WO2018161898A1 - 一种业务引流的方法和装置 - Google Patents

一种业务引流的方法和装置 Download PDF

Info

Publication number
WO2018161898A1
WO2018161898A1 PCT/CN2018/078159 CN2018078159W WO2018161898A1 WO 2018161898 A1 WO2018161898 A1 WO 2018161898A1 CN 2018078159 W CN2018078159 W CN 2018078159W WO 2018161898 A1 WO2018161898 A1 WO 2018161898A1
Authority
WO
WIPO (PCT)
Prior art keywords
user
service
target
information
behavior data
Prior art date
Application number
PCT/CN2018/078159
Other languages
English (en)
French (fr)
Inventor
丁伟伟
陶晨
金峰
鲁震宇
文佳俊
杜永刚
何文明
冯照临
杨志蓉
杨锴
Original Assignee
阿里巴巴集团控股有限公司
丁伟伟
陶晨
金峰
鲁震宇
文佳俊
杜永刚
何文明
冯照临
杨志蓉
杨锴
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 阿里巴巴集团控股有限公司, 丁伟伟, 陶晨, 金峰, 鲁震宇, 文佳俊, 杜永刚, 何文明, 冯照临, 杨志蓉, 杨锴 filed Critical 阿里巴巴集团控股有限公司
Priority to EP18764096.6A priority Critical patent/EP3525420B1/en
Priority to JP2019524372A priority patent/JP6765526B2/ja
Priority to KR1020197013403A priority patent/KR102239590B1/ko
Publication of WO2018161898A1 publication Critical patent/WO2018161898A1/zh
Priority to US16/562,911 priority patent/US10915925B2/en
Priority to US16/945,688 priority patent/US11062353B2/en

Links

Images

Classifications

    • 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
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • 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
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0255—Targeted advertisements based on user history
    • 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
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • 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
    • G06Q30/0241—Advertisements
    • G06Q30/0277—Online advertisement
    • 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
    • G06Q30/0282—Rating or review of business operators or products
    • 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
    • G06Q30/04—Billing or invoicing
    • 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
    • H04L67/51—Discovery or management thereof, e.g. service location protocol [SLP] or web services
    • 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
    • H04L67/535—Tracking the activity of the user
    • 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

