An after-sales service data analysis method, system, terminal and storage medium

Abstract

The application relates to an after-sales service data analysis method, system, terminal and storage medium. The after-sales service data analysis method comprises the following steps: constructing an after-sales service data analysis model; after the analysis model is constructed, target after-sales service data is extracted from a database according to an analysis direction of the analysis model; and the target after-sales service data is analyzed by the analysis model to determine an analysis result of the target after-sales service data. The after-sales service data analysis method, system, terminal and storage medium provided by the application can improve the accuracy and focus of after-sales service data analysis by constructing an after-sales service data analysis model, extracting target after-sales service data from the database according to the analysis direction of the analysis model, unifying data sources and index caliber, and improving the analysis efficiency of after-sales service data, which is beneficial to after-sales service management and decision-making.

Description

Technical Field

[0001] The present application belongs to the field of data analysis technology, and in particular relates to an after-sales service data analysis method, system, terminal and storage medium. Background Art

[0002] In the existing after-sales service analysis and decision-making system, some companies in the initial stage of "digitalization" use manual splicing, personal experience and other methods to process and analyze after-sales service data, and use the analysis results for after-sales service management and decision-making. This data processing and analysis method has the problem of distorted data sources, which leads to distorted data analysis results. In addition, data processing through manual splicing is not only inefficient but also prone to errors, resulting in erroneous data analysis results and ultimately errors in after-sales service management and decision-making.

[0003] Another group of companies with a certain "digital" foundation often conduct after-sales service data analysis by building a set of automated reports. This data analysis method is a data analysis process made according to a single analysis requirement. The data analysis is not systematic, and there are redundant statistical indicators in different data analysis reports, and the indicator caliber is inconsistent, which reduces the accuracy of data analysis. In addition, as time goes by, there will be more and more data analysis reports, resulting in a lack of focus on data analysis, making it difficult for decision makers to make decisions based on the analysis results. Summary of the Invention

[0004] In response to the above technical problems, the present application provides an after-sales service data analysis method, system, terminal and storage medium to improve the efficiency, accuracy and focus of after-sales service data analysis, which is conducive to after-sales service management and decision-making.

[0005] The present application provides an after-sales service data analysis method, comprising: constructing an after-sales service data analysis model; after the analysis model is constructed, extracting target after-sales service data from a database according to the analysis direction of the analysis model; analyzing the target after-sales service data through the analysis model to determine the analysis results of the target after-sales service data.

[0006] In one embodiment, the analysis model includes at least one of an after-sales service personnel, document, and equipment real-time monitoring and abnormality analysis model, an after-sales service key assessment indicator analysis model, an after-sales service personnel level analysis model, and an after-sales service cost analysis model.

[0007] In one embodiment, the target after-sales service data is extracted from the database according to the analysis direction of the analysis model, including at least one of the following: when the analysis direction of the analysis model is to perform real-time monitoring and abnormal analysis on after-sales service personnel, documents, and equipment, the location and status of the after-sales service personnel and equipment, and the content and status of the after-sales service documents are extracted from the database; when the analysis direction of the analysis model is to analyze the key assessment indicators of after-sales service, the key node data and key process data of the after-sales service process are extracted from the database; when the analysis direction of the analysis model is to analyze the level of after-sales service personnel, the execution process data and execution result data of the after-sales service personnel in the after-sales service process are extracted from the database; when the analysis direction of the analysis model is to analyze the after-sales service cost, the fixed input cost and dynamic input cost of the after-sales service process are extracted from the database.

[0008] In one embodiment, the step of analyzing the target after-sales service data through the analysis model and determining the analysis results of the target after-sales service data includes at least one of the following: real-time monitoring of the location and status of the after-sales service personnel and equipment, and the content and status of the after-sales service documents through the analysis model, and outputting a prompt message when any one of the location and status of the after-sales service personnel and equipment, and the content and status of the after-sales service documents does not meet the preset conditions; analyzing the key node data and key process data of the after-sales service process through the analysis model to determine and display the key assessment indicators of after-sales service; analyzing the execution process data and execution result data of the after-sales service personnel in the after-sales service process through the analysis model to determine and display the level of the after-sales service personnel; analyzing the fixed input cost and dynamic input cost of the after-sales service process through the analysis model to determine and display the after-sales service cost.

