Automatic customer satisfaction collection system and data analysis method

Abstract

The invention relates to the field of data processing, in particular to an automatic customer satisfaction collection system and a data analysis method.The system comprises a data collection module used for automatically collecting behavior data of customers in the software using process through an API interface of enterprise management software; the feedback triggering module is used for automatically sending a satisfaction questionnaire to the customer according to a preset triggering condition; the real-time analysis module is used for carrying out real-time analysis on the collected customer behavior data and satisfaction feedback data, generating a customer satisfaction score and identifying key factors influencing the satisfaction; and the automatic optimization suggestion module is used for automatically generating optimization suggestions for software functions, user experience or service processes according to the analysis result, and pushing the optimization suggestions to related development teams. According to the method, efficient and deep analysis of the satisfaction degree data of the customer to the business is realized through deep analysis of the data and a real-time feedback mechanism.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to an automated customer satisfaction collection system and a data analysis method. Background Art

[0002] As enterprises continue to increase their informatization, enterprise management software is increasingly being used across various industries. After purchasing and using these software, customer satisfaction becomes a crucial indicator of software quality and enterprise service levels. Traditional customer satisfaction surveys typically rely on manual questionnaires, phone calls, or email surveys. These methods are not only time-consuming and labor-intensive, but also inefficient in data collection, making it difficult to obtain real-time customer feedback. Furthermore, data analysis using these methods often lags, preventing timely support for enterprise decision-making.

[0003] In recent years, with the development of big data and artificial intelligence technologies, automated data collection and analysis has become increasingly feasible. However, existing automated customer satisfaction collection systems are mostly limited to simple questionnaire delivery and result statistics, lacking in-depth data analysis and real-time feedback mechanisms. In the field of enterprise management software, in particular, how to efficiently collect customer satisfaction data through automated means and conduct in-depth analysis based on business scenarios remains a pressing technical challenge. Summary of the Invention

[0004] In order to solve the problem of how to efficiently collect customer satisfaction data through automated means and conduct in-depth analysis based on business scenarios, this application provides a customer satisfaction automated collection system and data analysis method.

[0005] In the first aspect, the present application provides an automated customer satisfaction collection system and data analysis method, which adopts the following technical solutions:

[0006] A customer satisfaction automated collection system, comprising:

[0007] The data collection module is used to automatically collect customer behavior data during the use of the software through the API interface of the enterprise management software;

[0008] Feedback trigger module, used to automatically send satisfaction questionnaires to customers based on preset trigger conditions;

[0009] Real-time analysis module, used to analyze collected customer behavior data and satisfaction feedback data in real time, generate customer satisfaction scores, and identify key factors affecting satisfaction;

[0010] The automatic optimization suggestion module is used to automatically generate optimization suggestions for software functions, user experience or service processes based on the analysis results, and push them to the relevant development team, where the analysis includes the customer satisfaction score and the key factors affecting satisfaction.

[0011] Furthermore, the real-time analysis module includes a computing unit;

[0012] The calculation unit is used to calculate the customer satisfaction score according to the satisfaction calculation formula, wherein the satisfaction calculation formula is:

[0013] b=3M+F+6PL

[0014] Among them, b is the customer satisfaction score, M is the number of high satisfaction, F is the number of basic satisfaction, P is the number of abandonment, and L is the base number of abandonment;

[0015] The calculation formula of the high satisfaction level M is:

[0016]

[0017] Among them, M is high satisfaction, r is the lower limit of high satisfaction, n is the upper limit of high satisfaction, C1 is the constant compensation of high satisfaction, and x is the comprehensive behavioral data.

[0018] Furthermore, the real-time analysis module further includes an analysis unit, and the analysis unit is communicatively connected to the calculation unit;

[0019] The analyzing unit is configured to analyze a correlation data set between the customer satisfaction score and each of the behavioral data, and perform data analysis on the correlation data set to identify key factors affecting the satisfaction.

