CN108337316A - Information-pushing method, device, computer equipment and storage medium - Google Patents
Information-pushing method, device, computer equipment and storage medium Download PDFInfo
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- CN108337316A CN108337316A CN201810128050.6A CN201810128050A CN108337316A CN 108337316 A CN108337316 A CN 108337316A CN 201810128050 A CN201810128050 A CN 201810128050A CN 108337316 A CN108337316 A CN 108337316A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/55—Push-based network services
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0203—Market surveys; Market polls
Abstract
This application discloses a kind of information-pushing method, device, computer equipment and storage medium, wherein this method includes：Obtain the historic sales data of pre-set product；According in historic sales data customer profile data and customer action data determine customer data feature；Based on customer data feature, by preset algorithm modeling training to obtain the Sale Forecasting Model of pre-set product；The corresponding existing customer of pre-set product is predicted to export prediction result based on Sale Forecasting Model；And the corresponding customer information of existing customer and prediction result are pushed into the corresponding sales force of pre-set product.This method carries out analysis modeling to predict the prediction result of the purchase of the existing customer pre-set product using historic sales data, and the prediction result is sent to corresponding sales force, it is sold according to the default result by corresponding sales force, thus the marketing efficiency of the pre-set product can be improved, while saving the time of sales force.
Description
Technical field
This application involves Internet technical field more particularly to a kind of information-pushing method, device, computer equipment and deposit
Storage media.
Background technology
Currently, the subject of a sale of many online product sale is required to determine by expert opinion and previous experience, than
Such as fixed throwings of the finance product of bank and security company, fund intelligence, fund, Elder Security management product or regular etc., still
This there are prodigious subjectivity and unstability by way of expert opinion and previous experience, lack enough real data
Support is unfavorable for the sale of product to ensure feasibility and objectivity.It simultaneously cannot be very well using historic sales data into passing through
Accumulation is tested, the waste of resource is caused.
Invention content
This application provides a kind of information-pushing method, device, computer equipment and storage mediums, it is intended to utilize history number
According to marketing efficiency is provided, the wasting of resources is avoided.
In a first aspect, this application provides a kind of information-pushing methods, including：
The historic sales data for obtaining pre-set product, wherein the historic sales data includes customer profile data and client
Behavioral data；
Customer data feature is determined according to the customer profile data and customer action data；
Based on the customer data feature, by preset algorithm modeling training to obtain the sales forecast of the pre-set product
Model；
The corresponding existing customer of the pre-set product is predicted based on the Sale Forecasting Model to export prediction knot
Fruit；And
The corresponding customer information of the existing customer and prediction result are pushed into the corresponding sale people of the pre-set product
Member.
Second aspect, this application provides a kind of information push-delivery apparatus, including：
Data capture unit, the historic sales data for obtaining pre-set product, wherein the historic sales data includes
Customer profile data and customer action data；
Characteristics determining unit, for determining customer data feature according to the customer profile data and customer action data；
It is described to obtain to model training for being based on the customer data feature by preset algorithm for model training unit
The Sale Forecasting Model of pre-set product；
Prediction of result unit, for being carried out to the corresponding existing customer of the pre-set product based on the Sale Forecasting Model
Prediction is to export prediction result；And
Information push unit, it is described default for pushing to the corresponding customer information of the existing customer and prediction result
The corresponding sales force of product.
The third aspect present invention also provides a kind of computer equipment, including memory, processor and is stored in described deposit
On reservoir and the computer program that can run on the processor, the processor realize that the application carries when executing described program
Any one of them information-pushing method of confession.
Fourth aspect, present invention also provides a kind of storage mediums, wherein the storage medium is stored with computer program,
The computer program includes program instruction, and described program instruction makes the processor execute the application when being executed by a processor
Any one of them information-pushing method of offer.
The embodiment of the present application by obtain pre-set product historic sales data, wherein the historic sales data include visitor
Family information data and customer action data；Customer data feature is determined according to the customer profile data and customer action data；
Based on the customer data feature, by preset algorithm modeling training to obtain the Sale Forecasting Model of the pre-set product；Base
The corresponding existing customer of the pre-set product is predicted to export prediction result in the Sale Forecasting Model；And by institute
It states the corresponding customer information of existing customer and prediction result pushes to the corresponding sales force of the pre-set product.This method utilizes
Historic sales data carries out analysis modeling to predict the prediction result of the purchase of the existing customer pre-set product, and the prediction is tied
Fruit is sent to corresponding sales force, is sold according to the default result by corresponding sales force, it is possible thereby to improve this
The marketing efficiency of pre-set product, while saving the time of sales force.
