CN108428155A - A kind of behavior processing analysis method based on service feature model - Google Patents
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Abstract
The invention discloses a kind of, and the behavior based on service feature model handles analysis method, including analyzes the current present situation of the virtual network users of cluster；Build user's refined model；According to user's As-Is analysis result and user's refined model, user is finely divided, determines behavior of each user to the group of subscribers, such as the loss orientation and loyalty of user；According to subdivision as a result, structure user's relationship cycle model, recommended orientation and resource are provided for new business.The present invention builds user's relationship cycle model, not only promotes service quality and user experience, also provide marketing direction and resource for new business recommendation by being finely divided to user.
Description
Technical field
The present invention relates to a kind of, and the behavior based on service feature model handles analysis method, belongs to Internet of Things information network skill
Art field.
Background technology
Internet of Things just experience develops rank from infrastructure devices such as hardware, sensings to software platform and vertical industry application upgrade
Section, how effectively facilitating the technological innovation of group customer information network and operating service in the process will become particularly important.
Group's virtual net is also known as group's intelligence VPMN nets, is to aim at group customer to establish dedicated cluster virtual net, not only can be real
It is flexibly preferential to apply telephone expenses, saves unnecessary communication spending, moreover it is possible to which the group's business function for realizing multinomial personalization passes through group
The internal management of itself carrys out higher efficiency for enterprises communication band.As the development of customer service progresses into the gentle phase,
Significantly the improvement stage has switched to the period that data analysis carries out lean operation, how to carry out precise information recommendation, promotes clothes
Quality of being engaged in and user experience are critical issues.The virtual network service of cluster serves primarily in user speech communication, by logical to user
Letter behavior is analysed in depth, and is analyzed user demand, is the developing direction of intelligent network business.
Invention content
In order to solve the above technical problem, the present invention provides a kind of, and the behavior based on service feature model handles analysis side
Method.
In order to achieve the above object, the technical solution adopted in the present invention is：
A kind of behavior processing analysis method based on service feature model, including,
The current present situation of the virtual network users of cluster is analyzed；
Build user's refined model；
According to user's As-Is analysis result and user's refined model, user is finely divided, determines each user to cluster virtual net
Loss orientation and loyalty；
According to subdivision as a result, structure user's relationship cycle model, recommended orientation and resource are provided for new business.
The current As-Is analysis of user includes that user enlivens situation analysis and user's communication situation analysis.
User enlivens situation analysis,
Certain period user bill record is chosen, is distributed three from general speech situation, the frequent situation of call, call rush hour
Aspect understands user and enlivens situation；
Wherein,
General speech situation：The user's accounting for having call behavior is obtained within the period of selection, measuring and calculating wherein has virtual Netcom
User's accounting of words behavior；
Frequent situation of conversing includes call number of days, talk times and the secondary equal duration of call in the period chosen.
The signal intelligence of user and local handset user in a measurement period are analyzed in user's communication situation analysis, including right
Hold the distribution of call number, call relationship Annual distribution and call relationship call case；
Opposite end call number distribution, i.e., the distribution situation of daily opposite end contact number in measurement period；
The Annual distribution situation conversed in call relationship Annual distribution, i.e. measurement period；
Call relationship call case, i.e., user's calling and called number distribution situation in measurement period.
User's refined model uses RFM models, and using R as a dimension, using F as a dimension, each dimension divides
At several grades, palace lattice are established, realize subscriber segmentation；
Wherein, R is the number of days that cluster virtual net speech range observes day, judges whether user may be lost in；F be talk times,
The duration of call, selection period in talk times and the accumulative duration of call, to judge loyalty of the user to cluster virtual net
Really degree；M is customer charge information, weighs user's value.
User is to the loss orientation judgment rule of cluster virtual net,
The period that definition is chosen is N days shared, and N+1 days are observation day；
If without the virtual speech communication of cluster in user N days, the surface user is high loss orientation user；
If user has the virtual speech communication of cluster, the surface user in 2 ~ N days be general loss orientation user；
If user has the virtual speech communication of cluster, the surface user in nearest 1 day be that low-bleed is inclined to user；
User is to the loyalty judgment rule of cluster virtual net,
If user, within the period of selection, talk times are less than threshold value A 1, and the duration of call is less than B1, then the surface user is
Low loyalty user；
If user, within the period of selection, talk times are more than threshold value A 1, and the duration of call is less than B1, alternatively, talk times
Less than threshold value A 2, and the duration of call is more than B1, then the surface user is general loyalty user；
If user, within the period of selection, talk times are more than threshold value A 2, and the duration of call is more than B1, then the surface user is
High loyalty user.
