A method and system for recording and analyzing after-sales project data linked to a CRM system

Abstract

The present application relates to a method and system for recording and analyzing after-sales project data of a linkage CRM system, the method comprising obtaining current after-sales project data based on a CRM system, analyzing the current after-sales project data, and obtaining current data explanatory information; judging whether the current data explanatory information corresponds to a preset CRM explanatory index, marking the current after-sales project data based on the CRM explanatory index, and obtaining CRM explanatory corresponding data; judging whether the CRM explanatory corresponding data corresponds to a preset after-sales explanatory index, and recording the current after-sales project data based on the after-sales explanatory index; generating after-sales suggestion information based on the recorded current after-sales project data and the after-sales explanatory index. The present application has the effect of realizing accurate recording and efficient analysis of the full process data of after-sales projects, thereby improving the efficiency of enterprise after-sales service and product quality.

Description

Technical Field

[0001] The present invention relates to the technical field of data governance, and in particular to a method and system for recording and analyzing after-sales project data of a linked CRM system. Background Art

[0002] With the intensification of market competition and the diversification of consumer demands, improving user experience has become the key for companies to win market share and maintain competitiveness. In the field of after-sales service, by collecting and analyzing after-sales project data, companies can gain in-depth understanding of product performance, user feedback, and potential problems, thereby optimizing products and services in a targeted manner and improving user satisfaction.

[0003] At present, most companies use customer relationship management (CRM) systems to manage after-sales project data. CRM systems can record basic customer information, service requests, and maintenance records, etc., providing convenience for companies' after-sales services. However, existing CRM systems have obvious deficiencies in data processing and analysis.

[0004] Specifically, the existing CRM system mainly focuses on data collection and storage, but lacks the ability to deeply analyze and mine data. This makes it difficult for companies to extract valuable information from massive amounts of after-sales project data, and it is impossible to quickly locate product problems and make effective improvement suggestions. Summary of the invention

[0005] In order to achieve accurate recording and efficient analysis of the entire process data of after-sales projects, thereby improving the after-sales service efficiency and product quality of enterprises, the present application provides a method, system, device and storage medium for recording and analyzing after-sales project data in a linked CRM system.

[0006] In the first aspect, the above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0007] A method for recording and analyzing after-sales project data of a linkage CRM system, the method comprising:

[0008] Acquire current after-sales project data based on the CRM system, analyze the current after-sales project data, and obtain explanatory information of the current data;

[0009] Determine whether the current data explanatory information corresponds to a preset CRM explanatory index. If the determination is no, return the current after-sales project data to the CRM system and report an error. If the determination is yes, mark the current after-sales project data based on the CRM explanatory index and obtain CRM explanatory corresponding data.

[0010] Determine whether the CRM explanatory corresponding data corresponds to a preset after-sales explanatory index, if so, record the current after-sales project data based on the after-sales explanatory index, if not, determine a recordable position based on the current data explanatory information corresponding to the CRM explanatory corresponding data, and record the current after-sales project data based on the recordable position;

[0011] After-sales suggestion information is generated based on the recorded current after-sales project data and the after-sales explanatory index.

[0012] By adopting the above technical solution, when after-sales personnel perform after-sales processing, the generated materials, data and information are all recorded in the enterprise's CRM system. Therefore, the CRM system will obtain the after-sales project data collected in real time, and analyze the acquired after-sales project data, for example, semantic analysis or context analysis, so as to determine the specific after-sales processing matter explanation corresponding to the currently obtained after-sales project data; the preset CRM explanatory index refers to the meaning and purpose of each after-sales data recorded by the CRM system obtained by analyzing the current enterprise's CRM system. For example, the CRM system divides the after-sales data storage module into multiple sub-modules in advance, each sub-module corresponds to storing multiple specific after-sales processing matters, and each specific after-sales processing matter is associated with the matter explanation and purpose of the stored data. Therefore, it is determined whether the current data explanatory information corresponds to the CRM system. The preset CRM explanatory index is used to determine whether the after-sales staff has correctly recorded the after-sales data in the CRM system. Another after-sales explanatory index analyzes the interpretation and use of the after-sales project data of each after-sales project obtained from all after-sales projects in the production and operation process of the enterprise. The after-sales explanatory index is in line with the staff's habits of handling after-sales projects and the preferred process when the after-sales project is in progress. If the after-sales project data is collected according to the after-sales explanatory index, the formed data set is conducive to the machine learning model to learn and give suggestions, and in the process of the after-sales project, if the staff wants to review the previous data of the after-sales project or the customer makes a dissatisfied suggestion, this data set can enable the staff to quickly find every subtle processing matter in the entire after-sales project, thereby quickly discovering product problems and helping the company improve after-sales service efficiency and product quality.

[0013] In a preferred example, the present application may be further configured as follows: the CRM explanatory index is obtained by:

[0014] Analyze the current CRM system’s way of recording after-sales project data and obtain the CRM system’s data recording characteristics;

[0015] Based on the data record characteristics of the CRM system, analyzing the explanatory information of each after-sales project data in the CRM system;

[0016] Based on the explanatory information of each after-sales project data in the CRM system, a CRM explanatory index is generated.

[0017] By adopting the above technical solution, the current enterprise's CRM system is analyzed, that is, the way in which the current CRM system records after-sales project data is analyzed. For example, first, the number of modules divided into by the after-sales subsystem of the current CRM system is analyzed, and then which part of the after-sales project data each module is used to record, the type of data each module requires after-sales personnel to fill in, how each module processes the data filled in by after-sales personnel, and the characteristics of the after-sales project data recorded by each module are analyzed. Thus, based on the CRM system data recording characteristics of the data storage module representing the after-sales subsystem of the CRM system, the interpretation and use of each unit after-sales project data when recorded in the CRM system is analyzed on the basis of the data storage module of the after-sales subsystem of each CRM system, and then based on the interpretation and use of each unit after-sales project data when recorded in the CRM system, a CRM explanatory index is generated. Therefore, each time after-sales project data is obtained, the explanatory information of the after-sales project data can be queried and compared with the CRM explanatory index to determine whether the record of the after-sales project data is correct, thereby ensuring the accurate recording of the full process data of the after-sales project.