Description of the drawings
It, below will be to needed in embodiment description in order to illustrate more clearly of the embodiment of the present application technical solution
Attached drawing is briefly described, it should be apparent that, the accompanying drawings in the following description is some embodiments of the present application, general for this field
For logical technical staff, without creative efforts, other drawings may also be obtained based on these drawings.
Fig. 1 is the corresponding application scenarios schematic diagram of a kind of information-pushing method that embodiments herein provides；
Fig. 2 is a kind of schematic flow diagram for information-pushing method that one embodiment of the application provides；
Fig. 3 is the sub-step schematic flow diagram of information-pushing method in Fig. 2；
Fig. 4 is a kind of schematic block diagram for information push-delivery apparatus that one embodiment of the application provides；
Fig. 5 is a kind of schematic block diagram for computer equipment that one embodiment of the application provides.
Specific implementation mode
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation describes, it is clear that described embodiment is some embodiments of the present application, instead of all the embodiments.Based on this Shen
Please in embodiment, every other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, shall fall in the protection scope of this application.
It should be appreciated that ought use in this specification and in the appended claims, term " comprising " and "comprising" instruction
Described feature, entirety, step, operation, the presence of element and/or component, but one or more of the other feature, whole is not precluded
Body, step, operation, element, component and/or its presence or addition gathered.
It is also understood that the term used in this present specification is merely for the sake of the mesh for describing specific embodiment
And be not intended to limit the application.As present specification and it is used in the attached claims, unless on
Other situations are hereafter clearly indicated, otherwise " one " of singulative, "one" and "the" are intended to include plural form.
It will be further appreciated that the term "and/or" used in present specification and the appended claims is
Refer to any combinations and all possible combinations of one or more of associated item listed, and includes these combinations.
The embodiment of the present application provides a kind of information-pushing method, device, computer equipment and storage medium.
In order to make it easy to understand, being first situated between to the application scenarios composition that the information-pushing method of the embodiment of the present application is applicable in
It continues.As shown in Figure 1, the application scenarios include user terminal and server.
Wherein, user terminal can be smart mobile phone, tablet computer, laptop, desktop computer, personal digital assistant
With the electronic equipments such as Wearable；Server can be independent server, can also be the service of multiple server compositions
Device cluster.
Specifically, which may operate in server, by the interaction of server and user terminal with complete
It is pushed at the information, thereby aids in sales force and improve sales rate, and save a large amount of time.
Referring to Fig. 2, Fig. 2 is a kind of schematic flow diagram for information-pushing method that one embodiment of the application provides.It please be same
When refering to fig. 1, introduce the information-pushing method below with reference to application scenarios in Fig. 1, which operates in service
In device, sales force is pushed information to for passing through user terminal.Specifically as shown in Fig. 2, the information-pushing method includes step
Rapid S101~S105.
S101, the historic sales data for obtaining pre-set product, wherein the historic sales data includes customer profile data
With customer action data.
In the present embodiment, the pre-set product can be finance product, fund intelligence it is fixed throw, Elder Security management product or
Fund etc..The historic sales data refers to that client pays close attention to or buy these finance products, the fixed throwing of fund intelligence, Elder Security management
The historical data generated when product or fund, the wherein historic sales data include buying the pre-set product and not buying this to preset
The historical data that product generates, for example, whether client registers the finance product, the whether browsed finance product of client etc..
Wherein, customer profile data is the essential information of client, the essential information of client include client's essential attribute information,
Consumer product attribute information and client's specific properties information customer essential attribute information such as gender, age, region, occupation etc.；Visitor
Family product attribute information, such as safety product number, specialized company's number, degree of protection etc.；Client's specific properties information, such as bad note
Record, blacklist etc..Customer action data are included in consulting or buy the data information generated when the pre-set product, customer action number
According to the contact information including client to product, such as the visit of telephone call number, complaint number, website in certain a period of time in the past
Ask number etc..
S102, customer data feature is determined according to the customer profile data and customer action data.