[0020] Furthermore, the analysis unit is further configured to substitute the customer satisfaction score and each behavior data into an analysis factor formula to calculate associated data, wherein the analysis factor formula is:

[0021] f(x)=log a b+C

[0022] Among them, b is the customer satisfaction score, C is the compensation amount, a is the behavioral data, and f(x) is the correlation data between the behavioral factor a and the customer satisfaction score.

[0023] Furthermore, the data acquisition module includes an acquisition unit and a pre-processing unit;

[0024] The collection unit is used to automatically collect behavioral data of customers during the use of the software through the API interface of the enterprise management software;

[0025] The pre-processing unit is used to perform data pre-processing on the behavior data to obtain smooth behavior data, where the smooth behavior data includes browsing time and function usage level.

[0026] Furthermore, the customer satisfaction automated collection system also includes a visual reporting module;

[0027] The visualization report module is used to present the analysis results to the enterprise management in a visual form.

[0028] Furthermore, the preset trigger condition is reaching a preset browsing time or triggering a software shutdown event.

[0029] In a second aspect, the present application provides a data analysis method, which is executed in the aforementioned customer satisfaction automated collection system, comprising:

[0030] Automatically collect customer behavior data during software use through the API interface of enterprise management software;

[0031] Automatically send satisfaction surveys to customers based on preset trigger conditions;

[0032] Analyze collected customer behavior data and satisfaction feedback data in real time to generate customer satisfaction scores and identify key factors affecting satisfaction;

[0033] Present the analysis results to the company management in a visual form;

[0034] Based on the analysis results, optimization suggestions for software functions, user experience or service processes are automatically generated and pushed to the relevant development team, wherein the analysis includes the customer satisfaction score and the key factors affecting satisfaction.

[0035] In a third aspect, the present disclosure provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the customer satisfaction automated collection system described in any of the above embodiments when executing the computer program.

[0036] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the customer satisfaction automated collection system described in any of the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The diagram is a structural diagram of a customer satisfaction automated collection system in an embodiment.

[0038] Figure 2 The figure is a flowchart of a data analysis method in an embodiment.

[0039] Figure 3 The figure is a schematic diagram of the internal structure of a computer device in an embodiment. DETAILED DESCRIPTION

[0040] The following is combined with Figure 1-3 This application is described in further detail.

[0041] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0042] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0043] Example 1

[0044] The embodiment of the present application discloses a system for automatically collecting customer satisfaction.

[0045] Please refer to Figure 1 In one embodiment of the present application, a customer satisfaction automated collection system includes: a data collection module, a feedback triggering module, a real-time analysis module, and an automatic optimization suggestion module. The data collection module is in communication with the feedback triggering module, which is in communication with the real-time analysis module, which is in communication with the automatic optimization suggestion module.

[0046] Among them, the data collection module is used to automatically collect customer behavior data during the use of the software through the API interface of the enterprise management software; the feedback trigger module is used to automatically send satisfaction questionnaires to customers according to preset trigger conditions; the real-time analysis module is used to perform real-time analysis on the collected customer behavior data and satisfaction feedback data, generate customer satisfaction scores, and identify key factors affecting satisfaction; the automatic optimization suggestion module is used to automatically generate optimization suggestions for software functions, user experience or service processes based on the analysis results, and push them to the relevant development team, wherein the analysis includes the customer satisfaction score and the key factors affecting satisfaction.

[0047] In actual applications, by collecting customer operation records in software (such as clicks, browsing time, and degree of function usage), and analyzing user behavior patterns, the purpose is to help companies understand user preferences, optimize product design, and enhance user experience. In this embodiment, the degree of function usage is the number of functions used by the customer; that is, the more functions a customer uses, the greater the degree of function usage; conversely, the fewer functions a customer uses, the lower the degree of function usage.

