WO2018040997A1 - 漏斗模型的节点的评估系统、方法和装置 - Google Patents

漏斗模型的节点的评估系统、方法和装置 Download PDF

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Publication number
WO2018040997A1
WO2018040997A1 PCT/CN2017/098752 CN2017098752W WO2018040997A1 WO 2018040997 A1 WO2018040997 A1 WO 2018040997A1 CN 2017098752 W CN2017098752 W CN 2017098752W WO 2018040997 A1 WO2018040997 A1 WO 2018040997A1
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browsing
node
path
users
page
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English (en)
French (fr)
Inventor
胡于响
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Alibaba Group Holding Ltd
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Alibaba Group Holding Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/954—Navigation, e.g. using categorised browsing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
    • G06F16/9566—URL specific, e.g. using aliases, detecting broken or misspelled links
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/957—Browsing optimisation, e.g. caching or content distillation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking

Definitions

  • the present application relates to computer technology, and in particular, to an evaluation system, method and apparatus for a node of a funnel model.
  • each access path can be a funnel model.
  • a funnel model usually contains at least two nodes. Each node usually corresponds to one page. It is often necessary to evaluate each node of the funnel model, and then a funnel model is acquired. Parameters of each node, such as the number of page views (pv) and the number of users (unique visitor: uv), etc., to analyze the rationality of each node setting in the funnel model according to these parameters. Or set the value of the value and other issues.
  • a page is usually advertised on a page corresponding to each node to collect a browsing record corresponding to each browsing behavior of the page, and a browsing history of the page is generated by the user, and each browsing record is generated. It usually includes but is not limited to the following information: 1. cookie_id: user identifier, used to distinguish different browsing users; 2, page_id: the number of the current page, the page number is refreshed once every time the page is refreshed; 3. refer_page_id: The number of the guide page of the current page, that is, the number of the page that the user browsed before browsing the current page; 4. url: the URL of the page that is browsed.
  • the parameters of each node of the funnel model are sequentially obtained according to the order of the nodes of the funnel model, specifically:
  • the parameters of the first node specifically, obtain the first set of all browsing records whose browsing url is the same as the url of the first node; the number of browsing records in the first set is the pv of the first node The number of different cookie_ids in the browsing record in the first set is the number of uvs of the first node.
  • the parameters of the second node specifically, obtaining the second set of all browsing records with the same url of the browsing history as the url of the second node, and obtaining the browsing of the second set of the refer_page_id and the browsing in the first set
  • the parameter of the third node specifically, obtaining the fourth set of all browsing records whose browsing url is the same as the url of the third node, and obtaining the browsing in the third set of browsing records in the refer_page_id and the third set Recording the fifth set of browsing records with the same page_id; the number of browsing records in the fifth set is the number of pv times of the third node, and the number of different cookie_ids in the browsing record in the fifth set is the number of uvs of the third node .
  • the present application provides an evaluation system, method and apparatus for a node of a funnel model to solve the problem of low evaluation efficiency of nodes of the funnel model in the prior art.
  • the present application provides an evaluation system for a node of a funnel model, including:
  • a client an offline computing node, and a real-time computing node, wherein the client is connected to the real-time computing node, configured to define information of a funnel model, and send information of the funnel model to the real-time computing node;
  • the offline computing node is configured to acquire and store browsing paths of all users
  • the real-time computing node is connected to the offline computing node and the client, and configured to receive information about the funnel model sent by the client, according to the information of the funnel model and all users stored in the offline computing node.
  • the browsing path evaluates each node of the funnel model.
  • the offline computing node is specifically configured to acquire, for each user, a browsing record of the same user according to the user identifier in the browsing record, where the browsing record includes: the user identifier, a number of the current page. And the identifier of the current page and the identifier of the page; the order of the browsing page number of the same user is obtained according to the number of the current page in the browsing record of the same user and the number of the guiding page of the current page Obtaining a browsing path of the same user according to the identifier of the page and the node correspondence, and the order of browsing the page number of the user.
  • the offline computing node is specifically configured to be used for the browsing record of the same user. If the number of the current page in the first browsing record is the same as the number of the guiding page of the current page in the second browsing record, determining the first browsing record. The order of the number of the current page in the middle is before the number of the current page in the second browsing record.
  • the real-time computing node is specifically configured to acquire a screening path corresponding to the target node of the funnel model, where the target node is any node in the funnel model, and the filtering path corresponding to the target node is Describe the path from the first node of the funnel model to the target node; according to the first of the browsing paths of all users The number of paths is browsed, and the target node corresponding to the screening path is evaluated, wherein the first browsing path refers to a browsing path that includes the screening path.
  • the real-time computing node is specifically configured to determine, according to the number of different users corresponding to the first browsing path in the browsing path of all the users, the number of user uvs of the target node corresponding to the screening path; The number of the first browsing path in the browsing path of the user determines the number of browsing pages pv of the target node corresponding to the filtering path.
  • the real-time computing node is further configured to feed back the real-time evaluation result of the funnel model to the client.
  • the application provides a method for obtaining a user browsing path, including:
  • the browsing record includes: a user identifier, a current page number, a guide page number of the current page, and an identifier of the page;
  • the obtaining according to the number of the current page in the browsing record of the same user, and the number of the guiding page of the current page, the order of the browsing page number of the same user, including:
  • the number of the current page in the first browsing record is the same as the number of the leading page of the current page in the second browsing record, determining that the order of the number of the current page in the first browsing record is in the current page in the second browsing record Before the number.
  • the method includes:
  • the parameter of the target node corresponding to the screening path is determined according to the number of the first browsing paths included in the browsing path of all users, where the first browsing path refers to a browsing path that includes the filtering path.
  • the determining, according to the number of the first browsing paths included in the browsing path of all the users, the parameters of the target node corresponding to the filtering path including:
  • it also includes:
  • it also includes:
  • the conversion rate of the target node it is evaluated whether the target node setting is reasonable.
  • the method before the obtaining the filtering path corresponding to the target node of the funnel model, the method further includes:
  • the present application provides a parameter acquisition method for a node of a funnel model, including:
  • the number of user uvs and the number of browsing pages pv of each node of the funnel model are obtained.
  • the number of user uvs and the number of browsing pages pv of each node of the funnel model are obtained according to the browsing path of all the users, including:
  • the acquiring and offline storing browsing paths of all users includes:
  • the present application provides a method for obtaining a conversion rate of a node of a funnel model, including:
  • the number of user uvs and the number of browsing pages pv of each node of the funnel model are obtained.
  • the conversion rate of each node of the funnel model is determined according to the number of user uvs of each node and the number of browsing pages pv.
  • the present application provides a method for evaluating a node of a funnel model, including:
  • the application provides a browsing path obtaining device for a user, including:
  • a processing module configured to acquire browsing records of all users, where the browsing record includes: a user identifier, a current page number, a boot page number of the current page, and an identifier of the page;
  • the processing module is further configured to acquire, for each user, a browsing record of the same user according to the user identifier in the browsing record;
  • the processing module is further configured to obtain, according to the number of the current page in the browsing record of the same user and the number of the guiding page of the current page, the order of browsing the page number of the same user;
  • the processing module is further configured to acquire, according to the identifier of the page, the node correspondence, and the order of browsing the page number of the user, the browsing path of the same user;
  • a storage module configured to offline store the browsing path of the same user.
  • the present application provides a parameter acquisition device for a node of a funnel model, including:
  • a processing module configured to obtain a browsing path of all users
  • a storage module configured to offline store the browsing path of all the users
  • the processing module is further configured to obtain the number of user uvs and the number of browsing pages pv of each node of the funnel model according to the browsing path of all the users.
  • the present application provides a conversion rate obtaining device for a node of a funnel model, including:
  • a processing module configured to obtain a browsing path of all users
  • a storage module configured to offline store the browsing path of all the users
  • the processing module is further configured to obtain the number of user uvs and the number of browsing pages pv of each node of the funnel model according to the browsing path of all the users.
  • the processing module is further configured to determine a conversion rate of each node of the funnel model according to the number of users uv of each node and the number of times of browsing the page pv.
  • the present application provides an apparatus for evaluating a node of a funnel model, including:
  • a processing module configured to obtain a browsing path of all users
  • a storage module configured to offline store the browsing path of all the users
  • the processing module is further configured to obtain a transformation of each node of the funnel model according to the browsing path of all the users. rate;
  • the processing module is further configured to: according to the conversion rate of each node, whether the setting of each node of the funnel model is reasonable.
