Customer service personnel loss assessment early warning method and device based on big data

Abstract

The invention discloses a customer service personnel loss assessment and early warning method and device based on big data. The early warning method comprises the following steps: acquiring historical customer service personnel loss reason data, and integrating the data into a data region; wherein the data area comprises a plurality of sub-areas, and each sub-area comprises a plurality of data points; for the current customer service personnel loss reason, marking data points in the data area, and obtaining a filling area; presetting a threshold value, obtaining a statistical value of the historical filling area, and triggering an early warning signal according to a judgment condition of the statistical value and the threshold value; the early warning device comprises a data area integration module, a data point marking module and an early warning module. Through the mode, the loss risk of the customer service staff can be comprehensively and accurately evaluated according to the loss reason of the customer service staff, meanwhile, the specific loss reason of the customer service staff can be kept secret, and the protection performance of the privacy of the customer service staff is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of human resource management, and in particular to a customer service staff loss assessment and early warning method and device based on big data. Background Art

[0002] At present, enterprises are facing the serious problem of customer service staff loss, which not only increases the human resource costs of enterprises, but also affects the service quality and customer satisfaction of enterprises. Most of the existing loss warning methods rely on manual surveys and questionnaires, which have problems such as irregular data collection, inconsistent data, and missing core data, and cannot comprehensively and accurately assess the risk of customer service staff loss; at the same time, in manual surveys and questionnaires, the specific reasons for customer service staff loss are easily leaked, and the privacy of customer service staff cannot be guaranteed.

[0003] To sum up, it is very necessary to propose an evaluation and early warning method that can comprehensively and accurately evaluate the risk of customer service personnel loss based on the reasons for customer service personnel loss, while keeping the specific reasons for customer service personnel loss confidential and improving the protection performance of customer service personnel privacy. Summary of the invention

[0004] The purpose of the present invention is to provide a customer service staff loss assessment and early warning method, device and system based on big data, aiming to solve the problems in the prior art that rely on manual investigation and questionnaire survey, there are problems such as irregular data collection, inconsistent data, missing core data, etc., and it is impossible to comprehensively and accurately assess the risk of customer service staff loss; at the same time, in manual investigation and questionnaire survey, the specific reasons for the loss of customer service staff are easy to be leaked, and the privacy of customer service staff cannot be guaranteed.

[0005] To achieve the above purpose, the present invention adopts a customer service staff loss assessment and early warning method based on big data, comprising the following steps: (1) Obtaining historical customer service personnel loss reason data and integrating it into a data region; the data region includes multiple sub-regions, and the sub-region includes multiple data points; (2) Based on the current reasons for customer service staff turnover, mark the data points in the data area and obtain the filled area; (3) Preset thresholds and obtain statistical values ​​of historical filling areas, and trigger warning signals based on the judgment of statistical values ​​and thresholds.

[0006] Furthermore, step (1) is specifically as follows: Obtain historical reasons for customer service staff turnover, taking each loss reason as a data point; Query the features of each data point and aggregate multiple data points with the same features into a sub-region; Combine multiple sub-regions into a data region.

[0007] Furthermore, step (2) is specifically as follows: Obtain the reasons for the loss of current customer service staff and determine the data points based on the reasons for the loss; Mark the data points in the data area; According to the marked data points, fill the sub-region where they are located to obtain the filled region.

[0008] Furthermore, step (3) is specifically as follows: Preset a threshold for each filling area and obtain the statistical value of the historical filling area; By comparing the statistical value with the threshold, the warning situation of the filling area is obtained, and a warning signal corresponding to the warning level is issued.

[0009] Furthermore, the comparison of the statistical value and the threshold value is used to obtain the warning situation of the filling area, and a warning signal corresponding to the warning level is issued, specifically: When the comparison statistic value is less than the threshold, a warning signal of low warning level is issued; When the comparison statistic value is equal to the threshold, a warning signal of medium warning level is issued; When the comparison statistic value is greater than the threshold, a warning signal with a high warning level is issued.