[0018] In a preferred example, the present application can be further configured as follows: the after-sales explanatory index is obtained by:

[0019] Obtain historical after-sales event information and historical after-sales project data;

[0020] Analyze the historical after-sales event information and the historical after-sales project data to obtain first explanatory information corresponding to the historical after-sales event information and second explanatory information corresponding to the historical after-sales project data;

[0021] The CRM system data record feature is extracted, the first explanatory information and the second explanatory information are correspondingly associated with the CRM system data record feature, and a post-sales explanatory index is generated.

[0022] By adopting the above technical solution, the CRM explanatory index only analyzes the data storage characteristics of the CRM system, and the CRM system currently used by the enterprise may not match the after-sales projects generated by the current enterprise in the production and operation process. This will result in that even if all after-sales project data are completely and correctly recorded in the CRM system, it is still difficult to accurately query the required information from the CRM system. In addition, since the CRM system currently used by the enterprise may not match the after-sales projects generated by the current enterprise in the production and operation process, the product problems obtained by analyzing only the data recorded in the CRM system may not be the actual product problems. Therefore, the obtained Take historical after-sales event information and historical after-sales project data. At this time, the after-sales event information and after-sales project data are not based on the framework of the after-sales subsystem of the current CRM system, but each information and data is independent. Therefore, analyzing the historical after-sales event information and historical after-sales project data at this time is to analyze the characteristics of the after-sales projects and after-sales events themselves, that is, to analyze the logic of the after-sales events themselves and the after-sales problems targeted by the after-sales projects themselves. Therefore, the first explanatory information and the second explanatory information obtained can better reveal after-sales problems, and analyzing the data set generated based on the after-sales explanatory index can better locate product problems and put forward substantive and feasible improvement suggestions.

[0023] In a preferred example, the present application may be further configured as follows: the first explanatory information and the second explanatory information are associated with the CRM system data record features to generate a post-sales explanatory index, specifically including:

[0024] Analyze the historical after-sales event information to obtain after-sales event process information, and analyze the historical after-sales project data to obtain data process information;

[0025] Analyze the after-sales event process information and the data process information to obtain standardized process information;

[0026] Adjust the CRM system data record characteristics according to the standardized process information to obtain index process information;

[0027] The first explanatory information and the second explanatory information are correspondingly associated with the index process information to generate a post-sales explanatory index.

[0028] By adopting the above technical scheme, the first explanatory information, the second explanatory information and the CRM system data record characteristics all represent each different after-sales project data. Even if the first explanatory information and the second explanatory information are different from the CRM system data record characteristics, the first explanatory information, the second explanatory information and the CRM system data record characteristics are all obtained based on the analysis of the same after-sales project. Therefore, there is a certain correlation between the first explanatory information, the second explanatory information and the CRM system data record characteristics. For example, the modules divided by the after-sales subsystem of the CRM system are based on the different processes of the after-sales project, and the historical after-sales event information and the historical after-sales project data can also represent the different processes of the after-sales project. Therefore, based on the modules divided by the after-sales subsystem of the CRM system, the historical after-sales event information and the historical after-sales project data are analyzed, so that the levels of the after-sales event process information and the data process information can correspond to the levels of the data storage modules of the after-sales subsystem of the CRM system, that is, the after-sales event process information or the data process information represents The amount of after-sales project data covered by a process is the same as the amount of after-sales project data covered by a data storage module of the after-sales subsystem of the CRM system. Then, the after-sales event process information and the data process information are analyzed together to determine a preferred process information for the current after-sales project. The level of the preferred process information can also correspond to the level of the data storage module of the after-sales subsystem of the CRM system, and the preferred process information can better reveal after-sales problems. Therefore, under the same level, the standardized process information is used to adjust the data recording characteristics of the CRM system, that is, adjust the types of after-sales project data covered by the data storage module of the after-sales subsystem of the CRM system, and generate index process information, so that the types of after-sales data corresponding to the data recording characteristics of the CRM system are the same as the types of data corresponding to the standardized process information. In this way, when the after-sales project data is recorded in the CRM system, it is still based on the division of the storage module of the CRM system itself, but when analyzing the after-sales project data, it is analyzed based on the index process information to ensure the accuracy of the problem analysis.

[0029] In a preferred example, the present application may be further configured as follows: the step of correspondingly associating the first explanatory information and the second explanatory information with the index process information to generate a post-sales explanatory index specifically includes:

[0030] analyzing the first explanatory information and the second explanatory information together to obtain correlation information between the first explanatory information and the second explanatory information;

[0031] Based on the association information, associating the first explanatory information with the second explanatory information to obtain index explanatory information;

[0032] The index explanatory information is associated with the index process information to generate a post-sales explanatory index.

[0033] By adopting the above technical solution, the first explanatory information and the second explanatory information are obtained based on the historical after-sales event information and the historical after-sales project data, respectively. Although the historical after-sales event information and the historical after-sales project data are for the same after-sales project, the information density of the historical after-sales event information and the historical after-sales project data is different. Usually, the information density of the historical after-sales event information is lower than that of the historical after-sales project data. That is, for the same after-sales project, more second explanatory information is obtained by analyzing the historical after-sales project data, while relatively less first explanatory information is obtained by analyzing the historical after-sales event information. In addition, the second explanatory information and the CRM system data record characteristics are obtained by analyzing the unit data. After-sales event information is obtained by analyzing unit event information, so the granularity of the first explanatory information is greater than that of the second explanatory information, and the index process information is generated based on the CRM system data record characteristics obtained from the unit data analysis. Therefore, the granularity of the data types covered by the index process information is the same as the granularity of the second explanatory information, and the first explanatory information cannot be directly associated with the index process information. Therefore, if the first explanatory information and the second explanatory information are to be associated with the index process information accordingly, it is necessary to first associate the second explanatory information with the first explanatory information, and then associate the first explanatory information and the second explanatory information with the index process information based on the second explanatory information.

[0034] In the second aspect, the above invention objective of the present application is achieved through the following technical solutions:

[0035] A post-sales project data recording and analysis system of a linkage CRM system, the post-sales project data recording and analysis system of the linkage CRM system comprising:

[0036] A current interpretation analysis module is used to obtain current after-sales project data based on the CRM system, analyze the current after-sales project data, and obtain current data explanatory information;

[0037] A first judgment module is used to judge whether the current data explanatory information corresponds to a preset CRM explanatory index. If the judgment is no, the current after-sales project data is returned to the CRM system and an error is reported. If the judgment is yes, the current after-sales project data is marked based on the CRM explanatory index to obtain CRM explanatory corresponding data;

[0038] A second judgment module is used to judge whether the CRM explanatory corresponding data corresponds to a preset after-sales explanatory index. If it is judged to be yes, the current after-sales project data is recorded based on the after-sales explanatory index. If it is judged to be no, a recordable position is judged according to the current data explanatory information corresponding to the CRM explanatory corresponding data, and the current after-sales project data is recorded based on the recordable position.

