An ai user feedback analysis method, device, medium and product based on a supply and marketing platform

Abstract

This application discloses an AI-based user feedback analysis method, device, medium, and product based on a supply and sales platform, relating to the fields of internet data processing and human-computer interaction technology. The method includes configuring a floating window-style customer feedback module on each interface of the supply and sales platform; the floating window-style customer feedback module is configured with a red dot notification mechanism, and when the user's mouse moves to the location of the floating window-style customer feedback module, the floating window is triggered to obtain feedback data; the feedback data is preprocessed, including integrity verification, text cleaning, field standardization, and format conversion; the preprocessed feedback data is analyzed using an AI analysis model to obtain analysis results; and the analysis results are visualized through a data visualization platform. This application can improve the real-time performance, accuracy, and processing efficiency of user feedback processing on the supply and sales platform.

Description

Technical Field

[0001] This application relates to the fields of Internet data processing and human-computer interaction technology, and in particular to an AI user feedback analysis method, device, medium and product based on a supply and sales platform. Background Technology

[0002] As enterprises deepen their digital transformation, supply and marketing platforms, as core business systems connecting suppliers, distributors, and end customers, are becoming increasingly complex and iterating at an ever-accelerating pace. To ensure continuous optimization of product experience and to meet user needs, collecting and analyzing user feedback has become a crucial step in product operations.

[0003] In the user feedback handling scenario of a supply and sales platform, product managers need to collect a large amount of user feedback to quickly extract useful information, identify the user's true core needs, and formulate improvement strategies. However, existing feedback collection models and technical solutions have significant shortcomings in addressing this requirement.

[0004] From the perspective of feedback collection sources, the platform originally relied mainly on front-line sales and implementation personnel to transmit customer feedback. This model has significant problems of information transmission bias and lag. From the perspective of feedback analysis, the traditional processing method further amplifies the above problems, which not only has high labor costs and low processing efficiency, but also introduces personal subjective judgment.

[0005] To address the pain points of the above models, there is an urgent need for a method that can efficiently and accurately collect first-hand user feedback from supply and sales platforms, deeply summarize and mine core demands, help quickly respond to customer demands, and improve the real-time performance, accuracy, and efficiency of user feedback processing on supply and sales platforms. Summary of the Invention

[0006] The purpose of this application is to provide an AI-based user feedback analysis method, device, medium, and product based on a supply and sales platform, which can improve the real-time performance, accuracy, and processing efficiency of user feedback processing on the supply and sales platform.

[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides an AI user feedback analysis method based on a supply and sales platform, the AI ​​user feedback analysis method based on a supply and sales platform including: A floating window-style customer feedback module is configured on each interface of the supply and sales platform. The floating window-style customer feedback module is used to configure a red dot prompt mechanism, and when the user moves the mouse to the position of the floating window-style customer feedback module, the floating window is triggered to obtain feedback data. The feedback data includes interface identifier, satisfaction level, questionnaire content, submission time, and user ID. The feedback data is preprocessed; the preprocessing includes integrity verification, text cleaning, field standardization, and format conversion. The preprocessed feedback data is analyzed using an AI analysis model to obtain the analysis results; The analysis results are visualized using a data visualization platform.

[0008] Optionally, the process of obtaining feedback data specifically includes: When the user moves the mouse over the floating window-style customer feedback module, a pop-up window appears to select the satisfaction level; the floating window contains a "Satisfied" button and a "Dissatisfied" button. When a user clicks the "Dissatisfaction" button, a user feedback questionnaire pop-up is triggered, and the current interface identifier is recorded. The user feedback questionnaire pop-up includes a text feedback input box, an image upload field, and a contact information input box.

[0009] Optionally, the preprocessing of the feedback data specifically includes: The feedback data is stored in a temporary database and its integrity is verified to check whether the data submitted by the user is complete and whether the required fields contain the required information. The feedback data that passes the integrity check is cleaned, fields are standardized, and formats are converted. The preprocessed feedback data is then stored in the business database.

[0010] Optionally, the AI ​​analysis model is a pre-trained semantic recognition and statistical analysis model; the step of using the AI ​​analysis model to analyze the preprocessed feedback data and obtain analysis results specifically includes: The feedback data is grouped and statistically analyzed according to the interface identifier. The satisfaction level of a single interface is calculated based on the number of satisfied users and the total number of users who provided feedback on the corresponding interface. Filter feedback data within the corresponding submission time interval based on the time range filter parameters, and re-execute the statistics; Detailed analysis of questionnaires from dissatisfied users was conducted to summarize high-frequency issues and potential needs, and optimization strategies were generated by combining product information from the knowledge base.