Definitions

  • the present disclosure relates to the field of Internet application technologies, and in particular, to a method and apparatus for traffic diversion.
  • cross-flow between services is a way, for example, Users of another business can be drained to the business that is expected to grow.
  • users can pay water, electricity, gas, cable, and the like through an application installed on a mobile device.
  • the user may not use all types of services, and the user who has paid the electricity fee may not have opened the water fee payment service.
  • the present disclosure provides a method and apparatus for traffic diversion to improve the accuracy of drainage.
  • a method for service traffic comprising:
  • For a target service obtaining historical behavior data of a user who uses the target service
  • a method for service traffic comprising:
  • the associated service is a service other than the target service in the associated service group, and then sends service drainage information to the user to guide The user uses the associated service.
  • an apparatus for traffic diversion comprising:
  • a data acquisition module configured to acquire historical behavior data of a user who uses the target service for a target service
  • a data analysis module configured to analyze the historical behavior data, and obtain a user feature of the target user used to define the service drainage
  • a user selection module configured to select, by the user who does not use the target service, a user that meets the user feature as the target user;
  • the information sending module is configured to send the service diversion information to the target user to guide the target user to use the target service.
  • a fourth aspect provides an apparatus for traffic diversion, the apparatus comprising:
  • a user determining module configured to determine a user who uses the target service for any one of the associated service groups
  • An information obtaining module configured to obtain, according to historical behavior data of the user, service usage information of the user to the associated service group;
  • a drainage processing module configured to: if it is determined that the user does not use an associated service according to the service usage information, where the associated service is a service other than the target service in the associated service group, send the service to the user The service diverts information to guide the user to use the associated service.
  • the method and apparatus for traffic diversion of the present disclosure when selecting a target user for drainage, predicts a user who can use the target service as a target user through data analysis, and the method of not distinguishing the user in the prior art is more It is targeted so that the effect of drainage can be improved.
  • FIG. 1 is an application system of a service drainage method according to an embodiment of the present disclosure
  • FIG. 2 is a schematic diagram of application of an online payment service module according to an embodiment of the present disclosure
  • FIG. 3 is a schematic flowchart of a service drainage method according to an embodiment of the present disclosure
  • FIG. 4 is a schematic flowchart diagram of another service drainage method according to an embodiment of the present disclosure.
  • FIG. 5 is a schematic flowchart of still another service drainage method according to an embodiment of the present disclosure.
  • FIG. 6 is a schematic flowchart of still another service drainage method according to an embodiment of the present disclosure.
  • FIG. 7 is a schematic flowchart diagram of still another service drainage method according to an embodiment of the present disclosure.
  • FIG. 8 is a schematic structural diagram of a service traffic guiding apparatus according to an embodiment of the present disclosure.
  • FIG. 9 is a schematic structural diagram of a service traffic guiding apparatus according to an embodiment of the present disclosure.
  • Inter-business cross-draining is a way to lead new users between different businesses to achieve business growth.
  • an access portal for paying a water fee is provided when the electricity fee is paid, and the user may use the water fee to pay the service when the user sees the access, thereby increasing the traffic of the water fee payment service.
  • this way of providing access to each other through different services is passively diverted.
  • the service access portal there is no known whether the user will use the guided service through the portal, and some users may simply There is no interest in the service of the ingress, or the user may already be using the service.
  • the static drainage method of providing the access portal cannot achieve a good drainage effect, and it is easy to form a lack of targeted invalid recommendation, resulting in waste of resources.
  • the static flooding mode described above triggers a high threshold, which requires the user to use the service to trigger.
  • the embodiment of the present application provides a cross-draining method between services, which does not implement the traffic by statically providing a service access portal, but analyzes historical data to analyze which potential users have higher. Probability of the tendency to use a certain service, only the drainage notice is sent to these users when diverting. This method is more targeted in the user's choice, so it can improve the drainage effect.
  • the method of the present application will be described by taking the cross-flow between services of the online payment service as an example. However, the method can also be applied to other similar application scenarios, and is not limited to the online payment service.
  • the scale of the payment users has already reached tens of millions of scales, and the online payment service can include various types of payment services such as electricity, water, gas, and cable TV. Many users may only open the payment service. One or two services, not all services are used. For example, some users have opened gas tariffs but have not opened water tariffs. Some users have opened electricity tariff users but have not opened water tariffs.
  • Through the drainage method of the present application users using one type of service can be drained to another service, and the traffic volume of another service can be promoted.
  • the user of the electricity fee service can be guided to also use the water fee service; Or, the user who has not opened any service can be directed to a certain service, as long as the user uses the application where the online payment service is located, for example, one user installs and uses an application on his smart device.
  • the application includes the business entry of the online payment service, but the user has never used the online payment service, and the method of this example can also drain such users.
  • FIG. 1 illustrates an application system of a service drainage method, which can be used to describe cross-flow between services in an online payment service scenario.
  • the online payment service may be an application module installed in an APP of the user.
  • the user may click the “online payment” module in the APP, and after entering, the online payment service module may be seen. Including water and electricity, and other types of payment services.
  • the data warehouse 11 may store historical behavior data of a user who uses the electricity fee payment service.
  • the historical behavior data may include: a geographical location range of the user (eg, a certain cell in the province), history.
  • Payment records eg, dates for historical contributions
  • payment agencies eg, payment to a power company
  • the data warehouse 11 can also include a lot of other user information.
  • the drainage method in the example of the present application will use the data in the data warehouse for data analysis, and the result of the data analysis is applied to the cross-flow between services in the application, so that the drainage is performed. better result.
  • FIG. 3 and FIG. 1 The following can be combined with FIG. 3 and FIG. 1 to explain how data analysis provides assistance for cross-flow between services. As shown in FIG. 3, the method may include:
  • step 301 for a target service, historical behavior data of a user who uses the target service is acquired.
  • the analyzed electricity fee payment user may be referred to as a "seed user", and the electricity fee service may be referred to as The target business in this example.
  • the application APP can obtain data of the user who uses the electricity fee service therein.
  • the historical behavior data may include: user's geographical location information, historical payment records, paid power companies, and the like. These data can all be stored in the data warehouse 11 in FIG.
  • the seed user in this step may be only all users or a part of users of the target service.
  • step 302 the historical behavior data is analyzed to obtain user characteristics of the target user for defining the traffic diversion.
  • data analysis may be performed according to the data of the electricity fee user in step 301, and it is predicted that other users (ie, users who have not used the electricity fee service) can use the electricity fee service, and the predicted users may be referred to as target users, and the target users may be used. Diverted to the use of electricity tariff business.
  • step 301 by analyzing the historical behavior data of the seed user in step 301, some user features for matching the target user in subsequent steps can be obtained.
  • the user feature may be a geographic location range in which the user is located, and the geographic location may be, for example, cell information in which the user is located.
  • the regional characteristics may be strong, and the users of the A-cell and the users of the B-cell may not belong to the same power company when paying the payment, and when the drainage information is sent, for example, the payment reminder is sent. It is not suitable for sending the payment reminder of the A cell to the user of the B cell, because the payment of the A cell is the account of the company a, and the B cell is not the service coverage of the company a.
  • the data analysis and prediction in this step can select a user who can use the target service as the target user based on the regional characteristics described above, for example, if the data of the A-cell electricity fee user is used to analyze the result, the A-cell has not yet used the electricity fee service. Other users are the target users, and it is ok. The details will be detailed later.
  • step 303 among users who do not use the target service, a user who matches the user feature is selected as the target user. For example, a user who is in the same cell as the seed user can be selected as the target user.
  • the information of the target user analyzed in this step can be stored in the server 12 in FIG.
  • the traffic diversion information is sent to the target user to guide the target user to use the target service.
  • the service traffic information in this step may be information for guiding the target user to use the target service, and the online payment service is taken as an example.
  • the drainage information in this step may be a payment reminder of the electricity fee.
  • a certain electricity fee service The payment reminder can be sent not only to the electricity fee user of the A cell, but also to the user of the A cell who has not used the electricity fee service, so as to guide the user who has not used the electricity fee service to use the electricity fee service, thereby increasing the traffic volume of the electricity fee service.
  • some reminder or notification messages sent by the payment institution 13 may be sent to the aggregate data platform 14, and the aggregate data platform 14 may also send the payment mechanism 13 according to the data of the target user in the server 12.
  • the message is sent to the target user through the payment system 15 as drainage information. If the drainage is successful, for example, the target user who has not used the electricity fee service uses the electricity fee service to pay the electricity fee according to the drainage, the payment system 15 can deposit the latest data into the data warehouse 11, and the original target user has become the electricity fee user, and participates.
  • the user who can use the target service is predicted by the data analysis as the target user, and the way of not distinguishing the user in the prior art is more targeted. Sexuality, so that the effect of the drainage can be improved; and the method can receive the traffic diversion information even if the user does not use the service, which is easy to attract the attention of the user.
  • FIG. 4 illustrates a method flow of service diversion.
  • other potential users in the same geographic location may be selected to perform inter-service drainage by using the geographical location of the seed user.
  • the target is used to match the target.
  • the user's user characteristics may be geographic location information.
  • step 401 according to the historical behavior data of the seed user, the geographical location of the seed user is obtained.
  • the user who uses the electricity fee payment service in the APP may also use the APP to perform online shopping, etc., and may obtain the address information of the user, or may also pass LBS (Location Based Service) positioning, etc., obtain the geographic location of the user, and obtain the geographic location range of the user.
  • LBS Location Based Service
  • the geographical location range in this example is described by taking the cell in which the user lives as an example.
  • the cell of the user may be referred to as a target cell.
  • step 402 historical behavior data of the candidate users located within the same geographic location range is determined.
  • an APP user located in the same cell as the user of step 401 can be regarded as a candidate user.
  • These candidate users may be users who use the application APP but do not use any type of online payment service; or the candidate user may also be a user who uses at least one online payment service, but the used
  • the payment service is not an electricity fee, for example, the user uses a water fee or a gas fee service.
  • the historical behavior data of the candidate user in this step may also include various data related to the user, which may be obtained by the user using the historical behavior of the application APP. For example, the user's address information, login frequency, public opinion feedback, etc.
  • the historical behavior data of the candidate user will be applied in the next step.
  • step 403 a user having a target service usage tendency is selected as the target user according to the historical behavior data.
  • data analysis may be performed according to the historical behavior data of the candidate user obtained in step 402, and the target user is selected. For example, there may be many users to be selected in the same cell as the seed user in step 401. If all the users to be selected are prompted to be drained, it is not too accurate.
  • This step can further filter from the selected users and select from them.
  • the user service usage tendency described herein that is, the user has a higher probability of using the electricity fee service of the drainage reminder, and which users have such a tendency, can be obtained according to the data analysis in step 402.
  • the history behavior data of the candidate user obtained in step 402 may include: history release information of the candidate user, and the release information may be public opinion information.
  • the historical lyric information may be, for example, that the user has published some sensations, such as "When is the electricity fee payment service of our community opened, I hope to hurry up!, or "Who knows how to use the electricity fee business, I want to use” and so on.