[0010] The present invention also discloses a customer service staff loss assessment and early warning device based on big data, which is applied to the customer service staff loss assessment and early warning method based on big data. The device includes a data area integration module, a data point marking module and an early warning module; wherein: The data area integration module is used to obtain historical customer service staff loss reason data and integrate it into a data area; the data area includes multiple sub-areas, and the sub-areas include multiple data points; The data point marking module is used to mark data points in the data area according to the current reasons for the loss of customer service personnel and obtain a filled area; The early warning module is used to preset a threshold value, obtain the statistical value of the historical filling area, and trigger an early warning signal according to the judgment of the statistical value and the threshold value.

[0011] Furthermore, the data area integration module includes a historical data acquisition unit, an aggregation unit and an integration unit; wherein: The historical data acquisition unit is used to acquire the historical reasons for customer service personnel loss, with each loss reason being a data point; The aggregation unit is used to query the characteristics of each data point and aggregate multiple data points with the same characteristics into a sub-region; The integration unit is used to integrate multiple sub-areas into a data area.

[0012] Furthermore, the data point marking module includes a current data acquisition unit, a marking unit and a filling unit; wherein: The current data acquisition unit is used to acquire the current reasons for customer service staff turnover and determine the data points according to the reasons for turnover; The marking unit is used to mark the data points in the data area; The filling unit is used to fill the sub-region where the marked data point is located according to the marked data point to obtain the filled region.

[0013] Furthermore, the early warning module includes a statistical value acquisition unit and an early warning signal triggering unit; wherein: The statistical value acquisition unit is used to preset a threshold for each filling area and acquire the statistical value of the historical filling area; The warning signal triggering unit is used to compare the statistical value and the threshold value, obtain the warning situation of the filling area, and issue a warning signal corresponding to the warning level.

[0014] Furthermore, the warning signal triggering unit is specifically: When the comparison statistic value is less than the threshold, a warning signal of low warning level is issued; When the comparison statistic value is equal to the threshold, a warning signal of medium warning level is issued; When the comparison statistic value is greater than the threshold, a warning signal with a high warning level is issued.

[0015] The beneficial effects of the present invention are as follows: The present invention provides a customer service staff loss assessment and early warning method and device based on big data, which respectively uses the data area integration module, the data point marking module and the early warning module to perform the following steps: obtaining historical customer service staff loss reason data and integrating them into a data area; wherein the data area includes multiple sub-areas, and the sub-areas include multiple data points; for the current customer service staff loss reason, marking the data points in the data area to obtain the fill area; presetting the threshold, and obtaining the statistical value of the historical fill area, and triggering the early warning signal according to the judgment of the statistical value and the threshold; through the above method, the loss risk of customer service staff can be comprehensively and accurately assessed according to the customer service staff loss reason, and at the same time, the specific reasons for the customer service staff loss can be kept confidential, thereby improving the protection performance of the customer service staff privacy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flowchart of the steps of the customer service staff loss assessment and early warning method based on big data of the present invention; Figure 2 is a flow chart of the steps of S100 of the present invention; Figure 3 is a schematic diagram of the data area of ​​the present invention; Figure 4 is a flow chart of the steps of S200 of the present invention; Figure 5 is a flow chart of the steps of S300 of the present invention; Figure 6 is a schematic diagram of labeled data points of the present invention; Figure 7 is a schematic diagram of a filling area of ​​the present invention; Figure 8 It is a schematic diagram of the principle of the customer service staff loss assessment and early warning device based on big data of the present invention; Fig. 9 It is a schematic diagram of the principle of the electronic device of the present invention.

[0018] 400-data area integration module, 401-historical data acquisition unit, 402-aggregation unit, 403-integration unit, 500-data point marking module, 501-current data acquisition unit, 502-marking unit, 503-filling unit, 600-early warning module, 601-statistical value acquisition unit, 602-early warning signal triggering unit. DETAILED DESCRIPTION

[0019] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0020] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0021] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0022] See also Figure 1 to Figure 7 The present invention provides a customer service staff loss assessment and early warning method based on big data, comprising the following steps: S100: Acquire historical customer service staff loss reason data and integrate them into a data region; wherein the data region includes multiple sub-regions, and the sub-region includes multiple data points; In this embodiment, firstly, the historical customer service staff loss reason data is obtained and integrated into a data region; wherein the data region includes multiple sub-regions, and the sub-regions include multiple data points. The specific steps are as follows: S101: Obtain historical reasons for customer service staff turnover, with each loss reason being used as a data point; S102: querying the features of each data point, and aggregating multiple data points with the same features into a sub-region; S103: Integrate multiple sub-regions into a data region.