[0011] Optionally, visualizing the analysis results through a data visualization platform specifically includes: Create a user feedback analysis dashboard on a data visualization platform; Configure a module for displaying satisfaction with each interface and a module for displaying the results of potential demand mining in the user feedback analysis dashboard; Query the analysis results within a custom time range in the user feedback analysis dashboard.

[0012] Optionally, the data visualization platform is the Redash platform.

[0013] Secondly, this application provides an AI user feedback analysis device based on a supply and sales platform, the AI ​​user feedback analysis device based on the supply and sales platform comprising: The configuration unit is used to configure floating window-style customer feedback modules on various interfaces of the supply and sales platform. The floating window-style customer feedback module is used to configure a red dot prompt mechanism, and when the user's mouse moves to the position of the floating window-style customer feedback module, the floating window is triggered to obtain feedback data. The feedback data includes interface identifier, satisfaction level, questionnaire content, submission time, and user ID. A preprocessing unit is used to preprocess the feedback data; the preprocessing includes integrity verification, text cleaning, field standardization, and format conversion. The analysis unit is used to analyze the preprocessed feedback data using an AI analysis model to obtain analysis results; The visualization unit is used to visualize the analysis results through a data visualization platform.

[0014] Thirdly, this application provides a computer 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 AI ​​user feedback analysis method based on the supply and sales platform.

[0015] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned AI user feedback analysis method based on a supply and sales platform.

[0016] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned AI user feedback analysis method based on a supply and sales platform.

[0017] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides an AI-based user feedback analysis method, device, medium, and product based on a supply and sales platform. By constructing a full-process mechanism of "embedded feedback collection - AI intelligent analysis - visualization" on the supply and sales platform, it achieves real-time collection, accurate analysis, and efficient application of user feedback data, significantly solving the problems of information bias, processing lag, and superficial analysis in traditional feedback models. This technical solution provides data-driven, precise direction for the business iteration of supply and sales platforms, ultimately achieving a triple improvement in user feedback processing efficiency, targeted product optimization, and user experience, providing an intelligent feedback analysis solution for the refined operation of medium and large-sized supply and sales platforms. Attached Figure Description

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

[0019] Figure 1 This is a schematic diagram of an AI user feedback analysis method based on a supply and sales platform in one embodiment of this application; Figure 2 This is a flowchart illustrating step one of an AI user feedback analysis method based on a supply and sales platform according to an embodiment of this application. Figure 3 This is a flowchart illustrating step two of an AI user feedback analysis method based on a supply and sales platform in one embodiment of this application. Figure 4 This is a flowchart illustrating step three of an AI user feedback analysis method based on a supply and sales platform in one embodiment of this application. Figure 5 This is a flowchart illustrating step four of an AI user feedback analysis method based on a supply and sales platform in one embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] In one exemplary embodiment, such as Figure 1 As shown, an AI-based user feedback analysis method based on a supply and sales platform is provided, which includes the following steps S101 to S104. Wherein: S101, Configure a floating window-style customer feedback module on each interface of the supply and sales platform; the floating window-style customer feedback module is used to configure a red dot prompt mechanism, and when the user's mouse moves to the position of the floating window-style customer feedback module, the floating window is triggered to obtain feedback data; the feedback data includes interface identifier, satisfaction level, questionnaire content, submission time, and user ID. The process of obtaining feedback data specifically includes: S1, When the user's mouse moves to the floating window-style customer feedback module, a floating window for selecting satisfaction level pops up; the floating window contains a "Satisfied" button and a "Dissatisfied" button. S2, when the user clicks the "Dissatisfaction" button, a user feedback questionnaire pop-up window is triggered, and the interface identifier of the current interface is recorded; the user feedback questionnaire pop-up window includes a text feedback input box, an image upload field, and a contact information input box.

[0023] As a specific example, such as Figure 2 As shown, the computer equipment is configured with floating window-style customer feedback buttons on various interfaces of the supply and sales platform (including the Hot Item Zone, Distribution Price Limit, Quick Distribution, etc.). Specific parameters are as follows: ① Button display rules: By default, it floats in the lower right corner of the interface, with a red dot indicator (a red dot is displayed when no feedback is submitted, and disappears automatically after the user submits feedback); ② Floating window trigger logic: When the user's mouse hovers over the floating window-style customer feedback button, a "Satisfaction Selection Floating Window" automatically pops up. The title of the floating window is uniformly formatted as "Are you satisfied with [Interface Name]?"; ③ Floating window interaction configuration: The floating window contains two selection buttons: "Satisfied" and "Dissatisfied". Clicking "Satisfied" triggers a "Thank You Feedback Pop-up" (displaying "Thank you for your recognition, we will continue to optimize our service!"), and clicking "Dissatisfied" triggers a "Usage Problem Feedback Questionnaire Pop-up". Simultaneously, the front end automatically records the "Interface Identifier" of the current interface and associates it with subsequent feedback data. The "User Feedback Questionnaire Pop-up" includes the following fields: ① An input box stating "Please describe your suggestions and feedback on this page so we can improve it," with a character length limit of 0-1000 characters and a default prompt "Please enter content." This field is required to collect user feedback on page functionality and user experience. ② An image upload field: A "Upload Image" module is configured, supporting up to 9 images (formats are limited to common formats such as JPG and PNG). ③ A contact information field: An input box stating "Your company name or contact information," with a character length limit of 0-1000 characters and a default prompt "Please enter content." This field is optional to collect information such as the user's company name, phone number, and email address, facilitating follow-up by product managers for complex issues.