  • the application provides a browsing path obtaining device for a user, including:
  • a processor configured to acquire browsing records of all users, where the browsing record includes: a user identifier, a current page number, a boot page number of the current page, and an identifier of the page;
  • the processor is further configured to acquire, for each user, a browsing record of the same user according to the user identifier in the browsing record;
  • the processor is further configured to obtain an order of browsing page numbers of the same user according to the number of the current page in the browsing record of the same user and the number of the guiding page of the current page;
  • the processor is further configured to acquire, according to the identifier of the page, the node correspondence, and the order of browsing the page number of the user, the browsing path of the same user;
  • a memory coupled to the processor for offline storage of the browsing path of the same user.
  • the processor is specifically configured to: if the number of the current page in the first browsing record is the same as the number of the guiding page of the current page in the second browsing record, determine the order of the number of the current page in the first browsing record. Before the number of the current page in the second browsing record.
  • the processor is specifically configured to acquire a screening path corresponding to the target node of the funnel model, where the target node is any node in the funnel model, and the screening path corresponding to the target node is a path of the first node of the funnel model to the target node; determining a parameter of the target node corresponding to the screening path according to the number of the first browsing paths included in the browsing path of all users, wherein the first browsing A path refers to a browsing path that includes the filtering path.
  • the processor is further configured to determine, according to the number of different users corresponding to the first browsing path of the browsing paths of all the users, the number of user uvs of the target node corresponding to the screening path; The number of the first browsing path in the path determines the number of browsing pages pv of the target node corresponding to the filtering path.
  • the processor is further configured to determine, according to the number of users uv of the target node corresponding to the screening path and the number of browsing pages pv of the target node corresponding to the screening path, the conversion rate of the target node.
  • the processor is further configured to evaluate whether the target node setting is reasonable according to the conversion rate of the target node.
  • it also includes:
  • a receiving interface configured to receive information about a funnel model sent by the client, according to the information of the funnel model The funnel model is built.
  • the present application provides a parameter acquisition device for a node of a funnel model, including:
  • a memory coupled to the processor for offline storage of browsing paths of all of the users
  • the processor is further configured to obtain, according to the browsing path of all the users, the number of user uvs and the number of browsing pages pv of each node of the funnel model.
  • the processor is specifically configured to acquire a screening path corresponding to the target node of the funnel model, where the target node is any node in the funnel model, and the screening path corresponding to the target node is a path from the first node of the funnel model to the target node; determining the number of user uvs of the target node corresponding to the screening path according to the number of different users corresponding to the first browsing path in the browsing path of all users; The number of the first browsing paths in the browsing path of all the users determines the number of browsing pages pv of the target node corresponding to the filtering path.
  • the present application provides a conversion rate obtaining device for a node of a funnel model, including:
  • a memory coupled to the processor for offline storage of browsing paths of all of the users
  • the processor is further configured to obtain, according to the browsing path of all the users, the number of user uvs and the number of browsing pages pv of each node of the funnel model.
  • the processor is further configured to determine a conversion rate of each node of the funnel model according to the number of users uv of each node and the number of times of browsing the page pv.
  • the present application provides an apparatus for evaluating a node of a funnel model, including:
  • the processor is further configured to obtain a conversion rate of each node of the funnel model according to the browsing path of all the users;
  • the processor is further configured to: according to the conversion rate of each node, whether the setting of each node of the funnel model is reasonable.
  • the system, method and device for evaluating a node of a funnel model provided by the application, the system comprises: a client, an offline computing node and a real-time computing node, wherein the client is connected with a real-time computing node, the real-time computing node and the offline computing node and the client
  • the offline computing node is used to acquire and offline store the browsing path of all users
  • the client defines the information of the funnel model and sends the information of the funnel model to the real-time computing node
  • the real-time computing node receives the information of the funnel model sent by the client, According to the information of the funnel model and all the uses stored in the offline computing node
  • the browsing path of the user evaluates each node of the funnel model.
  • the screening path of each node of the funnel model can be filtered in parallel.
  • the parameters of each node are determined, and the operation of the screening operation is simple and the operation efficiency is high, thereby improving the evaluation efficiency of the nodes of the funnel model.
  • Figure 1 is a schematic view of a funnel model of the present application
  • FIG. 2 is a schematic diagram of a node definition of the present application
  • FIG. 3 is a schematic diagram of a screening path corresponding to node 2 of FIG. 1;
  • FIG. 4 is a schematic diagram of a screening path corresponding to node 3 of FIG. 1;
  • FIG. 5 is a schematic diagram of a screening path corresponding to node 4 of FIG. 1;
  • FIG. 6 is a schematic diagram of a screening path corresponding to node 5 of FIG. 1;
  • Figure 7 is a schematic diagram of a browsing path of the present application.
  • FIG. 8 is a schematic structural diagram of an embodiment of an evaluation system for a node of a funnel model of the present application
  • FIG. 9 is a schematic flowchart of an embodiment of a method for acquiring a browsing path of a user according to the present application.
  • FIG. 10 is a schematic flowchart of an embodiment of a method for acquiring a parameter of a node of a funnel model of the present application
  • FIG. 11 is a schematic flowchart of an embodiment of a method for obtaining a conversion rate of a node of a funnel model of the present application
  • FIG. 12 is a schematic flow chart of an embodiment of a method for evaluating a node of a funnel model of the present application
  • FIG. 13 is a schematic structural diagram of Embodiment 1 of an apparatus for acquiring a browsing path of a user according to the present application;
  • Embodiment 14 is a schematic structural diagram of Embodiment 1 of a node obtaining device of a node of a funnel model of the present application;
  • Embodiment 15 is a schematic structural diagram of Embodiment 1 of a conversion rate obtaining apparatus of a node of a funnel model of the present application;
  • Embodiment 16 is a schematic structural diagram of Embodiment 1 of an apparatus for evaluating a node of a funnel model of the present application;
  • Embodiment 17 is a schematic structural diagram of Embodiment 2 of an apparatus for acquiring a browsing path of a user according to the present application;
  • Embodiment 18 is a schematic structural diagram of Embodiment 2 of a parameter obtaining apparatus of a node of a funnel model of the present application;
  • Embodiment 19 is a schematic structural diagram of Embodiment 2 of a conversion rate obtaining device of a node of a funnel model of the present application;
  • FIG. 20 is a schematic structural diagram of Embodiment 2 of an evaluation apparatus for a node of a funnel model of the present application.
  • FIG. 1 is a schematic diagram of a funnel model of the present application, and the funnel model of FIG. 1 includes five nodes, each of which The page and definition corresponding to the node are as follows:
  • Node 1 The corresponding page is "Homepage"
  • Node 2 The corresponding page is "Product List Page"
  • Node 3 The corresponding page is "Product Details Page"
  • Node 5 The corresponding page is "Product Payment Page"
  • the browsing order of each node defined in the funnel model is node 1, node 2, node 3, node 4 and node 5 in order.
  • the funnel model can be constructed by defining individual nodes, as described above, according to node 1, node 2, The definition of node 3, node 4 and node 5, the funnel model can be constructed as shown in Fig. 1.
  • the funnel model can be constructed by defining each node, thereby obtaining the parameters of the funnel model.
  • FIG. 2 is a schematic diagram of a node definition of the present application; wherein node naming is used to distinguish different Node, node name: for example: node 1, node 2, node 3, node 4, node 5, etc.; node field binding is used to specify which field or fields in the browsing record are bound to the node, such as "url"; The field operator selects an operator for the field bound to the node.
  • Table 1 shows that the user has created a node 1, and defines that node 1 is all the web pages in the browsing record whose "url" field contains “detail”.
  • a node of the funnel model is referred to as a target node
  • a corresponding screening path of the target node of the funnel model refers to the The path from a node to the target node is combined with FIG. 1.
  • the corresponding filtering path of node 2 is as shown in FIG. 3, and FIG. 3 is a schematic diagram of the filtering path corresponding to node 2 of FIG. 1, including node 1 and node. 2; the corresponding filtering path of node 3 is shown in FIG. 4, FIG. 4 is a schematic diagram of the filtering path corresponding to node 3 of FIG.
  • FIG. 5 is a schematic diagram of a screening path corresponding to node 4 of FIG. 1, including: node 1, node 2, node 3, and node 4; the filtering path corresponding to node 5 is as shown in FIG. 6, and FIG. 6 is node 5 of FIG.
  • FIG. 7 is A schematic diagram of a browsing path of the application.
  • the browsing path of FIG. 7 includes the filtering path of node 2, and also includes the filtering path of node 3, but does not include the filtering path of node 4 and node 5.
  • the system includes: a client, an offline computing node, and a real-time computing node, wherein the client is connected to a real-time computing node for defining information of the funnel model. And sending the information of the funnel model to the real-time computing node; the offline computing node is configured to acquire and store a browsing path of all users; the real-time computing node is connected to the offline computing node and the client, and is configured to receive a client
  • the information of the funnel model sent by the end evaluates each node of the funnel model according to the information of the funnel model and the browsing path of all users stored in the offline computing node.
