CN109062984A - A kind of data analysing method and analysis system based on label - Google Patents
A kind of data analysing method and analysis system based on label Download PDFInfo
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- CN109062984A CN109062984A CN201810712481.7A CN201810712481A CN109062984A CN 109062984 A CN109062984 A CN 109062984A CN 201810712481 A CN201810712481 A CN 201810712481A CN 109062984 A CN109062984 A CN 109062984A
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Abstract
This application involves a kind of data analysing method and analysis system based on label, the former includes the weight coefficient that classification by geographical area presets each dimensional attribute；User is divided into several customers according to index classification；The customer attribute information of target user is received, and calculates the similarity of target user and some customers based on the customer attribute information；The relationship for comparing the similarity and preset threshold determines the priority level of target user；Matching policy information is exported according to the priority level of target user.In this way, the priority level for calculating user should be passed through based on the data analysing method of label, accurate data are provided for Making Enterprise Strategy policy and are supported, judge to determine enterprise potential customers, and improve data utilization efficiency.
Description
Technical field
This application involves field of computer technology, are more particularly to a kind of data analysing method based on label and analysis
System.
Background technique
Currently, realizing that customer care and popularization provide convenience to enterprises such as internets using label technique, obtain
To being more and more widely used.There is data analysis inaccuracy in traditional label data analysis method, cannot be fine
Offer data support, it is therefore desirable in due course introduces a kind of label data analysis method, to push the application of label technique.
Summary of the invention
Based on this, it is necessary to for the above technical issues, provide a kind of data analysing method based on label and analysis
System.
In order to solve the above technical problem, the present invention provides a kind of data analysing methods based on label, including following step
It is rapid:
Classification by geographical area presets the weight coefficient of each dimensional attribute；
User is divided into several customers according to index classification；
The customer attribute information of target user is received, and target user and some visitor are calculated based on the customer attribute information
The similarity of family group；
The relationship for comparing the similarity and preset threshold determines the priority level of target user；
Matching policy information is exported according to the priority level of target user.
In use, which prestores the weight coefficient of different dimensions attribute first and divides different types of
Customers, then the correlation attribute information of target user is inputted and pass through comparison and calculate determine the target customer and some or
The similarity of certain customers, that is, confirm the big of a possibility that target customer may belong to some or certain customers
It is small；Obtained similarity is compared with preset threshold then, and determines the priority level of target user, example according to comparison result
Such as it is determined as high medium priority when similarity is greater than preset threshold, is high when similarity is greater than 1.5 times of preset threshold
Priority etc.；Locating priority level and customers' type where it are finally used for according to target, exported matched
Policy information, targetedly the client can be pushed or be followed up according to the policy information of output.In this way, this is based on
The data analysing method of label provides accurate data by the priority level of calculating user for Making Enterprise Strategy policy
It supports, judges to determine enterprise potential customers, and improve data utilization efficiency.
Further, in the operation of weight coefficient that each dimensional attribute is preset in classification by geographical area, the dimension
Spend the second weight system of the first weight coefficient and corresponding platform that attribute is included at least to Yingcheng City.
Further, in the operation that user is divided into several customers according to index classification, what is be divided into is described
Customers, which include at least, complains customers and popularization customers.
Further, the behaviour that target user and the similarity of some customers are calculated based on the customer attribute information
Make, including be calculated by the following formula similarity: P=N × KX；
Wherein, P is the similarity that target user belongs to some customers；
N is target user for the click volume under customers' respective operations attribute；
KXFor the corresponding weight coefficient of dimensional attribute.
The present invention also provides a kind of data analysis systems based on label, comprising:
Weight setting module presets the weight coefficient of each dimensional attribute for classification by geographical area；
Customers' division module, for user to be divided into several customers according to index classification；
Similarity calculation module for receiving the customer attribute information of target user, and is based on the customer attribute information
Calculate the similarity of target user and some customers；
Priority Determination module, for the relationship of the similarity and preset threshold, to determine that target user's is excellent
First grade；
Tactful output module exports matching policy information for the priority level according to target user.