[0024] S102, preprocess the feedback data; the preprocessing includes integrity verification, text cleaning, field standardization, and format conversion; like Figure 3 As shown, S102 specifically includes: S21, the feedback data is stored in a temporary database and its integrity is verified to check whether the data submitted by the user is complete and whether the required fields have content; that is, when the user completes the "satisfaction status selection" or submits the "usage problem feedback questionnaire", the computer device receives the feedback data (including "interface identifier", "satisfaction status", "questionnaire content", "submission time" and "user ID") in real time, stores it in the temporary database, and performs integrity verification.

[0025] S22 performs text cleaning, field standardization, and format conversion on the feedback data that has passed the integrity check, and stores the preprocessed feedback data in the business database.

[0026] S103, use an AI analysis model to analyze the preprocessed feedback data and obtain the analysis results; like Figure 4 As shown, S103 specifically includes: S31, group and statistically analyze feedback data according to interface identifiers, and calculate the satisfaction level of a single interface based on the number of satisfied users and the corresponding total number of users who provide feedback on the interface. The process of calculating satisfaction for a single interface is as follows: For each "interface_id", count the number of users with "satisfaction = 1" (number of satisfied users) and the total number of users who provided feedback for that "interface_id", and calculate the satisfaction rate using the formula "satisfaction rate = (number of satisfied users / total number of users who provided feedback) × 100%". S32, filter feedback data within the corresponding submission time interval based on the time range filter parameters, and re-execute the statistics; The process of filtering the time range is as follows: It supports filtering feedback data within the corresponding "submit_time" interval based on the input time parameter, re-executing the above statistics, and generating a three-dimensional statistical result of "time-interface-satisfaction".

[0027] S33 involves performing detailed analysis on the questionnaires of dissatisfied users, summarizing high-frequency problems and potential needs, and generating optimization strategies by combining product information from the knowledge base, down to a specific detail of the product.

[0028] Specifically, the AI ​​analysis model loaded on the computer device (AI analysis server) is a pre-trained semantic recognition and statistical analysis model. The semantic recognition and statistical analysis model is built based on Alibaba Cloud's Bailian Big Model and uses the feedback data analysis agent provided by Alibaba Cloud Bailian to analyze the feedback data submitted by users. It is embedded into the computer by calling the interface of Alibaba Cloud Bailian Big Model. The core configuration parameters of the AI ​​analysis model include: ① Feedback classification dimension: divided according to the two dimensions of "interface identifier + problem type"; ② Satisfaction calculation rule: single interface satisfaction = (number of users who selected "satisfied" on the interface / total number of users who provided feedback on the interface) × 100%; ③ "Time range filtering parameter": supports setting time filtering conditions by "last 7 days", "last 30 days" and "custom date", which are associated with the "submission time" field in the feedback data.

[0029] S104, The analysis results are visualized using a data visualization platform; wherein, the data visualization platform is the Redash platform; like Figure 5 As shown, S104 specifically includes: S41, Create a user feedback analysis dashboard on the data visualization platform; S42, Configure the satisfaction display module for each interface and the potential demand mining conclusion display module in the user feedback analysis dashboard; S43, in the user feedback analysis dashboard, query the analysis results within a custom time range.

[0030] Based on the same inventive concept, this application also provides an AI user feedback analysis device based on a supply and sales platform for implementing the AI ​​user feedback analysis method based on a supply and sales platform described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the AI ​​user feedback analysis device based on a supply and sales platform provided below can be found in the limitations of the AI ​​user feedback analysis method based on a supply and sales platform described above, and will not be repeated here.