  • the offline computing node is mainly used to obtain and store the browsing path of all users, specifically: for each user, obtaining a browsing record of the same user according to the user identifier in the browsing record, wherein the browsing record is in the browsing record.
  • the user identifier, the number of the current page, the guide page number of the current page, and the identifier of the page; the number of the current page and the number of the guide page of the current page according to the browsing record of the same user, Acquiring the order of the browsing page number of the same user; obtaining the browsing path of the same user according to the identifier of the page and the node correspondence, and the order of the browsing page number of the user.
  • the offline computing node obtains the order of the browsing page number of the same user according to the number of the current page in the browsing record of the same user and the number of the guiding page of the current page, specifically, browsing for the same user. Recording, if the number of the current page in the first browsing record is the same as the number of the leading page of the current page in the second browsing record, determining the order of the number of the current page in the first browsing record in the second browsing record Before the number of the current page.
  • the real-time computing node is specifically configured to acquire a screening path corresponding to the target node of the funnel model, where the target node is any node in the funnel model, and the filtering path corresponding to the target node is the a path from the first node of the funnel model to the target node; the target node corresponding to the screening path is evaluated according to the number of the first browsing paths included in the browsing path of all the users, wherein the first browsing A path refers to a browsing path that includes the filtering path.
  • the real-time computing node is configured to: according to the number of the first browsing path included in the browsing path of all the users, the target node corresponding to the filtering path, specifically: according to the first browsing path in the browsing path of all users Determining, according to the number of different users, the number of users uv of the target node corresponding to the screening path; determining the browsing page of the target node corresponding to the screening path according to the number of the first browsing paths in the browsing path of all the users frequency.
  • the evaluation system of the node of the funnel model sets a client, an offline computing node and a real-time computing node, acquires and offline stores the browsing paths of all users through the offline computing node, and receives the funnel model sent by the client through the real-time computing node.
  • After the information, according to the information of the funnel model and the stored in the offline computing node There are user browsing paths, and each node of the funnel model is evaluated. Since the browsing paths of all users are obtained and stored offline, and the parameters of each node of the funnel model are calculated online, the nodes of the same funnel model can be screened in parallel.
  • the screening path, and the screening path of each node of the different funnel models in parallel that is, when obtaining the parameters of each node of each funnel model, the browsing paths of all users stored offline can be reused, and the parameters of each node are determined. Moreover, the operation of the screening operation is simple and the operation efficiency is high, thereby improving the evaluation efficiency of the nodes of the funnel model.
  • the real-time computing node is further configured to feed back the real-time evaluation result of the funnel model to the client.
  • FIG. 9 is a schematic flowchart of an embodiment of a method for obtaining a user browsing path according to the present application.
  • the present embodiment is as shown in FIG. 9 :
  • the browsing record includes: a user identifier, a current page number, a guide page number of the current page, and an identifier of the page;
  • the user identifier may be a cookie_id, the current page number is page_id, the current page's guide page number is refer_page_id, and the page identifier is url.
  • S902 For each user, obtain a browsing record of the same user according to the user identifier in the browsing record.
  • the browsing history with the same user ID is the browsing history of the same user.
  • S903 Obtain an order of browsing page numbers of the same user according to the number of the current page in the browsing record of the same user and the number of the guiding page of the current page.
  • the number of the current page in the first browsing record is the same as the number of the leading page of the current page in the second browsing record, determining the number of the current page in the first browsing record in the order of the current page in the second browsing record prior to.
  • S904 Acquire and offline store the browsing path of the same user according to the identifier of the page and the node correspondence, and the order of browsing the page number of the user.
  • the identifier of the page is “home” corresponding to node 1; the identifier of the page is “list” corresponding to node 2, and the identifier of the page is “detail” corresponding to the node. 3.
  • the identifier of the page is “pay” corresponding to node 5.
  • the browsing path of user A is node 1 -> node 2 -> node 2;
  • N browsing records of the same user are obtained, and the browsing page of the user is obtained according to the number of the current page in the browsing record and the number of the guiding page of the current page.
  • the order of the number is obtained according to the identifier of the page and the correspondence between the nodes, and the order of the browsing page number of the user, so as to obtain the browsing path of each user.
  • the browsing path of the acquiring user described in FIG. 9 can be obtained offline, and the obtained result is stored offline, thereby reducing the online calculation, and can be processed in parallel not only when evaluating different nodes of the same funnel model; When evaluating nodes of different funnel models, they can also be processed in parallel.
  • FIG. 10 is a schematic flowchart of a method for obtaining a parameter of a node of a funnel model according to the present application. As shown in FIG. 10, the method in this embodiment is as follows:
  • S1001 Acquire and store the browsing path of all users offline.
  • a possible implementation manner is: collecting a browsing record corresponding to each browsing behavior of the page by burying each webpage, and the user may generate a browsing record for a browsing behavior of one page, according to the browsing history. Get the user's browsing path.
  • Another possible implementation is to directly track and offline the user's browsing path.
  • a further possible implementation manner is: obtaining a browsing path of the user according to the access log of the user.
  • This application does not limit the manner in which the user's browsing path is obtained.
  • S1002 Acquire a screening path corresponding to the target node of the funnel model.
  • the target node is any node in the funnel model, and the screening path corresponding to the target node is a path from the first node of the funnel model to the target node.
  • each node is shown in FIG. 2-6.
  • S1003 Determine parameters of the target node corresponding to the screening path according to the number of the first browsing paths included in the browsing path of all users.
  • the first browsing path refers to a browsing path that includes a filtering path. More specifically, it means that all of the screening paths are included, or the screening path is a part of the browsing path.
  • the browsing path of the node 2 includes the filtering path of the node 2
  • the browsing paths of the user A and the user B respectively include the filtering path of the node 3
  • the browsing paths of the user A and the user B do not include the filtering path of the node 4
  • the browse path does not contain the filter path of node 5.
  • the parameter can be the number of user uvs and the number of times the page is browsed.
  • the number of user uvs and the number of browsing pages pv of each node of the funnel model are obtained.
  • the number of users uv of the target node corresponding to the screening path is determined according to the number of different users corresponding to the first browsing path of the browsing paths of all the users; and the number of the first browsing paths according to the browsing path of all the users And determining a number of browsing pages pv of the target node corresponding to the filtering path.
  • the method further includes: the real-time computing node receives the information of the funnel model sent by the client, and constructs the funnel model according to the information of the funnel model.
  • the screening path corresponding to the target node of the funnel model is obtained by acquiring and offline browsing the browsing paths of all the users, and determining the target corresponding to the filtering path according to the number of the first browsing paths included in the browsing path of all users.
  • the parameters of the node because the different nodes for the funnel model, screen out the first browsing path from all browsing paths, can be performed in parallel, and the filtering operation is simple and the operation efficiency is high. Therefore, the nodes of the acquisition funnel model are improved. The efficiency of the parameters.
  • FIG. 11 is a schematic flowchart of an embodiment of a method for obtaining a conversion rate of a node in a funnel model of the present application. As shown in FIG. 11, the method in this embodiment is as follows:
  • S1101 Acquire and offline store the browsing path of all users.
  • S1102 Obtain the number of user uvs and the number of browsing pages pv of each node of the funnel model according to the browsing path of all the users.
  • S1103 Determine a conversion rate of each node of the funnel model according to the number of user uvs of each node and the number of browsing pages pv.
  • the conversion rate includes the uv conversion rate and the pv conversion rate
  • the uv conversion rate is the ratio of the uv value of the current node to the uv value of the previous node
  • the pv conversion rate is the pv value of the current node and the pv value of the previous node. Conversion rates.
  • the number of users uv and the number of browsing pages pv of each node of the funnel model are obtained according to the browsing path of all users by acquiring and offline browsing the browsing paths of all users, according to the number of user uvs of each node.
  • the number of pvs of the page is determined, and the conversion rate of each node of the funnel model is determined.
  • the path screening method is obtained, and each of the nodes can be obtained in parallel.
  • the number of user uvs of the node and the number of pv pages are browsed, and the operation of the filtering operation is simple and the operation efficiency is high. Therefore, the efficiency of acquiring the number of users uv of each node and the number of times of browsing the page pv can be improved, thereby improving the nodes of the acquisition funnel model. The efficiency of the conversion rate.
  • FIG. 12 is a schematic flowchart diagram of an embodiment of a method for evaluating a node of a funnel model according to the present application. As shown in FIG. 12, the method in this embodiment is as follows:
  • S1201 Acquire and offline store the browsing path of all users.
  • S1202 Obtain a conversion rate of each node of the funnel model according to the browsing path of all users.