[0039] The suggestion analysis module is used to generate after-sales suggestion information based on the recorded current after-sales project data and the after-sales explanatory index.

[0040] Optionally, the after-sales project data recording and analysis system of the linkage CRM system further includes:

[0041] CRM analysis module, used to analyze the way the current CRM system records after-sales project data and obtain the data recording characteristics of the CRM system;

[0042] A CRM interpretation and analysis module, used to analyze the explanatory information of each after-sales project data in the CRM system based on the data record characteristics of the CRM system;

[0043] The first index generating module is used to generate a CRM explanatory index based on the explanatory information of each after-sales project data in the CRM system.

[0044] Optionally, the after-sales project data recording and analysis system of the linkage CRM system further includes:

[0045] A historical information acquisition module is used to obtain historical after-sales event information and historical after-sales project data;

[0046] A history analysis module, used for analyzing the historical after-sales event information and the historical after-sales project data, and obtaining first explanatory information corresponding to the historical after-sales event information and second explanatory information corresponding to the historical after-sales project data;

[0047] The second index generating module is used to extract the CRM system data record characteristics, associate the first explanatory information and the second explanatory information with the CRM system data record characteristics, and generate a post-sales explanatory index.

[0048] In the third aspect, the above invention objective of the present application is achieved through the following technical solutions:

[0049] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the after-sales project data record analysis method of the linkage CRM system when executing the computer program.

[0050] In a fourth aspect, the above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0051] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the after-sales project data record analysis method of the linkage CRM system.

[0052] In summary, the present application includes at least one of the following beneficial technical effects:

[0053] 1. When after-sales personnel perform after-sales processing, the information, data and information generated are all recorded in the company's CRM system. Therefore, the CRM system will obtain the after-sales project data collected in real time and analyze the acquired after-sales project data, for example, semantic analysis or context analysis, to determine the specific after-sales processing item explanation corresponding to the currently obtained after-sales project data; the preset CRM explanatory index refers to the information obtained by analyzing the current company's CRM system to explain the meaning and purpose of each after-sales data recorded in the CRM system. For example, the CRM system divides the after-sales data storage module into multiple sub-modules in advance, each sub-module corresponds to storing multiple specific after-sales processing items, and each specific after-sales processing item is associated with the item explanation and purpose of the stored data. Therefore, it is determined whether the current data explanatory information corresponds to the preset CRM explanatory index is used to determine whether after-sales personnel have correctly recorded after-sales data in the CRM system. Another after-sales explanatory index is to analyze the interpretation and use of after-sales project data of each after-sales project obtained by the company during the production and operation process. This after-sales explanatory index is in line with the staff's habits of handling after-sales projects and the preferred process when the after-sales project is in progress. If the after-sales project data is collected based on the after-sales explanatory index, the formed data set is conducive to the machine learning model to learn and give suggestions. In addition, during the after-sales project, if the staff wants to review the previous data of the after-sales project or the customer makes a dissatisfied suggestion, this data set can enable the staff to quickly find every subtle processing item in the entire after-sales project, thereby quickly discovering product problems and helping the company improve after-sales service efficiency and product quality.

[0054] 2. Analyze the current enterprise's CRM system, that is, analyze the way the current CRM system records after-sales project data. For example, first analyze how many modules the after-sales subsystem of the current CRM system is divided into, then analyze which part of the after-sales project data each module is used to record, the type of data each module requires after-sales personnel to fill in, how each module processes the data filled in by after-sales personnel, and the characteristics of the after-sales project data recorded by each module, etc. Based on the above-mentioned CRM system data recording characteristics of the data storage module representing the after-sales subsystem of the CRM system, analyze the interpretation and use of each unit's after-sales project data when recorded in the CRM system on the basis of the data storage module of the after-sales subsystem of each CRM system, and then generate a CRM explanatory index based on the interpretation and use of each unit's after-sales project data when recorded in the CRM system. Therefore, each time after-sales project data is obtained, the explanatory information of the after-sales project data can be queried and compared with the CRM explanatory index to determine whether the record of the after-sales project data is correct, thereby ensuring the accurate recording of the full process data of the after-sales project;

[0055] 3. The CRM explanatory index only analyzes the data storage characteristics of the CRM system. The CRM system currently used by the enterprise may not match the after-sales items generated by the current enterprise during the production and operation process. This will result in that even if all after-sales project data is completely and correctly recorded in the CRM system, it is still difficult to accurately query the required information from the CRM system. In addition, since the CRM system currently used by the enterprise may not match the after-sales items generated by the current enterprise during the production and operation process, the product problems obtained by analyzing only the data recorded in the CRM system may not be the actual product problems. Therefore, it is necessary to obtain historical after-sales information. Event information and historical after-sales project data. At this time, the after-sales event information and after-sales project data are not based on the framework of the after-sales subsystem of the current CRM system, but each information and data are independent. Therefore, analyzing the historical after-sales event information and historical after-sales project data at this time is to analyze the characteristics of the after-sales projects and after-sales events themselves, that is, to analyze the logic of the after-sales events themselves and the after-sales problems targeted by the after-sales projects themselves. Therefore, the first explanatory information and the second explanatory information obtained can better reveal after-sales problems, and analyzing the data set generated based on the after-sales explanatory index can better locate product problems and put forward substantive and feasible improvement suggestions. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flowchart of an implementation of the after-sales project data recording and analysis method of the linkage CRM system in the embodiment of the present application;

[0057] Figure 2 is a flowchart of the method for obtaining the CRM explanatory index in the embodiment of the present application;

[0058] Figure 3 is a flowchart of the method for obtaining the after-sales explanatory index in the embodiment of the present application;

[0059] Figure 4 It is a flowchart for implementing S06 of the after-sales project data recording and analysis method of the linkage CRM system in the embodiment of the present application;

[0060] Figure 5 This is a flowchart for implementing S064 of the after-sales project data recording and analysis method of the linkage CRM system in the embodiment of the present application;

[0061] Figure 6 This is a principle block diagram of the after-sales project data recording and analysis system of the linkage CRM system in the embodiment of the present application;

[0062] Figure 7 It is a diagram of the internal structure of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0063] The following is combined with Figure 1-7 This application is described in further detail.

