CN105139225A - Method and apparatus for determining the behavior capability grade of user - Google Patents
Method and apparatus for determining the behavior capability grade of user Download PDFInfo
- Publication number
- CN105139225A CN105139225A CN201510504669.9A CN201510504669A CN105139225A CN 105139225 A CN105139225 A CN 105139225A CN 201510504669 A CN201510504669 A CN 201510504669A CN 105139225 A CN105139225 A CN 105139225A
- Authority
- CN
- China
- Prior art keywords
- user
- grade
- buying behavior
- behavior number
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Abstract
The invention discloses a method and apparatus for determining the behavior capability grade of a user. The method comprises performing statistics of the frequency of purchasing behaviors in the recent unit statistical period of a user, and the accumulated amount data of the orders in each time segment during the same period; determining the comprehensive value of the user according to the accumulated amount data of the orders and the preset time forgetting factor of each time segment; and determining the behavior capability grade of the user by means of the comprehensive value, the frequency of purchasing behaviors, and according to the preset mapping relation between the behavior capability parameter grade combination and the capability grade. The method and apparatus for determining the behavior capability grade of a user can guarantee the accuracy of the behavior capability grade of the user.
Description
Technical field
The present invention relates to large market demand technology, particularly relate to a kind of method and apparatus determining user behavior ability rating.
Background technology
Under electric business's competitive environment of fierceness, electricity commercial business industry needs the capacity to user (as purchasing power) to assess usually, based on this assessment result, when resource-constrained for the user of different ability rating provides different marketing strategies, user can be kept here and the object improving consumer loyalty degree to reach.
At present, the method that the consumer behavior of a kind of RFM of utilization model to user is classified is proposed.RFM model is a kind of divided method based on customer consuming behavior, using the last consumption time, consuming frequency and spending amount as classification foundation, classifies to the consumer behavior feature of client.The method is applicable to the enterprise providing multiple commodity, these cargo prices are relatively not high, or there is complementarity each other, there is repeatedly necessity of repeat buying, such as, the users classification being applicable to the industries such as refuelling station, travel insurance, transport, express delivery, fast food restaurant, KTV, mobile phone credit card, securities broker company is compared.
The method that the above-mentioned RFM of utilization model is classified to customer consuming behavior ability, there are the following problems:
1, classify based on three classification indicators, each index is divided into 5 grades, and the number of combinations of three indexs will reach 125, that is, can obtain 125 customers.So many customers' quantity, by causing, the feature difference between distinct group is little, and group character is not obvious, is difficult to the consumptive characteristics of locating each customers, and then is difficult to formulate marketing strategy accurately and effectively for each customers.
2, in actual applications, the capacity of user can change, and accordingly, the consumer behavior of user also can change, and like this, the spending limit data of different time sections are to determining that the value of the capacity that user is current is also different.And in above-mentioned existing method, do not consider the mistiming alienation of above-mentioned data, just utilize consumption total value in unit period to determine the capacity of user, therefore, the problem of classification results inaccuracy can be there is, and then the accuracy of the various decision-makings done based on classification results can be affected.
Summary of the invention
In view of this, fundamental purpose of the present invention is to provide a kind of method and apparatus determining user behavior ability rating, can guarantee the accuracy of user behavior ability rating.
In order to achieve the above object, the technical scheme that the present invention proposes is:
Determine a method for user behavior ability, comprising:
The buying behavior number of times of counting user within nearest unit statistics period, and the order accumulated amount data in the same period each time slice;
According to the time forgetting factor of described order accumulated amount data with each time slice preset, determine the comprehensive value of described user;
Utilize described comprehensive value, described buying behavior number of times, according to the capacity parameter level combination preset and ability rating mapping relations, determine the performance capacity level of described user.
Determine a device for user behavior ability, comprising:
Data statistics unit, for the buying behavior number of times of counting user within nearest unit statistics period, and the order accumulated amount data in the same period each time slice;
Comprehensive value analytic unit, for the time forgetting factor according to described order accumulated amount data and default each time slice, determines the comprehensive value of described user;
Level de-termination unit, for utilizing described comprehensive value, described buying behavior number of times, according to the capacity parameter level combination preset and ability rating mapping relations, determines the performance capacity level of described user.