  • the browsing path of all users is obtained and stored offline, and the conversion rate of each node of the funnel model is obtained according to the browsing path of all users, and according to the conversion rate of each node, whether the setting of each node of the funnel model is reasonable is determined, according to the user.
  • the browsing path of the funnel model obtains a high conversion rate of each node, so that the efficiency of each node of the evaluation funnel model can be improved.
  • the application adopts a Spark computing framework, which has strong memory operation capability, thereby further improving computational efficiency.
  • FIG. 13 is a schematic structural diagram of Embodiment 1 of a device for acquiring a browsing path of a user in the present application.
  • the device in this embodiment includes: a processing module 1301 and a storage module 1302, where the processing module 1301 is configured to acquire browsing records of all users, and browse records.
  • the method includes: a user identifier, a current page number, a guide page number of the current page, and an identifier of the page.
  • the processing module 1301 is further configured to acquire, for each user, a browsing record of the same user according to the user identifier in the browsing record; the processing module 1301 And the step of obtaining the browsing page number of the same user according to the number of the current page in the browsing record of the same user and the number of the guiding page of the current page; the processing module 1301 is further configured to: according to the identifier of the page and the correspondence between the nodes, and The user browses the page number in the order of obtaining the browsing path of the same user; the storage module 1302 is configured to store the browsing path of the same user offline.
  • the device in this embodiment is applicable to the technical solution of the method embodiment shown in FIG. 9.
  • the implementation principle and technical effects are similar, and details are not described herein again.
  • FIG. 14 is a schematic structural diagram of Embodiment 1 of a node obtaining device of a funnel model of the present application.
  • the device in this embodiment includes a processing module 1401 and a storage module 1402, where the processing module 1401 is configured to acquire browsing paths of all users;
  • the 1402 is configured to store the browsing path of all users offline.
  • the processing module 1401 is further configured to obtain the number of user uvs and the number of browsing pages pv of each node of the funnel model according to the browsing path of all users.
  • the device in this embodiment is applicable to the technical solution of the method embodiment shown in FIG. 10, and the implementation principle and the technical effect are similar, and details are not described herein again.
  • the apparatus of this embodiment includes: a processing module 1501 and a storage module 1502, wherein the processing module 1501 is configured to acquire browsing paths of all users;
  • the storage module 1502 is configured to store the browsing path of all users offline.
  • the processing module 1501 is further configured to acquire the number of user uvs and the number of browsing pages pv of each node of the funnel model according to the browsing path of all users.
  • the processing module 1501 is further configured to determine a conversion rate of each node of the funnel model according to the number of users uv of each node and the number of times of browsing the page pv.
  • the device in this embodiment is applicable to the technical solution of the method embodiment shown in FIG. 11.
  • the implementation principle and technical effects are similar, and details are not described herein again.
  • the apparatus of this embodiment includes: a processing module 1601 and a storage module 1602, wherein the processing module 1601 is configured to acquire browsing paths of all users; 1602 is used to store the browsing path of all users offline; the processing module 1601 is further configured to obtain the conversion rate of each node of the funnel model according to the browsing path of all users; the processing module 1601 is further configured to evaluate the funnel model according to the conversion rate of each node. Whether the node settings are reasonable.
  • the device in this embodiment is applicable to the technical solution of the method embodiment shown in FIG. 12, and the implementation principle and the technical effect are similar, and details are not described herein again.
  • FIG. 17 is a schematic structural diagram of Embodiment 2 of a device for acquiring a browsing path of a user according to the present application.
  • the device in this embodiment includes: a processor 1701 and a memory 1702, where the processor 1701 is configured to acquire browsing records of all users, where the browsing record includes : user ID, the number of the current page, the guide page number of the current page, and the identifier of the page;
  • the processor 1701 is further configured to: for each user, obtain a browsing record of the same user according to the user identifier in the browsing record; the processor 1701 is further configured to use the current page number in the browsing record of the same user and the guiding page of the current page.
  • the number of the browsing page number of the same user is obtained; the processor 1701 is further configured to acquire the browsing path of the same user according to the identifier of the page and the node correspondence, and the order of the browsing page number of the user; the memory 1702 is coupled to the processing. For offline storage of the same user's browsing path.
  • the device in this embodiment is applicable to the technical solution of the method embodiment shown in FIG. 9.
  • the implementation principle and technical effects are similar, and details are not described herein again.
  • FIG. 18 is a schematic structural diagram of Embodiment 2 of a parameter obtaining apparatus of a node of a funnel model of the present application.
  • the apparatus of this embodiment includes a processor 1801 and a memory 1802, wherein the processor 1801 is configured to acquire browsing paths of all users; and the memory 1802 is configured.
  • the processor is coupled to the processor for offline storage of browsing paths of all users; the processor 1801 is further configured to obtain the number of user uvs and the number of browsing pages pv of each node of the funnel model according to the browsing path of all users.
  • the device in this embodiment is applicable to the technical solution of the method embodiment shown in FIG. 10, and the implementation principle and the technical effect are similar, and details are not described herein again.
  • FIG. 19 is a schematic structural diagram of Embodiment 2 of a conversion rate obtaining apparatus of a node of a funnel model of the present application.
  • the apparatus of this embodiment includes a processor 1901 and a memory 1902, wherein the processor 1901 is configured to acquire browsing paths of all users; and the memory 1902 The memory is coupled to the processor for offline storage of browsing paths of all users; the processor 1901 is further configured to obtain the number of user uvs and the number of browsing pages pv of each node of the funnel model according to the browsing path of all users.
  • the processor 1901 is further configured to determine a conversion rate of each node of the funnel model according to the number of users uv of each node and the number of times of browsing the page pv.
  • the device in this embodiment is applicable to the technical solution of the method embodiment shown in FIG. 11.
  • the implementation principle and technical effects are similar, and details are not described herein again.
  • FIG. 20 is a schematic structural diagram of Embodiment 2 of an apparatus for evaluating a node of a funnel model of the present application.
  • the apparatus of this embodiment includes a processor 2001 and a memory 2002, where the processor 2001 is configured to acquire browsing paths of all users; The browsing path of all users is stored offline; the processor 2001 is further configured to obtain the conversion rate of each node of the funnel model according to the browsing path of all users; the processor 2001 is further configured to evaluate whether the node settings of the funnel model are based on the conversion rate of each node. reasonable.
  • the device in this embodiment is applicable to the technical solution of the method embodiment shown in FIG. 12, and the implementation principle and the technical effect are similar, and details are not described herein again.
  • the aforementioned program can be stored in a computer readable storage medium.
  • the program when executed, performs the steps including the foregoing method embodiments; and the foregoing storage medium includes various media that can store program codes, such as a ROM, a RAM, a magnetic disk, or an optical disk.