[0048] Specifically, the data collection module is first used to automatically collect the customer's behavioral data during the use of the software through the API interface of the enterprise management software. First, determine the type of behavioral data that needs to be collected and the purpose of data use. In this embodiment, the behavioral data type includes browsing time and the degree of use of functions. Then confirm the relevant API interface provided by the software, as well as the restrictions and authentication methods of the API interface. Then obtain the API access rights, determine the data collection module that matches the API access rights, and finally the data collection module accepts and parses the data returned by the API, and extracts the required fields (browsing time and degree of use of functions) for preliminary cleaning. This allows the data collection module to automatically collect the customer's behavioral data during the use of the software through the API interface of the enterprise management software.

[0049] After the data collection module collects the user's behavior data, it transmits the behavior data to the feedback trigger module. The feedback trigger module automatically sends a satisfaction questionnaire to the customer according to the preset trigger conditions. The preset trigger condition can be reaching the preset browsing time; it can also be triggering the software shutdown event. Specifically, the system is set to monitor customer behavior data events (including customer browsing time and customer usage level of functions). When the monitored customer behavior data time has reached the preset trigger condition, the system automatically sends a satisfaction questionnaire to the customer through the feedback trigger module, allowing the customer to complete the satisfaction questionnaire. Finally, the satisfaction questionnaires completed by the customer are collected and the collected satisfaction questionnaires are transmitted to the real-time analysis module.

[0050] After receiving the satisfaction survey questionnaire, the real-time analysis module conducts real-time analysis of the collected customer behavior data and satisfaction feedback data to generate a customer satisfaction score and identify key factors influencing satisfaction. Specifically, the real-time analysis module calculates the customer satisfaction score based on a satisfaction calculation formula. First, the module categorizes the completed satisfaction survey questionnaires returned to customers to determine the number of highly satisfied questionnaires, the number of moderately satisfied questionnaires, and the number of abandoned questionnaires. These numbers are then substituted into the satisfaction calculation formula to calculate the customer satisfaction score.

[0051] The satisfaction calculation formula is:

[0052] b=3M+F+6PL

[0053] Among them, b is the customer satisfaction score, M is the number of high satisfaction, F is the number of basic satisfaction, P is the number of abandonment, and L is the base number of abandonment;

[0054] The calculation formula for the number of high satisfaction M is:

[0055]

[0056] Among them, M is the number of high satisfaction, r is the lower limit of high satisfaction, n is the upper limit of high satisfaction, C1 is the constant compensation amount of high satisfaction, and x is the comprehensive behavioral data.

[0057] In one embodiment, the lower limit of high satisfaction is set to 6, and the upper limit of high satisfaction is set to 10.

[0058] In this embodiment, a relationship between comprehensive behavior data and satisfaction is established. First, the comprehensive behavior data is calculated. Specifically, the browsing time and the degree of function usage of the customer are obtained, and the browsing time and the degree of function usage are substituted into the comprehensive behavior data calculation formula to calculate the comprehensive behavior data. The comprehensive behavior data calculation formula is:

[0059] x=(3Tp+7Fp) / 10

[0060] Among them, x is comprehensive behavioral data, Tp is browsing time, and Fp is the degree of functional use.

[0061] After the real-time analysis module calculates the customer satisfaction score, further analysis is performed on the factors affecting the customer satisfaction score and each behavioral data. Specifically, the customer satisfaction score and each behavioral data are substituted into the analysis factor formula to calculate the associated data, where the analysis factor formula is:

[0062] f(x)=log a b+C

[0063] Among them, b is the customer satisfaction score, C is the compensation amount, a is the behavioral data, and f(x) is the correlation data between the behavioral factor a and the customer satisfaction score.

[0064] After the real-time analysis module calculates the customer satisfaction score and the key factors affecting satisfaction, the customer satisfaction score and the key factors affecting satisfaction are transmitted to the automatic optimization suggestion module, so that the automatic optimization suggestion module automatically generates optimization suggestions for software functions, user experience or service processes based on the analysis results, and pushes them to the relevant development team, where the analysis includes the customer satisfaction score and the key factors affecting satisfaction.

[0065] Specifically, the key factors that affect satisfaction are sorted and arranged from large to small. The scope of optimization suggestions is determined based on the key factors with greater satisfaction, so as to improve customer satisfaction scores, increase the popularity of the software, and avoid optimizing all functions and causing data redundancy in the software.