[0023] like Figure 3 As shown, each grid is a sub-region, and the fifteen sub-regions in the figure are integrated into a data region; each sub-region has multiple data points with the same characteristics.

[0024] S200: Mark data points in the data area according to the current reasons for loss of customer service personnel, and obtain a filled area; In this embodiment, according to the current reasons for the loss of customer service personnel, data points are marked in the data area to obtain the filled area. The specific steps are as follows: S201: Obtain the current reasons for customer service staff turnover, and determine data points according to the reasons for turnover; S202: Marking data points in the data area; S203: Fill the sub-region where the marked data point is located to obtain a filled region.

[0025] like Figure 5As shown, the data points of the current reasons for the loss of customer service personnel are determined, and the data points are marked in the data area; for example, if the characteristic of a sub-area is emotion, the data points may be: family affection, love, friendship, etc. The customer service personnel with such reasons for loss may be: 1. Someone at home is sick and needs to be taken care of; 2. Because of the need to maintain or manage personal emotions, they need to go to another city or company; 3. They went to another company because of a friend's introduction, etc.

[0026] Fill the sub-area where the marked data point is located, such as Figure 6 As shown, there may be multiple reasons for the current loss of customer service personnel. Multiple data points can be marked in multiple sub-areas, and the sub-areas where they are located are filled to obtain the filled area.

[0027] S300: Preset a threshold value, obtain the statistical value of the historical filling area, and trigger an early warning signal based on the judgment of the statistical value and the threshold value.

[0028] In this embodiment, a threshold is preset at last, and the statistical value of the historical filling area is obtained, and an early warning signal is triggered according to the judgment of the statistical value and the threshold. The specific steps are as follows: S301: Preset a threshold for each filling area and obtain historical statistical values ​​of the filling areas; S302: Compare the statistical value with the threshold value, obtain the warning situation of the filling area, and issue a warning signal corresponding to the warning level.

[0029] When the comparison statistic value is less than the threshold, a warning signal of low warning level is issued; When the comparison statistic value is equal to the threshold, a warning signal of medium warning level is issued; When the comparison statistic value is greater than the threshold, a warning signal with a high warning level is issued.

[0030] In the present invention, firstly, the historical customer service personnel loss reason data is obtained and integrated into a data area. The data area includes multiple sub-areas, and the sub-areas include multiple data points. Then, according to the current customer service personnel loss reason, the data points are marked in the data area to obtain the filling area. Then, a threshold is preset, and the statistical value of the historical filling area is obtained, and an early warning signal is triggered according to the judgment of the statistical value and the threshold. According to the customer service personnel loss reason, the loss risk of customer service personnel can be comprehensively and accurately evaluated, and the specific reasons for the loss of customer service personnel can be kept confidential, thereby improving the protection performance of the privacy of customer service personnel.

[0031] Corresponding to the aforementioned embodiment of the customer service staff churn assessment and early warning method based on big data, the present application also provides an embodiment of the customer service staff churn assessment and early warning device based on big data.

[0032] Figure 8This is a block diagram of a customer service staff loss assessment and early warning device based on big data according to an exemplary embodiment. Figure 8 The system may include: a data region integration module 400, a data point marking module 500 and an early warning module 600; wherein: The data region integration module 400 is used to obtain historical customer service staff loss reason data and integrate it into a data region; the data region includes multiple sub-regions, and the sub-region includes multiple data points; The data point marking module 500 is used to mark data points in the data area according to the current customer service staff loss reasons and obtain the filling area; The warning module 600 is used to preset a threshold value, obtain the statistical value of the historical filling area, and trigger a warning signal according to the judgment of the statistical value and the threshold value.