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Abstract

本申请提供一种漏斗模型的节点的评估系统、方法和装置，该系统包括：客户端、离线计算节点和实时计算节点，其中，客户端与实时计算节点连接，实时计算节点与离线计算节点和客户端连接，离线计算节点用于获取并离线存储所有用户的浏览路径，客户端定义漏斗模型的信息并向实时计算节点发送漏斗模型的信息，实时计算节点接收客户端发送的漏斗模型的信息后，根据漏斗模型的信息和所述离线计算节点中存储的所有用户的浏览路径，对所述漏斗模型的各节点进行评估，从而，提高了漏斗模型的节点的评估效率。

Description

漏斗模型的节点的评估系统、方法和装置
本申请要求2016年08月31日递交的申请号为201610798815.8、发明名称为“漏斗模型的节点的评估系统、方法和装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及计算机技术，尤其涉及一种漏斗模型的节点的评估系统、方法和装置。
背景技术
在网站运行中，每条访问路径都可以是一个漏斗模型，一个漏斗模型通常包含至少两个节点，每个节点通常对应一个页面，经常需要对漏斗模型各节点进行评估，则要获取某漏斗模型的各节点的参数，参数例如：节点的浏览页面(page view，简称：pv)次数和用户(unique visitor，简称：uv)数量等，以根据这些参数分析该漏斗模型中各节点设置的合理性或设置的价值大小等问题。
现有技术中，通常会在各节点对应的页面进行埋点，以收集对该页面的每次浏览行为对应的浏览记录，用户对一个页面的一次浏览行为会产生一条浏览记录，每条浏览记录中通常包括但不限于下述信息：1、cookie_id：用户标识，用于区分不同的浏览用户；2、page_id：当前页面的编号，页面每刷新一次，页面的编号也刷新一次；3、refer_page_id：当前页面的引导页面的编号，即用户在浏览当前页面之前浏览的页面的编号；4、url：浏览的页面的网址。
在漏斗模型各节点进行评估时，按照漏斗模型的各节点的顺序依次获取漏斗模型的各节点的参数，具体地：
先获取第一个节点的参数，具体地，获取浏览记录的url与第一个节点的url相同的所有浏览记录的第一集合；第一集合中的浏览记录的数量为第一个节点的pv次数，第一集合中的浏览记录中不同的cookie_id的数量为第一节点的uv数量。
再获取第二个节点的参数，具体地，获取浏览记录的url与第二个节点的url相同的所有浏览记录的第二集合，获取第二集合的浏览记录中refer_page_id与第一集合中的浏览记录的page_id相同的浏览记录的第三集合；第三集合中的浏览记录的数量为第二个节点的pv次数，第三集合中的浏览记录中不同的cookie_id的数量为第二节点的uv数量。
再获取第三个节点的参数，具体地，获取浏览记录的url与第三个节点的url相同的所有浏览记录的第四集合，获取第四集合的浏览记录中refer_page_id与第三集合中的浏览记录的page_id相同的浏览记录的第五集合；第五集合中的浏览记录的数量为第三个节点的pv次数，第五集合中的浏览记录中不同的cookie_id的数量为第三节点的uv数量。
依次类推，直到获取到漏斗模型的最后一个节点的参数，可以看出，采用现有技术的方法，对漏斗模型的节点进行评估时，必须按照漏斗模型的各节点的顺序依次获取漏斗模型的各节点的参数，因此，对漏斗模型的节点的评估效率不高。
发明内容
本申请提供一种漏斗模型的节点的评估系统、方法和装置，以解决现有技术中漏斗模型的节点的评估效率不高的问题。
一个方面，本申请提供一种漏斗模型的节点的评估系统，包括：
客户端、离线计算节点和实时计算节点，其中，所述客户端与所述实时计算节点连接，用于定义漏斗模型的信息，并向所述实时计算节点发送所述漏斗模型的信息；
所述离线计算节点用于获取并存储所有用户的浏览路径；
所述实时计算节点与所述离线计算节点和所述客户端连接，用于接收客户端发送的所述漏斗模型的信息，根据所述漏斗模型的信息和所述离线计算节点中存储的所有用户的浏览路径，对所述漏斗模型的各节点进行评估。
可选地，所述离线计算节点具体用于针对每个用户，根据浏览记录中的用户标识，获取同一用户的浏览记录，其中，所述浏览记录中包括：所述用户标识、当前页面的编号、当前页面的引导页面编号和页面的标识；根据所述同一用户的浏览记录中的所述当前页面的编号和所述当前页面的引导页面的编号，获取所述同一用户的浏览页面编号的顺序；根据所述页面的标识和节点对应关系，以及所述用户的浏览页面编号的顺序，获取所述同一用户的浏览路径。
可选地，所述离线计算节点具体用于针对同一用户的浏览记录，若第一浏览记录中当前页面的编号和第二浏览记录中当前页面的引导页面的编号相同，则确定第一浏览记录中的当前页面的编号的顺序在所述第二浏览记录中的当前页面的编号之前。
可选地，所述实时计算节点具体用于获取所述漏斗模型的目标节点对应的筛选路径，所述目标节点为所述漏斗模型中的任一节点，所述目标节点对应的筛选路径为所述漏斗模型的第一个节点到所述目标节点的路径；根据所述所有用户的浏览路径中包含的第一 浏览路径的数量，评估所述筛选路径对应的目标节点，其中，所述第一浏览路径指包含所述筛选路径的浏览路径。
可选地，所述实时计算节点具体用于根据所述所有用户的浏览路径中第一浏览路径对应的不同用户的数量，确定所述筛选路径对应的目标节点的用户uv数量；根据所述所有用户的浏览路径中第一浏览路径的数量，确定所述筛选路径对应的目标节点的浏览页面pv次数。
可选地，所述实时计算节点还用于将所述漏斗模型的实时评估结果反馈给所述客户端。
另一个方面，本申请提供一种用户浏览路径的获取方法，包括：
获取所有用户的浏览记录，所述浏览记录中包括：用户标识、当前页面的编号、当前页面的引导页面编号和页面的标识；
针对每个用户，根据所述浏览记录中的用户标识，获取同一用户的浏览记录；
根据所述同一用户的浏览记录中的所述当前页面的编号和所述当前页面的引导页面的编号，获取所述同一用户的浏览页面编号的顺序；
根据所述页面的标识和节点对应关系，以及所述用户的浏览页面编号的顺序，获取并离线存储所述同一用户的浏览路径。
可选地，所述根据所述同一用户的浏览记录中的所述当前页面的编号和所述当前页面的引导页面的编号，获取所述同一用户的浏览页面编号的顺序，包括：
若第一浏览记录中当前页面的编号和第二浏览记录中当前页面的引导页面的编号相同，则确定第一浏览记录中的当前页面的编号的顺序在所述第二浏览记录中的当前页面的编号之前。
可选地，所述根据所述页面的标识和节点对应关系，以及所述用户的浏览页面编号的顺序，获取所述同一用户的浏览路径之后，包括：
获取所述漏斗模型的目标节点对应的筛选路径，所述目标节点为所述漏斗模型中的任一节点，所述目标节点对应的筛选路径为所述漏斗模型的第一个节点到所述目标节点的路径；
根据所有用户的浏览路径中包含的第一浏览路径的数量，确定所述筛选路径对应的目标节点的参数，其中，所述第一浏览路径指包含所述筛选路径的浏览路径。
可选地，所述根据所有用户的浏览路径中包含的第一浏览路径的数量，确定所述筛选路径对应的目标节点的参数，包括：
根据所有用户的浏览路径中第一浏览路径对应的不同用户的数量，确定所述筛选路径对应的目标节点的用户uv数量；根据所述所有用户的浏览路径中第一浏览路径的数量，确定所述筛选路径对应的目标节点的浏览页面pv次数。
可选地，还包括：
根据所述筛选路径对应的目标节点的用户uv数量和所述筛选路径对应的目标节点的浏览页面pv次数，确定所述目标节点的转化率。
可选地，还包括：
根据所述目标节点的转化率，评估所述目标节点设置是否合理。
可选地，所述获取所述漏斗模型的目标节点对应的筛选路径之前，还包括：
接收客户端发送的漏斗模型的信息，根据所述漏斗模型的信息，构建所述漏斗模型。
再一方面，本申请提供一种漏斗模型的节点的参数获取方法，包括：
获取并离线存储所有用户的浏览路径；
根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。
可选地，所述根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数，包括：
获取所述漏斗模型的目标节点对应的筛选路径，所述目标节点为所述漏斗模型中的任一节点，所述目标节点对应的筛选路径为所述漏斗模型的第一个节点到所述目标节点的路径；
根据所有用户的浏览路径中第一浏览路径对应的不同用户的数量，确定所述筛选路径对应的目标节点的用户uv数量；根据所述所有用户的浏览路径中第一浏览路径的数量，确定所述筛选路径对应的目标节点的浏览页面pv次数。
可选地，所述获取并离线存储所有用户的浏览路径，包括：
从离线计算节点中获取所述所有用户的浏览路径。
又一方面，本申请提供一种漏斗模型的节点的转化率获取方法，包括：
获取并离线存储所有用户的浏览路径；
根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。
根据所述各节点的用户uv数量和浏览页面pv次数，确定所述漏斗模型各节点的转化率。
