemplary methods for comparing these data: Clarify the expected goals: The expected goals usually include key performance indicators (KPIs) such as product sales volume, user growth, market share, brand awareness, etc. These goals should be reasonably set based on factors such as market research, historical data, competitor analysis and internal strategic planning before product placement.

[0068] Collect market feedback data, which can also cover user satisfaction with the product, function evaluation, price acceptance, brand impression, etc. Analyze target user behavior data, which is obtained through website analysis tools, APP data, social media monitoring and other channels. Analyze user access paths, dwell time, click-through rate, conversion rate, purchase behavior, etc. to understand users' actual use and preferences for the product.

[0069] Furthermore, compare data differences: compare market feedback data and target user behavior data with expected targets one by one. Identify data differences, including which aspects exceed expectations (such as high user satisfaction and strong purchase intention) and which aspects do not meet expectations (such as low function evaluation and insufficient conversion rate). Analyze the reasons for data differences, which may include market changes, competitor strategies, changes in user needs, product defects, etc.

[0070] Furthermore, the launch plan of the target products is re-formulated based on the market demand trend analysis report and data differences.

[0071] After identifying the data discrepancies and their causes, the next step is to re-formulate the target product launch plan based on the market demand trend analysis report and the data discrepancies. The following are the steps to develop a new plan: Interpretation of market demand trends: Market demand trend analysis reports should cover market growth trends, changes in consumer preferences, competitor dynamics, technology development trends, etc. Combined with data differences, analyze the impact of market demand trends on product launch strategies.

[0072] Adjust product strategy: Adjust product features, design, pricing and other strategies based on market demand trends and data differences. Prioritize solving problems in user feedback to improve product competitiveness.

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

[0074] Develop an implementation plan: clarify the implementation steps, timetable, division of responsibilities, etc. of the new plan. Ensure that team members have a clear understanding and consensus on the new plan.

[0075] Monitoring and evaluation: During the implementation of new solutions, we will continue to monitor market feedback and user behavior data. We will regularly evaluate the effectiveness of new solutions and adjust strategies in a timely manner to respond to market changes.

[0076] The above-mentioned new product launch plan can be an exemplary plan given by the system. During the application process, the user can also adjust it according to the actual situation.

[0077] The product delivery method provided by the present invention collects delivery feedback data after the target product is delivered based on the initial delivery plan. The delivery feedback data at least includes market feedback data and target user behavior data; analyzes the market feedback data and target user behavior data to generate corresponding data analysis charts; determines the market demand trend of the target product based on the data analysis charts; re-formulates the delivery plan based on the market demand trend, and conducts product delivery based on the re-formulated delivery plan. With this solution, in terms of accuracy, the delivery effect is improved through refined limiting conditions and optimized configuration methods. The system can effectively manage the product delivery volume, specific population, and delivery rules to achieve the best delivery effect. In terms of optimization effect, various limiting conditions are comprehensively considered, the strategy is significantly optimized, and the market conversion rate and user satisfaction are improved. The system can dynamically adjust the strategy according to the actual situation to ensure the maximization of the delivery effect. In terms of user experience, an intuitive visual configuration interface is provided, and users can conveniently set and adjust the push strategy. The operation interface is designed friendly, supporting users to get started quickly and operate efficiently.

[0078] The product delivery system provided by the present invention will be described below. The product delivery system described below can be correspondingly referred to the product delivery method described above.

[0079] Figure 3 It is a schematic diagram of the architecture of the product delivery system provided by the present invention, specifically including: The data integration module 301 is used to collect delivery feedback data after the target product is delivered based on the initial delivery plan. The delivery feedback data at least includes market feedback data and target user behavior data; The data analysis module 302 is used to analyze the market feedback data and target user behavior data; The traffic map module 303 is used to generate corresponding data analysis charts based on the data analysis results provided by the data analysis module and perform visual display; 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 plan formulation module 304 is used to re-formulate the delivery plan based on the market demand trend and conduct product delivery based on the re-formulated delivery 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-side interface and obtain the initial delivery plan of the target product input by the user-side, send a product delivery instruction to the market terminal device in the target delivery area based on the initial delivery plan, and identify the target user through the market terminal device according to the target product delivery volume and delivery time to complete the delivery of the target product; The placement details module 306 is used to monitor the product placement situation and provide market feedback data and progress tracking data after the product placement for the data integration module; The analysis report module 307 is used to analyze the data analysis chart based on a plurality of preset data analysis tools to obtain a market demand trend analysis report of the target product.

