CN108388955A - Customer service strategies formulating method, device based on random forest and logistic regression - Google Patents
Customer service strategies formulating method, device based on random forest and logistic regression Download PDFInfo
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Abstract
The invention discloses a kind of customer service strategies formulating method and device based on random forest and logistic regression, the method includes：Sample customer value feature is obtained, and carries out quality differentiation；Using sample customer data, top-tier customer identification model is built based on random forest and logistic regression algorithm；Using the value characteristic of client to be identified as input, it is based on the top-tier customer identification model, judges whether client is top-tier customer；The demand for services for obtaining sample client, is standardized classification analysis and establishes demand for services library；Demand for services in demand for services library is matched into service content, service content is matched into different grades of top-tier customer, establishes service strategy library；Based on demand for services library and service policy library, according to top-tier customer recognition result Auto-matching service strategy.The present invention is based on the precise positionings that big data realizes top-tier customer, and combine the analysis result of users service needs analysis, formulate personalized, immortalized service product and service strategy.
Description
Technical field
The invention belongs to machine learning field more particularly to a kind of customer service plans based on random forest and logistic regression
Slightly formulating method and device.
Background technology
With electric Power Reform in-depth, comprehensive relieving of sales market, electric companies at different levels of State Grid Corporation of China face
The market competitive pressure, to promote power grid enterprises' profitability and competitiveness, increase the loyalty of top-tier customer, satisfaction and
Client's stickiness, on the basis of carrying out whole society's universal service, it will be each sale of electricity master to provide good service for top-tier customer for enterprise
Body competes the main means and strategy of top-tier customer, it is necessary to formulate targetedly competitive service strategy, limited service is provided
Source is put on the body of top-tier customer, is established with it stable for electricity consumption relationship, is that power grid enterprises keep long-term sustainable development
Inevitable choice.
With the explosive growth of data volume and the continuous improvement of business need, traditional service system structure is more next
It is more difficult to meet the requirement of system operation.Big data technology has been reached common understanding in the world as important strategic resource,
This basic strategic resource of data is analysis customer demand and provides pertinent service, provides data supporting.
Therefore, the precise positioning that top-tier customer how is realized based on big data, is that the technology urgently solved is needed to ask at present
Topic.
Invention content
To overcome above-mentioned the deficiencies in the prior art, the present invention provides a kind of sale of electricity side groups in random forest and logistic regression
Customer service strategies formulating method and device, the method is with grid company client electrical properties, electricity consumption behavior, electricity consumption
Based on the mass datas such as feature, the customer evaluation index system of various dimensions is established, the visitor built in a manner of data analysis is passed through
Family evaluation model carries out comprehensive score to client, to realize the precise positioning to top-tier customer；, and user power utilization is combined to take
The analysis result of business demand analysis formulates personalized, immortalized service product and service strategy, timely according to Market Feedback
Service strategy is adjusted, increases the loyalty, client's viscosity and customer satisfaction of top-tier customer, establishes and stablize between top-tier customer
For electricity consumption relationship.
To achieve the above object, the present invention adopts the following technical scheme that：
A kind of customer service strategies formulating method based on random forest and logistic regression, includes the following steps：
Step 1：Sample customer value feature is obtained, and carries out quality differentiation；
Step 2：Using sample customer data, mould is identified based on random forest and logistic regression algorithm structure top-tier customer
Type；
Step 3：Using the value characteristic of client to be identified as input, it is based on the top-tier customer identification model, judges institute
State whether client is top-tier customer；
Step 4：The demand for services for obtaining sample client, is standardized classification analysis and establishes demand for services library；It will service
Demand for services in demand library matches service content, and service content is matched different grades of top-tier customer, establishes service strategy
Library；
Step 5：Based on demand for services library and service policy library, plan is serviced according to top-tier customer recognition result Auto-matching
Slightly.
Further, the step 1 includes：
Step 1.1：Customer value evaluating characteristic index system is built according to user's items power information of acquisition；
Step 1.2：According to the value characteristic of the index system statistical sample user, and carries out sample of users quality and sentence
Not.
Further, in the step 1 value characteristic include the corresponding essential attribute of user, economic value, Laden-Value,
Dynamogenetic value, credit worthiness, industry are worth data.
Further, the step 2 includes：
Step 2.1：Sample of users data are pre-processed；
Step 2.2：Top-tier customer judgment models are trained based on random forest method；
Step 2.3：Top-tier customer grade judgment models are built using logistic regression algorithm；
Step 2.4：Top-tier customer, which is obtained, in conjunction with top-tier customer judgment models and top-tier customer grade judgment models identifies mould
Type.
Further, the step 2.1 includes：Data cleansing, characteristic factor quantization, feature expand, feature selecting and different
Constant value processing.