  • the sensation information indicates that the candidate user desires to use the target service, that is, the electricity fee service in the present example, and the user is regarded as the target user of the drainage, and the accuracy is higher, and the successful drainage is easier to implement. Therefore, the historical public opinion information described above can be reflected as a target user with a user who has a tendency to use the target business.
  • the flow of the example is an example for selecting a user who has a tendency to use the target service.
  • the user can be the target user as long as the user meets the matching user characteristics described above. For example, as long as the user is in the same cell as the seed user, it can be selected as the target user.
  • step 404 traffic diversion information is sent to the target user to guide the target user to use the target service.
  • the reach of the organization service message will not only cover the electricity charge user, but also cover more users in the same cell.
  • the institution's payment reminder will no longer be limited to the electricity charge user, and the cell in which it is located.
  • Potential non-contributory users eg, users of water, gas, cable TV, etc. in the same community, or users who do not use any type of payment service
  • the business model characteristics of the organization may be combined, for example, the long-term period is posted once, or the account of the electricity-storage user who has been posted is sent, and the payment reminder is sent to the target user group.
  • FIG. 5 illustrates another method flow of service diversion.
  • a new user in a geographical location within the coverage of the service may be obtained as a target user according to the service coverage of the corresponding payment institution when the seed user pays the fee. , conduct traffic between businesses.
  • step 501 according to the historical behavior data of the seed user, the geographical location of the seed user and the business organization corresponding to the target service are obtained.
  • the geographical location ranges by taking the cell as an example.
  • the determining manner of the target cell can be referred to the example of FIG. 4, and the determination of the service organization can be comprehensively determined according to data of multiple seed users.
  • the data in the data warehouse includes many users.
  • the business unit in which the cell is located and the user pays the electricity fee "Y1 user is in D cell, the power company corresponding to the electricity bill is D1 company", "Y2 user is in B cell, and the power company corresponding to the payment of electricity fee is also D1 company".
  • the institution that pays the fee can be obtained from the information that the user pays for the service using the electricity fee.
  • step 502 the service coverage of the business organization is obtained according to the geographic location range and the business organization.
  • the service coverage of the D1 company includes the A cell and the B cell.
  • some data errors may occur, for example, where one user's location is located incorrectly, the real location is the A cell, and the result is incorrectly located in the C cell, and the data is "Y3 user in the C cell.
  • the power company corresponding to the payment of the electricity fee is also the D1 company.
  • the business organization can be determined based on the data of multiple seed users, if the majority of the users of the C cell are corresponding to the D2 power company, then it can be determined. This is a wrong data and will not be adopted. That is, a comprehensive analysis of a certain number of samples can ensure that the business organization determines accuracy.
  • step 503 the user activity information of the candidate user and the feedback behavior information of the historical marketing are obtained among the to-be-selected users located in the coverage of the service.
  • this step can obtain historical behavior data of the user to be selected, for example, including user activity information, and how often the user logs in. If the login is more, the user is more active; and the user can obtain the feedback behavior information of the historical marketing. To reflect whether the user is more sensitive to notification reminders.
  • This step may select a candidate user from the service coverage determined by step 502.
  • the service coverage of the D1 company may be selected from the "A cell and the B cell", and the application APP may be selected, but Any user who uses any type of online payment service; or the candidate user may also be a user who uses at least one online payment service, but the payment service used is not electricity, for example, the user uses water or gas. Fee business.
  • step 504 if the user activity information indicates that the business activity of the candidate user is high, and the feedback behavior information indicates that the candidate user is positive for the marketing feedback, the candidate user is determined to be the target user.
  • the example can also adopt the manner in which the target user is selected by the candidate users according to the public opinion described in FIG. 4 .
  • step 505 traffic diversion information is sent to the target user to guide the target user to use the seed service.
  • the business drainage method of the present example based on the analysis of the historical historical marketing feedback behavior of the user, mines the non-contributing users with higher sensitivity to the notification reminder, and finally pushes the information to the potential non-contributing users who are more sensitive to the notification reminder, and achieves drainage.
  • the purpose is to improve the effect of drainage.
  • FIG. 6 illustrates another method of traffic diversion, in which, in addition to the above-mentioned drainage method, the target user is selected according to the seed user, and in this example, the user is determined according to the service used by the user. Drainage business. As shown in FIG. 6, the method includes:
  • step 601 a user who uses the target service is determined for any one of the associated service groups.
  • the associated service group in this step can be a predefined service group.
  • the online payment service can be used as an example.
  • the online payment service can include multiple payment services such as electricity, water, and gas.
  • the multiple payment services can be called related services.
  • Water fee business and gas fee business can be called as related business of electricity fee business.
  • the user only some of the services may be used, for example, the user only uses the electricity fee payment service, or the electricity fee and the water fee service; in this example, one of the services used by the user may be referred to as the target service.
  • the associated business group may also be a business type other than the online payment service.
  • step 602 according to the historical behavior data of the user, the service usage information of the user to the associated service group is obtained.
  • the historical behavior data in this step may include a lot of data of the user, where the service usage information may be which services in the associated service group are used by the user, and which services are not used.
  • step 603 if it is determined that the user does not use the associated service according to the service usage information, the associated service is a service other than the target service in the associated service group, and then sends the service to the user.
  • the information is drained to direct the user to use the associated service.
  • the user may be guided to use the association.
  • the user when the service is diverted, the user is guided to use the service that he has not used yet, and the recommendation of the service is more targeted, and the accuracy of the drainage can be improved.
  • FIG. 7 illustrates yet another method of traffic diversion.
  • the associated service when recommending other related services in an associated service group that has not been used by the user, the associated service may be used according to the usage of the associated traffic.
  • User data to perform drainage As shown in FIG. 7, the method may include:
  • step 701 it is determined that the feature of the user matches the user, and the feature matches the user using the associated service.
  • the user in this step can be called a target user, and the target user should be directed to a certain service.
  • the feature matching user may be a user who is in the same cell as the target user.
  • the feature matches the service coverage of the associated service used by the user, including the cell where the target user is located, and the associated service is the service to be directed to the target user.
  • a user uses the electricity fee service in the online payment service and does not use the water fee service. If the user is to be diverted to use the water fee service, then the user who uses the water fee service is found as the feature matching user, and the water fee service is the business that leads the target business.
  • step 702 the service routing information corresponding to the associated service is analyzed according to the historical behavior data of the feature matching user.
  • the information related to the drainage can be analyzed and analyzed according to the historical behavior data of the matching user.
  • the data analysis of the user who uses the water fee service it can be known which water company jurisdiction the cell where the target user belongs, and then the water fee of the water company can be sent when the drainage information is subsequently sent. Drain the information of the business.
  • step 703 the traffic diversion information is sent to the user.
  • the drainage may be performed not using the data of the user of the associated service being drained, but may be based on the user's own data using the target service.
  • the service information related to the related service may be determined according to the historical behavior data of the user. For example, if the user is to be diverted to the water fee service, the cell of the user may be determined according to the cell information in the user data. The water company pays the fee, and the service information such as the time period of the water company payment; and then sends the service drainage information to the user according to the service information, for example, sending the water company reminder at a predetermined time.
  • the user may further determine whether the user has a usage tendency of the associated service, that is, whether the user has a requirement for using the associated service, and if necessary, Continue to recommend, if the user does not use the requirement, even if the user does not use the associated service in the associated service group, the user may not be recommended to the associated service.
  • the judgment of this tendency can be determined based on the historical behavior data of the user.
  • the information may be published according to the history of the user, if the posting information indicates that the user desires to use the associated service; or according to the activity information of the user and the feedback behavior information for historical marketing, if the user activity information indicates the user The business activity is relatively high, and the feedback behavior information of the user to the historical marketing indicates that the user is positive about the marketing feedback, and then the user has the requirement to use the associated business.
  • the user when the service is diverted, the user is guided to use the service that he has not used yet, and the recommendation of the service is more targeted, and the accuracy of the drainage can be improved; and, by determining whether the user has the requirement of using the associated service, The accuracy of the drainage can be further improved; in addition, by matching the historical behavior data of the user according to the feature, the drainage information can be assisted to be more accurate.
  • the present application further provides a service diversion device.
  • the device may include: a data acquisition module 81, a data analysis module 82, a user selection module 83, and an information transmission module 84.
  • the data obtaining module 81 is configured to acquire, for a target service, historical behavior data of a user who uses the target service;
  • the data analysis module 82 is configured to analyze the historical behavior data, and obtain user characteristics of the target user for defining service drainage;
  • a user selection module 83 configured to select, by the user who does not use the target service, a user that meets the user feature as the target user;
  • the information sending module 84 is configured to send service drainage information to the target user to guide the target user to use the target service.
  • the user selection module 83 is configured to obtain, as a candidate user, a user who does not use the target service and meets the user feature, and obtain historical behavior data of the candidate user; according to the historical behavior data. Select the user with the target business usage tendency as the target user.
  • the user selection module 83 is specifically configured to post information according to the history of the candidate user when the user having the target service usage tendency is selected as the target user, if the history release information indicates that the candidate user desires to use Determining, by the target service, the candidate user is the target user; or, according to the user activity information of the candidate user and the feedback behavior information of the historical marketing, if the user activity information indicates that the service of the candidate user is active If the feedback behavior information indicates that the candidate user is positive for the marketing feedback, the candidate user is determined to be the target user.
  • FIG. 9 exemplifies a structure of another service drainage device.
  • the device may include a user determination module 91, an information acquisition module 92, and a drainage processing module 93.
  • the user determining module 91 is configured to determine, for any target service in the associated service group, a user who uses the target service;
  • the information obtaining module 92 is configured to obtain, according to historical behavior data of the user, service usage information of the user to the associated service group;
  • the drainage processing module 93 is configured to determine, according to the service usage information, that the user does not use an associated service, where the associated service is a service other than the target service in the associated service group, and then the user is sent to the user The service routing information is sent to guide the user to use the associated service.
  • the traffic processing module 93 when used to send the traffic diversion information to the user, includes: determining that the feature of the user matches the user, the feature matching user uses the associated service; and matching according to the feature The historical behavior data of the user is analyzed, and the service drainage information corresponding to the associated service is obtained, and the service drainage information is sent to the user.
  • the information obtaining module 92 is further configured to determine, according to the historical behavior data of the user, that the user has a usage tendency of the associated service.
  • the apparatus or module illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product having a certain function.
  • a typical implementation device is a computer, and the specific form of the computer may be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email transceiver, and a game control.