又一方面，本申请提供一种漏斗模型的节点的评估方法，包括：
获取并离线存储所有用户的浏览路径；
根据所述所有用户的浏览路径，获取漏斗模型各节点的转化率；
根据所述各节点的转化率，评估所述漏斗模型各节点设置是否合理。
又一方面，本申请提供一种用户的浏览路径获取装置，包括：
处理模块，用于获取所有用户的浏览记录，所述浏览记录中包括：用户标识、当前页面的编号、当前页面的引导页面编号和页面的标识；
所述处理模块，还用于针对每个用户，根据所述浏览记录中的用户标识，获取同一用户的浏览记录；
所述处理模块还用于根据所述同一用户的浏览记录中的所述当前页面的编号和所述当前页面的引导页面的编号，获取所述同一用户的浏览页面编号的顺序；
所述处理模块，还用于根据所述页面的标识和节点对应关系，以及所述用户的浏览页面编号的顺序，获取所述同一用户的浏览路径；
存储模块，用于离线存储所述同一用户的浏览路径。
又一方面，本申请提供一种漏斗模型的节点的参数获取装置，包括：
处理模块，用于获取所有用户的浏览路径；
存储模块，用于离线存储所述所有用户的浏览路径；
所述处理模块，还用于根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。
又一方面，本申请提供一种漏斗模型的节点的转化率获取装置，包括：
处理模块，用于获取所有用户的浏览路径；
存储模块，用于离线存储所述所有用户的浏览路径；
所述处理模块，还用于根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。
所述处理模块，还用于根据所述各节点的用户uv数量和浏览页面pv次数，确定所述漏斗模型各节点的转化率。
又一方面，本申请提供一种漏斗模型的节点的评估装置，包括：
处理模块，用于获取所有用户的浏览路径；
存储模块，用于离线存储所述所有用户的浏览路径；
所述处理模块，还用于根据所述所有用户的浏览路径，获取漏斗模型各节点的转化 率；
所述处理模块，还用于根据所述各节点的转化率，评估所述漏斗模型各节点设置是否合理。
又一方面，本申请提供一种用户的浏览路径获取装置，包括：
处理器，用于获取所有用户的浏览记录，所述浏览记录中包括：用户标识、当前页面的编号、当前页面的引导页面编号和页面的标识；
所述处理器还用于针对每个用户，根据所述浏览记录中的用户标识，获取同一用户的浏览记录；
所述处理器还用于根据所述同一用户的浏览记录中的所述当前页面的编号和所述当前页面的引导页面的编号，获取所述同一用户的浏览页面编号的顺序；
所述处理器还用于根据所述页面的标识和节点对应关系，以及所述用户的浏览页面编号的顺序，获取所述同一用户的浏览路径；
存储器，所述存储器耦合至所述处理器，用于离线存储所述同一用户的浏览路径。
可选地，所述处理器具体用于若第一浏览记录中当前页面的编号和第二浏览记录中当前页面的引导页面的编号相同，则确定第一浏览记录中的当前页面的编号的顺序在所述第二浏览记录中的当前页面的编号之前。
可选地，所述处理器具体用于获取所述漏斗模型的目标节点对应的筛选路径，所述目标节点为所述漏斗模型中的任一节点，所述目标节点对应的筛选路径为所述漏斗模型的第一个节点到所述目标节点的路径；根据所有用户的浏览路径中包含的第一浏览路径的数量，确定所述筛选路径对应的目标节点的参数，其中，所述第一浏览路径指包含所述筛选路径的浏览路径。
可选地，所述处理器还用于根据所有用户的浏览路径中第一浏览路径对应的不同用户的数量，确定所述筛选路径对应的目标节点的用户uv数量；根据所述所有用户的浏览路径中第一浏览路径的数量，确定所述筛选路径对应的目标节点的浏览页面pv次数。
可选地，所述处理器还用于根据所述筛选路径对应的目标节点的用户uv数量和所述筛选路径对应的目标节点的浏览页面pv次数，确定所述目标节点的转化率。
可选地，所述处理器还用于根据所述目标节点的转化率，评估所述目标节点设置是否合理。
可选地，还包括：
接收接口，用于接收客户端发送的漏斗模型的信息，根据所述漏斗模型的信息，构 建所述漏斗模型。
又一方面，本申请提供一种漏斗模型的节点的参数获取装置，包括：
处理器，用于获取所有用户的浏览路径；
存储器，所述存储器耦合至所述处理器，用于离线存储所述所有用户的浏览路径；
所述处理器，还用于根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。
可选地，所述处理器具体用于获取所述漏斗模型的目标节点对应的筛选路径，所述目标节点为所述漏斗模型中的任一节点，所述目标节点对应的筛选路径为所述漏斗模型的第一个节点到所述目标节点的路径；根据所有用户的浏览路径中第一浏览路径对应的不同用户的数量，确定所述筛选路径对应的目标节点的用户uv数量；根据所述所有用户的浏览路径中第一浏览路径的数量，确定所述筛选路径对应的目标节点的浏览页面pv次数。
又一方面，本申请提供一种漏斗模型的节点的转化率获取装置，包括：
处理器，用于获取所有用户的浏览路径；
存储器，所述存储器耦合至所述处理器，用于离线存储所述所有用户的浏览路径；
所述处理器，还用于根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。
所述处理器还用于根据所述各节点的用户uv数量和浏览页面pv次数，确定所述漏斗模型各节点的转化率。
又一方面，本申请提供一种漏斗模型的节点的评估装置，包括：
处理器，用于获取所有用户的浏览路径；
存储器，用于离线存储所述所有用户的浏览路径；
所述处理器，还用于根据所述所有用户的浏览路径，获取漏斗模型各节点的转化率；
所述处理器，还用于根据所述各节点的转化率，评估所述漏斗模型各节点设置是否合理。
本申请提供的漏斗模型的节点的评估系统、方法和装置，该系统包括：客户端、离线计算节点和实时计算节点，其中，客户端与实时计算节点连接，实时计算节点与离线计算节点和客户端连接，离线计算节点用于获取并离线存储所有用户的浏览路径，客户端定义漏斗模型的信息并向实时计算节点发送漏斗模型的信息，实时计算节点接收客户端发送的漏斗模型的信息后，根据漏斗模型的信息和所述离线计算节点中存储的所有用 户的浏览路径，对所述漏斗模型的各节点进行评估，由于通过离线获取并存储了所有用户的浏览路径，在线计算漏斗模型的各节点的参数时，可以并行筛选漏斗模型各节点的筛选路径，确定各节点的参数，并且，筛选操作的运算简单，运算效率高，从而，提高了漏斗模型的节点的评估效率。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本申请的一种漏斗模型的示意图；
图2为本申请节点定义的示意图；
图3为图1的节点2对应的筛选路径的示意图；
图4为图1的节点3对应的筛选路径的示意图；
图5为图1的节点4对应的筛选路径的示意图；
图6为图1的节点5对应的筛选路径的示意图；
图7为本申请的一条浏览路径的示意图；
图8为本申请漏斗模型的节点的评估系统实施例的结构示意图；
图9为本申请用户浏览路径的获取方法实施例的流程示意图；
图10为本申请漏斗模型的节点的参数获取方法实施例的流程示意图；
图11为本申请漏斗模型的节点的转化率获取方法实施例的流程示意图；
图12为本申请漏斗模型的节点的评估方法实施例的流程示意图；
图13为本申请用户浏览路径的获取装置实施例一的结构示意图；
图14为本申请漏斗模型的节点的参数获取装置实施例一的结构示意图；
图15为本申请漏斗模型的节点的转化率获取装置实施例一的结构示意图；
图16为本申请漏斗模型的节点的评估装置实施例一的结构示意图；
图17为本申请用户浏览路径的获取装置实施例二的结构示意图；
图18为本申请漏斗模型的节点的参数获取装置实施例二的结构示意图；
图19为本申请漏斗模型的节点的转化率获取装置实施例二的结构示意图；
图20为本申请漏斗模型的节点的评估装置实施例二的结构示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
本发明的说明书和权利要求书及上述附图中的术语“第一”、“第二”、“第三”、“第四”等(如果存在)是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本发明的实施例例如能够以除了在这里图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
为了便于理解，以一个典型的漏斗模型为例，对漏斗模型进行说明，如图1所示，图1为本申请的一种漏斗模型的示意图，图1的漏斗模型中包含五个节点，各节点对应的页面和定义如下所示：
节点1：对应的页面为“首页”；
定义：url中含有“home”的页面；
节点2：对应的页面为“商品列表页”；
定义：由节点1引导而来，url中含有“list”的页面；
节点3：对应的页面为“商品详情页”；
定义：由节点1、节点2引导而来，url中含有“detail”的页面；
节点4：对应的页面为“商品下单页”；
定义：由节点1、节点2、节点3引导而来，url中含有“order”的页面；
节点5：对应的页面为“商品支付页”；