In sum; the method and apparatus of the determination user behavior ability that the present invention proposes; the impact that the consideration time is worth user behavior data; determine the comprehensive value of user; make it accurately can reflect user behavior capacity variation trend, thus effectively can guarantee the accuracy based on this determined performance capacity level.In addition, the present invention only utilizes two parameters that fully can reflect user behavior ability: comprehensive value and buying behavior number of times, determine the capacity of user, the too much caused user grouping of reference parameter effectively can be avoided meticulous to such an extent as to the not obvious problem of grouping feature, and then the accuracy that user behavior ability rating divides can be guaranteed.
Accompanying drawing explanation
Fig. 1 is the method flow schematic diagram of the embodiment of the present invention one;
Fig. 2 is the capabilities map rule schematic diagram of the embodiment of the present invention one;
Fig. 3 is the apparatus structure schematic diagram of the embodiment of the present invention one.
Embodiment
For making the object, technical solutions and advantages of the present invention clearly, the present invention is described in further detail below in conjunction with the accompanying drawings and the specific embodiments.
Core concept of the present invention is: consider that the data of different time sections are different to the assessment contribution of user behavior ability, the value data produced the user behavior of different time sections is weighted process by the present invention, and then the user behavior number of times of this result and the same period is combined, obtain the ability parameter combination of user, finally, according to this parameter combinations, the ability parameter combination that contrast is preset and the mapping relations of ability rating, can determine corresponding ability rating.Like this, taken into full account the change in time of user behavior ability, just can guarantee the accuracy of user behavior ability rating.
Fig. 1 is the schematic flow sheet of the embodiment of the present invention one, and as shown in Figure 1, the method for the determination user behavior ability that this embodiment realizes mainly comprises:
Step 101, the counting user buying behavior number of times within nearest unit statistics period, and the order accumulated amount data in the same period each time slice.
This step, for adding up the behavior situation of user within nearest a period of time (namely a unit adds up period) of current grade to be determined, i.e. buying behavior number of times and the order accumulated amount in this time in each segmentation, for the user behavior ability rating determining this user in subsequent step based on these data.
Here, the order accumulated amount data of each time slice are exactly the order amount of money sum that in this time slice, user submits to.
It should be noted that, in this step, consider that the different time sections customer consumption amount of money can change, accordingly can be different from the level of intimate that current behavior ability associates, namely stronger apart from the relevance of current nearest spending amount and user's current behavior ability, the accuracy of reflection user current behavior ability can be higher, otherwise, meeting relevance is more weak, and the accuracy of reflection user behavior ability can be more weak.Therefore, here when the order accumulated amount data of counting user, the different time segmentation can distinguished in unit statistics period is added up respectively, like this, the data can distinguishing different segmentation when determining the comprehensive value of user in subsequent step process, to guarantee the actual conditions of comprehensive value result close to user, thus improve the accuracy of performance capacity level result.
In actual applications, the length in described unit statistics period and the division of time slice in unit statistics period, can be determined according to actual needs by those skilled in the art.Preferably, segmentation can be carried out according to apart from current nearest time slice than the principle shorter apart from current time slice length far away.Such as, described unit adds up period can be 1 year; Segments can be 3, now unit can be added up Development stage to be between nearest 1 to 3 month, between nearest 3 to 6 months and three segmentations between nearest 6 to 12 months.
Step 102, time forgetting factor according to described order accumulated amount data and default each time slice, determine the comprehensive value of described user.
This step, for the order accumulated amount data according to user's each time slice within nearest unit statistics period, and the time forgetting factor of each time slice, determine the comprehensive value of this user.