Further, the step 2.2 includes：
Full feature training：Sample chooses whole sample of users data, and model enters ginseng for whole operational indicators；
Important feature is trained：Sample chooses whole sample of users data, and it is high preceding 40% index of importance that model, which enters ginseng,；
Full characteristic crossover training：Mix the sample with user data and averagely split into 10 parts, every time select wherein 9 parts as train
Sample, remaining 1 part is used as forecast sample, loop iteration 10 times, model to enter ginseng for whole operational indicators；
Important feature cross-training：Mix the sample with user data and averagely split into 10 parts, every time select wherein 9 parts as instruct
Practice sample, remaining 1 part is used as forecast sample, loop iteration 10 times, and it is high preceding 40% index of importance that model, which enters ginseng,.
Further, before model training, the method further includes：It is selected in such a way that MDA methods and MDG methods are combined
Importance index is taken, by model training, obtains index importance analysis result.
Further, the method further includes：Establish the permanent mechanism of the top-tier customer identification model upgrading optimization, base
Efficiency analysis is aperiodically carried out to the judging result of top-tier customer identification model in supervising professional method, and based on analysis knot
Fruit, re -training top-tier customer Statistical error model.
Further, the step 2.3 includes：The top-tier customer that top-tier customer judgment models obtain is passed through into logistic regression
Model carries out comprehensive score；Multiple comprehensive score sections are set, top-tier customer grade judgment models are obtained.
Further, the method further includes：Trained model is integrated, it is special to collect user by data-interface
Data are levied, the judgement of the high-quality grade of client is periodically carried out.
Further, the demand for services in the demand for services library include industry expansion do electricity and power grid construction, electricity charge electricity price and
Power information, power quality and power supply reliability, fault outage and scheduled outage, safety utilization of electric power and emergency guarantee, electric energy replace
Generation and electric power energy-saving, electricity consumption training troubleshooting and other etc. eight demand class.
Further, the specific steps of the demand for services library foundation include：
Obtain the demand for services of sample client；
The demand for services of sample client is pre-processed, effective demand for services is obtained；
Classification carries out classification analysis and duplicate removal to effective demand for services according to demand, and the natural language of user's description is turned
It is changed to the standard requirement item of technical term, demand for services library is established in the data clusters analysis based on expertise.
Further, the step 5 includes：
Step 5.1：After determining user gradation, the demand attention rate of the corresponding industry of the user is obtained based on demand for services library；
Step 5.2：Multiple demand classifications in the top are obtained according to the demand attention rate；
Step 5.3：According to the multiple demand classification and user gradation, serviced accordingly based on service strategy storehouse matching
Strategy.
Second purpose according to the present invention is taken the present invention also provides a kind of based on the client of random forest and logistic regression
It is engaged in policy development device, including memory, processor and storage are on a memory and the computer journey that can run on a processor
Sequence, the processor realize the method when executing described program.
Third purpose according to the present invention, the present invention also provides a kind of computer readable storage mediums, are stored thereon with
Computer program, it is a kind of based on the client of random forest and logistic regression clothes described in execution when which is executed by processor
Business policy development method.
Beneficial effects of the present invention
1, the present invention by grid company client electrical properties, electricity consumption behavior, with based on the mass datas such as electrical feature,
Using the technological means of machine learning, the identification of top-tier customer is realized, providing good service to be directed to top-tier customer provides
It ensures, helps to promote power grid enterprises' competitiveness.
2, the present invention carries out the training of client's identification model in such a way that random forest and logistic regression are combined, described
Identification model can judge the high-quality grade of client, high-quality visitor be furthermore achieved on the basis of identifying whether client is good
The precise positioning at family.
3, the present invention establishes the permanent mechanism of the top-tier customer identification model upgrading optimization, based on supervising professional method to excellent
The judging result of matter client's identification model aperiodically carries out efficiency analysis, and is based on analysis result, the high-quality visitor of re -training
Family Statistical error model achievees the purpose that model version upgrading and optimization by re -training model.
4, the present invention excavates and analyzes top-tier customer comprehensive value and power supply service demand using big data technology depth,
The accurately embodiment of analysis customer general value and customer electricity demand for services, high-quality visitor is focused on by limited Service Source
In the key service demand at family, changes competitiveness under the new situation in electricity to promote power grid enterprises, power grid enterprises is made to keep long-term
Sustainable development.
5, recognition result and the power supply service demand analysis of the invention according to top-tier customer is as a result, formulate personalized, increment
The service product and service strategy of change, and service strategy is adjusted according to Market Feedback in time, increase top-tier customer loyalty,
Client viscosity and customer satisfaction, establish top-tier customer between stablize for electricity consumption relationship.
Description of the drawings
The accompanying drawings which form a part of this application are used for providing further understanding of the present application, and the application's shows
Meaning property embodiment and its explanation do not constitute the improper restriction to the application for explaining the application.
Fig. 1 is that the present invention is based on the customer service strategies formulating method flow charts of random forest and logistic regression；
Fig. 2 is that top-tier customer identification model of the present invention builds flow chart；
Fig. 3 is that the present invention is based on client's grade trend schematic diagrames that logistic regression is formed；
Fig. 4 is top-tier customer industry distribution schematic diagram of the present invention；
Fig. 5 is demand for services of the present invention category distribution schematic diagram according to demand.