[0009] In one embodiment, before the step of analyzing the target after-sales service data through the analysis model to determine the analysis results of the target after-sales service data, it includes: preprocessing the target after-sales service data from different databases to unify the formats of the target after-sales service data from different databases.

[0010] The present application also provides an after-sales service data analysis system, which includes a model construction module, a data acquisition module and a data analysis module; wherein the model construction module is used to construct an after-sales service data analysis model; the data acquisition module is used to extract target after-sales service data from a database according to the analysis direction of the analysis model after the analysis model is constructed; the data analysis module is used to analyze the target after-sales service data through the analysis model to determine the analysis results of the target after-sales service data.

[0011] In one embodiment, the analysis system further includes a display module; the display module is used to display the analysis results of the target after-sales service data.

[0012] In one embodiment, the analysis system further includes a preprocessing module; the preprocessing module is used to preprocess the target after-sales service data in different databases and unify the formats of the target after-sales service data in different databases.

[0013] The present application also provides a terminal, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned after-sales service data analysis method are implemented.

[0014] The present application also provides a storage medium storing a computer program, which implements the steps of the above-mentioned after-sales service data analysis method when executed by a processor.

[0015] The present application provides an after-sales service data analysis method, system, terminal and storage medium. By constructing an after-sales service data analysis model and extracting target after-sales service data from a database according to the analysis direction of the analysis model, the data source and indicator caliber are unified, and the accuracy and focus of after-sales service data analysis are improved. In addition, the target after-sales service data is analyzed through the analysis model to determine the analysis results of the target after-sales service data, thereby improving the efficiency of after-sales service data analysis and facilitating after-sales service management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the after-sales service data analysis method provided in Example 1 of the present application;

[0017] Figure 2 This is a structural diagram of the after-sales service data analysis system provided in Example 2 of the present application;

[0018] Figure 3 This is a schematic diagram of the structure of the terminal provided in Example 3 of the present application. DETAILED DESCRIPTION

[0019] The technical solution of this application is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used herein, "and / or" includes any and all combinations of one or more of the relevant listed items.

[0020] Figure 1 This is a flow chart of the after-sales service data analysis method provided in Example 1 of this application. Figure 1 As shown, the after-sales service data analysis method of the present application may include the following steps:

[0021] Step S101: constructing an after-sales service data analysis model;

[0022] Optionally, an after-sales service data analysis model is constructed based on management needs such as real-time monitoring of after-sales service, abnormal supervision, KPI management, personnel management, equipment life cycle management and equipment failure management.

[0023] Optionally, the after-sales service data analysis model includes at least one of an after-sales service personnel, document, and equipment real-time monitoring and anomaly analysis model, an after-sales service key assessment indicator analysis model, an after-sales service personnel level analysis model, and an after-sales service cost analysis model.

[0024] Step S102: After the analysis model is constructed, target after-sales service data is extracted from the database according to the analysis direction of the analysis model;

[0025] Optionally, the database includes databases of different business systems, as well as different databases of the same business system, such as SAP, Oracle, MySQL, etc. Optionally, tools such as data embedding, NI FI, Sqoop, and Kafka are used to extract target after-sales service data from the database.

[0026] In one embodiment, in step S102, target after-sales service data is extracted from the database according to the analysis direction of the analysis model, including at least one of the following:

[0027] When the analysis model is designed to conduct real-time monitoring and abnormal analysis of after-sales service personnel, documents, and equipment, the location and status of after-sales service personnel and equipment, as well as the content and status of after-sales service documents, are extracted from the database;

[0028] When the analysis model is directed towards analyzing the key assessment indicators of after-sales service, key node data and key process data of the after-sales service process are extracted from the database;

[0029] When the analysis direction of the analysis model is to analyze the level of the after-sales service personnel, the execution process data and the execution result data of the after-sales service personnel in the after-sales service process are extracted from the database;

[0030] When the analysis direction of the analysis model is to analyze the after-sales service cost, the fixed input cost and the dynamic input cost of the after-sales service process are extracted from the database;

[0031] The fixed input cost includes the after-sales service personnel housing subsidy, the high-temperature subsidy, the remote subsidy and the like; the dynamic input cost includes the service personnel cost input, the service mileage cost input and the service working hour cost input of each after-sales service document and the like.