Preferably, following method can be adopted to determine the comprehensive value of described user:
According to
calculate described comprehensive value V, wherein, T is the time slice number in described unit statistics period, ρ
tfor the time forgetting factor of t time slice in described unit statistics period, V
tfor described user is in the described order accumulated amount data of described t time slice, 0 < ρ
t< T,
Here, the time forgetting factor ρ of each time slice
t, for being weighted the order accumulated amount data of each time slice, to embody different phase data to determining that the difference of active user's capacity is worth.This value specifically can be arranged according to the actual conditions of user behavior by those skilled in the art.Preferably, can be nearer according to distance current time, the principle that the value of time forgetting factor is larger is arranged, and has more valuable feature to embody the recent behavior of user, eliminates data saturated phenomenon, strengthens the impact of current data, reduces the impact of historical data.
Step 103, utilize described comprehensive value, described buying behavior number of times, according to the capacity parameter level combination preset and ability rating mapping relations, determine the performance capacity level of described user.
This step, for the combination according to the buying behavior number of times counted in the comprehensive value determined in step 102 and step 101, by inquiring about known capacity parameter level combination and ability rating mapping relations, determine the performance capacity level of user.
Here; due to based on parameter be only two, and taken into full account the change of user behavior ability when determining the comprehensive value of user, therefore; the matching degree of performance capacity level and the actual conditions determined based on this can be guaranteed, improve the accuracy of performance capacity level.
Preferably, the combination of the parameter level of capacity described in this step and ability rating mapping relations can adopt in advance and want following method to arrange:
Step x1, user is set in the comprehensive value in unit statistics period K grade presetting, wherein, the grade critical value v between kth level comprehensive value and kth+1 grade of comprehensive value
kfor λ
kwith the product of C, C is the cost of a current development new user or the maintenance cost of unique user, λ
kfor the kth multiplier factor preset, λ
k>0, k=1 ..., K-1; λ
1< λ
2< ... < λ
k-1.
Here, described multiplier factor λ
kcan be arranged according to actual needs by those skilled in the art, can take cost price as benchmark usually, along with the increase of grade, and λ
kvalue progressively increase.
Step x2, buying behavior number of times according to all users counted in current one statistics period, be divided into N group by described all users, wherein, the described buying behavior number of times of n-th group of user is less than the described buying behavior number of times of (n+1)th group of user, n=1 ..., N-1.
Step x3, for often organizing user, calculate the mean value of the described buying behavior number of times of this group user; According to the described mean value often organizing user, the buying behavior number of times that user adds up period in unit is set to N number of grade; Wherein, the grade critical value between n-th grade of buying behavior number of times and (n+1)th grade of buying behavior number of times is the described mean value of n-th group of user.
In this step, the grade critical value between buying behavior number of times be according to actual count to the mean value often organizing the buying behavior number of times of user determine, therefore effectively can guarantee the objectivity of grade critical value, guarantee the accuracy of grade classification.
Step x4, each comprehensive value grade to be combined with each buying behavior number of times grade respectively, and according to the capabilities map rule preset, determine the ability rating that each described combination is corresponding.
In said method, those skilled in the art can arrange the value of N and K according to actual conditions, and preferably, the span of N and K can between 3-8, the best, N=5, K=5.
Here, capabilities map rule can be preset according to actual needs by those skilled in the art.Such as, work as N=5, during K=5, can according to this mapping ruler of the arranged in matrix shown in Fig. 2.
Preferably, following method can be adopted to determine the performance capacity level of described user:
Step 1031, described grade critical value according to described comprehensive value, determine the grade belonging to comprehensive value of described user.
Step 1032, described grade critical value according to described buying behavior number of times, determine the grade belonging to buying behavior number of times of described user.
Step 1033, according to the combination of described capacity parameter level and ability rating mapping relations, determine that the comprehensive value grade of described user combines corresponding ability rating with buying behavior number of times grade, and using the performance capacity level of this ability rating as described user.
Can be found out by above-described embodiment, the present invention can effectively improve the accuracy determined user behavior ability rating.The client of different purchasing power grade, has value for electric business, but due to its type difference, marketing strategy can be distinguished and treat.Utilize the present invention, be conducive to formulating different marketing strategies for different brackets user.Such as, in limited resource, preferentially can meet the user of ability grade, in the market expansion phase, can take preferentially targetedly to wait the user of ability rating in Policy Encouraging to develop toward the user of ability grade.Simultaneously also can the user of capabilities grade that may run off of Timeliness coverage, adopt an effective measure in time.