Specific implementation mode
It is noted that described further below be all exemplary, it is intended to provide further instruction to the application.Unless another
It indicates, all technical and scientific terms used herein has logical with the application person of an ordinary skill in the technical field
The identical meanings understood.
It should be noted that term used herein above is merely to describe specific implementation mode, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singular shape
Formula is also intended to include plural form, additionally, it should be understood that, when in the present specification use term "comprising" and/or
When " comprising ", existing characteristics, step, operation, device, component and/or combination thereof are indicated.
In the absence of conflict, the features in the embodiments and the embodiments of the present application can be combined with each other.
Embodiment one
Present embodiment discloses a kind of customer service strategies formulating method based on random forest and logistic regression, such as Fig. 1
It is shown, include the following steps：
(1) data preparation stage
1, customer value evaluating characteristic index system is established：
By collecting User Profile information, economic value category information, Laden-Value category information, dynamogenetic value category information, letter
It is worth category information with value category information, industry, the various factors for influencing customer general value of comprehensive analysis establish customer value
Evaluating characteristic index system.Discussion and customer surveys are concentrated by client, realizes that prefectures and cities' sample of users quality differentiates, is
Model training provides data basis.
It is based on the various value characteristics that grid company is brought according to top-tier customer, the every electricity consumption for combing client refers to
Mark sorts out index according to customer value angle, builds customer evaluation index system, to criterionization processing, goes forward side by side
Row various dimensions summarize, to judge that the high-quality characteristic of client provides data basis.
2, model training sample is determined：
By the top-tier customer index system determined with districts and cities expert discussions, based on sales service application system, telecommunications is used
Acquisition system is ceased, counts the corresponding essential attribute of sample of users, economic value, Laden-Value, dynamogenetic value, credit respectively
Value, industry are worth data, in this, as model training sample.It is special to 47.4 ten thousand sample customer electricity behaviors in the present embodiment
Whether sign data have carried out expert judging, be labelled with high-quality.
User property：Family number, name in an account book, trade classification, whether highly energy-consuming and electricity consumption classification.
Economic value：Customer electricity to situation of getting a profit caused by power supply enterprise, as average electric sales rate is higher, electricity consumption compared with
Greatly, the more client of the electricity charge.Including：Current average electric sales rate, the current electricity charge, current electricity, accumulative average electric sales rate, accumulative electricity
Take, add up electricity, contract capacity and working capacity.
Laden-Value：The electric load value that client shows during electricity consumption, as power factor (PF) is larger, average negative
The high and low preferable client of paddy power consumption rate of lotus rate.Including：Average daily load rate, Peak power use rate, valley power consumption rate and power tune system
Number.
Dynamogenetic value：Client itself electricity consumption development is preferable, and future contributes larger client, can be brought to company lasting
Profit contribution.Including：Current electricity growth rate, nearly 3 months electricity growth rates, nearly 6 months electricity growth rates, nearly 1 year electricity
Growth rate, increase-volume number and volume reduction number.
Credit worthiness：Credit is that the basic guarantee of transaction is completed for electricity consumption both sides, can use electricity in accordance with the law, pay the electricity charge on time
Client.Including：The advance rate of carrying down of the electricity charge, the overdue number of days of electricity charge returned money, the overdue number of electricity charge returned money, electricity charge returned money phase, check
Returned ticket number and promise breaking stealing number.
Industry is worth：Consider that the industry development foreground of client, the development of industry entirety electricity consumption level are preferable.Including：Industry
Electricity growth rate, industry major class electricity growth rate and industry group electricity growth rate.
In data preparation stage, the standard formulation work of supervision source is also carried out, i.e., as effective supervision source, it is substantially answered
What the business of the satisfaction is, the supervision source of output, is just considered effective, Ke Yikai only in the business
Open up supervised learning.
Expert judging has been carried out to 47.4 ten thousand sample customer electricity behavioural characteristic data in the present embodiment, whether excellent has been labelled with
Matter.
(2) data processing stage
Current database is easily invaded and harassed by noise, loss data and inconsistent data, and quantity is too big, and comes from mostly
Multiple heterogeneous data sources cause the quality of data relatively low, and low-quality data will cause the result of data analysis inaccurate, therefore
Before model training, need to carry out data prediction.The data prediction of this programme is mainly from characteristic factor quantization, exception
Value processing, continuous variable processing etc. expansion.
1, data cleansing
It is examined by the inspection of data over run value, feature validation test, data null value, data is cleaned.
It transfinites inspection：Check that electricity consumption and electricity charge electricity price are 0 record and are deleted, electricity consumption and electricity charge electricity price are equal
Indicate that user without electricity, i.e., does not produce for 0, other related features also do not have characteristic.
Characteristic validity inspection：Check that the excessively single record of user's importance characteristic information, only minority belong to important
User.
Null value inspection：Check that the complete overdue number of days of empty and electricity charge returned money of pause day digital section lacks serious record.Suspend day
The complete empty expression pause full user of number of days of digital section lacks；It checks the overdue number of days of electricity charge returned money, it is found that field record is sky, but
Specific business is not overdue.