[0033] In this embodiment, the data area integration module 400 obtains historical customer service staff loss reasons data and integrates them into a data area; the data point marking module 500 marks data points in the data area according to the current customer service staff loss reasons and obtains the fill area; the early warning module 600 presets a threshold and obtains the statistical value of the historical fill area, and triggers a warning signal based on the judgment of the statistical value and the threshold.

[0034] Furthermore, the data area integration module 400 includes a historical data acquisition unit 401, an aggregation unit 402 and an integration unit 403; wherein: The historical data acquisition unit 401 is used to acquire historical reasons for customer service staff turnover, with each loss reason being a data point; The aggregation unit 402 is used to query the characteristics of each data point and aggregate multiple data points with the same characteristics into a sub-region; The integration unit 403 is used to integrate multiple sub-areas into a data area.

[0035] In this embodiment, the historical data acquisition unit 401 obtains the historical reasons for customer service staff loss, with each loss reason as a data point; the aggregation unit 402 queries the characteristics of each data point, and aggregates multiple data points with the same characteristics into a sub-area; the integration unit 403 integrates multiple sub-areas into a data area.

[0036] Furthermore, the data point marking module 500 includes a current data acquisition unit 501, a marking unit 502 and a filling unit 503; wherein: The current data acquisition unit 501 is used to acquire the current customer service personnel loss reasons and determine the data points according to the loss reasons; The marking unit 502 is used to mark data points in the data area; The filling unit 503 is used to fill the sub-region where the marked data point is located according to the marked data point to obtain a filled region.

[0037] In this embodiment, the current data acquisition unit 501 acquires the reasons for the loss of current customer service personnel and determines the data points according to the reasons for the loss; the marking unit 502 marks the data points in the data area; the filling unit 503 fills the sub-area where the marked data points are located according to the marked data points to obtain the filled area.

[0038] Furthermore, the warning module 600 includes a statistical value acquisition unit 601 and a warning signal triggering unit 602; wherein: The statistical value acquisition unit 601 is used to preset a threshold for each filling area and acquire the statistical value of the historical filling area; The warning signal triggering unit 602 is used to compare the statistical value with the threshold value, obtain the warning situation of the filling area, and issue a warning signal corresponding to the warning level.

[0039] In this embodiment, the statistical value acquisition unit 601 presets a threshold for each filling area and obtains the statistical value of the historical filling area; the warning signal triggering unit 602 compares the statistical value and the threshold, obtains the warning situation of the filling area, and issues a warning signal corresponding to the warning level.

[0040] Corresponding to the above-mentioned embodiment of the customer service staff loss assessment and early warning device based on big data, the present application also provides an embodiment of a customer service staff loss assessment and early warning system based on big data. The customer service staff loss assessment and early warning system based on big data includes the customer service staff loss assessment and early warning device based on big data.

[0041] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0042] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0043] Accordingly, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned customer service staff loss assessment and early warning method based on big data. Fig. 9 As shown in FIG. 1 , a hardware structure diagram of a customer service staff loss assessment and early warning system based on big data provided by an embodiment of the present invention is provided for any device with data processing capability, except Fig. 9 In addition to the processor, memory and network interface shown, any device with data processing capability in which the apparatus in the embodiment is located may also include other hardware, generally based on the actual functions of the device with data processing capability, which will not be described in detail.

[0044] Accordingly, the present application also provides a computer-readable storage medium on which computer instructions are stored, and when the instructions are executed by the processor, the customer service staff loss assessment and early warning method based on big data as described above is implemented. The computer-readable storage medium can be an internal storage unit of any device with data processing capabilities described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash card (Flash Card), etc. equipped on the device. Furthermore, the computer-readable storage medium can also include both an internal storage unit and an external storage device of any device with data processing capabilities. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and can also be used to temporarily store data that has been output or is to be output.