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Development Economics (AREA)
  • Finance (AREA)
  • Accounting & Taxation (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Game Theory and Decision Science (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Hardware Design (AREA)
  • General Engineering & Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Navigation (AREA)

Abstract

本公开提供一种业务引流的方法和装置，其中方法包括：对于一种目标业务，获取使用所述目标业务的用户的历史行为数据；分析所述历史行为数据，得到用于限定业务引流的目标用户的用户特征；由未使用所述目标业务的用户中，选择符合所述用户特征的用户作为所述目标用户；向所述目标用户发送业务引流信息，以引导所述目标用户使用目标业务。本公开引流时将更有针对性，从而能够提高引流的效果。

Description

一种业务引流的方法和装置 技术领域
本公开涉及互联网应用技术领域，特别涉及一种业务引流的方法和装置。
背景技术
不同的业务拥有的用户量是不同的，可能有些业务的用户多一些，有些业务的用户少一些，为了实现某种业务的业务量的增长，“业务间交叉引流”是一种方式，比如，可以将另一种业务的用户引流到所期望增长的业务上来。比如，以线上缴费业务为例，用户可以通过安装在移动设备上的应用缴纳水费、电费、煤气费、有线电视费等等。但是，用户不一定会使用全部类型的业务，开通了电费缴纳的用户可能并未开通水费缴纳业务。
发明内容
有鉴于此，本公开提供一种业务引流的方法和装置，以提高引流的准确率。
具体地，本公开是通过如下技术方案实现的：
第一方面，提供一种业务引流的方法，所述方法包括：
对于一种目标业务，获取使用所述目标业务的用户的历史行为数据；
分析所述历史行为数据，得到用于限定业务引流的目标用户的用户特征；
由未使用所述目标业务的用户中，选择符合所述用户特征的用户作为所述目标用户；
向所述目标用户发送业务引流信息，以引导所述目标用户使用目标业务。
第二方面，提供一种业务引流的方法，所述方法包括：
对于关联业务组中的任意一种目标业务，确定使用所述目标业务的用户；
根据所述用户的历史行为数据，得到所述用户对所述关联业务组的业务使用信息；
若根据所述业务使用信息确定所述用户未使用关联业务，所述关联业务是所述关联业务组中除所述目标业务之外的其他业务，则向所述用户发送业务引流信息，以引导所述用户使用所述关联业务。
第三方面，提供一种业务引流的装置，所述装置包括：
数据获取模块，用于对于一种目标业务，获取使用所述目标业务的用户的历史行为数据；
数据分析模块，用于分析所述历史行为数据，得到用于限定业务引流的目标用户的用户特征；
用户选择模块，用于由未使用所述目标业务的用户中，选择符合所述用户特征的用户作为所述目标用户；
信息发送模块，用于向所述目标用户发送业务引流信息，以引导所述目标用户使用目标业务。
第四方面，提供一种业务引流的装置，所述装置包括：
用户确定模块，用于对于关联业务组中的任意一种目标业务，确定使用所述目标业务的用户；
信息获取模块，用于根据所述用户的历史行为数据，得到所述用户对所述关联业务组的业务使用信息；
引流处理模块，用于若根据所述业务使用信息确定所述用户未使用关联业务，所述关联业务是所述关联业务组中除所述目标业务之外的其他业务，则向所述用户发送业务引流信息，以引导所述用户使用所述关联业务。
本公开的业务引流的方法和装置，在引流的目标用户的选择时，是通过数据分析预测能够使用目标业务的用户作为目标用户，相对于现有技术中不区分用户的方式，引流时将更有针对性，从而能够提高引流的效果。
附图说明
图1是本公开实施例提供的一个业务引流方法的应用系统；
图2是本公开实施例提供的一个线上缴费业务模块的应用示意图；
图3是本公开实施例提供的一个业务引流方法的流程示意图；
图4是本公开实施例提供的另一个业务引流方法的流程示意图；
图5是本公开实施例提供的又一个业务引流方法的流程示意图；
图6是本公开实施例提供的又一个业务引流方法的流程示意图；
图7是本公开实施例提供的又一个业务引流方法的流程示意图；
图8是本公开实施例提供的一个业务引流装置的结构示意图；
图9是本公开实施例提供的一个业务引流装置的结构示意图。
具体实施方式
“业务间交叉引流”是一种在不同业务间引导新用户，进而达到业务增长的方式。例如传统的业务间交叉引流的方式中，在缴纳电费时提供缴纳水费的访问入口，用户看到该访问入口就可能去使用水费缴纳业务，从而使得水费缴纳业务的流量增加。但是这种通过不同业务提供相互间的访问入口引流的方式，属于被动式引流，对于业务访问入口所展示的用户，完全不知道该用户是否会通过入口使用所引导的业务，也许有的用户根本就对入口引导的业务没有兴趣，或者可能用户已经在使用该业务，因此，上述的提供访问入口的静态引流方式不能达到很好的引流效果，容易形成缺乏针对性的无效推荐，导致资源浪费。此外，上述的静态引流方式触发门槛高，需要用户使用业务时才能触发。
基于此，本申请实施例提供了一种业务间交叉引流方法，该方法并不通过静态提供业务访问入口的方式实现引流，而是通过对历史数据的分析，分析出哪些潜在的用户具有较高概率的使用某业务的倾向，在引流时只向这些用户发送引流通知。该方法在用户的选择上更有针对性，所以能够提高引流效果。
如下将以线上缴费业务的业务间交叉引流为例，对本申请的方法进行描述；但是，该方法同样可以应用于其他类似的应用场景，不局限于线上缴费业务。在线上缴费业务中，缴费用户的规模早已达到千万级规模，且线上缴费业务可以包括电费、水费、燃气费、有线电视费等多种类型的缴费业务，很多用户可能只开通了其中一种或两种业务，并未使用全部的业务。比如，有些用户开通 了燃气费业务但没有开通水费业务，有些用户开通了电费用户但未开通水费业务。通过本申请的引流方法，可以将使用一种类型业务的用户引流到另一种业务上，促进另一种业务的业务量增长，比如，可以将电费业务用户引导至也使用水费业务；当然，也可以是将未开通任何业务的用户引导至某一种业务，只要该用户使用了线上缴费业务所在的应用即可，比如，有个用户在自己的智能设备上安装使用了一个应用，该应用中包括线上缴费业务的业务入口，但是用户从来没使用过线上缴费业务，本例子的方法也可以将这类用户进行引流。
图1示例了业务引流方法的一个应用系统，该应用系统可以用于描述线上缴费业务场景中的业务间交叉引流。例如，线上缴费业务可以是安装在用户某个APP中一个应用模块，如图2的示例，用户可以点击APP中的“线上缴费”模块，进入后可以看到该线上缴费业务模块可以包括水费、电费等多种类型的缴费业务。
只要用户注册了APP，用户使用该APP进行各种业务时的信息，都可以被获取并存储到图1的数据仓库11中，即使用户没有使用线上缴费业务，也可能使用了APP中的其他业务，比如网络购物、购火车票等，业务使用中涉及的信息都可以被存储到数据仓库11。在一个例子中，数据仓库11中可以存储有使用了电费缴纳业务的用户的历史行为数据，例如，该历史行为数据可以包括：用户的地理位置范围(如，所在省市的某小区)、历史缴费记录(如，历史缴费的日期)、缴费机构(如，向某个电力公司缴费)等。
数据仓库11中还可以包括很多其他的用户信息，本申请例子中的引流方法将使用数据仓库中的数据进行数据分析，将数据分析的结果应用于本申请中的业务间交叉引流，以使得引流效果更好。如下可以结合图3和图1，说明数据分析如何对业务间交叉引流提供辅助，如图3所示，该方法可以包括：
在步骤301中，对于一种目标业务，获取使用该目标业务的用户的历史行为数据。
例如，在一个例子中，假设通过分析电费缴纳用户的数据，去引导尚未使用电费业务的其他用户使用电费业务，则所分析的电费缴纳用户可以称为“种子 用户”，而电费业务可以称为本例子中的目标业务。
应用APP可以获取到使用其中的电费业务的用户的数据，比如，所述的历史行为数据可以包括：用户的地理位置信息、历史缴费记录、缴费的电力公司等诸多数据。这些数据都可以存储在图1中的数据仓库11中。此外，本步骤中的种子用户，可以只是目标业务的全部用户或者一部分用户。