[0032] Step S103: analyzing the target after-sales service data by the analysis model to determine the analysis result of the target after-sales service data.

[0033] Optionally, the target after-sales service data is analyzed by the analysis model using Spark SQL / streaming to determine the analysis result of the target after-sales service data, and the analysis result is pushed to the Dori s / Mysq l shared layer database, and finally the analysis result is displayed on the application layer such as the visual report system, the app system and the web system, so as to facilitate the after-sales service management and decision-making.

[0034] In an embodiment, step S103 includes at least one of the following:

[0035] The positions and states of the after-sales service personnel and the equipment, and the contents and states of the after-sales service documents are monitored in real time by the analysis model, and when any of the positions and states of the after-sales service personnel and the equipment, and the contents and states of the after-sales service documents does not meet the preset condition, a prompt information is outputted;

[0036] For example, if the idle time of the after-sales service personnel A is greater than a first preset time, and / or the distance between the real-time position and the target position is greater than a preset distance, and / or the order receiving and departure time is greater than a second preset time, the after-sales service personnel A is reminded to carry out the after-sales service work according to the requirements by the app message, the short message or the automatic voice call.

[0037] The key node data and the key process data of the after-sales service process are analyzed by the analysis model to determine and display the key evaluation index of the after-sales service;

[0038] Optionally, the key evaluation indicators of after-sales service cover several aspects such as after-sales service efficiency, quality, evaluation, and parts sales collection; through the analysis model, the key node data and key process data of the after-sales service process are sorted out to determine the process and result key evaluation indicators of after-sales service.

[0039] Analyze the execution process data and execution result data of after-sales service personnel in the after-sales service process through the analytical model to determine and demonstrate the level of after-sales service personnel;

[0040] Optionally, the execution process data includes execution process indicators such as the proportion of after-sales service personnel who depart on time and the proportion of after-sales service personnel who arrive on time after receiving an order; the execution result data includes execution result indicators such as after-sales service working hours and after-sales service evaluation; optionally, the indicator weighting model established by after-sales service management experts is used to determine the comprehensive level of after-sales service personnel based on their execution process indicators and execution result indicators.

[0041] The fixed input costs and dynamic input costs of the after-sales service process are analyzed through analytical models to determine and display the after-sales service costs.

[0042] Optionally, an after-sales service cost calculation model established based on the fixed input costs and dynamic input costs of historical after-sales service processes is used to determine the current after-sales service cost based on the fixed input costs and dynamic input costs of the current after-sales service process.

[0043] In one embodiment, before step S103, the following steps are included:

[0044] Preprocess the target after-sales service data of different databases and unify the formats of the target after-sales service data of different databases.

[0045] Optionally, cleaning calculations are performed on the target after-sales service data in different databases to unify the formats of the target after-sales service data in different databases.

[0046] It is worth mentioning that after the after-sales service data analysis model is constructed, the after-sales service data analysis model will be iteratively optimized by continuously following up on the usage effect and usage needs of the after-sales service data analysis model to continuously meet the after-sales service decision-making and management needs.

[0047] The after-sales service data analysis method provided in Example 1 of the present application constructs an after-sales service data analysis model, and extracts target after-sales service data from the database according to the analysis direction of the analysis model, thereby unifying the data sources and indicator caliber, and improving the accuracy and focus of after-sales service data analysis; analyzes the target after-sales service data through the analysis model, determines the analysis results of the target after-sales service data, improves the efficiency of after-sales service data analysis, and is beneficial to after-sales service management and decision-making; in addition, the location and status of after-sales service personnel and equipment, and the content and status of after-sales service documents are monitored in real time through the analysis model, and when any of the location and status of after-sales service personnel and equipment, and the content and status of after-sales service documents do not meet the preset conditions, a prompt message is output, which can promptly discover and correct abnormal situations in the after-sales service process, thereby improving the after-sales service supervision effect.