In the present embodiment, which refers to buying the data characteristics of the corresponding client of the pre-set product, is
All data acquisition systems of customers buying behavior are influenced, the spy in customer profile data and/or customer action data can be specifically used
Data are levied to indicate, for example, gender, region, occupation, record of bad behavior or blacklist etc..
Specifically, the client for having bought the pre-set product is determined according to the customer profile data and customer action data
Corresponding historic sales data；Customer profile data and customer action from the corresponding historic sales data of client bought
The corresponding data characteristics of extracting data.Since customer data is characterized in buying the data of the pre-set product, such as client's purchase
Finance product needs the essential information registered and fill in client of opening an account on the corresponding website of the finance product, such as name, property
Not, age, occupation and educational background etc. are filled on the website that these information pass through the pre-set product, which can be as unit of client
The essential information of client is stored in corresponding database, the form that similar table specifically may be used is stored, so
Corresponding client characteristics can easily be extracted by obtaining the customer profile data recorded in the table and customer action data
Data.
S103, it is based on the customer data feature, by preset algorithm modeling training to obtain the pin of the pre-set product
Sell prediction model.
In the present embodiment, using machine learning method, customer data feature is divided into training data sample and verification number
According to sample, it is trained by preset algorithm using training data sample and is verified by verify data sample to obtain
The Sale Forecasting Model of the pre-set product, the Sale Forecasting Model are used to predict that the existing customer of product to intend to buy the product
Purchase probability.
Specifically, by desired value and client characteristics data of result it is change when being trained by the preset algorithm
Amount is trained and verifies to obtain final Sale Forecasting Model, which is that basis once buys the default production
The customer data feature of product predicts the possibility of the purchase of new client, and the concrete form of output is percentage, i.e. purchase is general
Rate.
In the present embodiment, the preset algorithm includes that gradient promotes decision tree (Gradient Boosting
DecisionTree, GBDT) and logistic regression (Logistic Regression, LR) combinational algorithm.Specifically based on institute
Customer data feature is stated, decision tree and the modeling training of logistic regression combinational algorithm are promoted to obtain the pre-set product by gradient
Sale Forecasting Model.
In the present embodiment, described that decision tree and the modeling training of logistic regression combinational algorithm are promoted to obtain by gradient
The Sale Forecasting Model for stating pre-set product includes modeling training method.The modeling training method, as shown in figure 3, i.e. step S103
Including sub-step S103a to S103c.Wherein, S103a, according to gradient promoted decision Tree algorithms generate promoted tree-model；S103b、
Efficient combination feature is obtained based on the promotion tree-model；S103c, the customer data feature and efficient combination feature are set as
The training characteristics of the logistic regression algorithm are trained to generate Sale Forecasting Model.
Specifically, GBDT and LR combinational algorithms are two sort merge algorithms, first pass through the GBDT algorithms and use original visitor
Found in user data feature with the relevant assemblage characteristic of desired value depth, then obtain by the LR algorithm target of all samples
The probability of happening of event.Particularly referring to GBDT algorithm principles, it is assumed that Tree1, Tree2 be by GBDT models out two
Tree, x be an input sample, traverse two tree after, x samples fall on respectively two tree leaf node on, each leaf section
The corresponding LR one-dimensional characteristics of point have just obtained the corresponding all LR features of the sample then being set by traversing.Due to the roads Shu Meitiao
Diameter is that have distinction path by minimize that the methods of mean square deviation finally splits, the feature that is obtained according to the path, spy
Sign combination is all opposite distinction, and the processing mode of artificial experience will not be second on effect theory.Therefore the combinational algorithm is utilized
Customer data feature is handled, it can be found that there are many kinds of the feature for having distinction and feature combination, the path of decision tree
The step of being used directly as LR algorithm input feature vector, saving the feature manually found and feature combination, accelerate modeling
Speed.
Wherein, it first uses GBDT algorithms to generate and promotes tree-model, find out the assemblage characteristic being had a significant impact to desired value.Example
Such as：Whether age and occupation are to having bought significantly correlated, the assemblage characteristic (such as after 80s+financial white collar) of age that year and occupation also one
Surely work；Secondly, in conjunction with primitive character and newborn assemblage characteristic, the training characteristics as LR algorithm model.For example, former
Beginning is characterized in age and occupation, becomes the combination of age, occupation and age and occupation after increasing.