[0066] In one embodiment, the automated customer satisfaction collection system further includes a visual reporting module;

[0067] The visualization report module is used to present the analysis results to the enterprise management in a visual form. In this embodiment, presenting the analysis results to the enterprise management in a visual form can help the management quickly understand the insights behind the data and make more informed decisions.

[0068] In one embodiment, the data acquisition module includes an acquisition unit and a pre-processing unit;

[0069] The collection unit automatically collects behavioral data about customers using the enterprise management software through the API. Specifically, it identifies the relevant APIs provided by the software, as well as their restrictions and authentication methods. It then obtains API access rights and identifies a data collection module that matches these rights. Finally, the data collection module accepts and parses the data returned by the API, extracting the required fields (browsing time and level of functional usage) for preliminary cleaning. This allows the data collection module to automatically collect behavioral data about customers using the software through the enterprise management software's API.

[0070] The preprocessing unit is used to preprocess the behavior data to obtain smoothed behavior data, which includes browsing time and the degree of functional usage. In this embodiment, since comprehensive behavior data needs to be calculated, the behavior data needs to be preprocessed to ensure that the format of each behavior data is consistent and to eliminate interference data. This includes smoothing the browsing time and the degree of functional usage so that the data formats of browsing time and functional usage are consistent, which facilitates the calculation of comprehensive behavior data. In this embodiment, the calculation formula for comprehensive behavior data is:

[0071] x=(3Tp+7Fp) / 10

[0072] Among them, x is comprehensive behavioral data, Tp is browsing time, and Fp is the degree of functional use.

[0073] The implementation principle of the customer satisfaction automatic collection system in the embodiment of the present application is as follows: through the API interface of the enterprise management software, automatically collect the behavioral data of customers in the process of using the software (using the API interface to call customer behavior data to achieve high efficiency and real-time data collection; while improving the flexibility and programmability of data processing); automatically send satisfaction survey questionnaires to customers according to preset trigger conditions (to obtain feedback in a timely manner, compared with traditional survey methods, reduce survey costs and improve survey efficiency); conduct real-time analysis of the collected customer behavior data and satisfaction feedback data to generate customer satisfaction scores, and identify key factors affecting satisfaction (through real-time analysis of customer behavior data, to accurately grasp customer needs and preferences, thereby enabling enterprises to quickly respond to market changes and customer needs); based on the analysis results, automatically generate optimization suggestions for software functions, user experience or service processes, and push them to relevant development teams (optimization suggestions generated based on data analysis can often accurately locate specific problems in software functions, user experience or service processes, thereby ensuring that optimization measures can effectively solve user pain points and improve software functions and user experience; through continuous data analysis and optimization suggestion generation, enterprises can form a closed-loop mechanism for continuous improvement, continuously iterate software functions and service processes, ensure that the software always remains at the forefront of the industry, and meet the ever-changing needs of users). Therefore, this embodiment achieves efficient in-depth analysis of customer satisfaction data on the business through in-depth analysis of data and real-time feedback mechanism.

[0074] Example 2

[0075] An embodiment of the present application discloses a data analysis method, which is executed in the customer satisfaction automatic collection system of the above embodiment.

[0076] See also Figure 2 In one embodiment of the present application, a data analysis method includes:

[0077] Step 111, automatically collect customer behavior data during the use of the software through the API interface of the enterprise management software.

[0078] In actual applications, by collecting customers' operation records in the software (such as clicks, browsing time, degree of function usage, etc.) and analyzing user behavior patterns, the purpose is to help companies understand user preferences, optimize product design, and improve user experience.

[0079] Identify the relevant API interfaces provided by the software, as well as their restrictions and authentication methods. Then, obtain API access permissions and identify a data collection module that matches these permissions. Finally, the data collection module receives and parses the data returned by the API, extracting the required fields (browsing time and level of functionality used) for preliminary cleaning. This allows the data collection module to automatically collect behavioral data about customers as they use the software through the enterprise management software's API interfaces.