The process for establishing relationship cycle model is,
Time division is carried out to a measurement period, is divided into working day and day off, and it is T1 and T2 that weight, which is respectively set,；
The weight for calculating each frequency class index working day and day off calculates the power on each viscosity class index working day and day off
Weight；
Evaluation work day, user was to the contact frequency F1 of a certain local handset user, i.e., by user in working day to the local handset
The weight of all frequency class indexs of user is weighted summation operation；
Contact frequency F2 of the day off user to a certain local handset user is calculated, i.e., by user in day off to the local handset
The weight of all frequency class indexs of user is weighted summation operation；
Evaluation work day, user was to the contact viscosity D1 of a certain local handset user, i.e., by user in working day to the local handset
The weight of all viscosity class indexs of user is weighted summation operation；
Contact viscosity D2 of the day off user to a certain local handset user is calculated, i.e., by user in day off to the local handset
The weight of all viscosity class indexs of user is weighted summation operation；
It is by frequency class setup measures weightl, viscosity class index weights are set as m, calculate user and local handset user's
Contact tight ness rating score, formula T1（lF1+m D1）+ T2（l F2+m D2）；
It is ranked up according to contact tight ness rating score, using M before score rank corresponding local handset users as the user's
Relationship cycle.
The duration of call is defined as viscosity class index, talk times and call frequency are defined as frequency class index.
The advantageous effect that the present invention is reached：The present invention builds user's relationship cycle model, no by being finely divided to user
Service quality and user experience are only promoted, also provides marketing direction and resource for new business recommendation.
Description of the drawings
Fig. 1 is the flow chart of the present invention.
Specific implementation mode
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and not intended to limit the protection scope of the present invention.
As shown in Figure 1, a kind of behavior based on service feature model handles analysis method, include the following steps：
Step 1, the current present situation of the virtual network users of cluster is analyzed.
The current As-Is analysis of user includes that user enlivens situation analysis and user's communication situation analysis.
User enlivens situation analysis：Certain period user bill record is chosen, from general speech situation, the frequent feelings of call
Condition, call rush hour are distributed three aspect understanding users and enliven situation.
a）General speech situation：The user's accounting for having call behavior is obtained within the period of selection, measuring and calculating wherein has void
User's accounting of quasi- speech communication behavior；
b）Frequent situation of conversing includes call number of days, talk times and the secondary equal duration of call in the period chosen；
User's communication situation analysis：The signal intelligence of user and local handset user in a measurement period are analyzed, including opposite end leads to
The distribution of words number, call relationship Annual distribution and call relationship call case.
Opposite end call number distribution：The distribution situation of daily opposite end contact number i.e. in measurement period；
Call relationship Annual distribution：The Annual distribution situation conversed in measurement period；
Call relationship call case：User's calling and called number distribution situation i.e. in measurement period.
Step 2, user's refined model is built.
User's refined model uses RFM models, and using R as a dimension, using F as a dimension, each dimension divides
At several grades, palace lattice are established, realize subscriber segmentation.Wherein, R is the number of days that cluster virtual net speech range observes day, is sentenced
Whether disconnected user may be lost in；F be talk times, the duration of call, selection period in talk times with accumulative call when
It is long, to judge loyalty of the user to cluster virtual net；M is customer charge information, weighs user's value.
Step 3, according to user's As-Is analysis result and user's refined model, user is finely divided, determines each user couple
The loss orientation and loyalty of cluster virtual net.
User is to the loss orientation judgment rule of cluster virtual net：
The period that definition is chosen is N days shared, and N is generally 14, N+1 days as observation day；
If without the virtual speech communication of cluster in user N days, the surface user is high loss orientation user；
If user has the virtual speech communication of cluster, the surface user in 2 ~ N days be general loss orientation user；
If user has the virtual speech communication of cluster, the surface user in nearest 1 day be that low-bleed is inclined to user.