In one embodiment, described to be trained by preset algorithm modeling to obtain the sales forecast mould of the pre-set product
Type further includes：Judge that the performance parameter of the Sale Forecasting Model meets preset condition, wherein the performance parameter includes gradient
Promote the regularization coefficient of the depth value and logistic regression algorithm of decision tree；If the performance parameter of the Sale Forecasting Model meets
The preset condition, deconditioning is to obtain best sale preset model.
For example, by parameter trial and error method repetition training GBDT and LR algorithm model until performance has most in validation data set
Good classification performance.For example, when boosted tree includes 50 trees, depth 5, when LR regularization coefficients are 0.2, model is best.
S104, the corresponding existing customer of the pre-set product is predicted to export in advance based on the Sale Forecasting Model
Survey result.
In the present embodiment, Sale Forecasting Model is used for the data characteristics according to the corresponding existing customer of the pre-set product
Predicted that wherein default result is for showing that client buys the pre-set product to analyze whether it intends to buy the pre-set product
Possibility, the prediction result such as be purchase probability, or for purchase with do not buy two kinds of results.
S105, the corresponding customer information of the existing customer and prediction result are pushed into the corresponding pin of the pre-set product
Sell personnel.
In the present embodiment, for prediction result for showing whether the client is potential purchase client, customer information is specific
Can be customer name and communication mode etc..It can be specifically sent to by the mode of Email modes or product client communication
The user terminal that sales force uses.Thus sales force can carry out marketing activity according to the default result, obtain big number
According to the support of technology, and then the sales volume of product can be improved.
Specifically, which is used for being carried out according to the data characteristics of the corresponding existing customer of the pre-set product
Purchase probability in the existing customer is met the corresponding customer information of client of preset condition by prediction to obtain its purchase probability
And purchase probability pushes to the corresponding sales force of the pre-set product, purchase probability meets preset condition and refers to purchase probability
More than predetermined threshold value, illustrate that the client is potential purchase client more than the predetermined threshold value.Or there is specified specific sale
Number of objects, such as 1000, so that it may to take before purchase probability ranking 1000 client as subject of a sale.For example, will buy general
Rate is more than that 70% client thinks that user has and prodigious may carry out buying the product.Customer information is specifically as follows customer name
With communication mode etc..Sales force specifically can be sent to by way of Email and product client communication.
In addition, the historic sales data for obtaining pre-set product, including：It obtains and the relevant product of the pre-set product
Sales data within a preset period of time；And using the sales data in the preset time period as the history pin of pre-set product
Sell data.
Specifically, which is the case where reaching the standard grade for new product, which does not have the case where historical sales record, this
When the sales data of the product similar with the product may be used modeled.Specifically, can according to product information determine with
The similar product of the pre-set product, such as the product information of two finance products are substantially the same, difference may be release when
Between it is different.
Above-described embodiment by obtain pre-set product historic sales data, wherein the historic sales data includes client
Information data and customer action data；Customer data feature is determined according to the customer profile data and customer action data；Base
In the customer data feature, by preset algorithm modeling training to obtain the Sale Forecasting Model of the pre-set product；It is based on
The Sale Forecasting Model predicts to export prediction result the corresponding existing customer of the pre-set product；And it will be described
The corresponding customer information of existing customer and prediction result push to the corresponding sales force of the pre-set product.This method is utilized and is gone through
History sales data carries out analysis modeling to predict the prediction result of the purchase of the existing customer pre-set product, and by the prediction result
It is sent to corresponding sales force, is sold according to the default result by corresponding sales force, it is possible thereby to which it is pre- to improve this
If the marketing efficiency of product, while saving the time of sales force.
Referring to Fig. 4, Fig. 4 is a kind of schematic block diagram of information push-delivery apparatus provided by the embodiments of the present application.The information
Pusher 400 can be installed on server, and wherein the server can be independent server, can also be multiple servers
The server cluster of composition.As shown in figure 4, the information push-delivery apparatus 400 includes：Data capture unit 401, characteristics determining unit
402, model training unit 403, prediction of result unit 404 and information push unit 405.
Data capture unit 401, the historic sales data for obtaining pre-set product, wherein the historic sales data packet
Include customer profile data and customer action data.
Characteristics determining unit 402, for determining customer data spy according to the customer profile data and customer action data
Sign.