[0048] Figure 2 This is a structural diagram of the after-sales service data analysis system provided in Example 2 of this application. The after-sales service data analysis system of this application includes a model building module, a data acquisition module and a data analysis module;

[0049] Among them, the model building module is used to build an after-sales service data analysis model;

[0050] The data acquisition module is used to extract target after-sales service data from the database according to the analysis direction of the analysis model after the analysis model is built;

[0051] The data analysis module is used to analyze the target after-sales service data through the analysis model and determine the analysis results of the target after-sales service data.

[0052] In one embodiment, the analysis system further includes a display module; the display module is used to display the analysis results of the target after-sales service data.

[0053] In one embodiment, the analysis system further includes a preprocessing module; the preprocessing module is used to preprocess the target after-sales service data in different databases and unify the formats of the target after-sales service data in different databases.

[0054] In one embodiment, the analysis system also includes a model optimization module; the model optimization module is used to continuously follow up on the usage effect and usage requirements of the after-sales service data analysis model after the after-sales service data analysis model is constructed, and iteratively optimize the after-sales service data analysis model based on the usage effect and usage requirements to continuously meet after-sales service decision-making and management needs.

[0055] The specific implementation process of this embodiment is described in detail in Example 1 and will not be further elaborated here.

[0056] The after-sales service data analysis system provided in Example 2 of the present application constructs an after-sales service data analysis model through the interaction between the model construction module, the data acquisition module and the data analysis module, and extracts target after-sales service data from the database according to the analysis direction of the analysis model, thereby unifying the data source and indicator caliber, and improving the accuracy and focus of the after-sales service data analysis; in addition, the target after-sales service data is analyzed through the analysis model to determine the analysis results of the target after-sales service data, thereby improving the efficiency of the after-sales service data analysis and facilitating after-sales service management and decision-making.

[0057] Figure 3 1 is a schematic diagram of the structure of a terminal provided in Embodiment 3 of the present application. The terminal of the present application includes: a processor 110, a memory 111, and a computer program 112 stored in the memory 111 and executable by the processor 110. When the processor 110 executes the computer program 112, the steps of the above-mentioned embodiment of the after-sales service data analysis method are implemented.

[0058] The terminal may include, but is not limited to, a processor 110 and a memory 111. Those skilled in the art will appreciate that Figure 3 These are merely examples of terminals and do not constitute a limitation on the terminals. The terminals may include more or fewer components than shown in the figures, or a combination of certain components, or different components. For example, the terminals may also include input and output devices, network access devices, buses, etc.

[0059] The processor 110 may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0060] The storage 111 can be an internal storage unit of the terminal, such as a hard disk or a memory of the terminal. The storage 111 can also be an external storage device of the terminal, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, or the like equipped on the terminal. Further, the storage 111 can include both an internal storage unit and an external storage device of the terminal. The storage 111 is used to store a computer program and other programs and data required by the terminal. The storage 111 can also be used to temporarily store data that has been output or will be output.

[0061] The application further provides a storage medium, and a computer program is stored on the storage medium. The computer program is executed by a processor to implement the steps in the method for analyzing after-sales service data.

[0062] The technical features of the above-described embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described, but it should be considered that any combination of the technical features is within the scope of the present disclosure, as long as the combination does not result in contradictions.

[0063] In this document, the terms "comprise", "contain", or any other variant thereof are intended to cover non-exclusive inclusion, in addition to the listed elements, other elements that are not explicitly listed can also be included.