定义：由节点1、节点2、节点3、节点4引导而来，url中含有“pay”的页面。
从上述各节点的定义可以看出，该漏斗模型中定义的各节点的浏览顺序依次为节点1、节点2、节点3、节点4和节点5。
相应地，可以通过定义各个节点来构建漏斗模型，如前所述，根据节点1、节点2、 节点3、节点4和节点5的定义，即可构建漏斗模型如图1所示。
因此，当用户需要获取某个漏斗模型的参数时，可以通过定义各节点，构建漏斗模型，从而，获取漏斗模型的参数。
在定义每个节点时，至少包括节点命名、节点字段绑定、字段运算符选择和字段输入值，如图2所示，图2为本申请节点定义的示意图；其中，节点命名用于区分不同的节点，节点命名例如：节点1、节点2、节点3、节点4、节点5等；节点字段绑定用于指定该节点与浏览记录中哪个字段或哪些字段绑定的，如“url”；字段运算符选择用于给节点绑定的字段选择一个运算符，运算符是预先定义好的，例如：＝,>,<,>＝,<＝,！＝,like,not like，用户只能为一个字段选择其中一个运算符；字段输入值用于指定字段的运算对象，即运算符右边的部分，如表1所示：
表1
节点命名 节点字段绑定 字段运算符选择 字段输入值
节点1 url like detail
表1表示用户创建了一个节点1、定义节点1为浏览记录中“url”字段包含“detail”的所有网页。
为了便于理解，先对本申请所描述的漏斗模型的某个节点对应的筛选路径进行说明：称漏斗模型的某个节点为目标节点，漏斗模型的目标节点对应的筛选路径是指该漏斗模型的第一个节点到该目标节点的路径，结合图1，举例来说，节点2对应的筛选路径如图3所示，图3为图1的节点2对应的筛选路径的示意图，包括节点1和节点2；节点3对应的筛选路径如图4所示，图4为图1的节点3对应的筛选路径的示意图，包括节点1、节点2和节点3；节点4对应的筛选路径如图5所示，图5为图1的节点4对应的筛选路径的示意图，包括：节点1、节点2、节点3和节点4；节点5对应的筛选路径如图6所示，图6为图1的节点5对应的筛选路径的示意图，包括：节点1、节点2、节点3、节点4和节点5。
本申请所描述的包含某个节点对应的筛选路径的浏览路径，是指该浏览路径中包含筛选路径的全部，或者，筛选路径为该浏览路径的一部分，如图7所示，图7为本申请的一条浏览路径的示意图，图7的浏览路径包含节点2的筛选路径，也包含节点3的筛选路径，但不包含节点4和节点5的筛选路径。
图8为本申请漏斗模型的节点的评估系统实施例的结构示意图，该系统包括：客户端、离线计算节点和实时计算节点，其中，客户端与实时计算节点连接，用于定义漏斗模型的信息，并向所述实时计算节点发送所述漏斗模型的信息；离线计算节点用于获取并存储所有用户的浏览路径；实时计算节点与所述离线计算节点和所述客户端连接，用于接收客户端发送的所述漏斗模型的信息，根据所述漏斗模型的信息和所述离线计算节点中存储的所有用户的浏览路径，对所述漏斗模型的各节点进行评估。
在图8中，离线计算节点主要用于获取并存储所有用户的浏览路径，具体为：针对每个用户，根据浏览记录中的用户标识，获取同一用户的浏览记录，其中，所述浏览记录中包括：所述用户标识、当前页面的编号、当前页面的引导页面编号和页面的标识；根据所述同一用户的浏览记录中的所述当前页面的编号和所述当前页面的引导页面的编号，获取所述同一用户的浏览页面编号的顺序；根据所述页面的标识和节点对应关系，以及所述用户的浏览页面编号的顺序，获取所述同一用户的浏览路径。
其中，离线计算节点根据同一用户的浏览记录中的所述当前页面的编号和所述当前页面的引导页面的编号，获取所述同一用户的浏览页面编号的顺序时，具体为针对同一用户的浏览记录，若第一浏览记录中当前页面的编号和第二浏览记录中当前页面的引导页面的编号相同，则确定第一浏览记录中的当前页面的编号的顺序在所述第二浏览记录中的当前页面的编号之前。
在图8中，实时计算节点具体用于获取所述漏斗模型的目标节点对应的筛选路径，所述目标节点为所述漏斗模型中的任一节点，所述目标节点对应的筛选路径为所述漏斗模型的第一个节点到所述目标节点的路径；根据所述所有用户的浏览路径中包含的第一浏览路径的数量，评估所述筛选路径对应的目标节点，其中，所述第一浏览路径指包含所述筛选路径的浏览路径。
其中，实时计算节点根据所述所有用户的浏览路径中包含的第一浏览路径的数量，评估所述筛选路径对应的目标节点时，具体为：根据所述所有用户的浏览路径中第一浏览路径对应的不同用户的数量，确定所述筛选路径对应的目标节点的用户uv数量；根据所述所有用户的浏览路径中第一浏览路径的数量，确定所述筛选路径对应的目标节点的浏览页面pv次数。
本申请提供的漏斗模型的节点的评估系统，设置客户端、离线计算节点和实时计算节点，通过离线计算节点获取并离线存储所有用户的浏览路径，通过实时计算节点接收客户端发送的漏斗模型的信息后，根据漏斗模型的信息和所述离线计算节点中存储的所 有用户的浏览路径，对所述漏斗模型的各节点进行评估，由于通过离线获取并存储了所有用户的浏览路径，在线计算漏斗模型的各节点的参数时，可以并行筛选同一个漏斗模型各节点的筛选路径，以及并行筛选不同的漏斗模型的各节点的筛选路径，即：在获取各漏斗模型的各节点的参数时，可以复用离线存储的所有用户的浏览路径，确定各节点的参数，并且，筛选操作的运算简单，运算效率高，从而，提高了漏斗模型的节点的评估效率。
在上述实施例中，进一步地，实时计算节点还用于将所述漏斗模型的实时评估结果反馈给所述客户端。
下面以具体地实施例对本发明的技术方案进行详细说明。下面这几个具体的实施例可以相互结合，对于相同或相似的概念或过程可能在某些实施例不再赘述。
图9为本申请用户浏览路径的获取方法实施例的流程示意图，本实施例如图9所示：
S901：获取所有用户的浏览记录。
其中，浏览记录中包括：用户标识、当前页面的编号、当前页面的引导页面编号和页面的标识；
其中，用户标识可以为cookie_id，当前页面的编号为page_id，当前页面的引导页面编号为refer_page_id，页面的标识为url。
例如：用户A和用户B的浏览记录如表2所示：
表2
Figure PCTCN2017098752-appb-000001
从表2中可以看出，用户A有3条浏览记录，其中，“1#-1#home”表示当前页面的编号为1，当前页面的引导页面编号为-1，其中，“-1”表示没有引导页，url中包含“home”；“3#2#list”表示当前页面的编号为3，当前页面的引导页面编号为2，url中包含“list”；“2#1#list”表示当前页面的编号为2，当前页面的引导页面编号为1， url中包含“list”。用户B有4条浏览记录，其中，“3#-1#home”表示当前页面的编号为3，当前页面的引导页面编号为-1，url中包含“home”；“4#3#list”表示当前页面的编号为4，当前页面的引导页面编号为3，url中包含“list”；“8#12#pay”表示当前页面的编号为8，当前页面的引导页面编号为12，url中包含“pay”。
S902：针对每个用户，根据所述浏览记录中的用户标识，获取同一用户的浏览记录。
即：用户标识相同的浏览记录为同一用户的浏览记录。
S903：根据所述同一用户的浏览记录中的所述当前页面的编号和所述当前页面的引导页面的编号，获取同一用户的浏览页面编号的顺序。
具体地，
若第一浏览记录中当前页面的编号和第二浏览记录中当前页面的引导页面的编号相同，则确定第一浏览记录中的当前页面的编号的顺序在第二浏览记录中的当前页面的编号之前。
结合表2，可以确定用户A的浏览页面编号的顺序为：1、2、3；
可以确定用户B的浏览页面编号的顺序为：3、4、12、8。
S904：根据所述页面的标识和节点对应关系，以及所述用户的浏览页面编号的顺序，获取并离线存储所述同一用户的浏览路径。
结合图1中各节点的定义，可以看出，页面的标识为“home”对应的为节点1；页面的标识为“list”对应的为节点2，页面的标识为“detail”对应的为节点3，页面的标识为“pay”对应的为节点5。
从而，可以看出用户A的浏览路径为节点1——>节点2——>节点2；
用户B的浏览路径为节点1——>节点2——>节点3——>节点5。
本实施例，通过针对每个用户，根据浏览记录中的用户标识，获取同一用户的N条浏览记录，根据浏览记录中的当前页面的编号和当前页面的引导页面的编号，获取用户的浏览页面编号的顺序，根据页面的标识和节点对应关系，以及用户的浏览页面编号的顺序，获取用户的浏览路径，从而，获取每个用户的浏览路径。
图9中所描述的获取用户的浏览路径可以离线获取，并将获取结果进行离线存储，从而，轻量化在线计算，并且，不仅可以在评估同一个漏斗模型的不同节点时，可以并行处理；在评估不同的漏斗模型的节点时，也可以并行处理。
图10为本申请漏斗模型的节点的参数获取方法实施例的流程示意图，如图10所示，本实施例的方法如下：
S1001：获取并离线存储所有用户的浏览路径。
包括但不限于以下几种可能的实现方式：
一种可能的实现方式为：通过在每个网页进行埋点，以收集用户对该页面的每次浏览行为对应的浏览记录，用户对一个页面的一次浏览行为会产生一条浏览记录，根据浏览记录获取用户的浏览路径。详细介绍参见图9所示的实现方式，此处不再赘述。
