CN111563754A - Service sequence driven power customer appeal perception system - Google Patents
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Abstract
The invention discloses a power client appeal perception system driven by a service sequence, which comprises a data preparation module, a service sequence generation module, a data set construction module and a service sequence appeal perception module, wherein the data preparation module comprises: based on business analysis and modeling abstraction, a business data sheet related to a client appeal in a marketing system base table is collected around a client service center, key fields such as client service time, a work order identifier, a client service subclass and a business expansion service category are obtained from data sheets such as user power consumption data, client service work order data and business expansion work order data, and multiple tables are related through user marks and metering point numbers to form an initial data set. The intelligent perception model for the power customer appeal is provided, new potential modes and rules are discovered in time through the historical behaviors of the user and the corresponding business promotion conditions, the potential service appeal of the user is actively perceived, the service level is improved, and the customer power utilization satisfaction is comprehensively improved.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a power client appeal perception system driven by a service sequence.
Background
With the further development of the integrated work of the power grid company in the marketing field, the concept of ' taking internal services as a center to ' taking customers as a center ' is changed, the concept of ' functional management ' is changed to ' process management ', the concept of ' extensive management ' is changed to ' lean management ', and the like, and the concept is further enjoyed. However, under the background of large concentration of customer service, with the increasing service demands and higher service quality requirements of power customers, the electronic channels cannot effectively distribute manual customer service pressure and cannot meet customer demands in time, so that customer complaints are caused, and complaint phenomena occur frequently.
Traditional service statistics and preference analysis based on service category distribution cannot realize effective analysis of the root cause of user appeal. The working mode is that the customer service seat can identify the appeal of the client after inquiring once by the user actively through related services such as call consultation charge and the like. The mode based on the passive service brings huge pressure to the customer service seat, the pressure of the customer service seat cannot be effectively relieved, and the customer cannot sense the appeal of the customer in advance when the customer does not actively call.
At present, in the field of intelligent customer service, enterprises establish comprehensive customer understanding to a certain extent based on customer figures, and are beneficial to establishing, executing and optimizing related strategies of customer selection, product design, marketing plan, interactive experience, relationship maintenance, risk management and service operation of organizations. In practical application, a client portrait has certain defects, on one hand, the number of target clients is more and more huge, the formation of a client group is more complex, and meanwhile, the behavior of the client is continuously changed, so that the accurate description degree of any user is very difficult to realize; on the other hand, the client portrait is an offline processing of a series of static data based on consumption requirements, purchase preference and behavior tendency information of the client, and communication and feedback between enterprises and users cannot be realized, interaction between the business-driven enterprises and the client is a fundamental source of client appeal, the enterprises have an absolute advantage of service initiation, and the reason for passively receiving the user appeal is no reason.
Disclosure of Invention
The invention aims to provide a power client appeal perception system driven by a service sequence, which comprises a data preparation module, a service sequence generation module, a data set construction module and a service sequence appeal perception module,
a data preparation module: based on business analysis and modeling abstraction, a business data sheet related to a client appeal in a marketing system base table is collected around a client service center, key fields such as client service time, a work order identifier, a client service subclass and a business expansion service category are obtained from data sheets such as user power consumption data, client service work order data and business expansion work order data, and multiple tables are associated through user marks and metering point numbers to form an initial data set;
a service sequence generation module: the business sequence generation module converts an original data set into a standard data set, and 3 key steps of null value removal, data type conversion and data merging are required:
null value removing processing, namely removing null values from key fields of data tables such as user electricity consumption data, customer service work order data, business expansion work order data and the like, and sequencing according to dates;
data type conversion, wherein the key field of the user electricity consumption data is the user electricity consumption and is numerical data, calculating the power consumption fluctuation rate level according to the service logic, and dividing the power consumption fluctuation rate level into 20 grade codes [ P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, N1, N2, N3, N4, N5, N6, N7, N8, N9, N10], wherein P represents that the fluctuation rate is a positive value, the power consumption is increased, N represents that the fluctuation rate is a negative value, the power consumption is reduced, the data size represents the fluctuation rate change level of the power consumption, the data core field of the customer service work order is a work order type code, the classified data can be directly used, KF marks are uniformly added before customer service worksheet type codes in order to distinguish type codes among different data tables, the core field of the worksheet data table is a worksheet type code, and YK marks are uniformly added to preprocessing results before codes;
merging data, namely taking the processed data as a user number as a primary key, and sorting the data into a service sequence data set according to the sequence of the power service;
a data set construction module: because the sequence data per se contains the development rule of an object, the prediction of the sequence is to presume the future development trend of the object according to the development continuity of the object, certain continuity must be ensured in the sequence, the pre-processed sequence data only has the time context, the interval between specific time points is uneven, the time interval is too long, the continuity between the previous and subsequent events is weakened, the long-time service sequence is divided into continuous subsequences according to the interval size of each time point, on one hand, strong correlation is ensured, on the other hand, the data complexity is reduced, the sequence segmentation is carried out by taking 3 months as a time threshold, and a data set is constructed by the short sequence;
the service sequence appeal perception module: early association analysis was a task to find interesting relationships in large-scale datasets, in two forms: a frequent set of items and association rules. A frequent item set refers to a collection of items that often appear in a block; the association rule implies a strong relationship possibly existing between two articles, and the hidden strong association rule among the items can be found out through association analysis, but the items in the strong association rule only have a frequent co-occurrence relationship in space but not have a temporal front-back association relationship;
the sequence pattern mining analysis is used for a data set with a certain sequence relation among all items in a transaction, and strong association rules are found through association analysis, all items not only have frequent co-occurrence relations in space, but also have temporal context relation, and the business analysis can know that a client appeal is caused by business promotion, and the method is a typical event chain consisting of different events with context time relation: the method is characterized in that a service is taken as a source, a certain service appeal of a client is taken as a key point, so that the service problem finally identified by service deduction is changed into a service sequence pattern mining problem of identifying rules from a series of time sequence events, an appeal perception model based on service sequence data is constructed by a sequence pattern mining analysis method, the appeal conversion rule of the client can be identified, and the root of the client appeal is traced.
Compared with the prior art, the invention has the beneficial effects that: the intelligent perception model for the power customer appeal is provided, new potential modes and rules are discovered in time through the historical behaviors of the user and the corresponding business promotion conditions, the potential service appeal of the user is actively perceived, the service level is improved, and the customer power utilization satisfaction is comprehensively improved. The customer appeal prejudgment is realized, and the aims of reducing the number of customer complaints and lightening the working pressure are achieved by early warning in time to drive marketing initiative service.
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FIG. 1 is a schematic view of a client of the present invention;
FIG. 2 is a circuit flow diagram of the present invention;
FIG. 3 is a block diagram of the system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
A power client appeal perception system driven by a service sequence comprises a data preparation module, a service sequence generation module, a data set construction module and a service sequence appeal perception module,