Fig. 3 is the apparatus structure schematic diagram corresponding with said method embodiment, and as shown in Figure 3, this device comprises:
Data statistics unit, for the buying behavior number of times of counting user within nearest unit statistics period, and the order accumulated amount data in the same period each time slice;
Comprehensive value analytic unit, for the time forgetting factor according to described order accumulated amount data and default each time slice, determines the comprehensive value of described user;
Level de-termination unit, for utilizing described comprehensive value, described buying behavior number of times, according to the capacity parameter level combination preset and ability rating mapping relations, determines the performance capacity level of described user.
Preferably, described comprehensive value analytic unit, be further used for according to
calculate described comprehensive value V, wherein, T is the time slice number in described unit statistics period, ρ
tfor the time forgetting factor of t time slice in described unit statistics period, V
tfor described user is in the described order accumulated amount data of described t time slice, 0 < ρ
t< T,
Preferably, described level de-termination unit, be further used for arranging the combination of capacity parameter level and ability rating mapping relations, described setting comprises: user is set to K the grade preset in the comprehensive value that unit adds up period, wherein, the grade critical value v between kth level comprehensive value and kth+1 grade of comprehensive value
kfor λ
kwith the product of C, C is the cost of a current development new user or the maintenance cost of unique user, λ
kfor the kth multiplier factor preset, λ
k>0, k=1 ..., K-1; λ
1< λ
2< ... < λ
k-1; According to the buying behavior number of times of all users counted in current one statistics period, described all users are divided into N group, and wherein, the described buying behavior number of times of n-th group of user is less than the described buying behavior number of times of (n+1)th group of user, n=1 ..., N-1; For often organizing user, calculate the mean value of the described buying behavior number of times of this group user; According to the described mean value often organizing user, the buying behavior number of times that user adds up period in unit is set to N number of grade; Wherein, the grade critical value between n-th grade of buying behavior number of times and (n+1)th grade of buying behavior number of times is the described mean value of n-th group of user; Each comprehensive value grade is combined with each buying behavior number of times grade respectively, and according to the capabilities map rule preset, determines the ability rating that each described combination is corresponding.
Preferably, described level de-termination unit, is further used for the described grade critical value according to described comprehensive value, determines the grade belonging to comprehensive value of described user; According to the described grade critical value of described buying behavior number of times, determine the grade belonging to buying behavior number of times of described user; According to described capacity parameter level combination and ability rating mapping relations, determine that the comprehensive value grade of described user combines corresponding ability rating with buying behavior number of times grade, and using the performance capacity level of this ability rating as described user.
In sum, these are only preferred embodiment of the present invention, be not intended to limit protection scope of the present invention.Within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. determine a method for user behavior ability, it is characterized in that, comprising:
The buying behavior number of times of counting user within nearest unit statistics period, and the order accumulated amount data in the same period each time slice;
According to the time forgetting factor of described order accumulated amount data with each time slice preset, determine the comprehensive value of described user;
Utilize described comprehensive value, described buying behavior number of times, according to the capacity parameter level combination preset and ability rating mapping relations, determine the performance capacity level of described user.
2. method according to claim 1, is characterized in that, describedly determines that the comprehensive value of described user comprises:
According to
calculate described comprehensive value V, wherein, T is the time slice number in described unit statistics period, ρ
tfor the time forgetting factor of t time slice in described unit statistics period, V
tfor described user is in the described order accumulated amount data of described t time slice, 0 < ρ
t< T,
3. method according to claim 1, is characterized in that, the setting of described capacity parameter level combination and ability rating mapping relations comprises:
User is set to K the grade preset in the comprehensive value that unit adds up period, wherein, the grade critical value v between kth level comprehensive value and kth+1 grade of comprehensive value
kfor λ
kwith the product of C, C is the cost of a current development new user or the maintenance cost of unique user, λ
kfor the kth multiplier factor preset, λ
k>0, k=1 ..., K-1; λ
1< λ
2< ... < λ
k-1;
According to the buying behavior number of times of all users counted in current one statistics period, described all users are divided into N group, and wherein, the described buying behavior number of times of n-th group of user is less than the described buying behavior number of times of (n+1)th group of user, n=1 ..., N-1;
For often organizing user, calculate the mean value of the described buying behavior number of times of this group user; According to the described mean value often organizing user, the buying behavior number of times that user adds up period in unit is set to N number of grade; Wherein, the grade critical value between n-th grade of buying behavior number of times and (n+1)th grade of buying behavior number of times is the described mean value of n-th group of user;
Each comprehensive value grade is combined with each buying behavior number of times grade respectively, and according to the capabilities map rule preset, determines the ability rating that each described combination is corresponding.