2, characteristic factor quantifies
The information such as files on each of customers, festivals or holidays and the weather come from marketing system or other systems acquisition are all to use word or generation
Number indicate, need to carry out numeralization expression to this class variable.
42 field spies such as name in an account book, family number, industry, industry group, industry major class, highly energy-consuming trade, importance rate
Sign.It is classified as follows：1) customer attribute information；2) economic value；3) Laden-Value；4) dynamogenetic value；5) credit worthiness；6) row
Industry is worth.
Factorization is converted：(being expressed using 0/1/2/3... digital codings) industry, industry group, industry major class, high consumption
It can industry, importance rate, electricity consumption classification, voltage class, region, scale of investment, the size of capacity, load character；
3, feature is expanded：
1) normalization is expanded：(setting within [0-1] user data value to data as feature) electricity charge, contract capacity,
Nearly annual electricity sales amount, nearly 6 monthly average electricity sales amounts, nearly 3 monthly average electricity sales amounts, working capacities；
2) discretization is expanded：(be segmented user data value by size and be used as feature) electricity charge, are put down at contract capacity for nearly 1 year
Equal electricity sales amount, nearly 6 monthly average electricity sales amounts, nearly 3 monthly average electricity sales amounts, working capacities；
3) sequencing feature is expanded：It is (sorting by size user data value as feature) electricity charge, contract capacity, 1 year nearly
Average electricity sales amount, nearly 6 monthly average electricity sales amounts, nearly 3 monthly average electricity sales amounts, working capacities；
4) few data encoding is measured to expand：(codings of onehot 0/1) increase-volume number, volume reduction number, the old deficient electricity charge, Chen Qian electricity
Take accounting, promise breaking stealing number.
4, feature selecting：
For user property feature, the distributing equilibrium situation of data is observed, whether these dimensional characteristics of preliminary analysis are to excellent
The influence of matter and requirement item.
For 5 class value characteristics, the distributing equilibrium situation of data is observed, whether these dimensional characteristics of preliminary analysis are to high-quality
With the influence of requirement item.It checks whether with associate feature.
Comprehensive dimensionality reduction, explores a variety of methods of attempting, and the result of comprehensive various methods carries out dimensionality reduction.
5, outlier processing
Gathered data, which exists, not to be acquired or the case where abnormal data, archives class data the case where there is also missings, needs needle
Missing values processing is carried out to this partial data, different missing values processing methods is selected according to different business rule：
Default value is replaced：For such as the case where load character, voltage class, being set by universal business rule in certain archives
Default value is set to be calculated.
Case scalping method：If missing values proportion is fewer, and certain attribute is important, then is picked using case
Division weeds out the data.If such as user id loses in User Profile information, directly weeds out the data.
Mean value Shift Method：If missing values are value types, the number of missing is filled with the average value of front and back data
According to.
If missing values are non-numeric types, the data that are lacked come polishing with the mode of the attribute.
Calorie completion method：An object most like with missing data object is selected in data set, with the value of the object
Instead of missing values.
(3) model training stage
The present embodiment carries out model training using random forest and logistic regression, as shown in Figure 2.
1, it is based on random forest method and trains top-tier customer judgment models
Importance index is chosen
Importance index selection is carried out using following two methods：One is the methods based on OOB errors, referred to as MDA
(Mean Decrease Accuracy)；Another kind is the method based on Gini impurity levels, referred to as MDG (Mean Decrease
Gini).Both of which is that the bigger expression variable of scalar value is more important.By model training, index importance analysis knot is obtained
Two methods of fruit, the importance index that comparison obtain, table specific as follows：
Table 1
| Ranking | MDA | MDG |
| 1 | Accumulative electricity | Accumulative electricity |
| 2 | The accumulative electricity charge | The accumulative electricity charge |
| 3 | The current electricity charge | The current electricity charge |
| 4 | Current electricity | Current electricity |
| 5 | Working capacity | Working capacity |
| 6 | It dishonours a cheque number | Power tune coefficient |
| 7 | Accumulative average electric sales rate | Industry major class electricity growth rate |
| 8 | Industry major class electricity growth rate | Annual daily load rate |
| 9 | Power tune coefficient | The electricity charge returned money phase |
| 10 | Accumulative electricity price growth rate | Industry group electricity growth rate |
In conjunction with the above importance index, determine that 13 indexs are importance index, it is specific as follows：
Table 2
| Serial number | Importance index | Corresponding data arranges |
| 1 | Accumulative electricity | 7 |
| 2 | The accumulative electricity charge | 8 |
| 3 | The current electricity charge | 5 |
| 4 | Current electricity | 4 |
| 5 | Working capacity | 10 |
| 6 | Power tune coefficient | 15 |
| 7 | It dishonours a cheque number | 35 |
| 8 | Accumulative average electric sales rate | 9 |
| 9 | Industry major class electricity growth rate | 39 |
| 10 | Accumulative electricity price growth rate | 24 |
| 11 | Annual daily load rate | 11 |
| 12 | The electricity charge returned money phase | 34 |
| 13 | Industry group electricity growth rate | 38 |
Training data is trained and is optimized by random forest method, it is whether excellent with user to find out electricity consumption behavioural characteristic value
Correspondence between matter, generation judge the whether good model of client.