a data preparation module: based on business analysis and modeling abstraction, a business data sheet related to a client appeal in a marketing system base table is collected around a client service center, key fields such as client service time, a work order identifier, a client service subclass and a business expansion service category are obtained from data sheets such as user power consumption data, client service work order data and business expansion work order data, and multiple tables are associated through user marks and metering point numbers to form an initial data set;
a service sequence generation module: the business sequence generation module converts an original data set into a standard data set, and 3 key steps of null value removal, data type conversion and data merging are required:
null value removing processing, namely removing null values from key fields of data tables such as user electricity consumption data, customer service work order data, business expansion work order data and the like, and sequencing according to dates;
data type conversion, wherein the key field of the user electricity consumption data is the user electricity consumption and is numerical data, calculating the power consumption fluctuation rate level according to the service logic, and dividing the power consumption fluctuation rate level into 20 grade codes [ P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, N1, N2, N3, N4, N5, N6, N7, N8, N9, N10], wherein P represents that the fluctuation rate is a positive value, the power consumption is increased, N represents that the fluctuation rate is a negative value, the power consumption is reduced, the data size represents the fluctuation rate change level of the power consumption, the data core field of the customer service work order is a work order type code, the classified data can be directly used, KF marks are uniformly added before customer service worksheet type codes in order to distinguish type codes among different data tables, the core field of the worksheet data table is a worksheet type code, and YK marks are uniformly added to preprocessing results before codes;
merging data, namely taking the processed data as a user number as a primary key, and sorting the data into a service sequence data set according to the sequence of the power service;
a data set construction module: because the sequence data per se contains the development rule of an object, the prediction of the sequence is to presume the future development trend of the object according to the development continuity of the object, certain continuity must be ensured in the sequence, the pre-processed sequence data only has the time context, the interval between specific time points is uneven, the time interval is too long, the continuity between the previous and subsequent events is weakened, the long-time service sequence is divided into continuous subsequences according to the interval size of each time point, on one hand, strong correlation is ensured, on the other hand, the data complexity is reduced, the sequence segmentation is carried out by taking 3 months as a time threshold, and a data set is constructed by the short sequence; in the table below, a short sequence with user number xxx00004903xxx shows that the interval between the occurrence times of the states does not exceed 3 months.
The service sequence appeal perception module: early association analysis was a task to find interesting relationships in large-scale datasets, in two forms: a frequent set of items and association rules. A frequent item set refers to a collection of items that often appear in a block; the association rule implies a strong relationship possibly existing between two articles, and the hidden strong association rule among the items can be found out through association analysis, but the items in the strong association rule only have a frequent co-occurrence relationship in space but not have a temporal front-back association relationship;
the sequence pattern mining analysis is used for a data set with a certain sequence relation among all items in a transaction, and strong association rules are found through association analysis, all items not only have frequent co-occurrence relations in space, but also have temporal context relation, and the business analysis can know that a client appeal is caused by business promotion, and the method is a typical event chain consisting of different events with context time relation: the method is characterized in that a service is taken as a source, a certain service appeal of a client is taken as a key point, so that the service problem finally identified by service deduction is changed into a service sequence pattern mining problem of identifying rules from a series of time sequence events, an appeal perception model based on service sequence data is constructed by a sequence pattern mining analysis method, the appeal conversion rule of the client can be identified, and the root of the client appeal is traced.
And modeling analysis is carried out on a data set constructed on the basis of the business expansion worksheet, the customer service worksheet data and the user power consumption data to obtain an appeal perception model, and the possible future appeal is predicted according to the recent business state of the user. The frequent sequence relationships obtained by the service sequence mining are as follows:
the method can predict that when the fluctuation rate of the continuous electric quantity is high, a user can probably call a customer service telephone to complain about the abnormal situation of the electric quantity and the electric charge change.