[0045] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the contents disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0046] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A customer service staff loss assessment and early warning method based on big data, characterized in that: The steps include: (1) Obtaining historical customer service personnel loss reason data and integrating it into a data region; the data region includes multiple sub-regions, and the sub-region includes multiple data points; (2) Based on the current reasons for customer service staff turnover, mark the data points in the data area and obtain the filled area; (3) Preset thresholds and obtain statistical values ​​of historical filling areas, and trigger warning signals based on the judgment of statistical values ​​and thresholds.

2. The customer service staff loss assessment and early warning method based on big data as claimed in claim 1 is characterized in that: Step (1) is as follows: Obtain historical reasons for customer service staff turnover, taking each loss reason as a data point; Query the features of each data point and aggregate multiple data points with the same features into a sub-region; Combine multiple sub-regions into a data region.

3. The customer service staff loss assessment and early warning method based on big data as claimed in claim 1, characterized in that: Step (2) is as follows: Obtain the reasons for the loss of current customer service staff and determine the data points based on the reasons for the loss; Mark the data points in the data area; According to the marked data points, fill the sub-region where the data points are located to obtain the filled region.

4. The customer service staff loss assessment and early warning method based on big data as claimed in claim 1, characterized in that: Step (3) is as follows: Preset a threshold for each filling area and obtain the statistical value of the historical filling area; By comparing the statistical value and the threshold, the warning situation of the filling area is obtained, and a warning signal corresponding to the warning level is issued.

5. The customer service staff loss assessment and early warning method based on big data as claimed in claim 4 is characterized in that: The comparison of the statistical value and the threshold value is used to obtain the warning situation of the filling area, and a warning signal corresponding to the warning level is issued, specifically: When the comparison statistic value is less than the threshold, a warning signal of low warning level is issued; When the comparison statistic value is equal to the threshold, a warning signal of medium warning level is issued; When the comparison statistic value is greater than the threshold, a warning signal with a high warning level is issued.

6. A customer service staff loss assessment and early warning device based on big data, applied to the customer service staff loss assessment and early warning method based on big data as described in any one of claims 1 to 5, characterized in that: The device includes a data area integration module, a data point marking module and an early warning module; wherein: The data area integration module is used to obtain historical customer service staff loss reason data and integrate it into a data area; the data area includes multiple sub-areas, and the sub-areas include multiple data points; The data point marking module is used to mark data points in the data area according to the current reasons for the loss of customer service personnel and obtain a filled area; The early warning module is used to preset a threshold value, obtain the statistical value of the historical filling area, and trigger an early warning signal according to the judgment of the statistical value and the threshold value.

7. The customer service staff loss assessment and early warning device based on big data as claimed in claim 6, characterized in that: The data area integration module includes a historical data acquisition unit, an aggregation unit and an integration unit; wherein: The historical data acquisition unit is used to acquire the historical reasons for customer service personnel loss, with each loss reason being a data point; The aggregation unit is used to query the characteristics of each data point and aggregate multiple data points with the same characteristics into a sub-region; The integration unit is used to integrate multiple sub-areas into a data area.

8. The customer service staff loss assessment and early warning device based on big data as claimed in claim 6, characterized in that: The data point marking module includes a current data acquisition unit, a marking unit and a filling unit; wherein: The current data acquisition unit is used to acquire the current reasons for customer service staff turnover and determine the data points according to the reasons for turnover; The marking unit is used to mark the data points in the data area; The filling unit is used to fill the sub-region where the marked data point is located according to the marked data point to obtain the filled region.

9. The customer service staff loss assessment and early warning device based on big data as claimed in claim 6, characterized in that: The early warning module includes a statistical value acquisition unit and an early warning signal triggering unit; wherein: The statistical value acquisition unit is used to preset a threshold for each filling area and acquire the statistical value of the historical filling area; The warning signal triggering unit is used to compare the statistical value and the threshold value, obtain the warning situation of the filling area, and issue a warning signal corresponding to the warning level.

10. The customer service staff loss assessment and early warning device based on big data according to claim 9, characterized in that: The early warning signal triggering unit is specifically: When the comparison statistic value is less than the threshold, a warning signal of low warning level is issued; When the comparison statistic value is equal to the threshold, a warning signal of medium warning level is issued; When the comparison statistic value is greater than the threshold, a warning signal with a high warning level is issued.

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