Preferably, using following training process, implementation model gradually adjusts, from two dimensions of model stability and accuracy
Carry out model validation analysis：
Full feature training：Sample chooses all 47.4 ten thousand families, and model enters ginseng for whole operational indicators；
Important feature is trained：Sample chooses all 47.4 ten thousand families, and it is high preceding 40% index of importance that model, which enters ginseng,；
Full characteristic crossover training：Whole sample means are split into 10 parts, select every time wherein 9 parts as training sample,
Remaining 1 part is used as forecast sample, loop iteration 10 times, model to enter ginseng for whole operational indicators；
Important feature cross-training：Whole sample means are split into 10 parts, select every time wherein 9 parts as trained sample
This, remaining 1 part is used as forecast sample, loop iteration 10 times, and it is high preceding 40% index of importance that model, which enters ginseng,.
Wherein, noise identification is carried out by the notable property coefficient p of analysis model input variable, noise variance will not be included in mould
Type.
The present embodiment amounts to 47.4 ten thousand datas of collection and weeds out 3.94 ten thousand sample of users by data cleansing.Mould
Type training process is total to apply 43.5 samples, wherein 10.06 ten thousand families are top-tier customer, 33.39 ten thousand families are non-prime client,
The high-quality ratio 0.3 to 1 with non-prime sample.
2, top-tier customer grade judgment models are built using logistic regression algorithm
The probability P and comprehensive score Y that user is top-tier customer, wherein probability P=1/ (1 are obtained using logistic regression algorithm
+ exp (- Y)) it is about mono- nonlinear function of comprehensive score Y.Comprehensive score Y is a continuous variable, different by being arranged
Comprehensive score section, for further subdivision client's high-quality grade numerical basis is provided.Whole top-tier customers are returned by logic
Model is returned to carry out comprehensive score, score value Y to form client's grade trend figure according to being ranked up from high to low, by top-tier customer
It is divided according to quartile method, determines four grade top-tier customer scorings section (such as Fig. 3), form top-tier customer grading mark
It is accurate.The Y value that storage top-tier customer is calculated with Logic Regression Models, judges the high-quality grade of the client by its Y value.
Top-tier customer identification model falls into 5 types all high voltage customers, is respectively：Non-prime client, level-one top-tier customer
(grade is low), two level top-tier customer (grade is relatively low), three-level top-tier customer (higher ranked), level Four top-tier customer (grade is high).
In 47.4 ten thousand current training samples, probability P is divided into top-tier customer more than 0.5, and probability is less than or equal to
0.5 is divided into non-prime client, the category of model result rate of accuracy reached based on important feature to 99.1%.Probability P=1/ (1
+ exp (- Y)) it is comprehensive score Y can be used as further subdivision client high-quality etc. about mono- nonlinear function of comprehensive score Y
The numerical basis of grade.Score value Y to form client's grade trend figure according to being ranked up from high to low, by top-tier customer according to four
Period in arithmetric is divided, and is determined four grade top-tier customer scorings section (such as Fig. 3), is formed top-tier customer rating scale.To patrol
The Y value that regression model calculates storage top-tier customer is collected, judges the high-quality grade of the client by its Y value.
The logistic regression is additionally operable to the high-quality evaluation of single client：
Specific to single top-tier customer, the high-quality solution for differentiating result of sole user is carried out using logistic regression as auxiliary
It releases.By the analysis to sample data, the model coefficient K of each index is obtained.And the size of the product Hi of K values and characteristic value
Contribution degree of the index in the reflection high-quality degree of client is represented, influences the good principal element of client, i.e. user to analyze
High-quality speciality.
(4) model iteration optimization
The permanent mechanism of modeler model edition upgrading optimization.Carry out model by supervising professional and judges that result is corrected, it is indefinite
Phase carries out efficiency analysis to model judgement result and reaches model version by re -training model on the basis of analysis result
The purpose of upgrading and optimization.
(5) modelling effect is assessed
With the data of expert estimation, test to accuracy rate, the recall rate of best model, assessment models effect.
(6) model application deployment
Trained model is integrated, user characteristic data is collected by data-interface, it is high-quality periodically to carry out client
The judgement of grade.
From the point of view of the big data analysis and overall distribution situation of top-tier customer, the whole province high pressure client amounts to 47.4 ten thousand families,
10.06 ten thousand family of middle top-tier customer, after eight large user's trade classifications, top-tier customer distribution is as shown in Figure 4；Industrial trade it is excellent
The 46% of matter client's whole, public utilities and management industry account for 18%, and communications and transportation and construction industry are minimum, respectively 2%,
3%.From the point of view of every profession and trade top-tier customer accounting situation, informationization transmission, computer software industry and public utilities and management industry
Top-tier customer accounting highest, respectively reaches 74.54%, 35.21%；Industry, finance, real estate, commercial affairs and neghborhood services industry,
Communications and transportation, warehousing industry take second place, and respectively reach 20.17%, 20.64%, 15.23%；Two construction industry, agriculture, forestry, animal husbandry and fishery rows
The top-tier customer accounting of industry is minimum, and respectively 11.58%, 6.97%.