[0080] Step 112: Automatically send a satisfaction survey questionnaire to the customer based on the preset triggering conditions.

[0081] Set up monitoring of customer behavior data events (including customer browsing time and customer usage level of functions). When the monitored customer behavior data time has reached the preset trigger condition, the system automatically sends a satisfaction questionnaire to the customer through the feedback trigger module, allowing the customer to complete the satisfaction questionnaire, and finally collects the satisfaction questionnaires completed by the customer.

[0082] In this embodiment, the preset trigger condition may be reaching a preset browsing time; or may be triggering a software closing event, which is not specifically limited here.

[0083] Step 113 , performing real-time analysis on the collected customer behavior data and satisfaction feedback data, generating a customer satisfaction score, and identifying key factors affecting satisfaction.

[0084] The satisfaction questionnaires completed by the collected customers were classified to obtain the number of questionnaires with high satisfaction, the number of questionnaires with basic satisfaction and the number of questionnaires that were abandoned. The number of questionnaires with high satisfaction, the number of questionnaires with basic satisfaction and the number of questionnaires that were abandoned were substituted into the satisfaction calculation formula to obtain the customer satisfaction score.

[0085] The satisfaction calculation formula is:

[0086] b=3M+F+6PL

[0087] Among them, b is the customer satisfaction score, M is the number of high satisfaction, F is the number of basic satisfaction, P is the number of abandonment, and L is the base number of abandonment;

[0088] The calculation formula for the number of high satisfaction M is:

[0089]

[0090] Among them, M is the number of high satisfaction, r is the lower limit of high satisfaction, n is the upper limit of high satisfaction, C1 is the constant compensation amount of high satisfaction, and x is the comprehensive behavioral data.

[0091] After the real-time analysis module calculates the customer satisfaction score, further analysis is performed on the factors affecting the customer satisfaction score and each behavioral data. Specifically, the customer satisfaction score and each behavioral data are substituted into the analysis factor formula to calculate the associated data, where the analysis factor formula is:

[0092] f(x)=log a b+C

[0093] Among them, b is the customer satisfaction score, C is the compensation amount, a is the behavioral data, and f(x) is the correlation data between the behavioral factor a and the customer satisfaction score.

[0094] Step 114: Present the analysis results to the enterprise management in a visual form.

[0095] In this embodiment, the analysis results are presented to the enterprise management in a visual form. In this embodiment, the analysis results are presented to the enterprise management in a visual form, which can help the management quickly understand the insights behind the data and make more informed decisions.

[0096] Step 115 , automatically generating optimization suggestions for software functions, user experience, or service processes based on the analysis results, and pushing them to the relevant development team, wherein the analysis includes the customer satisfaction score and the key factors affecting satisfaction.

[0097] In this embodiment, the key factors affecting satisfaction are sorted and arranged from large to small, and the scope of optimization suggestions is determined based on the key factors with greater satisfaction, so as to improve the customer satisfaction score and increase the popularity of the software, avoiding the data redundancy of the software caused by optimizing all functions.

[0098] The implementation principle of a data analysis method in the embodiment of the present application is as follows: through the API interface of the enterprise management software, automatically collect the behavioral data of customers in the process of using the software (using the API interface to call customer behavior data to achieve high efficiency and real-time data collection; while improving the flexibility and programmability of data processing); automatically send satisfaction survey questionnaires to customers according to preset trigger conditions (to obtain feedback in a timely manner, compared with traditional survey methods, reduce survey costs and improve survey efficiency); conduct real-time analysis of the collected customer behavior data and satisfaction feedback data to generate customer satisfaction scores, and identify key factors affecting satisfaction (through real-time analysis of customer behavior data, To accurately grasp customer needs and preferences, so that enterprises can quickly respond to market changes and customer needs); based on the analysis results, automatically generate optimization suggestions for software functions, user experience or service processes, and push them to the relevant development team (optimization suggestions generated based on data analysis can often accurately locate specific problems in software functions, user experience or service processes, thereby ensuring that optimization measures can effectively solve user pain points and improve software functions and user experience; through continuous data analysis and optimization suggestion generation, enterprises can form a closed-loop mechanism for continuous improvement, continuously iterate software functions and service processes, ensure that the software always remains at the forefront of the industry, and meet the ever-changing needs of users). Therefore, this embodiment achieves efficient in-depth analysis of customer satisfaction data on the business through in-depth analysis of data and real-time feedback mechanism.