Further, the dimensional attribute includes at least the second power of the first weight coefficient and corresponding platform to Yingcheng City
Weight system.
Further, the customers being divided into customers' division module, which include at least, complains customers and pushes away
Wide customers.
Further, formula P=N × K is prestored in the similarity calculation moduleX, to calculate similarity；
Wherein, P is the similarity that target user belongs to some customers；
N is target user for the click volume under customers' respective operations attribute；
KXFor the corresponding weight coefficient of dimensional attribute.
The present invention also provides a kind of computer equipment, including memory and processor, the memory is stored with computer
The step of program, the processor realizes method as described above when executing the computer program.
The present invention also provides a kind of computer readable storage mediums, are stored thereon with computer program, the computer journey
The step of method as described above is realized when sequence is executed by processor.
Detailed description of the invention
Fig. 1 is a kind of flow chart of specific embodiment of the data analysing method provided by the present invention based on label；
Fig. 2 is a kind of structural representation of specific embodiment of the data analysis system provided by the present invention based on label
Figure.
Description of symbols:
Data analysis system of the 1000- based on label
100- weight setting module
200- customers division module
300- similarity calculation module
400- Priority Determination module
500- strategy output module
Specific embodiment
It is with reference to the accompanying drawings and embodiments, right in order to which the objects, technical solutions and advantages of the application are more clearly understood
The application is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain the application, not
For limiting the application.
Referring to FIG. 1, Fig. 1 is a kind of specific embodiment of the data analysing method provided by the present invention based on label
Flow chart.
In a specific embodiment, the data analysing method provided by the invention based on label the following steps are included:
S1: classification by geographical area presets the weight coefficient of each dimensional attribute；The dimensional attribute can there are many draw
Point mode, such as divide according to city level, a line city weight coefficient are K11, and tier 2 cities weight coefficient is K12, three or four
Line city is K13 etc.；Or divided according to default group of cities, key cities are prestored, key cities' weight coefficient is K11', other
City weight coefficient is K12' etc.；It also or according to city and platform divides, such as is K1 to the first weight coefficient of Yingcheng City,
Second weight system of corresponding platform is K2.
S2: user is divided by several customers according to index classification；Customers are complained for example, customers are divided into
With promote customers etc..
S3: the customer attribute information of target user is received, and target user and certain are calculated based on the customer attribute information
The similarity of a customers；The similarity is to pass through formula: the similarity that P=N × KX is calculated；
Wherein, P is the similarity that target user belongs to some customers；
N is target user for the click volume under customers' respective operations attribute；
KX is the corresponding weight coefficient of dimensional attribute.
S4: the relationship of the similarity and preset threshold determines the priority level of target user；Such as work as similarity
It is determined as high medium priority when greater than preset threshold, is high isopreference level etc. when similarity is greater than 1.5 times of preset threshold.
S5: matching policy information is exported according to the priority level of target user；Pass through calculating formula of similarity meter
It calculates target user and belongs to the similarity of some customers, the target user for enterprise potential customers, formulates phase accordingly if judging
The sales tactics answered；If judging, the target user is high to enterprises service requirement, formulates corresponding service strategy accordingly.
In use, which prestores the weight coefficient of different dimensions attribute first and divides different types of
Customers, then the correlation attribute information of target user is inputted and pass through comparison and calculate determine the target customer and some or
The similarity of certain customers, that is, confirm the big of a possibility that target customer may belong to some or certain customers
It is small；Obtained similarity is compared with preset threshold then, and determines the priority level of target user according to comparison result；Most
Locating priority level and customers' type where it are used for according to target afterwards, export matched policy information,
Targetedly the client can be pushed or be followed up according to the policy information of output.In this way, should the data based on label
Analysis method is provided accurate data for Making Enterprise Strategy policy and is supported by the priority level of calculating user, judgement
It determines enterprise potential customers, and improves data utilization efficiency.
In addition to above-mentioned data analysing method, the present invention also provides a kind of data analysis systems based on the data analysing method
1000, as shown in Fig. 2, the system include weight setting module 100, customers' division module 200, similarity calculation module 300,
Priority Determination module 400 and tactful output module 500.