(7) demand for services library establishment stage
The demand for services for obtaining sample client, is standardized classification analysis and establishes demand for services library：
To promote customer experience as core, customer's requirement setting for enterprises service and product for the marketing of Internet era
Meter and planning, current electric grid enterprise have faced the rich and varied and personalized demand for services of client, and ring is competed in future marketization
Will be even more important to the assurance of customer demand under border, therefore from the accurate angle for holding customer demand, make sustainable answer
Customer Requirement Management system, using Customer Requirement Management as the work of normalization.
Make standardization demand management process.It is needed to change conventional requirement management dispersion, ununified storage, servicing for giving
The prominent questions such as disconnection are sought, plan the demand for services library of supporting policy development, standard requirement item is established, user's industry, uses
Relationship maps between the information such as capacitance, high-quality grade, demand attention rate are made and collect, demand pretreatment, need including demand
The standardization demand management process for asking classification, standardization, demand to be put in storage, and passed through to put into practice to execute and completed demand data
Primitive accumulation.
It is the unified standard of requirements of support information first, business expert meaning is gathered according to power business distribution and business characteristic
See, demand for services, which is divided into industry expansion, does electricity and power grid construction, electricity charge electricity price and power information, power quality and power supply reliably
Property, fault outage and scheduled outage, safety utilization of electric power and emergency guarantee, electric energy substitutes and electric power energy-saving, electricity consumption training troubleshooting
And other etc. eight demand class；As shown in Figure 5.
In terms of demand collection different industries, the demand 1.67 ten thousand of different scales power customer have been collected by multiple means
Item；
After demand filtration treatment in terms of demand pretreatment to describing unintelligible, improper verbal description, it is 9800 to arrange
Effective demand；
Based on demand class standard to forming 8156 complete demands after effective demand classification analysis and duplicate removal；
The standard requirement item of technical term, and the number based on expertise are converted to by the natural language for describing user
According to clustering, 378 standard requirement storages are completed, the digital management of demand information is realized；
The standard requirement item of formulation, original demands, user property and high-quality grade are associated, demand for services is formed
Library.
The demand of more means is excavated by all kinds of means.
First, current demand is collected, by the way of on-line off-line be combined, visited, sought by base business personnel scene
The collection of demand under the approach that industry Room staff waits for gap, key customer manager periodically to visit in client realizes line；Utilize the palm
Channel functions realize the collection of demand on line on the lines such as upper electric power.
Second is that potential demand is excavated, starts with from service is abnormal with problem, utilize text semantic excavation, clustering, association
The modes such as rule analysis carry out customer service big data analysis, extract demand for services.
The demand fine-grained management of various visual angles.
First, ensureing requirement quality, by the depth analysis to demand, multi-angle is precisely held the true service of client and is told
It asks, includes the angle from demand, refinement navigates to attention rate of the different industries to specific requirements, goes out from the angle of industry
Hair, refinement navigate to attention rate of the specific industry to different demands, by grasping the dependence of industry and demand, in city
Field, which is decontroled, formulates targetedly competitive strategy offer foundation under environment.
Second is that ensureing demand timeliness, demand is excavated and analyzed and is carried out as normalization work, by current demand
Periodic harvest and periodicity analysis to potential demand, constantly update and perfect service demand library, grasp customer demand
Variation tendency ensures the timely, effective of need-based competitive service strategy.
(8) service strategy library establishment stage
Demand for services in demand for services library is matched into service content, service content is matched into different grades of high-quality visitor
Service strategy library is established at family；Preferably, in conjunction with districts and cities' expert's business experience, the service strategy library includes being directed to each demand
The competitive strategy of five kinds of grades is formulated in classification, and high-grade competitive strategy enjoys inferior grade strategy, passes through the top-tier customer identified
Grade, the strategy of Auto-matching appropriate level, the accurate push of implementation strategy.
Client is waited for find and call as passive entity service in Traditional Marketing service, enterprise fails obtaining for active
It takes the dynamic requests of service and is difficult to change the dynamic evolution for realizing service strategy according to customer demand, it is difficult to adapt to the marketization
Changeable Market Situation and customer competition under environment.On the basis of realizing customer demand digital management, by being needed with client
The method for asking driving service to gather and combine, builds the service supply and demand Matching Model of client-demand-service three-dimensional, gradually forms
The competitive service policy library of architecture is realized the active response to customer service demand and is quickly executed.
First, the matching for establishing demand for services and service content, based on the different demands in demand for services library, comprehensive analysis
It disclosure satisfy that the service content and service form of demand, form the correspondence of demand and service.
Secondly, the matching of customer grade and service strategy is established, it is on the basis of demand and Service Matching, client is high-quality
Grade combines the external competitives factors such as market relieving, Industry Policy, competition temperature as important references dimension, in service
Hold and form refines, differentiated service strategy is formulated according to the high-quality grade of different clients, such as the service of " policy discloses " needs
Ask to different brackets user with explanation of visiting in real time, periodically visit inform, tell by telephone, information push, the strategies such as channel publicity
It is achieved.