在步骤302中，分析历史行为数据，得到用于限定业务引流的目标用户的用户特征。本例子中，可以根据步骤301中的电费用户的数据进行数据分析，预测哪些其他用户(即尚未使用电费业务的用户)能够使用电费业务，可以将预测的这些用户称为目标用户，将目标用户引流到使用电费业务上。
其中，在目标用户的确定过程中，需要选择将哪些用户作为目标用户，则需要一些用户选择时的依据。本步骤中，可以通过分析步骤301中的种子用户的历史行为数据，得到一些用于在后续步骤匹配目标用户的用户特征。
比如，该用户特征可以是用户所在的地理位置范围，该地理位置范围例如可以是用户所在的小区信息。例如，对于线上缴费业务来说，可能地域性特征比较强，A小区的用户与B小区的用户在缴费时可能并不属于同一个电力公司，在引流信息发送时，例如，发送出账提醒时，并不适于将A小区的出账提醒发送至B小区的用户，因为A小区的出账是a公司的出账，B小区不是a公司的业务覆盖范围。本步骤的数据分析预测，可以基于上述的这种地域性特征，选择能够使用目标业务的用户作为目标用户，比如，如果根据A小区电费用户的数据分析结果，选择A小区的尚未使用电费业务的其他用户作为目标用户，是可以的。后续将举例详述。
在步骤303中，由未使用目标业务的用户中，选择符合所述用户特征的用户作为所述目标用户。比如，可以选择与种子用户在同一个小区的用户作为目标用户。本步骤分析到的目标用户的信息，可以存储在图1中的服务器12中。
在步骤304中，向所述目标用户发送业务引流信息，以引导目标用户使用目标业务。本步骤的业务引流信息可以是用于引导目标用户使用目标业务的信息，以线上缴费业务为例，例如，本步骤的引流信息可以是电费的出账提醒， 在一个例子中，某电费业务的出账提醒，可以不仅发送至A小区的电费用户，还可以发送至A小区的尚未使用电费业务的用户，以引导尚未使用电费业务的用户使用电费业务，从而实现电费业务流量的增加。
结合图1来看，缴费机构13发送的一些提醒或者通知的消息，可以发送至聚合数据平台14，该聚合数据平台14还可以综合根据服务器12中的目标用户的数据，将缴费机构13要发送的消息作为引流信息，通过缴费系统15发送至目标用户。如果引流成功，例如，尚未使用电费业务的目标用户根据引流，使用了电费业务缴纳电费，则缴费系统15可以将最新数据沉淀到数据仓库11，此时原来的目标用户已经成为电费用户，参与下一次的数据分析和目标用户选取。
本例子的业务引流的方法，在引流的目标用户的选择时，是通过数据分析预测能够使用目标业务的用户作为目标用户，相对于现有技术中不区分用户的方式，引流时将更有针对性，从而能够提高引流的效果；并且，该方法即使用户不使用业务也可以接收到业务引流信息，容易引起用户关注。
如下通过两个例子描述本申请的业务引流的方法，但是实际实施中并不局限于这些方式。
图4示例了一种业务引流的方法流程，该例子中，可以利用种子用户的所在地理位置范围，选取同一地理位置范围内的其他潜在用户进行业务间引流，在该例子中，用于匹配目标用户的用户特征可以是地理位置范围信息。
在步骤401中，根据种子用户的历史行为数据，得到种子用户所在的地理位置范围。
例如，以线上缴费业务中的电费业务用户为例，使用了APP中的电费缴纳业务的用户，可能还使用APP进行过网络购物等行为，可以获取到用户的住址信息，或者，还可以通过LBS(Location Based Service)定位等方式，获取用户地理位置，得到用户的地理位置范围。本例子中的地理位置范围以用户所住的小区为例进行描述，本例子可以将用户的小区称为目标小区。
在步骤402中，确定位于同一地理位置范围内的待选用户的历史行为数据。
例如，可以将与步骤401的用户位于同一小区的APP用户，作为待选用户。 这些待选用户可以是使用了应用APP，但是并未使用其中的任何类型线上缴费业务的用户；或者，待选用户还可以是使用了至少一种线上缴费业务的用户，但是所使用的缴费业务不是电费，比如用户使用的是水费或燃气费业务。
本步骤中的待选用户的历史行为数据，同样可以包括与该用户相关的多种数据，都是可以由用户使用应用APP的历史行为中获取。例如，用户的住址信息、登录频度、舆情反馈等。待选用户的历史行为数据将应用在下一步骤中。
在步骤403中，根据所述历史行为数据，选择具有目标业务使用倾向的用户作为目标用户。
本步骤中，可以根据步骤402中获得的待选用户的历史行为数据进行数据分析，选取目标用户。例如，与步骤401中的种子用户在同一小区的待选用户可以有很多，如果将所有的待选用户都进行引流提醒，不是太精准，本步骤可以从这些待选用户中进一步筛选，从中选择具有目标业务使用倾向的用户作为目标用户。这里所述的具有目标业务使用倾向，即用户具有较高的概率使用引流提醒的电费业务，而哪些用户具有这种倾向，则可以根据步骤402中的数据分析得到。
举一个例子如下：假设在步骤402中获得的待选用户的历史行为数据，可以包括：所述待选用户的历史发布信息，该发布信息可以是舆情信息。所述的历史舆情信息例如可以是，用户曾经发布过一些舆情，如“咱们小区的电费缴纳业务什么时候开通，期望快点！”，或者“谁知道怎么使用电费业务，我想使用”等。这些舆情信息表明所述待选用户期望使用目标业务即本例子中的电费业务，那么将此类用户作为引流的目标用户，准确度更高，更容易实现成功引流。因此，可以将上述的历史舆情信息反映出具有目标业务使用倾向的用户作为目标用户。
当然，由待选用户中选择目标用户的方式可以有很多，不限制于上述的根据舆情信息来选择的例子。比如，在后续的图5所示的例子中，还将描述一种选择目标用户的方式，这种方式同样可以应用于本例子中。此外，本例子的流程是举例可以选择具有目标业务使用倾向的用户，实际实施中，即使不能确定 用户是否具有这种倾向，只要用户符合上述匹配的用户特征，就可以作为目标用户。比如，只要用户与种子用户在同一个小区，即可选为目标用户。
在步骤404中，向所述目标用户发送业务引流信息，以引导所述目标用户使用目标业务。
例如，在本例子中，机构服务消息的触达面积将不仅仅覆盖电费存量用户，还可覆盖更多同小区用户，如机构的出账提醒将不再局限于电费存量用户，其所在小区的潜在非缴费用户(如，相同小区的水费、煤气、有线电视等业务的用户，或者未使用任何类型缴费业务的用户)也将能同时收到，进而将目标非缴费用户群转换为缴费用户。其中，在出账提醒发送时，可以结合机构的业务模型特征，比如多长周期出账一次，或者结合已出账的电费存量用户的记录，向目标用户群发送出账提醒。
图5示例了另一种业务引流的方法流程，该例子中，可以根据种子用户在缴费时对应的缴费机构的业务覆盖范围，获取其业务覆盖范围内的地理位置范围中的新用户作为目标用户，进行业务间引流。
在步骤501中，根据种子用户的历史行为数据，得到所述种子用户所在的地理位置范围以及目标业务对应的业务机构。
例如，地理位置范围以小区为例，目标小区的确定方式可以参见图4的例子，对于业务机构的确定，可以综合根据多个种子用户的数据确定，比如，数据仓库中的数据包括很多用户的所在小区和该用户缴电费的业务机构，“Y1用户在A小区，其缴纳电费对应的电力公司是D1公司”、“Y2用户在B小区，其缴纳电费对应的电力公司也是D1公司”。缴费的机构可以从用户使用电费缴纳业务的信息中得到。
在步骤502中，根据地理位置范围和业务机构，得到所述业务机构的业务覆盖范围。
例如，综合步骤501中的两个例子，可以得到“D1公司的业务覆盖范围包括A小区和B小区”。此外，在步骤501中的数据分析时，可能会出现部分数据错误，比如，其中一个用户的位置定位错误，真实所在地是A小区，结果错误的 定位成C小区，数据为“Y3用户在C小区，其缴纳电费对应的电力公司也是D1公司”，这种情况下，由于可以综合根据多个种子用户的数据确定业务机构，如果C小区的绝大多数用户都是对应D2电力公司，那么可以确定出这是个错误数据，是不会采纳的。即一定数量的样本综合分析可以保证业务机构确定准确。
在步骤503中，在位于所述业务覆盖范围内的待选用户中，获取所述待选用户的用户活跃度信息、以及对历史营销的反馈行为信息。
例如，本步骤可以获取待选用户的历史行为数据，比如包括用户活跃度信息，用户的登录频度多少，如果登录较多，说明该用户较为活跃；还可以获取用户对历史营销的反馈行为信息，以反映用户是否对通知提醒较为敏感。
本步骤可以由步骤502确定的业务覆盖范围内选择待选用户，比如，上述例子中可以从D1公司的业务覆盖范围“A小区和B小区”中选择，可以选择使用了应用APP，但是并未使用其中的任何类型线上缴费业务的用户；或者，待选用户还可以是使用了至少一种线上缴费业务的用户，但是所使用的缴费业务不是电费，比如用户使用的是水费或燃气费业务。