[0064] In one embodiment, if Figure 1 As shown, the present application discloses a method for recording and analyzing after-sales project data of a linkage CRM system, which specifically includes the following steps:

[0065] S10: Acquire current after-sales project data based on the CRM system, analyze the current after-sales project data, and obtain explanatory information of the current data.

[0066] In this embodiment, the enterprise uses the after-sales subsystem in the CRM system to collect and record after-sales project data. The current after-sales project data refers to the after-sales project data currently collected by the CRM system. The current data explanatory information refers to the explanation and use information of the after-sales project data.

[0067] Specifically, when after-sales personnel handle after-sales projects, the generated materials, data, and information are all recorded in the after-sales subsystem of the enterprise's CRM system. Therefore, based on the CRM system, the after-sales project data currently collected by the after-sales subsystem of the CRM system, that is, the current after-sales project data, is obtained, and the current after-sales project data is analyzed, for example, semantic analysis or context analysis, to determine the explanation and usage information of the specific after-sales processing items corresponding to the current after-sales project data, and obtain the current data explanatory information. For example, the current after-sales project data is the amount of compensation claimed by the customer, then the corresponding current data explanatory information may be text mark information that explains the current after-sales project data, text mark information that indicates the specific after-sales processing items corresponding to the current after-sales project data, and text mark information that indicates the specific after-sales processing items corresponding to the current after-sales project data. Textual marking information on the purpose of after-sales processing items, etc. In addition, the current after-sales project data also includes customer feedback information, but it should be noted that customer feedback information does not exist independently, that is, customer feedback information is always associated with specific after-sales processing items that represent the problems handled by after-sales personnel. Therefore, when analyzing specific after-sales processing items containing customer feedback information, it is necessary to jointly analyze the customer feedback information and the specific after-sales processing items, that is, to further determine the explanation and purpose information of the specific after-sales processing items through the customer feedback information, so as to improve the accuracy of the current data explanatory information judged, and through the after-sales process represented by the specific after-sales processing items, further judge the true intention of the customer represented by the customer feedback information, thereby enriching the content of the current data explanatory information and improving the accuracy of the current data explanatory information.

[0068] S20: Determine whether the current data explanatory information corresponds to the preset CRM explanatory index. If it is not, return the current after-sales project data to the CRM system and report an error. If it is yes, mark the current after-sales project data based on the CRM explanatory index and obtain the CRM explanatory corresponding data.

[0069] In this embodiment, the preset CRM explanatory index refers to an index obtained by analyzing the current enterprise's CRM system and used to explain the meaning and purpose of each after-sales project data recorded by the CRM system.

[0070] Specifically, after obtaining the current after-sales project data, it is necessary to determine whether the after-sales personnel have correctly recorded the current after-sales project data in the CRM system. Therefore, a preset CRM explanatory index is extracted. The preset CRM explanatory index refers to an index obtained by analyzing the current enterprise's CRM system for explaining the meaning and purpose of each after-sales project data recorded in the CRM system. For example, the after-sales project data storage module of the after-sales subsystem in the CRM system is divided into multiple sub-modules, and each sub-module stores a variety of after-sales project data and specific after-sales processing matters. Therefore, each sub-module and the corresponding after-sales project data are analyzed to obtain the specific after-sales processing matters corresponding to the multiple after-sales project data, and then each specific after-sales processing matter is analyzed to obtain the CRM explanatory index representing the matter explanation and purpose of each after-sales project data. It should be noted that the matter explanation and purpose information of each after-sales project data in the CRM explanatory index also includes the text mark information of each after-sales project data, the text mark information representing the specific after-sales processing matter corresponding to each current after-sales project data, and the text mark information representing the purpose of the specific after-sales processing matter corresponding to each current after-sales project data, etc.

[0071] Therefore, it is determined whether the current data explanatory information corresponds to the preset CRM explanatory index, that is, whether the current after-sales project data is correctly stored in the after-sales project data storage module of the corresponding after-sales subsystem. If the judgment is no, the current after-sales project data is returned to the CRM system and an error is reported to remind the staff to verify the current after-sales project data; if the judgment is yes, based on the explanation and usage information of the current after-sales project data in the CRM explanatory index, the current after-sales project data is associated and marked to obtain the CRM explanatory corresponding data. At this time, the CRM explanatory corresponding data includes the current after-sales project data, and the matter explanation and usage information of the after-sales project data corresponding to the current after-sales project data obtained from the CRM explanatory index.

[0072] S30: Determine whether the CRM explanatory corresponding data corresponds to the preset after-sales explanatory index. If it is, record the current after-sales project data based on the after-sales explanatory index. If it is not, determine the recordable position based on the current data explanatory information corresponding to the CRM explanatory corresponding data, and record the current after-sales project data based on the recordable position.

[0073] In this embodiment, the preset after-sales explanatory index refers to an index of the interpretation and usage of after-sales project data of each after-sales project obtained by analyzing all after-sales projects of the enterprise during the production and operation process.

[0074] Specifically, a preset after-sales explanatory index is extracted. The preset after-sales explanatory index refers to an index of the interpretation and use of after-sales project data of each after-sales project obtained by analyzing all after-sales projects of the enterprise during the production and operation process. The after-sales explanatory index conforms to the staff's habit of handling after-sales projects and the preferred process when the after-sales project is in progress. If the after-sales project data is collected according to the after-sales explanatory index, the formed data set is conducive to the machine learning model to learn and give suggestions. In the process of the after-sales project, if the staff wants to review the previous data of the after-sales project or the customer makes a dissatisfied suggestion, this data set enables the staff to quickly find every subtle processing item in the entire after-sales project. Therefore, it is necessary to re-collect and manage all the current after-sales project data obtained based on the preset after-sales explanatory index. It should be noted that the item explanation and use information of each after-sales project data in the after-sales explanatory index also includes the text mark information of each after-sales project data, the text mark information indicating the specific after-sales processing item corresponding to each current after-sales project data, and the text mark information indicating the use of the specific after-sales processing item corresponding to each current after-sales project data, etc.;

[0075] Therefore, it is determined whether the CRM explanatory corresponding data corresponds to the preset after-sales explanatory index. If it is determined to be yes, the current after-sales project data is recorded based on the after-sales explanatory index, and the current data explanatory information corresponding to the current after-sales project data is recorded together. If it is determined to be no, the recordable position of the current after-sales project data is determined according to the current data explanatory information corresponding to the CRM explanatory corresponding data, that is, the newly inserted position in the after-sales explanatory index, because the preset after-sales explanatory index corresponds to the processing flow of the current after-sales project. If it is determined that the CRM explanatory corresponding data does not correspond to the preset after-sales explanatory index, it means that the after-sales matter corresponding to the current after-sales project data is a new matter that has never appeared. Therefore, according to the current data explanatory information corresponding to the CRM explanatory corresponding data, it is determined at which position in the processing flow of the current after-sales project the new matter represented by the current after-sales project data should be located, and the current after-sales project data is recorded at that position, and the current data explanatory information corresponding to the current after-sales project data is recorded at that position together.