User is to the loyalty judgment rule of cluster virtual net：
If user, within the period of selection, talk times are less than threshold value A 1, and the duration of call is less than B1, then the surface user is
Low loyalty user, A1 are generally 75, B1 and are generally 110 minutes；
If user, within the period of selection, talk times are more than threshold value A 1, and the duration of call is less than B1, alternatively, talk times
Less than threshold value A 2, and the duration of call is more than B1, then the surface user is general loyalty user, and A2 is generally 124；
If user, within the period of selection, talk times are more than threshold value A 2, and the duration of call is more than B1, then the surface user is
High loyalty user.
Step 4, according to subdivision as a result, structure user's relationship cycle model, recommended orientation and resource are provided for new business.
The process that all users are established with relationship cycle model is：
1）Time division is carried out to a measurement period, is divided into working day and day off, and it is T1 and T2 that weight, which is respectively set,.
2）The duration of call is defined as viscosity class index, talk times and call frequency are defined as frequency class index, are counted
The weight for calculating each frequency class index working day and day off calculates the weight on each viscosity class index working day and day off；
Weight calculation step is：
21）According to viscosity and frequency index classification, the total amount of all sub- indicator-specific statistics amounts under same index is calculated；
22）Obtained with tenths discrete method each sub- indicator-specific statistics amount under same index it is discrete after statistic；Tenths
Presidential gauging calibration under same index is 10 by discrete method, and the ratio of each sub- indicator-specific statistics amount and total statistic is multiplied by
10 as sub- indicator-specific statistics amount it is discrete after statistic；
23）According to the above method, you can obtain the tenths of each sub- indicator-specific statistics amount under viscosity index and frequency index from
Statistical value after dissipating；
24）Sub- indicator-specific statistics amount after various discrete based on viscosity index and frequency index uses the discrete side of tenths again
Method, the statistical value after each new tenths of sub- indicator-specific statistics amount of acquisition is discrete；
25）By above-mentioned statistical value divided by 10, the index weights of each sub- indicator-specific statistics amount are obtained.
3）Evaluation work day, user was to the contact frequency F1 of a certain local handset user, i.e., by user in working day to this
The weight of all frequency class indexs of ground mobile phone user is weighted summation operation.
4）Contact frequency F2 of the day off user to a certain local handset user is calculated, i.e., by user in day off to this
The weight of all frequency class indexs of ground mobile phone user is weighted summation operation.
5）Evaluation work day, user was to the contact viscosity D1 of a certain local handset user, i.e., by user in working day to this
The weight of all viscosity class indexs of ground mobile phone user is weighted summation operation.
6）Contact viscosity D2 of the day off user to a certain local handset user is calculated, i.e., by user in day off to this
The weight of all viscosity class indexs of ground mobile phone user is weighted summation operation.
7）It is by frequency class setup measures weightl, viscosity class index weights are set as m, calculate user and the local handset
The contact tight ness rating score of user, formula T1（lF1+m D1）+ T2（l F2+m D2）.
8）It is ranked up according to contact tight ness rating score, using M before score rank corresponding local handset users as this
The relationship cycle of user.
The above method builds user's relationship cycle model, not only promotes service quality and user by being finely divided to user
Experience also provides marketing direction and resource for new business recommendation.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improvement and deformations
Also it should be regarded as protection scope of the present invention.
Claims (8)
1. a kind of behavior based on service feature model handles analysis method, it is characterised in that：Including,
The current present situation of the virtual network users of cluster is analyzed；
Build user's refined model；
According to user's As-Is analysis result and user's refined model, user is finely divided, determines each user to cluster virtual net
Loss orientation and loyalty；
According to subdivision as a result, structure user's relationship cycle model, recommended orientation and resource are provided for new business.
2. a kind of behavior based on service feature model according to claim 1 handles analysis method, it is characterised in that：With
The current As-Is analysis in family includes that user enlivens situation analysis and user's communication situation analysis.