在步骤504中，若用户活跃度信息表明待选用户的业务活跃度较高，且反馈行为信息表明待选用户对营销反馈积极，则确定所述待选用户为目标用户。
例如，如果用户较为活跃，且对营销积极的反馈，向这类用户发送引流信息，相比于不活跃的用户，更有可能成功引流。因此可以将这类用户确定为具有种子业务使用倾向的用户，作为目标用户。
此外，本例子同样可以采用图4所述的根据舆情由待选用户中选择目标用户的方式。
在步骤505中，向所述目标用户发送业务引流信息，以引导所述目标用户使用种子业务。
本例子的业务引流方法，通过根据对用户历史营销反馈行为分析，挖掘出对通知提醒敏感度较高的非缴费用户，最终将信息精准推送到对通知提醒较为敏感的潜在非缴费用户，达到引流的目的，提高了引流的效果。
在另一个例子中，图6示例了另一种业务引流的方法，在该方法中，不同 于上面的引流方法中根据种子用户选择目标用户，本例子中将根据用户使用的业务确定需要将用户引流的业务。如图6所示，该方法包括：
在步骤601中，对于关联业务组中的任意一种目标业务，确定使用所述目标业务的用户。
本步骤中的关联业务组，可以是预定义的业务组。比如，仍以线上缴费业务为例，线上缴费业务中可以包括电费、水费、燃气费等多种缴费业务，该多种缴费业务可以称为关联业务，比如，对于电费业务来说，水费业务、燃气费业务都可以称为电费业务的关联业务。
对于一个用户来说，可能只使用了其中部分业务，例如用户只使用了其中的电费缴纳业务，或者电费和水费业务；本例子可以将用户使用的其中一种业务称为目标业务。在其他的例子中，关联业务组也可以是线上缴费业务之外的其他业务类型。
在步骤602中，根据所述用户的历史行为数据，得到所述用户对所述关联业务组的业务使用信息。
本步骤中的历史行为数据可以包括该用户的很多数据，其中的业务使用信息，可以是用户使用了关联业务组中的哪些业务，未使用哪些业务。
在步骤603中，若根据所述业务使用信息确定所述用户未使用关联业务，所述关联业务是所述关联业务组中除所述目标业务之外的其他业务，则向所述用户发送业务引流信息，以引导所述用户使用所述关联业务。
本步骤中，如果根据业务使用信息确定用户未使用关联业务组中的至少一个关联业务，比如，使用了电费业务的用户，尚未使用水费和燃气费业务，那么可以引导该用户去使用该关联业务组中他尚未使用的业务。例如，可以引导使用了电费业务的用户去使用水费或燃气费业务。
本例子的方法中，在业务引流时，是引导用户使用他尚未使用的业务，在业务的推荐上更有针对性，可以提高引流准确性。
在又一个例子中，图7示例了又一种业务引流的方法，在本例子中，在向用户推荐其尚未使用的关联业务组中的其他关联业务时，可以根据使用所引流 的关联业务的用户的数据来执行引流。如图7所示，该方法可以包括：
在步骤701中，确定用户的特征匹配用户，所述特征匹配用户使用所述关联业务。
本步骤中的用户，可以称为目标用户，要将该目标用户引流到某种业务上去。所述特征匹配用户，可以是与该目标用户位于同一小区的用户。在另一个例子中，该特征匹配用户使用的关联业务的业务覆盖范围，包括目标用户所在的小区，而该关联业务即为要将目标用户引流的业务。
比如，一个用户使用了线上缴费业务中的电费业务，没使用水费业务。如果要将该用户引流到使用水费业务，那么找到使用了水费业务的用户作为特征匹配用户，而水费业务时将目标业务引流的业务。
在步骤702中，根据所述特征匹配用户的历史行为数据，分析得到所述关联业务对应的业务引流信息。
比如，本步骤可以根据特征匹配用户的历史行为数据，分析得到与引流相关的信息。例如，在步骤701的例子中，根据使用了水费业务的用户的数据分析时，可以知道目标用户所在小区属于哪个自来水公司管辖，那么在后续发送引流信息时，可以发送该自来水公司的水费缴纳业务的引流信息。
在步骤703中，向所述用户发送所述业务引流信息。
此外，在其他例子中，也可以不是使用所引流的关联业务的用户的数据来执行引流，而是根据使用目标业务的用户自己的数据来引流。比如，可以根据所述用户的历史行为数据，确定与关联业务相关的业务信息，例如，假设要将用户引流到水费业务，可以根据用户数据中的所在小区信息，确定该用户的小区应向哪个自来水公司缴费，以及该自来水公司缴费的时间周期等业务信息；然后根据所述业务信息向所述用户发送业务引流信息，例如，在预定的时间发送该自来水公司的出账提醒。
在另一个例子中，在本步骤向所述用户发送业务引流信息之前，还可以预先确定下该用户是否具有所述关联业务的使用倾向，即用户是否有使用关联业务的需求，如有需求可以继续推荐，如果没有使用需求，即使用户未使用关联 业务组中的关联业务，也可以不向用户做引流至关联业务的推荐。这种倾向的判断可以根据用户的历史行为数据确定。
例如，可以根据用户的历史发布信息，如果该发布信息表明所述用户期望使用所述关联业务；或者根据用户的活跃度信息和对历史营销的反馈行为信息，如果所述用户活跃度信息表明用户的业务活跃度较高，且所述用户对历史营销的反馈行为信息表明所述用户对营销反馈积极，则确定用户具有使用关联业务的需求。
本例子的方法中，在业务引流时，是引导用户使用他尚未使用的业务，在业务的推荐上更有针对性，可以提高引流准确性；并且，通过判断用户是否具有使用关联业务的需求，可以进一步提高引流的准确性；此外，通过根据特征匹配用户的历史行为数据的分析，可以辅助使得引流信息更加准确。
为了实现上述的业务引流方法，本申请还提供一种业务引流装置，如图8所示，该装置可以包括：数据获取模块81、数据分析模块82、用户选择模块83和信息发送模块84。
数据获取模块81，用于对于一种目标业务，获取使用所述目标业务的用户的历史行为数据；
数据分析模块82，用于分析所述历史行为数据，得到用于限定业务引流的目标用户的用户特征；
用户选择模块83，用于由未使用所述目标业务的用户中，选择符合所述用户特征的用户作为所述目标用户；
信息发送模块84，用于向所述目标用户发送业务引流信息，以引导所述目标用户使用目标业务。
在一个例子中，用户选择模块83，具体用于将未使用所述目标业务且符合所述用户特征的用户作为待选用户，获取所述待选用户的历史行为数据；根据所述历史行为数据，选择具有目标业务使用倾向的用户作为目标用户。
在一个例子中，用户选择模块83，在选择具有目标业务使用倾向的用户作为目标用户时，具体用于根据待选用户的历史发布信息，若所述历史发布信息 表明所述待选用户期望使用所述目标业务，则确定所述待选用户为目标用户；或者，根据待选用户的用户活跃度信息以及对历史营销的反馈行为信息，若所述用户活跃度信息表明待选用户的业务活跃度较高，且所述反馈行为信息表明待选用户对营销反馈积极，则确定所述待选用户为目标用户。
图9示例了另一种业务引流装置的结构，如图9所示，该装置可以包括：用户确定模块91、信息获取模块92和引流处理模块93。
用户确定模块91，用于对于关联业务组中的任意一种目标业务，确定使用所述目标业务的用户；
信息获取模块92，用于根据所述用户的历史行为数据，得到所述用户对所述关联业务组的业务使用信息；
引流处理模块93，用于若根据所述业务使用信息确定所述用户未使用关联业务，所述关联业务是所述关联业务组中除所述目标业务之外的其他业务，则向所述用户发送业务引流信息，以引导所述用户使用所述关联业务。
在一个例子中，引流处理模块93，在用于向所述用户发送业务引流信息时，包括：确定所述用户的特征匹配用户，所述特征匹配用户使用所述关联业务；根据所述特征匹配用户的历史行为数据，分析得到所述关联业务对应的业务引流信息，向所述用户发送所述业务引流信息。
在一个例子中，信息获取模块92，还用于根据所述用户的历史行为数据，确定所述用户具有所述关联业务的使用倾向。
上述实施例阐明的装置或模块，具体可以由计算机芯片或实体实现，或者由具有某种功能的产品来实现。一种典型的实现设备为计算机，计算机的具体形式可以是个人计算机、膝上型计算机、蜂窝电话、相机电话、智能电话、个人数字助理、媒体播放器、导航设备、电子邮件收发设备、游戏控制台、平板计算机、可穿戴设备或者这些设备中的任意几种设备的组合。
为了描述的方便，描述以上装置时以功能分为各种模块分别描述。当然，在实施本公开时可以把各模块的功能在同一个或多个软件和/或硬件中实现。
以上所述仅为本公开的较佳实施例而已，并不用以限制本公开，凡在本公 开的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本公开保护的范围之内。