Wherein, weight setting module 100 presets the weight coefficient of each dimensional attribute for classification by geographical area；It should
Dimensional attribute can there are many division modes, such as divide according to city level, and a line city weight coefficient is K11, two wires city
City's weight coefficient is K12, and three or four line cities are K13 etc.；Or divided according to default group of cities, prestore key cities, emphasis city
City's weight coefficient is K11', other city weight coefficients are K12' etc.；It also or according to city and platform divides, such as to Yingcheng City
First weight coefficient in city is K1, and the second weight system of corresponding platform is K2.
Customers' division module 200 is used to that user to be divided into several customers according to index classification；For example, by client
Group, which is divided into, to be complained customers and promotes customers etc..
Similarity calculation module 300 is used to receive the customer attribute information of target user, and is believed based on the user property
Breath calculates the similarity of target user and some customers；The similarity is to pass through formula: P=N × KX is calculated similar
Degree；
Wherein, P is the similarity that target user belongs to some customers；
N is target user for the click volume under customers' respective operations attribute；
KX is the corresponding weight coefficient of dimensional attribute.
Priority Determination module 400 is used for the relationship of the similarity and preset threshold, to determine target user's
Priority level；Such as it is determined as high medium priority when similarity is greater than preset threshold, when similarity is greater than the 1.5 of preset threshold
Times when for high isopreference level etc..
Tactful output module 500, matching policy information is exported for the priority level according to target user；Pass through
Calculating formula of similarity calculates target user and belongs to the similarity of some customers, if judging the target user for the potential visitor of enterprise
Corresponding sales tactics is then formulated at family accordingly；If judging, the target user is high to enterprises service requirement, formulates accordingly corresponding
Service strategy.
In use, which prestores the weight coefficient of different dimensions attribute first and divides different types of
Customers, then the correlation attribute information of target user is inputted and pass through comparison and calculate determine the target customer and some or
The similarity of certain customers, that is, confirm the big of a possibility that target customer may belong to some or certain customers
It is small；Obtained similarity is compared with preset threshold then, and determines the priority level of target user according to comparison result；Most
Locating priority level and customers' type where it are used for according to target afterwards, export matched policy information,
Targetedly the client can be pushed or be followed up according to the policy information of output.In this way, should the data based on label
Analysis method is provided accurate data for Making Enterprise Strategy policy and is supported by the priority level of calculating user, judgement
It determines enterprise potential customers, and improves data utilization efficiency.
To sum up, in data analysing method provided by the present invention and analysis system, portrait label includes that dimension index is closed
System and customer attribute information preset the weight coefficient of each dimensional attribute (for example, dimensional attribute is the weight coefficient in city
For K1, dimensional attribute is that the weight coefficient of platform is K2,.)；User is divided into several clients according to index classification
Group, for example complain customers, promote customers；The phase for belonging to some customers of target user is calculated according to customer attribute information
Like degree, for example target user Zhang San is submitted by Android App and complains work order times N time, by preset calculation formula and
Dimensional attribute is that the weight coefficient K2 of platform calculates similarity P, P=N*K2 that target user belongs to the complaint customers, according to P
With the relationship of preset threshold, it is determined that the priority level of target user；According to determining user's priority level, corresponding plan is formulated
Slightly, such as, the user's priority level for the determination illustrated in step 3 judges that the user is relatively high to enterprises service requirement, can be with
Relative strategy is targetedly formulated, for another example, if it is customers are promoted, the phase that user belongs to this group is calculated by preset formula
Like degree, judge that the user for enterprise potential customers, can similarly formulate corresponding sales tactics accordingly.
To provide accurate data for Making Enterprise Strategy policy and support by the priority level for calculating user,
Judge to determine enterprise potential customers, and data utilization efficiency also can be improved by this method.
The embodiment of the present application also provides a kind of computer equipment, including memory and processor, and the memory is stored with
The step of computer program, the processor realizes above-mentioned any means embodiment when executing the computer program.