[0081] Figure 4 An example of a schematic physical structure diagram of an electronic device is shown as Figure 4 shown. The electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840. Among them, the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call the logical instructions in the memory 830 to execute a product placement method, which includes: collecting placement feedback data after the target product is placed based on an initial placement plan, where the placement feedback data at least includes 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 a placement plan based on the market demand trend, and performing product placement based on the re - formulated placement plan.

[0082] In addition, when the logical instructions in the above - mentioned memory 830 are implemented in the form of software functional units and sold or used as an independent product, they can be stored in a computer - readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read - only memories (ROM, Read - Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical disks, etc., which can store program codes.

[0083] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program 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 placement method provided by each of the above methods. The method includes: collecting placement feedback data after the target product is placed based on the initial placement plan, where the placement feedback data at least includes 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 the market demand trend of the target product based on the data analysis charts; re-formulating a placement plan based on the market demand trend, and performing product placement based on the re-formulated placement plan.

[0084] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the product placement method provided by each of the above methods. The method includes: collecting placement feedback data after the target product is placed based on the initial placement plan, where the placement feedback data at least includes 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 the market demand trend of the target product based on the data analysis charts; re-formulating a placement plan based on the market demand trend, and performing product placement based on the re-formulated placement plan.

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

[0086] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, also by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment 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 are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. However, such modifications or replacements 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: Collecting delivery feedback data after the target product is delivered based on the initial delivery plan, wherein the delivery feedback data at least includes 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; Determine the market demand trend of the target product based on the data analysis chart; A launch plan is re-formulated based on the market demand trend, and product launch is carried out based on the re-formulated launch plan.

2. The method according to claim 1, characterized in that Before collecting the delivery feedback data after the target product is delivered based on the initial delivery plan, the process includes: Acquire an initial delivery plan of the target product input by the user, wherein the initial delivery plan at least includes the delivery amount of the target product, target users, delivery region and delivery time; Based on the initial delivery plan, a product delivery instruction is sent to the market terminal device in the target delivery area, and the target product delivery is completed by identifying the target user according to the delivery amount and delivery time of the target product through the market terminal device.

3. The method according to claim 2, characterized in that The data analysis of the market feedback data and the target user behavior data to generate corresponding data analysis charts includes: Determine the traffic conversion rate based on the market feedback data, and determine the user click rate based on the target user behavior data; Draw a traffic map and traffic trend chart based on the market feedback data, target user behavior data, traffic conversion rate and user click-through rate.

4. The method according to claim 3, characterized in that Determining the market demand trend of the target product based on the data analysis chart includes: The traffic map and traffic trend chart are analyzed based on a plurality of preset data analysis tools to obtain a market demand trend analysis report for the target product.

5. The method according to claim 4, characterized in that The re-formulating a launch plan based on the market demand trend, and launching products based on the re-formulated launch plan, includes: Compare the market feedback data and target user behavior data with the expected data; Based on the market demand trend analysis report and the data differences, the launch plan of the target product is re-formulated.

6. A product delivery system, characterized in that: include: A data integration module, used to collect delivery feedback data after the target product is delivered based on the initial delivery plan, wherein the delivery feedback data at least includes market feedback data and target user behavior data; A data analysis module, used to analyze the market feedback data and target user behavior data; The traffic map module is used to generate corresponding data analysis charts and perform visual display based on the data analysis results provided by the data analysis module; The data analysis module is further used to determine the market demand trend of the target product based on the data analysis chart; The plan formulation module is used to re-formulate the launch plan based on the market demand trend and launch products based on the re-formulated launch plan.

7. The system according to claim 6, characterized in that The system further comprises: A configuration management module is used to provide a user interface and obtain an initial delivery plan of the target product input by the user, send a product delivery instruction to a market terminal device in a target delivery area based on the initial delivery plan, and complete the delivery of the target product by identifying the target user through the market terminal device according to the delivery amount and delivery time of the target product; The 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 product launch; The analysis report module is used to analyze the data analysis chart based on a plurality of preset data analysis tools to obtain a market demand trend analysis report for the target product.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the product delivery method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the product delivery method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the product delivery method according to any one of claims 1 to 6 is implemented.

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