[0076] Furthermore, when it is determined that the after-sales matter corresponding to the current after-sales project data is a new matter that has never appeared, the after-sales explanatory index is updated based on the current after-sales project data and the corresponding current data explanatory information.

[0077] S40: Generate after-sales suggestion information based on the recorded current after-sales project data and the after-sales explanatory index.

[0078] Specifically, after completing the recording of the current after-sales project data, all the current after-sales project data recorded based on the after-sales explanatory index are analyzed to determine the suggestion information for the next after-sales work of the after-sales personnel, that is, the after-sales suggestion information. For example, all the current after-sales project data recorded based on the after-sales explanatory index can be input into a preset after-sales analysis model, and the after-sales analysis model analyzes the currently completed after-sales matters, determines the next appropriate after-sales behavior, and outputs the after-sales suggestion information. In addition, for the current after-sales project data including customer feedback information, the after-sales analysis model will perform additional contextual analysis on all customer feedback information, that is, analyze all types of customer feedback information, and generate additional after-sales suggestion information based on all types of customer feedback information, and then determine whether the additional after-sales suggestion information conflicts with the generated after-sales suggestion information, and use the additional after-sales suggestion information as a high-weight suggestion. When it is determined that there is a conflict, the additional after-sales suggestion information generated based on the customer feedback information is used as the main suggestion, and the conflicting generated after-sales suggestion information can be deleted.

[0079] Furthermore, if the current after-sales project data is recorded in the last position in the after-sales explanatory index, that is, the after-sales matter corresponding to the current after-sales project data is the last step of the current after-sales project, then after completing the recording of the current after-sales project data, when analyzing all the current after-sales project data recorded based on the after-sales explanatory index, the problem of improper after-sales handling of the entire after-sales project is analyzed.

[0080] In one embodiment, if Figure 2 As shown, the CRM explanatory index is obtained in the following way:

[0081] S01: Analyze the current CRM system's way of recording after-sales project data and obtain the CRM system's data recording characteristics.

[0082] In this embodiment, the CRM system data record characteristics refer to the type and characteristic information of each after-sales project data covered by each after-sales project data storage module of the after-sales subsystem in the CRM system.

[0083] Specifically, the current enterprise's CRM system is analyzed, that is, the way the current CRM system records after-sales project data is analyzed. For example, first, it is analyzed how many modules the after-sales subsystem of the current CRM system is divided into, and then it is analyzed which part of the after-sales project data each module is used to record, the type of data each module requires after-sales personnel to fill in, how each module processes the data filled in by after-sales personnel, and the type of after-sales project data recorded by each module, etc., to obtain the data recording characteristics of the CRM system.

[0084] S02: Based on the data record characteristics of the CRM system, analyze the explanatory information of each after-sales project data in the CRM system.

[0085] Specifically, based on the type and characteristic information of each after-sales project data covered by each after-sales project data storage module of the after-sales subsystem in the CRM system represented by the data record characteristics of the CRM system, the interpretation and purpose of each after-sales project data in the CRM system is analyzed to obtain the explanatory information of each after-sales project data in the CRM system.

[0086] Furthermore, exemplary data can be generated based on the type and characteristic information of each after-sales project data covered by each after-sales project data storage module of the after-sales subsystem in the CRM system represented by the CRM system data record characteristics, and then the interpretation and use of each exemplary data in the CRM system can be analyzed.

[0087] S03: Generate a CRM explanatory index based on the explanatory information of each after-sales project data in the CRM system.

[0088] Specifically, based on the explanatory information of each after-sales project data in the CRM system, a CRM explanatory index is generated under the framework of each after-sales project data storage module of the after-sales subsystem in the CRM system.

[0089] It should be noted that different after-sales projects in the current enterprise's production and operation process have different CRM explanatory indexes.

[0090] In one embodiment, if Figure 3 As shown, the post-sale explanatory index is obtained by:

[0091] S04: Obtain historical after-sales event information and historical after-sales project data.

[0092] Specifically, historical after-sales event information and historical after-sales project data are obtained, wherein historical after-sales event information refers to the after-sales event marking information corresponding to historical after-sales projects, and historical after-sales project data refers to all data generated by historical after-sales projects. For example, historical after-sales event information may be matter information manually marked by after-sales personnel or the process for handling after-sales projects drawn by after-sales personnel, while historical after-sales project data refers to all data and information of after-sales projects obtained from all channels, including numbers, text, and charts.

[0093] S05: Analyze the historical after-sales event information and the historical after-sales project data to obtain first explanatory information corresponding to the historical after-sales event information and second explanatory information corresponding to the historical after-sales project data.

[0094] Specifically, the current historical after-sales event information and historical after-sales project data are not based on the framework of the after-sales subsystem of the current CRM system, but each information and data is independent. Therefore, analyzing the historical after-sales event information and historical after-sales project data at this time is to analyze the characteristics of the after-sales projects and after-sales events themselves, that is, to analyze the logic of the after-sales events themselves and the after-sales problems targeted by the after-sales projects themselves, so as to obtain the first explanatory information corresponding to the historical after-sales event information and the second explanatory information corresponding to the historical after-sales project data.

[0095] S06: Extracting CRM system data record features, correspondingly associating the first explanatory information and the second explanatory information with the CRM system data record features, and generating a post-sales explanatory index.