Model training unit 403, for being based on the customer data feature, by preset algorithm modeling training to obtain
State the Sale Forecasting Model of pre-set product；The wherein described preset algorithm includes that gradient promotion decision tree and logistic regression group are worthwhile
Method；The model training unit is specifically used for：Based on the customer data feature, decision tree and logistic regression are promoted by gradient
Combinational algorithm modeling training is to obtain the Sale Forecasting Model of the pre-set product.
In one embodiment, model training unit 403 includes：First model generates subelement 4031, validity feature obtains
Subelement 4032 and the second model generate subelement 4033.Wherein the first model generates subelement 4031, for being carried according to gradient
It rises decision Tree algorithms and generates promotion tree-model；Validity feature obtains subelement 4032, for being obtained based on the promotion tree-model
Efficient combination feature；Second model generates subelement 4033, for the customer data feature and efficient combination feature to be set as
The training characteristics of the logistic regression algorithm are trained to generate Sale Forecasting Model.
Prediction of result unit 404, for being based on the Sale Forecasting Model to the corresponding existing customer of the pre-set product
It is predicted to export prediction result.
Information push unit 405, it is described for pushing to the corresponding customer information of the existing customer and prediction result
The corresponding sales force of pre-set product.
It is apparent to those skilled in the art that for convenience of description and succinctly, the letter of foregoing description
The specific work process for ceasing pusher and unit, can refer to corresponding processes in the foregoing method embodiment, no longer superfluous herein
It states.
Above-mentioned apparatus can be implemented as a kind of form of computer program, and computer program can be in meter as shown in Figure 5
It calculates and is run on machine equipment.
Referring to Fig. 5, Fig. 5 is a kind of schematic block diagram of computer equipment provided by the embodiments of the present application.The computer
500 equipment of equipment can be server.
With reference to Fig. 5, which includes processor 520, memory and the net connected by system bus 510
Network interface 550, wherein memory may include non-volatile memory medium 530 and built-in storage 540.
The non-volatile memory medium 530 can storage program area 531 and computer program 532.The computer program 532
It is performed, processor 520 may make to execute a kind of information-pushing method.
The processor 520 supports the operation of entire computer equipment 500 for providing calculating and control ability.
The built-in storage 540 provides environment for the operation of the computer program 532 in non-volatile memory medium 530, should
When computer program 532 is executed by processor 520, processor 520 may make to execute a kind of information-pushing method.
The network interface 550 such as sends the task dispatching of distribution for carrying out network communication.Those skilled in the art can manage
It solves, structure is not constituted only with the block diagram of the relevant part-structure of application scheme to the application side shown in Fig. 5
The restriction for the computer equipment 500 that case is applied thereon, specific computer equipment 500 may include more than as shown in the figure
Or less component, it either combines certain components or is arranged with different components.
Wherein, the processor 520 is for running program code stored in memory, to realize following steps：
The historic sales data for obtaining pre-set product, wherein the historic sales data includes customer profile data and client
Behavioral data；
Customer data feature is determined according to the customer profile data and customer action data；
Based on the customer data feature, by preset algorithm modeling training to obtain the sales forecast of the pre-set product
Model；
The corresponding existing customer of the pre-set product is predicted based on the Sale Forecasting Model to export prediction knot
Fruit；And
The corresponding customer information of the existing customer and prediction result are pushed into the corresponding sale people of the pre-set product
Member.
In one embodiment, the preset algorithm includes that gradient promotes decision tree and logistic regression combinational algorithm, processor
520 are based on the customer data feature described, pre- to obtain the sale of the pre-set product by preset algorithm modeling training
When surveying model, following steps are executed：Based on the customer data feature, decision tree is promoted by gradient and logistic regression group is worthwhile
Method modeling training is to obtain the Sale Forecasting Model of the pre-set product.
In one embodiment, processor 520 is based on the customer data feature described in executing, and decision is promoted by gradient
When tree is trained with the modeling of logistic regression combinational algorithm to obtain the Sale Forecasting Model of the pre-set product, also specific execution is as follows
Step：
Decision Tree algorithms, which are promoted, according to gradient generates promotion tree-model；It is special that efficient combination is obtained based on the promotion tree-model
Sign；The training characteristics that the customer data feature and efficient combination feature are set as to the logistic regression algorithm are trained with life
At Sale Forecasting Model.