3. a kind of behavior based on service feature model according to claim 2 handles analysis method, it is characterised in that：With
Situation analysis is enlivened at family,
Certain period user bill record is chosen, is distributed three from general speech situation, the frequent situation of call, call rush hour
Aspect understands user and enlivens situation；
Wherein,
General speech situation：The user's accounting for having call behavior is obtained within the period of selection, measuring and calculating wherein has virtual Netcom
User's accounting of words behavior；
Frequent situation of conversing includes call number of days, talk times and the secondary equal duration of call in the period chosen.
4. a kind of behavior based on service feature model according to claim 2 handles analysis method, it is characterised in that：With
The signal intelligence of user and local handset user in a measurement period, including opposite end call number are analyzed in family call scenarios analysis
Distribution, call relationship Annual distribution and call relationship call case；
Opposite end call number distribution, i.e., the distribution situation of daily opposite end contact number in measurement period；
The Annual distribution situation conversed in call relationship Annual distribution, i.e. measurement period；
Call relationship call case, i.e., user's calling and called number distribution situation in measurement period.
5. a kind of behavior based on service feature model according to claim 1 handles analysis method, it is characterised in that：With
Family refined model uses RFM models, and using R as a dimension, using F as a dimension, each dimension is divided into several etc.
Grade establishes palace lattice, realizes subscriber segmentation；
Wherein, R is the number of days that cluster virtual net speech range observes day, judges whether user may be lost in；F be talk times,
The duration of call, selection period in talk times and the accumulative duration of call, to judge loyalty of the user to cluster virtual net
Really degree；M is customer charge information, weighs user's value.
6. a kind of behavior based on service feature model according to claim 5 handles analysis method, it is characterised in that：With
Family is to the loss orientation judgment rule of cluster virtual net,
The period that definition is chosen is N days shared, and N+1 days are observation day；
If without the virtual speech communication of cluster in user N days, the surface user is high loss orientation user；
If user has the virtual speech communication of cluster, the surface user in 2 ~ N days be general loss orientation user；
If user has the virtual speech communication of cluster, the surface user in nearest 1 day be that low-bleed is inclined to user；
User is to the loyalty judgment rule of cluster virtual net,
If user, within the period of selection, talk times are less than threshold value A 1, and the duration of call is less than B1, then the surface user is
Low loyalty user；
If user, within the period of selection, talk times are more than threshold value A 1, and the duration of call is less than B1, alternatively, talk times
Less than threshold value A 2, and the duration of call is more than B1, then the surface user is general loyalty user；
If user, within the period of selection, talk times are more than threshold value A 2, and the duration of call is more than B1, then the surface user is
High loyalty user.
7. a kind of behavior based on service feature model according to claim 5 handles analysis method, it is characterised in that：
The process for establishing relationship cycle model is,
Time division is carried out to a measurement period, is divided into working day and day off, and it is T1 and T2 that weight, which is respectively set,；
The weight for calculating each frequency class index working day and day off calculates the power on each viscosity class index working day and day off
Weight；
Evaluation work day, user was to the contact frequency F1 of a certain local handset user, i.e., by user in working day to the local handset
The weight of all frequency class indexs of user is weighted summation operation；
Contact frequency F2 of the day off user to a certain local handset user is calculated, i.e., by user in day off to the local handset
The weight of all frequency class indexs of user is weighted summation operation；
Evaluation work day, user was to the contact viscosity D1 of a certain local handset user, i.e., by user in working day to the local handset
The weight of all viscosity class indexs of user is weighted summation operation；
Contact viscosity D2 of the day off user to a certain local handset user is calculated, i.e., by user in day off to the local handset
The weight of all viscosity class indexs of user is weighted summation operation；
It is by frequency class setup measures weightl, viscosity class index weights are set as m, calculate user and local handset user's
Contact tight ness rating score, formula T1（lF1+m D1）+ T2（l F2+m D2）；
It is ranked up according to contact tight ness rating score, using M before score rank corresponding local handset users as the user's
Relationship cycle.
8. a kind of behavior based on service feature model according to claim 7 handles analysis method, it is characterised in that：It will
The duration of call is defined as viscosity class index, and talk times and call frequency are defined as frequency class index.
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Application publication date: 20180821 |