[0031] In one exemplary embodiment, an AI user feedback analysis device based on a supply and sales platform is provided, comprising: The configuration unit is used to configure floating window-style customer feedback modules on various interfaces of the supply and sales platform. The floating window-style customer feedback module is used to configure a red dot prompt mechanism, and when the user's mouse moves to the position of the floating window-style customer feedback module, the floating window is triggered to obtain feedback data. The feedback data includes interface identifier, satisfaction level, questionnaire content, submission time, and user ID. A preprocessing unit is used to preprocess the feedback data; the preprocessing includes integrity verification, text cleaning, field standardization, and format conversion. The analysis unit is used to analyze the preprocessed feedback data using an AI analysis model to obtain analysis results; The visualization unit is used to visualize the analysis results through a data visualization platform.

[0032] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an AI user feedback analysis method based on a supply and sales platform.

[0033] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0034] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0035] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0036] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0037] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0038] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0039] In this application, all actions to acquire signals, information, or data are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization granted by the owner of the relevant device.

[0040] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0041] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An AI-based user feedback analysis method based on a supply and sales platform, characterized in that, The AI-based user feedback analysis method based on the supply and sales platform includes: A floating window-style customer feedback module is configured on each interface of the supply and sales platform. The floating window-style customer feedback module is used to configure a red dot prompt mechanism, and when the user moves the mouse to the position of the floating window-style customer feedback module, the floating window is triggered to obtain feedback data. The feedback data includes interface identifier, satisfaction level, questionnaire content, submission time, and user ID. The feedback data is preprocessed; the preprocessing includes integrity verification, text cleaning, field standardization, and format conversion. The preprocessed feedback data is analyzed using an AI analysis model to obtain the analysis results; The analysis results are visualized using a data visualization platform.

2. The AI ​​user feedback analysis method based on a supply and sales platform according to claim 1, characterized in that, The process of obtaining feedback data specifically includes: When the user moves the mouse over the floating window-style customer feedback module, a pop-up window appears to select the satisfaction level; the floating window contains a "Satisfied" button and a "Dissatisfied" button. When a user clicks the "Dissatisfaction" button, a user feedback questionnaire pop-up is triggered, and the current interface identifier is recorded. The user feedback questionnaire pop-up includes a text feedback input box, an image upload field, and a contact information input box.

3. The AI ​​user feedback analysis method based on a supply and sales platform according to claim 1, characterized in that, The preprocessing of the feedback data specifically includes: The feedback data is stored in a temporary database and its integrity is verified to check whether the data submitted by the user is complete and whether the required fields contain the required information. The feedback data that passes the integrity check is cleaned, fields are standardized, and formats are converted. The preprocessed feedback data is then stored in the business database.

4. The AI ​​user feedback analysis method based on a supply and sales platform according to claim 1, characterized in that, The AI ​​analysis model is a pre-trained semantic recognition and statistical analysis model; the analysis of the pre-processed feedback data using the AI ​​analysis model to obtain analysis results specifically includes: The feedback data is grouped and statistically analyzed according to the interface identifier. The satisfaction level of a single interface is calculated based on the number of satisfied users and the total number of users who provided feedback on the corresponding interface. Filter feedback data within the corresponding submission time interval based on the time range filter parameters, and re-execute the statistics; Detailed analysis of questionnaires from dissatisfied users was conducted to summarize high-frequency issues and potential needs, and optimization strategies were generated by combining product information from the knowledge base.

5. The AI ​​user feedback analysis method based on a supply and sales platform according to claim 1, characterized in that, The visualization of the analysis results through a data visualization platform specifically includes: Create a user feedback analysis dashboard on a data visualization platform; Configure a module for displaying satisfaction with each interface and a module for displaying the results of potential demand mining in the user feedback analysis dashboard; Query the analysis results within a custom time range in the user feedback analysis dashboard.

6. The AI ​​user feedback analysis method based on a supply and sales platform according to claim 1 or claim 5, characterized in that, The data visualization platform is the Redash platform.

7. An AI-powered user feedback analysis device based on a supply and sales platform, characterized in that, The AI ​​user feedback analysis device based on the supply and sales platform includes: The configuration unit is used to configure floating window-style customer feedback modules on various interfaces of the supply and sales platform. The floating window-style customer feedback module is used to configure a red dot prompt mechanism, and when the user's mouse moves to the position of the floating window-style customer feedback module, the floating window is triggered to obtain feedback data. The feedback data includes interface identifier, satisfaction level, questionnaire content, submission time, and user ID. A preprocessing unit is used to preprocess the feedback data; the preprocessing includes integrity verification, text cleaning, field standardization, and format conversion. The analysis unit is used to analyze the preprocessed feedback data using an AI analysis model to obtain analysis results; The visualization unit is used to visualize the analysis results through a data visualization platform.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the AI ​​user feedback analysis method based on a supply and sales platform as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the AI ​​user feedback analysis method based on a supply and sales platform as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the AI ​​user feedback analysis method based on a supply and sales platform as described in any one of claims 1-6.

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