The embodiment of the present application also provides a kind of computer readable storage medium, is stored thereon with computer program, feature
It is, the step of computer program is executed by processor to realize above-mentioned any means embodiment.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computer
In read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein,
To any reference of memory, storage, database or other media used in each embodiment provided herein,
Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM
(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include
Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,
Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancing
Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Each technical characteristic of above embodiments can be combined arbitrarily, for simplicity of description, not to above-described embodiment
In each technical characteristic it is all possible combination be all described, as long as however, the combination of these technical characteristics be not present lance
Shield all should be considered as described in this specification.
The several embodiments of the application above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the concept of this application, various modifications and improvements can be made, these belong to the protection of the application
Range.Therefore, the scope of protection shall be subject to the appended claims for the application patent.
Claims (10)
1. a kind of data analysing method based on label, which comprises the following steps:
Classification by geographical area presets the weight coefficient of each dimensional attribute；
User is divided into several customers according to index classification；
The customer attribute information of target user is received, and target user and some customers are calculated based on the customer attribute information
Similarity；
The relationship for comparing the similarity and preset threshold determines the priority level of target user；And
Matching policy information is exported according to the priority level of target user.
2. the data analysing method according to claim 1 based on label, which is characterized in that preparatory in classification by geographical area
In the operation for setting the weight coefficient of each dimensional attribute, the dimensional attribute includes at least the first weight coefficient to Yingcheng City
With the second weight system of corresponding platform.
3. the data analysing method according to claim 1 based on label, which is characterized in that will be used according to index classification
Family is divided into the operation of several customers, and the customers being divided into, which include at least, complains customers and popularization client
Group.
4. the data analysing method according to claim 1 based on label, which is characterized in that described to be belonged to based on the user
Property information calculate target user and some customers similarity operation, including be calculated by the following formula similarity: P=N
×Kx,
Wherein, P is the similarity that target user belongs to some customers；
N is target user for the click volume under customers' respective operations attribute；
KxFor the corresponding weight coefficient of dimensional attribute.
5. a kind of data analysis system based on label characterized by comprising
Weight setting module (100), the weight coefficient of each dimensional attribute is preset for classification by geographical area；
Customers' division module (200), for user to be divided into several customers according to index classification；
Similarity calculation module (300) for receiving the customer attribute information of target user, and is based on the customer attribute information
Calculate the similarity of target user and some customers；
Priority Determination module (400), for the relationship of the similarity and preset threshold, to determine that target user's is excellent
First grade；And
Tactful output module (500), matching policy information is exported for the priority level according to target user.
6. the data analysis system according to claim 5 based on label, which is characterized in that the dimensional attribute at least wraps
Include the second weight system of the first weight coefficient and corresponding platform to Yingcheng City.
7. the data analysis system according to claim 5 based on label, which is characterized in that customers' division module
(200) customers being divided into, which include at least, complains customers and popularization customers.
8. the data analysis system according to claim 5 based on label, which is characterized in that the similarity calculation module
(300) include computational submodule, be configured to following formula and calculate similarity: P=N × Kx,
Wherein, P is the similarity that target user belongs to some customers；
N is target user for the click volume under customers' respective operations attribute；
KxFor the corresponding weight coefficient of dimensional attribute.
9. a kind of computer equipment, which is characterized in that including memory and processor, the memory is stored with computer journey
The step of sequence, the processor realizes Claims 1 to 4 described in any item methods when executing the computer program.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of Claims 1 to 4 described in any item methods are realized when being executed by processor.
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| CN109978608A (en) * | 2019-03-05 | 2019-07-05 | 广州海晟科技有限公司 | The marketing label analysis extracting method and system of target user's portrait |
| CN110119876A (en) * | 2019-04-03 | 2019-08-13 | 口碑(上海)信息技术有限公司 | Worksheet method and device |
| CN110728453A (en) * | 2019-10-14 | 2020-01-24 | 山东嘉熙信息科技有限公司 | Big data based policy automatic matching analysis system and method |
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Application publication date: 20181221 |