[0096] Specifically, since the historical after-sales event information and the historical after-sales project data are different, the corresponding first explanatory information and the second explanatory information are also different from each other. Therefore, in order to associate the first explanatory information and the second explanatory information with the CRM system data record characteristics, it is necessary to associate the first explanatory information and the second explanatory information with the CRM system data record characteristics respectively. Therefore, firstly, the degree of correspondence between the first explanatory information and the second explanatory information and the CRM system data record characteristics is determined, that is, the second explanatory information corresponds to the type and characteristic information of each after-sales project data storage module of the after-sales subsystem in the CRM system represented by the CRM system data record characteristics, while the first explanatory information only corresponds to the type and characteristic information of each after-sales project data storage module of the after-sales subsystem in the CRM system represented by the CRM system data record characteristics. Each after-sales project data storage module, therefore, analyze the after-sales process corresponding to each after-sales project data storage module of the after-sales subsystem in the CRM system represented by the CRM system data record characteristics, and then analyze the explanation and purpose of the after-sales process corresponding to each after-sales project data storage module of the after-sales subsystem in the CRM system represented by the CRM system data record characteristics; based on this, associate the first explanatory information with the explanation and purpose information of the after-sales process corresponding to each after-sales project data storage module of the after-sales subsystem in the CRM system represented by the CRM system data record characteristics, and associate the second explanatory information with the type and characteristic information of each after-sales project data covered by each after-sales project data storage module of the after-sales subsystem in the CRM system represented by the CRM system data record characteristics, so as to generate an after-sales explanatory index.

[0097] It should be noted that different after-sales projects in the current enterprise's production and operation process have different CRM explanatory indexes, as well as after-sales explanatory indexes corresponding to the CRM explanatory indexes.

[0098] Furthermore, when analyzing historical after-sales event information and historical after-sales project data, customer feedback information can be extracted separately and analyzed to obtain third explanatory information corresponding to the customer feedback information, and the third explanatory information is associated with the CRM system data record characteristics to generate a customer feedback explanatory index. It should be noted that when analyzing customer feedback information, each customer feedback information is not analyzed as an independent information, but the customer feedback information of the entire after-sales closed-loop process, as well as the after-sales event information and historical after-sales project data representing the specific work content of the after-sales personnel are analyzed to determine which solution obtains more positive customer feedback when facing the same specific process of the after-sales project, so as to obtain the preferred solution information for each specific process of the corresponding after-sales project. Therefore, the third explanatory information includes the true meaning of the customer feedback information and the solution information for the customer feedback, and the customer feedback explanatory index is connected in parallel with the after-sales explanatory index.

[0099] In one embodiment, if Figure 4 As shown, in step S06, the first explanatory information and the second explanatory information are associated with the CRM system data record characteristics to generate a post-sales explanatory index, which specifically includes:

[0100] S061: Analyze historical after-sales event information, obtain after-sales event process information, analyze historical after-sales project data, and obtain data process information.

[0101] In this embodiment, the after-sales event process information refers to the processing flow of after-sales items obtained based on the analysis of historical after-sales event information. The data process information refers to the processing flow of after-sales items obtained based on the analysis of historical after-sales item data.

[0102] Specifically, the first explanatory information, the second explanatory information and the CRM system data record characteristics all represent each different after-sales project data. Even if the first explanatory information and the second explanatory information are different from the CRM system data record characteristics, the first explanatory information, the second explanatory information and the CRM system data record characteristics are all obtained based on the analysis of the same after-sales project. Therefore, there is a certain correlation between the first explanatory information, the second explanatory information and the CRM system data record characteristics. For example, the after-sales project data storage module divided by the after-sales subsystem of the CRM system is based on different processes of the after-sales project, and the historical after-sales event information and historical after-sales project data can also represent after-sales. Different processes of the project, therefore, the after-sales project data storage module divided based on the after-sales subsystem of the CRM system analyzes the historical after-sales event information and historical after-sales project data according to the different processes of the after-sales projects, and obtains the after-sales event process information corresponding to the historical after-sales event information, and the data flow information corresponding to the historical after-sales project data, so that the levels of the after-sales event process information and the data flow information can correspond to the level of the data storage module of the after-sales subsystem of the CRM system, that is, the number of after-sales project data types covered by a process represented by the after-sales event process information or the data flow information is the same as the number of after-sales project data types covered by a data storage module of the after-sales subsystem of the CRM system.

[0103] S062: Analyze the after-sales event process information and data process information to obtain standardized process information.

[0104] Specifically, the after-sales event process information and the data flow information respectively represent the processing flows of two different after-sales projects. Therefore, the after-sales event process information and the data flow information are analyzed together to determine a preferred process information for the current after-sales project and obtain standardized process information. For example, the after-sales event process information and the data flow information are input into a preset after-sales analysis model for analyzing the problem of improper after-sales processing of the entire after-sales project. Based on the after-sales analysis model, the unreasonable parts of the processing flows of the after-sales projects respectively represented by the after-sales event process information and the data flow information are determined. Then, the unreasonable flows are removed from the after-sales event process information and the data flow information and then merged to obtain standardized process information. The number of after-sales project data types covered by the standardized process information is the same as the number of after-sales project data types covered by a data storage module of the after-sales subsystem of the CRM system.

[0105] S063: Adjust the CRM system data record characteristics according to the standardized process information to obtain index process information.

[0106] Specifically, the number of after-sales project data types covered by the standardized process information is the same as the number of after-sales project data types covered by a data storage module of the after-sales subsystem of the CRM system, but the standardized process information does not actually include the after-sales project data types and feature information. Therefore, the data record characteristics of the CRM system are adjusted according to the standardized process information, that is, the after-sales project data types and feature information covered by the data storage module of the after-sales subsystem of the CRM system are adjusted to obtain the indexed process information, so that the after-sales data types corresponding to the CRM system data record characteristics are the same as the data types corresponding to the standardized process information. The indexed process information includes the preferred process information for the current after-sales project, as well as the after-sales project data types and feature information covered by each step of the process.

[0107] S064: Correspondingly associating the first explanatory information and the second explanatory information with the index process information to generate an after-sales explanatory index.

[0108] Specifically, since the historical after-sales event information and the historical after-sales project data are different, the corresponding first explanatory information and second explanatory information are also different from each other. Therefore, in order to associate the first explanatory information and the second explanatory information with the index process information, it is necessary to associate the first explanatory information and the second explanatory information with the index process information respectively. Therefore, firstly determine the degree of correspondence between the first explanatory information and the second explanatory information and the index process information, that is, the second explanatory information corresponds to the type and characteristic information of the after-sales project data covered by each step of the process represented by the index process information, and the first explanatory information corresponds to the preferred process information of the after-sales project represented by the index process information. Therefore, the first explanatory information is respectively associated with the preferred process information of the after-sales project represented by the index process information, and the second explanatory information is respectively associated with the type and characteristic information of the after-sales project data covered by each step of the process represented by the index process information, so as to generate an after-sales explanatory index.