4. method according to claim 3, is characterized in that, describedly determines that the performance capacity level of described user comprises:
According to the described grade critical value of described comprehensive value, determine the grade belonging to comprehensive value of described user;
According to the described grade critical value of described buying behavior number of times, determine the grade belonging to buying behavior number of times of described user;
According to described capacity parameter level combination and ability rating mapping relations, determine that the comprehensive value grade of described user combines corresponding ability rating with buying behavior number of times grade, and using the performance capacity level of this ability rating as described user.
5. method according to claim 1, is characterized in that, it is 1 year that described unit adds up period.
6. method according to claim 1, is characterized in that, described N=5, described K=5.
7. determine a device for user behavior ability, it is characterized in that, comprising:
Data statistics unit, for the buying behavior number of times of counting user within nearest unit statistics period, and the order accumulated amount data in the same period each time slice;
Comprehensive value analytic unit, for the time forgetting factor according to described order accumulated amount data and default each time slice, determines the comprehensive value of described user;
Level de-termination unit, for utilizing described comprehensive value, described buying behavior number of times, according to the capacity parameter level combination preset and ability rating mapping relations, determines the performance capacity level of described user.
8. device according to claim 7, is characterized in that, described comprehensive value analytic unit, be further used for according to
calculate described comprehensive value V, wherein, T is the time slice number in described unit statistics period, ρ
tfor the time forgetting factor of t time slice in described unit statistics period, V
tfor described user is in the described order accumulated amount data of described t time slice, 0 < ρ
t< T,
9. device according to claim 7, it is characterized in that, described level de-termination unit, be further used for arranging the combination of capacity parameter level and ability rating mapping relations, described setting comprises: user is set to K the grade preset in the comprehensive value that unit adds up period, wherein, the grade critical value v between kth level comprehensive value and kth+1 grade of comprehensive value
kfor λ
kwith the product of C, C is the cost of a current development new user or the maintenance cost of unique user, λ
kfor the kth multiplier factor preset, λ
k>0, k=1 ..., K-1; λ
1< λ
2< ... < λ
k-1; According to the buying behavior number of times of all users counted in current one statistics period, described all users are divided into N group, and wherein, the described buying behavior number of times of n-th group of user is less than the described buying behavior number of times of (n+1)th group of user, n=1 ..., N-1; For often organizing user, calculate the mean value of the described buying behavior number of times of this group user; According to the described mean value often organizing user, the buying behavior number of times that user adds up period in unit is set to N number of grade; Wherein, the grade critical value between n-th grade of buying behavior number of times and (n+1)th grade of buying behavior number of times is the described mean value of n-th group of user; Each comprehensive value grade is combined with each buying behavior number of times grade respectively, and according to the capabilities map rule preset, determines the ability rating that each described combination is corresponding.