Claims (20)

  1. 一种业务引流的方法，其特征在于，所述方法包括：
    对于一种目标业务，获取使用所述目标业务的用户的历史行为数据；
    分析所述历史行为数据，得到用于限定业务引流的目标用户的用户特征；
    由未使用所述目标业务的用户中，选择符合所述用户特征的用户作为所述目标用户；
    向所述目标用户发送业务引流信息，以引导所述目标用户使用目标业务。
  2. 根据权利要求1所述的方法，其特征在于，所述用户特征，包括：使用所述目标业务的用户所在的地理位置范围；
    所述选择符合所述用户特征的用户作为所述目标用户，包括：将位于同一地理位置范围内的用户，作为所述目标用户。
  3. 根据权利要求1所述的方法，其特征在于，
    所述分析所述历史行为数据，得到用于限定业务引流的目标用户的用户特征，包括：根据所述历史行为数据，得到使用所述目标业务的用户所在的地理位置范围、以及所述目标业务对应的业务机构；根据所述地理位置范围和业务机构，得到所述业务机构的业务覆盖范围中包括的地理位置范围；
    所述选择符合所述用户特征的用户作为所述目标用户，包括：将位于所述业务覆盖范围中包括的地理位置范围内的用户，作为所述目标用户。
  4. 根据权利要求1～3任一所述的方法，其特征在于，所述由未使用所述目标业务的用户中，选择符合所述用户特征的用户作为所述目标用户，包括：
    将未使用所述目标业务且符合所述用户特征的用户作为待选用户，获取所述待选用户的历史行为数据；
    根据所述历史行为数据，选择具有目标业务使用倾向的用户作为目标用户。
  5. 根据权利要求4所述的方法，其特征在于，所述待选用户的历史行为数据，包括：所述待选用户的历史发布信息；
    所述选择具有目标业务使用倾向的用户作为目标用户，包括：
    若所述历史发布信息表明所述待选用户期望使用所述目标业务，则确定所述待选用户为目标用户。
  6. 根据权利要求4所述的方法，其特征在于，所述待选用户的历史行为数据，包括：用户活跃度信息、以及对历史营销的反馈行为信息；
    所述选择具有目标业务使用倾向的用户作为目标用户，包括：
    若所述用户活跃度信息表明待选用户的业务活跃度较高，且所述反馈行为信息表明待选用户对营销反馈积极，则确定所述待选用户为目标用户。
  7. 一种业务引流的方法，其特征在于，所述方法包括：
    对于关联业务组中的任意一种目标业务，确定使用所述目标业务的用户；
    根据所述用户的历史行为数据，得到所述用户对所述关联业务组的业务使用信息；
    若根据所述业务使用信息确定所述用户未使用关联业务，所述关联业务是所述关联业务组中除所述目标业务之外的其他业务，则向所述用户发送业务引流信息，以引导所述用户使用所述关联业务。
  8. 根据权利要求7所述的方法，其特征在于，所述向所述用户发送业务引流信息，包括：
    确定所述用户的特征匹配用户，所述特征匹配用户使用所述关联业务；
    根据所述特征匹配用户的历史行为数据，分析得到所述关联业务对应的业务引流信息，向所述用户发送所述业务引流信息。
  9. 根据权利要求8所述的方法，其特征在于，所述特征匹配用户，是与所述用户位于同一地理位置范围小区的用户。
  10. 根据权利要求8所述的方法，其特征在于，所述特征匹配用户使用的所述关联业务的业务覆盖范围，包括所述用户所在的地理位置范围小区。
  11. 根据权利要求7所述的方法，其特征在于，所述向所述用户发送业务引流信息，包括：根据所述用户的历史行为数据，确定与关联业务相关的业务信息；根据所述业务信息向所述用户发送业务引流信息。
  12. 根据权利要求7所述的方法，其特征在于，在向所述用户发送业务引 流信息之前，还包括：根据所述用户的历史行为数据，确定所述用户具有所述关联业务的使用倾向。
  13. 根据权利要求12所述的方法，其特征在于，所述根据所述用户的历史行为数据，确定所述用户具有所述关联业务的使用倾向，包括：所述用户的历史发布信息表明所述用户期望使用所述关联业务。
  14. 根据权利要求12所述的方法，其特征在于，所述根据所述用户的历史行为数据，确定所述用户具有所述关联业务的使用倾向，包括：
    所述用户活跃度信息表明用户的业务活跃度较高，且所述用户对历史营销的反馈行为信息表明所述用户对营销反馈积极。
  15. 一种业务引流的装置，其特征在于，所述装置包括：
    数据获取模块，用于对于一种目标业务，获取使用所述目标业务的用户的历史行为数据；
    数据分析模块，用于分析所述历史行为数据，得到用于限定业务引流的目标用户的用户特征；
    用户选择模块，用于由未使用所述目标业务的用户中，选择符合所述用户特征的用户作为所述目标用户；
    信息发送模块，用于向所述目标用户发送业务引流信息，以引导所述目标用户使用目标业务。
  16. 根据权利要求15所述的装置，其特征在于，
    所述用户选择模块，具体用于将未使用所述目标业务且符合所述用户特征的用户作为待选用户，获取所述待选用户的历史行为数据；根据所述历史行为数据，选择具有目标业务使用倾向的用户作为目标用户。
  17. 根据权利要求16所述的装置，其特征在于，
    所述用户选择模块，在选择具有目标业务使用倾向的用户作为目标用户时，具体用于根据待选用户的历史发布信息，若所述历史发布信息表明所述待选用户期望使用所述目标业务，则确定所述待选用户为目标用户；或者，根据待选用户的用户活跃度信息以及对历史营销的反馈行为信息，若所述用户活跃度信 息表明待选用户的业务活跃度较高，且所述反馈行为信息表明待选用户对营销反馈积极，则确定所述待选用户为目标用户。
  18. 一种业务引流的装置，其特征在于，所述装置包括：
    用户确定模块，用于对于关联业务组中的任意一种目标业务，确定使用所述目标业务的用户；
    信息获取模块，用于根据所述用户的历史行为数据，得到所述用户对所述关联业务组的业务使用信息；
    引流处理模块，用于若根据所述业务使用信息确定所述用户未使用关联业务，所述关联业务是所述关联业务组中除所述目标业务之外的其他业务，则向所述用户发送业务引流信息，以引导所述用户使用所述关联业务。
  19. 根据权利要求18所述的装置，其特征在于，
    所述引流处理模块，在用于向所述用户发送业务引流信息时，包括：确定所述用户的特征匹配用户，所述特征匹配用户使用所述关联业务；根据所述特征匹配用户的历史行为数据，分析得到所述关联业务对应的业务引流信息，向所述用户发送所述业务引流信息。
  20. 根据权利要求18所述的装置，其特征在于，
    所述信息获取模块，还用于根据所述用户的历史行为数据，确定所述用户具有所述关联业务的使用倾向。
PCT/CN2018/078159 2017-03-09 2018-03-06 一种业务引流的方法和装置 WO2018161898A1 (zh)

Priority Applications (5)

Application Number Priority Date Filing Date Title
EP18764096.6A EP3525420B1 (en) 2017-03-09 2018-03-06 Method and apparatus for guiding service flow
JP2019524372A JP6765526B2 (ja) 2017-03-09 2018-03-06 サービスフローを案内するための方法および装置
KR1020197013403A KR102239590B1 (ko) 2017-03-09 2018-03-06 서비스 흐름을 안내하기 위한 방법 및 장치
US16/562,911 US10915925B2 (en) 2017-03-09 2019-09-06 Method and apparatus for guiding service flow
US16/945,688 US11062353B2 (en) 2017-03-09 2020-07-31 Method and apparatus for service diversion in connection with mobile payment transactions

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201710139131.1 2017-03-09
CN201710139131.1A CN107087017B (zh) 2017-03-09 2017-03-09 一种业务引流的方法和装置

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US16/562,911 Continuation US10915925B2 (en) 2017-03-09 2019-09-06 Method and apparatus for guiding service flow

Publications (1)

Publication Number Publication Date
WO2018161898A1 true WO2018161898A1 (zh) 2018-09-13

Family

ID=59615098

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/078159 WO2018161898A1 (zh) 2017-03-09 2018-03-06 一种业务引流的方法和装置

Country Status (7)

Country Link
US (2) US10915925B2 (zh)
EP (1) EP3525420B1 (zh)
JP (1) JP6765526B2 (zh)
KR (1) KR102239590B1 (zh)
CN (1) CN107087017B (zh)
TW (1) TWI706356B (zh)
WO (1) WO2018161898A1 (zh)

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107087017B (zh) 2017-03-09 2020-02-21 阿里巴巴集团控股有限公司 一种业务引流的方法和装置
CN110390577A (zh) * 2018-04-23 2019-10-29 北京嘀嘀无限科技发展有限公司 订单的分配方法及装置
CN109302377B (zh) * 2018-06-13 2021-01-15 百度在线网络技术（北京）有限公司 一种应用功能实现方法、装置、设备和存储介质
CN109214663B (zh) * 2018-08-20 2023-07-25 中国平安人寿保险股份有限公司 计算业务引流的方法、装置、计算机设备和存储介质
CN112395486B (zh) * 2019-08-12 2023-11-03 中国移动通信集团重庆有限公司 一种宽带业务推荐方法、系统、服务器和存储介质
CN111277868B (zh) * 2020-01-21 2022-01-28 聚好看科技股份有限公司 一种音视频点播服务开通方法及装置
CN111968302B (zh) * 2020-08-28 2022-08-12 支付宝(杭州)信息技术有限公司 缴费提醒方法及装置

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102026151A (zh) * 2009-09-16 2011-04-20 中国移动通信集团公司 一种基于进程监测的服务推送方法、装置和系统
CN104636164A (zh) * 2015-01-29 2015-05-20 小米科技有限责任公司 启动页面生成方法及装置
CN105227445A (zh) * 2015-10-23 2016-01-06 中国联合网络通信集团有限公司 应用推荐方法和应用推荐平台
CN105323322A (zh) * 2015-11-17 2016-02-10 中国联合网络通信集团有限公司 一种信息推送方法及装置
CN106126537A (zh) * 2016-06-14 2016-11-16 中国联合网络通信集团有限公司 一种应用推荐方法及装置
CN107087017A (zh) * 2017-03-09 2017-08-22 阿里巴巴集团控股有限公司 一种业务引流的方法和装置