[0064] The above description is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for analyzing after-sales service data, characterized in that: include: Constructing an after-sales service data analysis model, wherein the analysis model includes at least one of a real-time monitoring and anomaly analysis model for after-sales service personnel, documents, and equipment, an analysis model for key assessment indicators of after-sales service, an analysis model for the level of after-sales service personnel, and an analysis model for after-sales service cost; After the analysis model is constructed, target after-sales service data is extracted from the database according to the analysis direction of the analysis model; Analyzing the target after-sales service data using the analysis model to determine an analysis result of the target after-sales service data; The extracting target after-sales service data from the database according to the analysis direction of the analysis model includes at least one of the following: When the analysis direction of the analysis model is to perform real-time monitoring and abnormal analysis on after-sales service personnel, documents, and equipment, the location and status of after-sales service personnel and equipment, and the content and status of after-sales service documents are extracted from the database; When the analysis direction of the analysis model is to analyze the key assessment indicators of after-sales service, key node data and key process data of the after-sales service process are extracted from the database; When the analysis direction of the analysis model is to analyze the level of after-sales service personnel, extracting the execution process data and execution result data of the after-sales service personnel in the after-sales service process from the database; When the analysis direction of the analysis model is to analyze the after-sales service cost, the fixed input cost and the dynamic input cost of the after-sales service process are extracted from the database.

2. The analysis method according to claim 1, wherein The step of analyzing the target after-sales service data using the analysis model to determine the analysis result of the target after-sales service data includes at least one of the following: The analysis model is used to monitor the location and status of the after-sales service personnel and equipment, and the content and status of the after-sales service document in real time, and outputs a prompt message when any of the location and status of the after-sales service personnel and equipment, and the content and status of the after-sales service document do not meet preset conditions; Analyze the key node data and key process data of the after-sales service process through the analysis model to determine and display the key assessment indicators of the after-sales service; Analyzing the execution process data and execution result data of the after-sales service personnel in the after-sales service process by using the analysis model to determine and display the level of the after-sales service personnel; The fixed input cost and dynamic input cost of the after-sales service process are analyzed through the analysis model to determine and display the after-sales service cost.

3. The analysis method according to claim 1, wherein Before the step of analyzing the target after-sales service data by using the analysis model to determine the analysis result of the target after-sales service data, the method includes: Preprocessing is performed on target after-sales service data in different databases to unify formats of the target after-sales service data in the different databases.

4. An after-sales service data analysis system, characterized in that: The analysis system includes a model building module, a data acquisition module and a data analysis module; The model building module is used to build an after-sales service data analysis model, wherein the analysis model includes at least one of an after-sales service personnel, document, and equipment real-time monitoring and abnormality analysis model, an after-sales service key assessment indicator analysis model, an after-sales service personnel level analysis model, and an after-sales service cost analysis model; The data acquisition module is used to extract target after-sales service data from the database according to the analysis direction of the analysis model after the analysis model is built; The data analysis module is used to analyze the target after-sales service data using the analysis model to determine the analysis results of the target after-sales service data; The step of extracting target after-sales service data from a database according to the analysis direction of the analysis model includes at least one of the following: When the analysis direction of the analysis model is to perform real-time monitoring and abnormal analysis on after-sales service personnel, documents, and equipment, the location and status of after-sales service personnel and equipment, and the content and status of after-sales service documents are extracted from the database; When the analysis direction of the analysis model is to analyze the key assessment indicators of after-sales service, key node data and key process data of the after-sales service process are extracted from the database; When the analysis direction of the analysis model is to analyze the level of after-sales service personnel, extracting the execution process data and execution result data of the after-sales service personnel in the after-sales service process from the database; When the analysis direction of the analysis model is to analyze the after-sales service cost, the fixed input cost and the dynamic input cost of the after-sales service process are extracted from the database.

5. The analysis system according to claim 4, wherein The analysis system further includes a display module; The display module is used to display the analysis results of the target after-sales service data.

6. The analysis system according to claim 5, wherein The analysis system also includes a pre-processing module; The preprocessing module is used to preprocess the target after-sales service data in different databases and unify the formats of the target after-sales service data in different databases.

7. A terminal, characterized in that: The terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the analysis method according to any one of claims 1 to 3 are implemented.

8. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the analysis method according to any one of claims 1 to 3 are implemented.

Images