The electric energy metering device periodically rotates business expansion business to easily cause voltage fluctuation and electric quantity increase, and further cause customer complaints. Therefore, after the electric energy metering device periodically rotates the service, the electric charge of the electric quantity of the customer can be observed in advance, and active customer service is provided for relevant problems.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (1)
1. A power customer appeal perception system driven by service sequences is characterized in that: comprises a data preparation module, a service sequence generation module, a data set construction module and a service sequence appeal perception module,
a data preparation module: based on business analysis and modeling abstraction, a business data sheet related to a client appeal in a marketing system base table is collected around a client service center, key fields such as client service time, a work order identifier, a client service subclass and a business expansion service category are obtained from data sheets such as user power consumption data, client service work order data and business expansion work order data, and multiple tables are associated through user marks and metering point numbers to form an initial data set;
a service sequence generation module: the business sequence generation module converts an original data set into a standard data set, and 3 key steps of null value removal, data type conversion and data merging are required:
null value removing processing, namely removing null values from key fields of data tables such as user electricity consumption data, customer service work order data, business expansion work order data and the like, and sequencing according to dates;
data type conversion, wherein the key field of the user electricity consumption data is the user electricity consumption and is numerical data, calculating the power consumption fluctuation rate level according to the service logic, and dividing the power consumption fluctuation rate level into 20 grade codes [ P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, N1, N2, N3, N4, N5, N6, N7, N8, N9, N10], wherein P represents that the fluctuation rate is a positive value, the power consumption is increased, N represents that the fluctuation rate is a negative value, the power consumption is reduced, the data size represents the fluctuation rate change level of the power consumption, the data core field of the customer service work order is a work order type code, the classified data can be directly used, KF marks are uniformly added before customer service worksheet type codes for distinguishing type codes among different data tables, the core field of the worksheet data table is a worksheet type code, and YK marks are uniformly added before codes are coded according to preprocessing results;
merging data, namely taking the processed data as a user number as a primary key, and sorting the data into a service sequence data set according to the sequence of the power service;
a data set construction module: because the sequence data per se contains the development rule of an object, the prediction of the sequence is to presume the future development trend of the object according to the development continuity of the object, certain continuity must be ensured in the sequence, the pre-processed sequence data only has the time context, the interval between specific time points is uneven, the time interval is too long, the continuity between the previous and subsequent events is weakened, the long-time service sequence is divided into continuous subsequences according to the interval size of each time point, on one hand, strong correlation is ensured, on the other hand, the data complexity is reduced, the sequence segmentation is carried out by taking 3 months as a time threshold, and a data set is constructed by the short sequence;
the service sequence appeal perception module: early association analysis was a task to find interesting relationships in large-scale datasets, in two forms: a frequent set of items and association rules. A frequent item set refers to a collection of items that often appear in a block; the association rule implies a strong relationship possibly existing between two articles, and the hidden strong association rule among the items can be found out through association analysis, but the items in the strong association rule only have a frequent co-occurrence relationship in space but not have a temporal front-back association relationship;
the sequence pattern mining analysis is used for a data set with a certain sequence relation among all items in a transaction, and strong association rules are found through association analysis, all items not only have frequent co-occurrence relations in space, but also have temporal context relation, and the business analysis can know that a client appeal is caused by business promotion, and the method is a typical event chain consisting of different events with context time relation: the method is characterized in that a service is taken as a source, a certain service appeal of a client is taken as a key point, so that the service problem finally identified by service deduction is changed into a service sequence pattern mining problem of identifying rules from a series of time sequence events, an appeal perception model based on service sequence data is constructed by a sequence pattern mining analysis method, the appeal conversion rule of the client can be identified, and the root of the client appeal is traced.
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| CN112365365A (en) * | 2020-11-10 | 2021-02-12 | 贵州电网有限责任公司 | Method for counting business expansion file accuracy of marketing system |
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| CN109934469A (en) * | 2019-02-25 | 2019-06-25 | 国网河南省电力公司电力科学研究院 | Based on the heterologous power failure susceptibility method for early warning and device for intersecting regression analysis |
| CN110889526A (en) * | 2018-09-07 | 2020-03-17 | 中国移动通信集团有限公司 | Method and system for predicting user upgrade complaint behavior |
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| CN106971310A (en) * | 2017-03-16 | 2017-07-21 | 国家电网公司 | A kind of customer complaint quantitative forecasting technique and device |
| CN110889526A (en) * | 2018-09-07 | 2020-03-17 | 中国移动通信集团有限公司 | Method and system for predicting user upgrade complaint behavior |
| CN109934469A (en) * | 2019-02-25 | 2019-06-25 | 国网河南省电力公司电力科学研究院 | Based on the heterologous power failure susceptibility method for early warning and device for intersecting regression analysis |
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| CN112365365A (en) * | 2020-11-10 | 2021-02-12 | 贵州电网有限责任公司 | Method for counting business expansion file accuracy of marketing system |
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Application publication date: 20200821 |