[0109] In one embodiment, if Figure 5 As shown, in step S064, the first explanatory information and the second explanatory information are associated with the index process information to generate a post-sales explanatory index, which specifically includes:

[0110] S0641: Analyze the first explanatory information and the second explanatory information together to obtain correlation information between the first explanatory information and the second explanatory information.

[0111] Specifically, the first explanatory information and the second explanatory information are obtained based on the historical after-sales event information and the historical after-sales project data, respectively. Although the historical after-sales event information and the historical after-sales project data are for the same after-sales project, the information density of the historical after-sales event information and the historical after-sales project data is different. Usually, the information density of the historical after-sales event information is lower than that of the historical after-sales project data. That is, for the same after-sales project, more second explanatory information is obtained by analyzing the historical after-sales project data, while relatively less first explanatory information is obtained by analyzing the historical after-sales event information. In addition, the second explanatory information and the CRM system data record characteristics are both obtained by analyzing unit data. The historical after-sales event information is obtained by analyzing the unit event information, so the granularity of the first explanatory information is greater than that of the second explanatory information, and the index process information is generated based on the CRM system data record characteristics obtained by analyzing the unit data. Therefore, the granularity of the data types covered by the index process information is the same as that of the second explanatory information. The first explanatory information cannot be directly associated with the index process information. Therefore, if the first explanatory information and the second explanatory information are to be associated with the index process information accordingly, the first explanatory information and the second explanatory information are first analyzed together to determine the correlation information between the first explanatory information and the second explanatory information.

[0112] S0642: Based on the association information, associate the first explanatory information with the second explanatory information to obtain index explanatory information.

[0113] Specifically, based on the association information, it can be known that one first explanatory information corresponds to a plurality of second explanatory information, so each first explanatory information is associated with the corresponding plurality of second explanatory information to obtain index explanatory information.

[0114] S0643: Associating the index explanatory information with the index process information to generate a post-sales explanatory index.

[0115] Specifically, the index process information includes preferred process information for the current after-sales project, and the types and characteristic information of after-sales project data covered by each step of the process, and the index explanatory information includes first explanatory information and multiple second explanatory information corresponding to the first explanatory information, wherein the preferred process information for the current after-sales project in the index process information corresponds to the first explanatory information in the index explanatory information, and the types and characteristic information of after-sales project data covered by each step of the process in the index process information corresponds to the second explanatory information; therefore, the index explanatory information is associated with the index process information to generate an after-sales explanatory index.

[0116] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0117] In one embodiment, a post-sales project data recording and analysis system of a linkage CRM system is provided, and the post-sales project data recording and analysis system of the linkage CRM system corresponds one-to-one to the post-sales project data recording and analysis method of the linkage CRM system in the above embodiment. Figure 6 As shown, the after-sales project data record analysis system of the linkage CRM system includes a current interpretation analysis module, a first judgment module, a second judgment module and a suggestion analysis module. The detailed description of each functional module is as follows:

[0118] The current interpretation analysis module is used to obtain the current after-sales project data based on the CRM system, analyze the current after-sales project data, and obtain the current data explanatory information;

[0119] The first judgment module is used to judge whether the current data explanatory information corresponds to the preset CRM explanatory index. If it is judged as no, the current after-sales project data is returned to the CRM system and an error is reported. If it is judged as yes, the current after-sales project data is marked based on the CRM explanatory index to obtain the CRM explanatory corresponding data;

[0120] The second judgment module is used to judge whether the CRM explanatory corresponding data corresponds to a preset after-sales explanatory index. If it is judged to be yes, the current after-sales project data is recorded based on the after-sales explanatory index. If it is judged to be no, the recordable position is judged according to the current data explanatory information corresponding to the CRM explanatory corresponding data, and the current after-sales project data is recorded based on the recordable position.

[0121] The suggestion analysis module is used to generate after-sales suggestion information based on the recorded current after-sales project data and the after-sales explanatory index.

[0122] Optionally, the after-sales project data recording and analysis system linked to the CRM system also includes:

[0123] CRM analysis module, used to analyze the way the current CRM system records after-sales project data and obtain the data recording characteristics of the CRM system;

[0124] CRM interpretation and analysis module, which is used to analyze the explanatory information of each after-sales project data in the CRM system based on the data record characteristics of the CRM system;

[0125] The first index generating module is used to generate a CRM explanatory index based on the explanatory information of each after-sales project data in the CRM system.

[0126] Optionally, the after-sales project data recording and analysis system linked to the CRM system also includes:

[0127] A historical information acquisition module is used to obtain historical after-sales event information and historical after-sales project data;

[0128] A historical analysis module, used for analyzing historical after-sales event information and historical after-sales project data, and obtaining first explanatory information corresponding to the historical after-sales event information and second explanatory information corresponding to the historical after-sales project data;

[0129] The second index generation module is used to extract CRM system data record features, associate the first explanatory information and the second explanatory information with the CRM system data record features, and generate a post-sales explanatory index.

[0130] Optionally, the second index generating module includes:

[0131] The process analysis submodule is used to analyze historical after-sales event information, obtain after-sales event process information, analyze historical after-sales project data, and obtain data process information;

[0132] The standard process submodule is used to analyze the after-sales event process information and data process information to obtain standardized process information;

[0133] The feature adjustment submodule is used to adjust the CRM system data record features according to the standardized process information to obtain the index process information;

[0134] The associating submodule is used to associate the first explanatory information and the second explanatory information with the index process information to generate a post-sales explanatory index.

[0135] Optionally, associated submodules include:

[0136] A joint analysis unit, used for jointly analyzing the first explanatory information and the second explanatory information to obtain correlation information between the first explanatory information and the second explanatory information;

[0137] A first associating unit, configured to associate the first explanatory information with the second explanatory information based on the association information to obtain index explanatory information;

[0138] The second associating unit is used to associate the index explanatory information with the index process information to generate an after-sales explanatory index.

[0139] For the specific definition of the after-sales project data recording and analysis system of the linkage CRM system, please refer to the definition of the after-sales project data recording and analysis method of the linkage CRM system mentioned above, which will not be repeated here. Each module in the after-sales project data recording and analysis system of the linkage CRM system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0140] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store current data explanatory information, CRM explanatory corresponding data and after-sales suggestion information, etc. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for recording and analyzing after-sales project data of a linked CRM system is implemented.