Family Cites Families (30)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2000038090A1 (en) * 1998-12-21 2000-06-29 International Business Machines Corporation Method for trying to supply a potential customer with at least one selected product offer
US10185936B2 (en) * 2000-06-22 2019-01-22 Jpmorgan Chase Bank, N.A. Method and system for processing internet payments
JP2002334201A (ja) * 2001-01-10 2002-11-22 Supreme System Consulting Corp 営業支援システム及び営業支援方法
TW571209B (en) 2001-04-27 2004-01-11 Net Force Inc Method and system of forwarding system diagnostic message
JP2003281449A (ja) * 2002-03-26 2003-10-03 Nec Engineering Ltd 料金支払い方法及び料金支払いシステム
US7526448B2 (en) * 2002-11-01 2009-04-28 Checkfree Corporation Matching consumers with billers having bills available for electronic presentment
JP2005157690A (ja) * 2003-11-25 2005-06-16 Sony Corp 電子機器装置及び情報提供方法
JP2005208943A (ja) * 2004-01-22 2005-08-04 Denso It Laboratory Inc サービス候補提供システム及びユーザ側通信装置並びにサービス候補提供サーバ
US20060064346A1 (en) * 2004-08-31 2006-03-23 Qualcomm Incorporated Location based service (LBS) system and method for targeted advertising
US20060111960A1 (en) * 2004-11-22 2006-05-25 International Business Machines Corporation Performance prediction service using business-process information
JP2010237305A (ja) * 2009-03-30 2010-10-21 Zenrin Datacom Co Ltd 広告システムおよび方法
US20110112981A1 (en) * 2009-11-09 2011-05-12 Seung-Taek Park Feature-Based Method and System for Cold-Start Recommendation of Online Ads
CN102163205A (zh) * 2010-02-21 2011-08-24 施章祖 一种类似客户群体的自动挖掘系统
JP5408570B2 (ja) * 2010-06-25 2014-02-05 株式会社日立製作所 属性情報更新方法及び情報更新方法
CN102881239A (zh) * 2011-07-15 2013-01-16 鼎亿数码科技(上海)有限公司 基于图像识别的广告投播系统及方法
US9092801B2 (en) * 2011-09-23 2015-07-28 24/7 Customer, Inc. Customer journey prediction and resolution
US8665743B2 (en) * 2011-10-28 2014-03-04 Cisco Technology, Inc. User behavior model and statistical transition map to assist advanced WLAN applications
CN103150696A (zh) * 2011-12-06 2013-06-12 中兴通讯股份有限公司 选择目标增值业务潜在客户的方法及装置
TWI526963B (zh) * 2012-11-13 2016-03-21 財團法人資訊工業策進會 目標客戶搜尋方法、目標客戶搜尋裝置及其記錄媒體
US20140172573A1 (en) * 2012-12-05 2014-06-19 The Rubicon Project, Inc. System and method for planning and allocating location-based advertising
JP6246657B2 (ja) 2013-05-20 2017-12-13 パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカＰａｎａｓｏｎｉｃ Ｉｎｔｅｌｌｅｃｔｕａｌ Ｐｒｏｐｅｒｔｙ Ｃｏｒｐｏｒａｔｉｏｎ ｏｆ Ａｍｅｒｉｃａ 情報提供方法、情報提供装置
US10546326B2 (en) * 2013-09-26 2020-01-28 Mark W. Publicover Providing targeted content based on a user's preferences
US9084082B2 (en) * 2013-11-18 2015-07-14 Aol Inc. Systems and methods for optimizing message notification timing based on geographic location
CN105338480B (zh) * 2014-06-24 2020-01-24 创新先进技术有限公司 基于lbs的用户匹配方法、消息客户端、服务器及系统
US20160034968A1 (en) 2014-07-31 2016-02-04 Huawei Technologies Co., Ltd. Method and device for determining target user, and network server
CN104239571B (zh) * 2014-09-30 2018-04-24 北京奇虎科技有限公司 一种进行应用推荐的方法和装置
CN105989004B (zh) * 2015-01-27 2020-04-14 阿里巴巴集团控股有限公司 一种信息投放的预处理方法和装置
KR101680445B1 (ko) * 2015-02-05 2016-11-28 주식회사 디워프 지역 공동체 네트워킹 시스템 및 그 방법
EP3073421A1 (en) 2015-03-25 2016-09-28 Facebook, Inc. Techniques for automated determination of form responses
CN105246101B (zh) * 2015-09-01 2019-01-18 厦门大学 一种面向次等移动内容分发系统的内容推荐装置及其方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102026151A (zh) * 2009-09-16 2011-04-20 中国移动通信集团公司 一种基于进程监测的服务推送方法、装置和系统
CN104636164A (zh) * 2015-01-29 2015-05-20 小米科技有限责任公司 启动页面生成方法及装置
CN105227445A (zh) * 2015-10-23 2016-01-06 中国联合网络通信集团有限公司 应用推荐方法和应用推荐平台
CN105323322A (zh) * 2015-11-17 2016-02-10 中国联合网络通信集团有限公司 一种信息推送方法及装置
CN106126537A (zh) * 2016-06-14 2016-11-16 中国联合网络通信集团有限公司 一种应用推荐方法及装置
CN107087017A (zh) * 2017-03-09 2017-08-22 阿里巴巴集团控股有限公司 一种业务引流的方法和装置

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3525420A4 *

Also Published As

Publication number Publication date
US20200364748A1 (en) 2020-11-19
US11062353B2 (en) 2021-07-13
KR102239590B1 (ko) 2021-04-15
KR20190060841A (ko) 2019-06-03
JP2020504361A (ja) 2020-02-06
US10915925B2 (en) 2021-02-09
EP3525420A4 (en) 2019-11-06
TWI706356B (zh) 2020-10-01
TW201833839A (zh) 2018-09-16
EP3525420B1 (en) 2022-06-08
CN107087017B (zh) 2020-02-21
US20190392480A1 (en) 2019-12-26
JP6765526B2 (ja) 2020-10-07
CN107087017A (zh) 2017-08-22
EP3525420A1 (en) 2019-08-14

Similar Documents

Publication Publication Date Title
WO2018161898A1 (zh) 一种业务引流的方法和装置
CN103327063B (zh) 用户存在检测和事件发现
Dillon Using mobile phones to collect panel data in developing countries
Cvrcek et al. A study on the value of location privacy
Gupta et al. mClerk: enabling mobile crowdsourcing in developing regions
US8259915B2 (en) System and method to analyze calls to advertised telephone numbers
CN107038620B (zh) 基于用户打车偏好的信息推送及装置
CN102137155A (zh) 一种基于客户感知的通信网络质量投诉处理方法
Helderop et al. Data deluge or data trickle? Difficulties in acquiring public data for telecommunications policy analysis
TW201732727A (zh) 自動繳費的方法和裝置
US8433048B2 (en) System and method to direct telephone calls to advertisers
US20140188716A1 (en) Automated first party debt collection system
US20120002664A1 (en) System and method for calling advertised telephone numbers on a computing device
Huang et al. Pinning down abuse on google maps
KR101475353B1 (ko) 전화 마케팅과 모바일 페이지 마케팅을 모두 지원하는 제휴마케팅서비스 시스템
CN113256330A (zh) 信息投放效果归因方法和装置
US11195235B2 (en) Location-based tracking system
US20140279378A1 (en) Model performance simulator
Sultan et al. Mobile phone price as a proxy for socio-economic indicators
US20230298074A1 (en) Analysis and recommendation systems, and related methods and computer-readable media
JP5264806B2 (ja) 課金装置、課金方法及びプログラム
Nguyen Impacts of Information and Communication Technologies infrastructure development on economic growth: An empirical study of Southeast Asian countries
CN113822586B (zh) 一种任务悬赏方法及系统
Tilabov PROSPECTS FOR THE USE OF DIGITAL TECHNOLOGIES IN SECURING TAX REVENUES
KR20140146686A (ko) 질의응답 형식의 고객 맞춤형 광고 중개 서비스 제공 방법

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18764096

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 20197013403

Country of ref document: KR

Kind code of ref document: A

Ref document number: 2019524372

Country of ref document: JP

Kind code of ref document: A

ENP Entry into the national phase

Ref document number: 2018764096

Country of ref document: EP

Effective date: 20190509

NENP Non-entry into the national phase

Ref country code: DE