In one embodiment, processor 520 is trained by preset algorithm modeling to obtain the default production described in executing
When the Sale Forecasting Model of product, following steps are specifically executed：
Judge that the performance parameter of the Sale Forecasting Model meets preset condition；If the performance of the Sale Forecasting Model is joined
Number meets the preset condition, and deconditioning is to obtain best sale preset model.
In one embodiment, the prediction result includes purchase probability, and processor 520 is described by the current visitor in execution
When the corresponding customer information in family and prediction result push to the pre-set product corresponding sales force, following steps are executed：It will
In the existing customer purchase probability meet the corresponding customer information of client of preset condition and purchase probability push to it is described pre-
If the corresponding sales force of product.
It should be appreciated that in the embodiment of the present application, processor 520 can be central processing unit (Central
ProcessingUnit, CPU), which can also be other general processors, digital signal processor (Digital
Signal Processor, DSP), application-specific integrated circuit (Application Specific Integrated Circuit,
ASIC), ready-made programmable gate array (Field-Programmable GateArray, FPGA) or other programmable logic devices
Part, discrete gate or transistor logic, discrete hardware components etc..Wherein, general processor can be microprocessor or
The processor can also be any conventional processor etc..
It will be understood by those skilled in the art that 500 structure of computer equipment shown in Fig. 5 is not constituted and is set to computer
Standby 500 restriction may include either combining certain components or different component cloth than illustrating more or fewer components
It sets.
In several embodiments provided herein, it should be understood that disclosed information push-delivery apparatus and method, it can
To realize by another way.For example, information push-delivery apparatus embodiment described above is only schematical.For example,
The division of each unit, only a kind of division of logic function, formula that in actual implementation, there may be another division manner.Such as it is multiple
Unit or component can be combined or can be integrated into another system, or some features can be ignored or not executed.
Step in the embodiment of the present application method can be sequentially adjusted, merged and deleted according to actual needs.
Unit in the embodiment of the present application device can be combined, divided and deleted according to actual needs.
In addition, each functional unit in each embodiment of the application can be integrated in a processing unit, it can also
It is that each unit physically exists alone, can also be during two or more units are integrated in one unit.It is above-mentioned integrated
The form that hardware had both may be used in unit is realized, can also be realized in the form of SFU software functional unit.
If the integrated unit is realized in the form of SFU software functional unit and when sold or used as an independent product,
It can be stored in a computer read/write memory medium.Based on this understanding, the technical solution of the application substantially or
Person says that all or part of the part that contributes to existing technology or the technical solution can body in the form of software products
Reveal and, which is stored in a storage medium, including some instructions are with so that a computer is set
Standby (can be personal computer, terminal or the network equipment etc.) execute each embodiment the method for the application whole or
Part steps.
One of ordinary skill in the art will appreciate that be realize above-described embodiment method in all or part of flow, be
Relevant hardware can be instructed to complete by computer program, program can be stored in a storage medium, the storage medium
For computer readable storage medium.In the embodiment of the present invention, which can be stored in the storage medium of computer system, and
It is executed by least one of computer system processor, to realize including that the flow of the embodiment of above-mentioned each method such as walks
Suddenly.
The computer readable storage medium can be magnetic disc, CD, USB flash disk, mobile hard disk, random access memory
The various media that can store program code such as (RandomAccess Memory, RAM), magnetic disc or CD.
Those of ordinary skill in the art may realize that lists described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware
With the interchangeability of software, each exemplary composition and step are generally described according to function in the above description.This
A little functions are implemented in hardware or software actually, depend on the specific application and design constraint of technical solution.Specially
Industry technical staff can use different methods to achieve the described function each specific application, but this realization is not
It is considered as exceeding scope of the present application.
The above, the only specific implementation mode of the application, but the protection domain of the application is not limited thereto, it is any
Those familiar with the art can readily occur in various equivalent modifications or replace in the technical scope that the application discloses
It changes, these modifications or substitutions should all cover within the protection domain of the application.Therefore, the protection domain of the application should be with right
It is required that protection domain subject to.