10. device according to claim 9, is characterized in that, described level de-termination unit, is further used for the described grade critical value according to described comprehensive value, determines the grade belonging to comprehensive value of described user; According to the described grade critical value of described buying behavior number of times, determine the grade belonging to buying behavior number of times of described user; According to described capacity parameter level combination and ability rating mapping relations, determine that the comprehensive value grade of described user combines corresponding ability rating with buying behavior number of times grade, and using the performance capacity level of this ability rating as described user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510504669.9A CN105139225A (en) | 2015-08-17 | 2015-08-17 | Method and apparatus for determining the behavior capability grade of user |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510504669.9A CN105139225A (en) | 2015-08-17 | 2015-08-17 | Method and apparatus for determining the behavior capability grade of user |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105139225A true CN105139225A (en) | 2015-12-09 |
Family
ID=54724561
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510504669.9A Pending CN105139225A (en) | 2015-08-17 | 2015-08-17 | Method and apparatus for determining the behavior capability grade of user |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105139225A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105490823A (en) * | 2015-12-24 | 2016-04-13 | 原肇 | Data processing method and device |
| CN107093091A (en) * | 2016-11-17 | 2017-08-25 | 北京小度信息科技有限公司 | A kind of data processing method and device |
| CN107633419A (en) * | 2017-08-10 | 2018-01-26 | 无锡雅座在线科技股份有限公司 | Member is lost in treating method and apparatus |
| CN108090800A (en) * | 2017-11-27 | 2018-05-29 | 珠海金山网络游戏科技有限公司 | A kind of game item method for pushing and device based on player's consumption potentiality |
| CN108269118A (en) * | 2017-01-03 | 2018-07-10 | 中兴通讯股份有限公司 | A kind of method and apparatus of data analysis |
| CN110827070A (en) * | 2019-10-30 | 2020-02-21 | 神州数码融信软件有限公司 | User growth calculation method and device based on dynamic expansion factor |
| CN110991875A (en) * | 2019-11-29 | 2020-04-10 | 广州市百果园信息技术有限公司 | Platform user quality evaluation system |
| CN112070548A (en) * | 2020-09-11 | 2020-12-11 | 上海风秩科技有限公司 | User layering method, device, equipment and storage medium |
| CN113849730A (en) * | 2021-09-06 | 2021-12-28 | 北京妙医佳健康科技集团有限公司 | Method for layering user value in health management service and corresponding portrait device |
| CN115829639A (en) * | 2023-02-27 | 2023-03-21 | 企迈科技有限公司 | Shop member management system based on SAAS cloud platform |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101046857A (en) * | 2006-03-28 | 2007-10-03 | 腾讯科技（深圳）有限公司 | Setting method for grade system and its system |
| CN101483531A (en) * | 2008-10-16 | 2009-07-15 | 北京四达时代软件技术股份有限公司 | Method and system for prediction type account balance alarm |
| CA2212574C (en) * | 1995-02-13 | 2010-02-02 | Electronic Publishing Resources, Inc. | Systems and methods for secure transaction management and electronic rights protection |
| CN102419851A (en) * | 2011-11-04 | 2012-04-18 | 韩壮 | Method and device for collecting consumption data of consumers |
-
2015
- 2015-08-17 CN CN201510504669.9A patent/CN105139225A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2212574C (en) * | 1995-02-13 | 2010-02-02 | Electronic Publishing Resources, Inc. | Systems and methods for secure transaction management and electronic rights protection |
| CN101046857A (en) * | 2006-03-28 | 2007-10-03 | 腾讯科技（深圳）有限公司 | Setting method for grade system and its system |
| CN101483531A (en) * | 2008-10-16 | 2009-07-15 | 北京四达时代软件技术股份有限公司 | Method and system for prediction type account balance alarm |
| CN102419851A (en) * | 2011-11-04 | 2012-04-18 | 韩壮 | Method and device for collecting consumption data of consumers |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105490823A (en) * | 2015-12-24 | 2016-04-13 | 原肇 | Data processing method and device |
| CN107093091A (en) * | 2016-11-17 | 2017-08-25 | 北京小度信息科技有限公司 | A kind of data processing method and device |
| CN107093091B (en) * | 2016-11-17 | 2021-08-10 | 北京星选科技有限公司 | Data processing method and device |