Finally, competitive service policy library update mechanism is established, is improved to service by strategy execution, policy feedback, strategy
Strategy realizes closed loop management, is adjusted in real time as the progress of reference pair strategy using the executive condition of service strategy and customer evaluation and complete
It is kind, it is ensured that competitive service strategy can meet market competition needs in real time.
(8) service strategy matching stage
According to top-tier customer recognition result search service policy library, obtain and the matched service strategy of existing customer.
Specifically, the competitive strategy for matching appropriate level includes：
After determining user gradation, the demand attention rate of the corresponding industry of the user is obtained based on demand for services library；
Multiple demand classifications in the top are obtained according to the demand attention rate；
According to the multiple demand classification and user gradation, it is based on the corresponding service strategy of service strategy storehouse matching.
Such as certain structured metal product manufacturer, it is three-level to go out the high-quality grade in certain family according to Model Identification；Pass through service
Learn that 20 class demand of the sector the sector pair compares concern in demand library；Preferential preceding 5 demands of selection, are looked into based on service strategy library
This 5 corresponding strategies of demand three-level client are looked for recommend.It is three-level, three-level to go out the high-quality grade in certain family according to Model Identification
Client enjoys the strategy of subordinate client simultaneously, and corresponding competitive strategy is got with competitive strategy library by demand.
Embodiment two
The purpose of the present embodiment is to provide a kind of computing device.
A kind of customer service strategies making device based on random forest and logistic regression, including memory, processor and
Storage on a memory and the computer program that can run on a processor, when the processor executes described program realization with
Lower step, including：
Step 1：Sample customer value feature is obtained, and carries out quality differentiation；
Step 2：Using sample customer data, mould is identified based on random forest and logistic regression algorithm structure top-tier customer
Type；
Step 3：Efficiency analysis is carried out to the judging result of top-tier customer identification model based on supervising professional method, and is based on
Analysis result trains top-tier customer Statistical error model；
Step 4：Using the value characteristic of client to be identified as input, it is based on the top-tier customer identification model, judges institute
State whether client is top-tier customer.
Embodiment three
The purpose of the present embodiment is to provide a kind of computer readable storage medium.
A kind of computer readable storage medium, is stored thereon with computer program, which executes when being executed by processor
Following steps：
Step 1：Sample customer value feature is obtained, and carries out quality differentiation；
Step 2：Using sample customer data, mould is identified based on random forest and logistic regression algorithm structure top-tier customer
Type；
Step 3：Efficiency analysis is carried out to the judging result of top-tier customer identification model based on supervising professional method, and is based on
Analysis result trains top-tier customer Statistical error model；
Step 4：Using the value characteristic of client to be identified as input, it is based on the top-tier customer identification model, judges institute
State whether client is top-tier customer.
Each step involved in the device of above example two and three is corresponding with embodiment of the method one, specific implementation mode
It can be found in the related description part of embodiment one.It includes one or more that term " computer readable storage medium ", which is construed as,
The single medium or multiple media of a instruction set；Any medium is should also be understood as including, any medium can be deposited
Storage, coding carry the instruction set for being executed by processor and processor are made to execute the either method in the present invention.
Beneficial effects of the present invention
1, the present invention by grid company client electrical properties, electricity consumption behavior, with based on the mass datas such as electrical feature,
Using the technological means of machine learning, the identification of top-tier customer is realized, providing good service to be directed to top-tier customer provides
It ensures, helps to promote power grid enterprises' competitiveness.
2, the present invention carries out the training of client's identification model in such a way that random forest and logistic regression are combined, described
Identification model can judge the high-quality grade of client, high-quality visitor be furthermore achieved on the basis of identifying whether client is good
The precise positioning at family.
3, the present invention establishes the permanent mechanism of the top-tier customer identification model upgrading optimization, based on supervising professional method to excellent
The judging result of matter client's identification model aperiodically carries out efficiency analysis, and is based on analysis result, the high-quality visitor of re -training
Family Statistical error model achievees the purpose that model version upgrading and optimization by re -training model.
4, the present invention excavates and analyzes top-tier customer comprehensive value and power supply service demand using big data technology depth,
The accurately embodiment of analysis customer general value and customer electricity demand for services, high-quality visitor is focused on by limited Service Source
In the key service demand at family, changes competitiveness under the new situation in electricity to promote power grid enterprises, power grid enterprises is made to keep long-term
Sustainable development.
5, recognition result and the power supply service demand analysis of the invention according to top-tier customer is as a result, formulate personalized, increment
The service product and service strategy of change, and service strategy is adjusted according to Market Feedback in time, increase top-tier customer loyalty,
Client viscosity and customer satisfaction, establish top-tier customer between stablize for electricity consumption relationship.