Claims (10)
1. a kind of information-pushing method, which is characterized in that including：
The historic sales data for obtaining pre-set product, wherein the historic sales data includes customer profile data and customer action
Data；
Customer data feature is determined according to the customer profile data and customer action data；
Based on the customer data feature, by preset algorithm modeling training to obtain the sales forecast mould of the pre-set product
Type；
The corresponding existing customer of the pre-set product is predicted to export prediction result based on the Sale Forecasting Model；With
And
The corresponding customer information of the existing customer and prediction result are pushed into the corresponding sales force of the pre-set product.
2. information-pushing method according to claim 1, which is characterized in that the preset algorithm includes that gradient promotes decision
Tree and logistic regression combinational algorithm；
It is described to be based on the customer data feature, by preset algorithm modeling training to obtain the sales forecast of the pre-set product
Model, including：
Based on the customer data feature, decision tree and the modeling training of logistic regression combinational algorithm are promoted to obtain by gradient
State the Sale Forecasting Model of pre-set product.
3. information-pushing method according to claim 2, which is characterized in that described to promote decision tree and logic by gradient
The modeling of regression combination algorithm is trained to obtain the Sale Forecasting Model of the pre-set product, including：
Decision Tree algorithms, which are promoted, according to the gradient generates promotion tree-model；
Efficient combination feature is obtained based on the promotion tree-model；
The customer data feature and efficient combination feature are set as the logistic regression algorithm training characteristics be trained with
Generate Sale Forecasting Model.
4. information-pushing method according to claim 3, which is characterized in that described to be trained by preset algorithm modeling to obtain
To the Sale Forecasting Model of the pre-set product, including：
Judge that the performance parameter of the Sale Forecasting Model meets preset condition, determines wherein the performance parameter includes gradient promotion
The regularization coefficient of the depth value and logistic regression algorithm of plan tree；
If the performance parameter of the Sale Forecasting Model meets the preset condition, deconditioning is default to obtain best sale
Model.
5. information-pushing method according to claim 1, which is characterized in that the prediction result includes purchase probability；
It is described that the corresponding customer information of the existing customer and prediction result are pushed into the corresponding sale people of the pre-set product
Member, including：
Purchase probability in the existing customer is met into the corresponding customer information of client of preset condition and purchase probability pushes to
The corresponding sales force of the pre-set product.
6. a kind of information push-delivery apparatus, which is characterized in that including：
Data capture unit, the historic sales data for obtaining pre-set product, wherein the historic sales data includes client
Information data and customer action data；
Characteristics determining unit, for determining customer data feature according to the customer profile data and customer action data；
Model training unit, for being based on the customer data feature, it is described default to obtain to model training by preset algorithm
The Sale Forecasting Model of product；
Prediction of result unit predicts the corresponding existing customer of the pre-set product for being based on the Sale Forecasting Model
To export prediction result；And
Information push unit, for the corresponding customer information of the existing customer and prediction result to be pushed to the pre-set product
Corresponding sales force.
7. information push-delivery apparatus according to claim 6, which is characterized in that the preset algorithm includes that gradient promotes decision
Tree and logistic regression combinational algorithm；
The model training unit promotes decision tree and logistic regression group for being based on the customer data feature by gradient
Hop algorithm modeling training is to obtain the Sale Forecasting Model of the pre-set product.
8. information push-delivery apparatus according to claim 7, which is characterized in that the model training unit, including：
First model generates subelement, and promotion tree-model is generated for promoting decision Tree algorithms according to gradient；
Validity feature obtains subelement, for obtaining efficient combination feature based on the promotion tree-model；
Second model generates subelement, is calculated for the customer data feature and efficient combination feature to be set as the logistic regression
The training characteristics of method are trained to generate Sale Forecasting Model.
9. a kind of computer equipment, which is characterized in that including memory, processor and be stored on the memory and can be in institute
The computer program run on processor is stated, the processor is realized when executing the computer program as in claim 1 to 5
Any one of them information-pushing method.
10. a kind of storage medium, which is characterized in that the storage medium is stored with computer program, the computer program packet
Program instruction is included, described program instruction makes the processor execute such as any one of claim 1 to 5 institute when being executed by a processor
The information-pushing method stated.
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| CN201810128050.6A CN108337316A (en) | 2018-02-08 | 2018-02-08 | Information-pushing method, device, computer equipment and storage medium |
| PCT/CN2018/084308 WO2019153518A1 (en) | 2018-02-08 | 2018-04-25 | Information pushing method and device, computer device and storage medium |
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Application publication date: 20180727 |