| CN108269118A (en) * | 2017-01-03 | 2018-07-10 | 中兴通讯股份有限公司 | A kind of method and apparatus of data analysis |
| CN108269118B9 (en) * | 2017-01-03 | 2024-01-02 | 中兴通讯股份有限公司 | Data analysis method and device |
| CN108269118B (en) * | 2017-01-03 | 2023-11-17 | 中兴通讯股份有限公司 | Data analysis method and device |
| CN107633419A (en) * | 2017-08-10 | 2018-01-26 | 无锡雅座在线科技股份有限公司 | Member is lost in treating method and apparatus |
| CN108090800A (en) * | 2017-11-27 | 2018-05-29 | 珠海金山网络游戏科技有限公司 | A kind of game item method for pushing and device based on player's consumption potentiality |
| CN108090800B (en) * | 2017-11-27 | 2021-12-03 | 珠海金山网络游戏科技有限公司 | Game prop pushing method and device based on player consumption potential |
| CN110827070A (en) * | 2019-10-30 | 2020-02-21 | 神州数码融信软件有限公司 | User growth calculation method and device based on dynamic expansion factor |
| CN110827070B (en) * | 2019-10-30 | 2023-05-23 | 神州数码融信软件有限公司 | User growth calculation method and device based on dynamic expansion factors |
| CN110991875B (en) * | 2019-11-29 | 2023-09-26 | 广州市百果园信息技术有限公司 | Platform user quality evaluation system |
| CN110991875A (en) * | 2019-11-29 | 2020-04-10 | 广州市百果园信息技术有限公司 | Platform user quality evaluation system |
| CN112070548A (en) * | 2020-09-11 | 2020-12-11 | 上海风秩科技有限公司 | User layering method, device, equipment and storage medium |
| CN112070548B (en) * | 2020-09-11 | 2024-02-20 | 上海秒针网络科技有限公司 | User layering method, device, equipment and storage medium |
| CN113849730A (en) * | 2021-09-06 | 2021-12-28 | 北京妙医佳健康科技集团有限公司 | Method for layering user value in health management service and corresponding portrait device |
| CN115829639A (en) * | 2023-02-27 | 2023-03-21 | 企迈科技有限公司 | Shop member management system based on SAAS cloud platform |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105139225A (en) | Method and apparatus for determining the behavior capability grade of user | |
| Ju et al. | Macroeconomic effects of oil price shocks in China: An empirical study based on Hilbert–Huang transform and event study | |
| Lin et al. | Trump economics and China–US trade imbalances | |
| US20130151423A1 (en) | Valuation of data | |
| US10372580B2 (en) | Optimization of power and computational density of a data center | |
| CN108364191A (en) | Top-tier customer Optimum Identification Method and device based on random forest and logistic regression | |
| CN103970747A (en) | Data processing method for network side computer to order search results | |
| Chang et al. | The impact of the bullwhip effect on sales and earnings prediction using order backlog | |
| CN109993380A (en) | A kind of information processing method, device and computer readable storage medium | |
| CN107705175B (en) | Method and device for determining similarity between user and merchant and electronic equipment | |
| CN106372964A (en) | Behavior loyalty identification and management method, system and terminal | |
| CN104820942A (en) | Electricity market trade evaluation criterion measuring and calculating method based on hierarchical clustering | |
| Granell et al. | Power-use profile analysis of non-domestic consumers for electricity tariff switching | |
| CN105245351A (en) | Charging method and device | |
| CN105574599A (en) | Method and apparatus for estimating sales return rate of order | |
| Silberberger et al. | The aftermath of anti-dumping: Are temporary trade barriers really temporary? | |
| Wang et al. | The working capital management based on vendor relationship–“icing on the cake” or “timely help” | |
| He et al. | Green power pricing and matching efficiency optimization for peer-to-peer trading platforms considering heterogeneity of supply and demand sides | |
| CN103246987A (en) | Sales information management system and method | |
| CN109903070A (en) | Data digging method, device and computer readable storage medium | |
| Mountain et al. | Do Victoria’s households leave less money on the table when they switch electricity retailers | |
| Grassano et al. | Productivity in services twenty years on. A review of conceptual and measurement issues and a way forward | |
| CN116070781B (en) | Electric energy metering equipment demand prediction method and device and computer equipment | |
| Kharvi et al. | Ordering policies under currency risk sharing agreements: a Markov chain approach | |
| Banker et al. | Sticky cost behavior: A comprehensive global test |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20151209 |