It will be understood by those skilled in the art that each module or each step of aforementioned present invention can be filled with general computer
It sets to realize, optionally, they can be realized with the program code that computing device can perform, it is thus possible to which they are deposited
Storage be performed by computing device in the storage device, either they are fabricated to each integrated circuit modules or by it
In multiple modules or step be fabricated to single integrated circuit module to realize.The present invention is not limited to any specific hard
The combination of part and software.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art
The various modifications or changes that can be made need not be made the creative labor still within protection scope of the present invention.
Claims (10)
1. a kind of customer service strategies formulating method based on random forest and logistic regression, which is characterized in that including following step
Suddenly：
Step 1：Sample customer value feature is obtained, and carries out quality differentiation；
Step 2：Using sample customer data, top-tier customer identification model is built based on random forest and logistic regression algorithm；
Step 3：Using the value characteristic of client to be identified as input, it is based on the top-tier customer identification model, judges the visitor
Whether family is top-tier customer；
Step 4：The demand for services for obtaining sample client, is standardized classification analysis and establishes demand for services library；By demand for services
Demand for services in library matches service content, and service content is matched different grades of top-tier customer, establishes service strategy library；
Step 5：Based on demand for services library and service policy library, according to top-tier customer recognition result Auto-matching service strategy.
2. a kind of customer service strategies formulating method based on random forest and logistic regression as described in claim 1, special
Sign is that the step 1 includes：
Step 1.1：Customer value evaluating characteristic index system is built according to user's items power information of acquisition；
Step 1.2：According to the value characteristic of the index system statistical sample user, and carry out sample of users quality differentiation.
3. a kind of customer service strategies formulating method based on random forest and logistic regression as claimed in claim 1 or 2,
It is characterized in that, value characteristic includes the corresponding essential attribute of user, economic value, Laden-Value, development valence in the step 1
Value, credit worthiness, industry are worth data.
4. a kind of customer service strategies formulating method based on random forest and logistic regression as described in claim 1, special
Sign is that the step 2 includes：
Step 2.1：Sample of users data are pre-processed；
Step 2.2：Top-tier customer judgment models are trained based on random forest method；
Step 2.3：Top-tier customer grade judgment models are built using logistic regression algorithm；
Step 2.4：Top-tier customer identification model is obtained in conjunction with top-tier customer judgment models and top-tier customer grade judgment models.
5. a kind of customer service strategies formulating method based on random forest and logistic regression as claimed in claim 4, special
Sign is that the step 2.1 includes：Data cleansing, characteristic factor quantization, feature expansion, feature selecting and outlier processing.
6. a kind of customer service strategies formulating method based on random forest and logistic regression as claimed in claim 4, special
Sign is that the step 2.2 includes：
Full feature training：Sample chooses whole sample of users data, and model enters ginseng for whole operational indicators；
Important feature is trained：Sample chooses whole sample of users data, and it is high preceding 40% index of importance that model, which enters ginseng,；
Full characteristic crossover training：Mix the sample with user data and averagely split into 10 parts, select every time wherein 9 parts as training sample,
Remaining 1 part is used as forecast sample, loop iteration 10 times, model to enter ginseng for whole operational indicators；
Important feature cross-training：Mix the sample with user data and averagely split into 10 parts, select every time wherein 9 parts as trained sample
This, remaining 1 part is used as forecast sample, loop iteration 10 times, and it is high preceding 40% index of importance that model, which enters ginseng,.
7. a kind of customer service strategies formulating method based on random forest and logistic regression as claimed in claim 4, special
Sign is that the step 2.3 includes：The top-tier customer that top-tier customer judgment models are obtained is carried out comprehensive by Logic Regression Models
Close scoring；Multiple comprehensive score sections are set, top-tier customer grade judgment models are obtained.
8. a kind of customer service strategies formulating method based on random forest and logistic regression as described in claim 1, special
Sign is that the demand for services in the demand for services library includes that electricity and power grid construction, electricity charge electricity price and power information, electricity are done in industry expansion
Can quality and power supply reliability, fault outage and scheduled outage, safety utilization of electric power and emergency guarantee, electric energy substitutes and electric power energy-saving,
Electricity consumption training troubleshooting and other etc. eight demand class.Or
The specific steps that the demand for services library is established include：
Obtain the demand for services of sample client；
The demand for services of sample client is pre-processed, effective demand for services is obtained；
Classification carries out classification analysis and duplicate removal to effective demand for services according to demand, and the natural language that user describes is converted to
The standard requirement item of technical term, the data clusters analysis based on expertise, establishes demand for services library.Or
The step 5 includes：
Step 5.1：After determining user gradation, the demand attention rate of the corresponding industry of the user is obtained based on demand for services library；
Step 5.2：Multiple demand classifications in the top are obtained according to the demand attention rate；
Step 5.3：According to the multiple demand classification and user gradation, plan is serviced based on service strategy storehouse matching accordingly
Slightly.
9. a kind of customer service strategies making device based on random forest and logistic regression, including memory, processor and deposit
Store up the computer program that can be run on a memory and on a processor, which is characterized in that the processor executes described program
Shi Shixian such as claim 1-8 any one of them methods.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor
A kind of such as customer service strategies system based on random forest and logistic regression of claim 1-8 any one of them is executed when execution
Determine method.
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