[0099] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A customer satisfaction automated collection system, characterized in that: include: The data collection module is used to automatically collect customer behavior data during the use of the software through the API interface of the enterprise management software; Feedback trigger module, used to automatically send satisfaction questionnaires to customers based on preset trigger conditions; Real-time analysis module, used to analyze collected customer behavior data and satisfaction feedback data in real time, generate customer satisfaction scores, and identify key factors affecting satisfaction; The automatic optimization suggestion module is used to automatically generate optimization suggestions for software functions, user experience or service processes based on the analysis results, and push them to the relevant development team, where the analysis includes the customer satisfaction score and the key factors affecting satisfaction.

2. The customer satisfaction automated collection system according to claim 1, characterized in that: The real-time analysis module includes a computing unit; The calculation unit is used to calculate the customer satisfaction score according to the satisfaction calculation formula, wherein the satisfaction calculation formula is: b=3M+F+6PL Among them, b is the customer satisfaction score, M is the number of high satisfaction, F is the number of basic satisfaction, P is the number of abandonment, and L is the base number of abandonment; The calculation formula of the high satisfaction level M is: Among them, M is high satisfaction, r is the lower limit of high satisfaction, n is the upper limit of high satisfaction, C1 is the constant compensation of high satisfaction, and x is the comprehensive behavioral data.

3. The customer satisfaction automated collection system according to claim 2, characterized in that: The real-time analysis module further includes an analysis unit, which is communicatively connected to the calculation unit; The analyzing unit is configured to analyze a correlation data set between the customer satisfaction score and each of the behavioral data, and perform data analysis on the correlation data set to identify key factors affecting the satisfaction.

4. The customer satisfaction automated collection system according to claim 3, characterized in that: The analysis unit is further configured to substitute the customer satisfaction score and each behavioral data into an analysis factor formula to calculate associated data, wherein the analysis factor formula is: f(x)=log a b+C Among them, b is the customer satisfaction score, C is the compensation amount, a is the behavioral data, and f(x) is the correlation data between the behavioral factor a and the customer satisfaction score.

5. The customer satisfaction automated collection system according to claim 1, characterized in that: The data acquisition module includes an acquisition unit and a preprocessing unit; The collection unit is used to automatically collect behavioral data of customers during the use of the software through the API interface of the enterprise management software; The pre-processing unit is used to perform data pre-processing on the behavior data to obtain smooth behavior data, where the smooth behavior data includes browsing time and function usage level.

6. The customer satisfaction automated collection system according to claim 3, characterized in that: The customer satisfaction automated collection system also includes a visual reporting module; The visualization report module is used to present the analysis results to the enterprise management in a visual form.

7. The customer satisfaction automated collection system according to claim 1, characterized in that: The preset triggering condition is reaching a preset browsing time or triggering a software closing event.

8. A data analysis method, characterized in that: The customer satisfaction automated collection system implemented in any one of claims 1 to 9 comprises: Automatically collect customer behavior data during software use through the API interface of enterprise management software; Automatically send satisfaction surveys to customers based on preset trigger conditions; Analyze collected customer behavior data and satisfaction feedback data in real time to generate customer satisfaction scores and identify key factors affecting satisfaction; Present the analysis results to the company management in a visual form; Based on the analysis results, optimization suggestions for software functions, user experience or service processes are automatically generated and pushed to the relevant development team, wherein the analysis includes the customer satisfaction score and the key factors affecting satisfaction.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the customer satisfaction automatic collection system according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the customer satisfaction automatic collection system according to any one of claims 1 to 7 is implemented.

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