[0141] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0142] Obtain current after-sales project data based on the CRM system, analyze the current after-sales project data, and obtain explanatory information of the current data;

[0143] Determine whether the current data explanatory information corresponds to the preset CRM explanatory index. If it is not, return the current after-sales project data to the CRM system and report an error. If it is yes, mark the current after-sales project data based on the CRM explanatory index and obtain the CRM explanatory corresponding data.

[0144] Determine whether the CRM explanatory corresponding data corresponds to a preset after-sales explanatory index. If so, record the current after-sales project data based on the after-sales explanatory index. If not, determine a recordable position based on the current data explanatory information corresponding to the CRM explanatory corresponding data, and record the current after-sales project data based on the recordable position.

[0145] After-sales suggestion information is generated based on the recorded current after-sales project data and the after-sales explanatory index.

[0146] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0147] Obtain current after-sales project data based on the CRM system, analyze the current after-sales project data, and obtain explanatory information of the current data;

[0148] Determine whether the current data explanatory information corresponds to the preset CRM explanatory index. If it is not, return the current after-sales project data to the CRM system and report an error. If it is yes, mark the current after-sales project data based on the CRM explanatory index and obtain the CRM explanatory corresponding data.

[0149] Determine whether the CRM explanatory corresponding data corresponds to a preset after-sales explanatory index. If so, record the current after-sales project data based on the after-sales explanatory index. If not, determine a recordable position based on the current data explanatory information corresponding to the CRM explanatory corresponding data, and record the current after-sales project data based on the recordable position.

[0150] After-sales suggestion information is generated based on the recorded current after-sales project data and the after-sales explanatory index.

[0151] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0152] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0153] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for recording and analyzing after-sales project data in a linkage CRM system, characterized in that: The after-sales project data recording and analysis method of the linkage CRM system includes: Acquire current after-sales project data based on the CRM system, analyze the current after-sales project data, and obtain explanatory information of the current data; Determine whether the current data explanatory information corresponds to a preset CRM explanatory index. If the determination is no, return the current after-sales project data to the CRM system and report an error. If the determination is yes, mark the current after-sales project data based on the CRM explanatory index to obtain CRM explanatory corresponding data. The CRM explanatory index is obtained in the following manner: Analyze the current CRM system’s way of recording after-sales project data and obtain the CRM system’s data recording characteristics; Based on the data record characteristics of the CRM system, analyzing the explanatory information of each after-sales project data in the CRM system; Generate a CRM explanatory index based on the explanatory information of each after-sales project data in the CRM system; Determine whether the CRM explanatory corresponding data corresponds to a preset after-sales explanatory index. If so, record the current after-sales project data based on the after-sales explanatory index. If not, determine a recordable location based on the current data explanatory information corresponding to the CRM explanatory corresponding data, and record the current after-sales project data based on the recordable location. The after-sales explanatory index is obtained in the following manner: Obtain historical after-sales event information and historical after-sales project data; Analyze the historical after-sales event information and the historical after-sales project data to obtain first explanatory information corresponding to the historical after-sales event information and second explanatory information corresponding to the historical after-sales project data; Extracting the CRM system data record feature, correspondingly associating the first explanatory information and the second explanatory information with the CRM system data record feature, and generating a post-sales explanatory index; After-sales suggestion information is generated based on the recorded current after-sales item data and the after-sales explanatory index.

2. The after-sales project data recording and analysis method of the linkage CRM system according to claim 1 is characterized in that: The step of correspondingly associating the first explanatory information and the second explanatory information with the CRM system data record feature to generate a post-sales explanatory index specifically includes: Analyze the historical after-sales event information to obtain after-sales event process information, and analyze the historical after-sales project data to obtain data process information; Analyze the after-sales event process information and the data process information to obtain standardized process information; Adjust the CRM system data record characteristics according to the standardized process information to obtain index process information; The first explanatory information and the second explanatory information are correspondingly associated with the index process information to generate a post-sales explanatory index.

3. The after-sales project data recording and analysis method of the linkage CRM system according to claim 2 is characterized in that: The step of correspondingly associating the first explanatory information and the second explanatory information with the index process information to generate a post-sales explanatory index specifically includes: analyzing the first explanatory information and the second explanatory information together to obtain correlation information between the first explanatory information and the second explanatory information; Based on the association information, associating the first explanatory information with the second explanatory information to obtain index explanatory information; The index explanatory information is associated with the index process information to generate a post-sales explanatory index.

4. A post-sales project data recording and analysis system linked to a CRM system, characterized in that: The after-sales project data recording and analysis system of the linkage CRM system includes: A current interpretation analysis module is used to obtain current after-sales project data based on the CRM system, analyze the current after-sales project data, and obtain current data explanatory information; A first judgment module is used to judge whether the current data explanatory information corresponds to a preset CRM explanatory index. If the judgment is no, the current after-sales project data is returned to the CRM system and an error is reported. If the judgment is yes, the current after-sales project data is marked based on the CRM explanatory index to obtain CRM explanatory corresponding data; CRM analysis module, used to analyze the way the current CRM system records after-sales project data and obtain the data recording characteristics of the CRM system; A CRM interpretation and analysis module, used to analyze the explanatory information of each after-sales project data in the CRM system based on the data record characteristics of the CRM system; A first index generating module, configured to generate a CRM explanatory index based on the explanatory information of each after-sales project data in the CRM system; A second judgment module is used to judge whether the CRM explanatory corresponding data corresponds to a preset after-sales explanatory index. If it is judged to be yes, the current after-sales project data is recorded based on the after-sales explanatory index. If it is judged to be no, a recordable position is judged according to the current data explanatory information corresponding to the CRM explanatory corresponding data, and the current after-sales project data is recorded based on the recordable position. A historical information acquisition module is used to obtain historical after-sales event information and historical after-sales project data; A history analysis module, used for analyzing the historical after-sales event information and the historical after-sales project data, and obtaining first explanatory information corresponding to the historical after-sales event information and second explanatory information corresponding to the historical after-sales project data; A second index generating module, configured to extract the CRM system data record feature, associate the first explanatory information and the second explanatory information with the CRM system data record feature, and generate a post-sales explanatory index; The suggestion analysis module is used to generate after-sales suggestion information based on the recorded current after-sales project data and the after-sales explanatory index.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the after-sales project data record analysis method of the linkage CRM system according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the after-sales project data record analysis method of the linkage CRM system as claimed in any one of claims 1 to 3 are implemented.

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