另一种可能的实现方式为：直接追踪并离线存储用户的浏览路径。
再一种可能的实现方式为：根据用户的访问日志获取用户的浏览路径。
本申请对获取用户的浏览路径的方式不做限制。
S1002：获取漏斗模型的目标节点对应的筛选路径。
其中，所述目标节点为所述漏斗模型中的任一节点，所述目标节点对应的筛选路径为所述漏斗模型的第一个节点到所述目标节点的路径。
结合图1，各节点对应的筛选路径如图2-图6所示。
S1003：根据所有用户的浏览路径中包含的第一浏览路径的数量，确定所述筛选路径对应的目标节点的参数。
其中，第一浏览路径指包含筛选路径的浏览路径。更具体地是指包含筛选路径的全部，或者，筛选路径为该浏览路径的一部分，结合图1的漏斗模型和表2的用户A和用户B的浏览路径，可以看出，用户A和用户B的浏览路径均包含节点2的筛选路径，用户A和用户B的浏览路径均包含节点3的筛选路径，用户A和用户B的浏览路径均不包含节点4的筛选路径，用户A和用户B的浏览路径均不包含节点5的筛选路径。
其中，参数可以为用户uv数量和浏览页面pv次数。
根据所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。
具体地，根据所有用户的浏览路径中第一浏览路径对应的不同用户的数量，确定所述筛选路径对应的目标节点的用户uv数量；根据所述所有用户的浏览路径中第一浏览路径的数量，确定所述筛选路径对应的目标节点的浏览页面pv次数。
结合表2中用户A和用户B的浏览路径，和图1所示的漏斗模型，可以看出，节点1的pv为2、uv为2；节点2的pv为2、uv为2；节点3的pv为1、uv为1；节点4的pv为0、uv为0；节点5的pv为0、uv为0。
在S1001之前，还包括：实时计算节点接收客户端发送的漏斗模型的信息，根据所述漏斗模型的信息，构建所述漏斗模型。
本实施例，通过获取并离线存储所有用户的浏览路径，获取漏斗模型的目标节点对应的筛选路径，根据所有用户的浏览路径中包含的第一浏览路径的数量，确定所述筛选路径对应的目标节点的参数，由于针对漏斗模型的不同节点，从所有浏览路径中筛选出第一浏览路径，可以并行进行，并且，筛选操作的运算简单，运算效率高，因此，提高了获取漏斗模型的各节点的参数的效率。
图11为本申请漏斗模型的节点的转化率获取方法实施例的流程示意图，如图11所示，本实施例的方法如下：
S1101：获取并离线存储所有用户的浏览路径。
S1102：根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。
S1101、S1102可以参见图10所示实施例中的相应部分的详细描述，此处不再赘述。
S1103：根据所述各节点的用户uv数量和浏览页面pv次数，确定所述漏斗模型各节点的转化率。
其中，转化率包括uv转化率和pv转化率，uv转化率为当前节点的uv值与前一节点的uv值的比值，pv转化率，为当前节点的pv值与前一节点的pv值的转化率。
本实施例，通过获取并离线存储所有用户的浏览路径，根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数，根据所述各节点的用户uv数量和浏览页面pv次数，确定所述漏斗模型各节点的转化率，根据所有用户的浏览路径获取漏斗模型的各节点的用户uv数量和浏览页面pv次数时，通过路径筛选的方式获得，可以并行获取各节点的用户uv数量和浏览页面pv次数，并且，筛选操作的运算简单，运算效率高，因此，可以提高获取各节点的用户uv数量和浏览页面pv次数的效率，从而，提高获取漏斗模型各节点的转化率的效率。
图12为本申请漏斗模型的节点的评估方法实施例的流程示意图，如图12所示，本实施例的方法如下：
S1201：获取并离线存储所有用户的浏览路径。
S1202：根据所有用户的浏览路径，获取漏斗模型各节点的转化率。
其中，S1201、S1202可以参见图11所示实施例中的相应部分的详细描述，此处不再赘述。
S1203：根据所述各节点的转化率，评估所述漏斗模型各节点设置是否合理。
转化率越高，说明节点设置越合理，转化率越低，说明节点设置越不合理。
本实施例，通过获取并离线存储所有用户的浏览路径，根据所有用户的浏览路径，获取漏斗模型各节点的转化率，根据各节点的转化率，评估漏斗模型各节点设置是否合理，由于根据用户的浏览路径获取漏斗模型的各节点的转化率效果高，因此，可以提高评估漏斗模型各节点的效率。
在上述实施例中，进一步地，还包括实时计算节点将所述漏斗模型的实时评估结果反馈给所述客户端。
可选地，本申请采用Spark计算框架，该框架具有极强的内存操作能力，从而，可以进一步提高计算效率。
本申请还对上述技术方案进行了实验，以7亿条pv数据为例，按照传统计算方法，计算一个6节点的漏斗模型，耗时需2小时，若计算100个6节点的漏斗模型，耗时200小时；以本申请的技术方案处理这7亿条pv数据，离线计算部分需要15分钟，在线部分计算一个6节点的漏斗模型需1分钟(一个模型共计15+1＝16分钟)，在线部分计算100个6节点的漏斗模型需要100分钟(100个模型需要15+100＝115分钟)。可见，采用本申请的技术方案，能够提高评估漏斗模型各节点的效率。
图13为本申请用户浏览路径的获取装置实施例一的结构示意图，本实施例的装置包括：处理模块1301和存储模块1302，其中，处理模块1301用于获取所有用户的浏览记录，浏览记录中包括：用户标识、当前页面的编号、当前页面的引导页面编号和页面的标识；处理模块1301还用于针对每个用户，根据浏览记录中的用户标识，获取同一用户的浏览记录；处理模块1301还用于根据同一用户的浏览记录中的当前页面的编号和当前页面的引导页面的编号，获取同一用户的浏览页面编号的顺序；处理模块1301还用于根据页面的标识和节点对应关系，以及用户的浏览页面编号的顺序，获取同一用户的浏览路径；存储模块1302用于离线存储同一用户的浏览路径。
本实施例的装置，对应的可用于执行图9所示方法实施例的技术方案，其实现原理和技术效果类似，此处不再赘述。
图14为本申请漏斗模型的节点的参数获取装置实施例一的结构示意图，本实施例的装置包括处理模块1401和存储模块1402，其中，处理模块1401用于获取所有用户的浏览路径；存储模块1402用于离线存储所有用户的浏览路径；处理模块1401还用于根据所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。
本实施例的装置，对应的可用于执行图10所示方法实施例的技术方案，其实现原理和技术效果类似，此处不再赘述。
图15为本申请漏斗模型的节点的转化率获取装置实施例一的结构示意图，本实施例的装置包括：处理模块1501和存储模块1502，其中，处理模块1501用于获取所有用户的浏览路径；存储模块1502用于离线存储所有用户的浏览路径；处理模块1501还用于根据所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。处理模块1501还用于根据各节点的用户uv数量和浏览页面pv次数，确定漏斗模型各节点的转化率。
本实施例的装置，对应的可用于执行图11所示方法实施例的技术方案，其实现原理和技术效果类似，此处不再赘述。
图16为本申请漏斗模型的节点的评估装置实施例一的结构示意图，本实施例的装置包括：处理模块1601和存储模块1602，其中，处理模块1601用于获取所有用户的浏览路径；存储模块1602用于离线存储所有用户的浏览路径；处理模块1601还用于根据所有用户的浏览路径，获取漏斗模型各节点的转化率；处理模块1601还用于根据各节点的转化率，评估漏斗模型各节点设置是否合理。
本实施例的装置，对应的可用于执行图12所示方法实施例的技术方案，其实现原理和技术效果类似，此处不再赘述。
图17为本申请用户浏览路径的获取装置实施例二的结构示意图，本实施例的装置包括：处理器1701和存储器1702，其中，处理器1701用于获取所有用户的浏览记录，浏览记录中包括：用户标识、当前页面的编号、当前页面的引导页面编号和页面的标识； 处理器1701还用于针对每个用户，根据浏览记录中的用户标识，获取同一用户的浏览记录；处理器1701还用于根据同一用户的浏览记录中的当前页面的编号和当前页面的引导页面的编号，获取同一用户的浏览页面编号的顺序；处理器1701还用于根据页面的标识和节点对应关系，以及用户的浏览页面编号的顺序，获取同一用户的浏览路径；存储器1702存储器耦合至处理器，用于离线存储同一用户的浏览路径。
本实施例的装置，对应的可用于执行图9所示方法实施例的技术方案，其实现原理和技术效果类似，此处不再赘述。
图18为本申请漏斗模型的节点的参数获取装置实施例二的结构示意图，本实施例的装置包括处理器1801和存储器1802，其中，处理器1801用于获取所有用户的浏览路径；存储器1802存储器耦合至处理器，用于离线存储所有用户的浏览路径；处理器1801还用于根据所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。
本实施例的装置，对应的可用于执行图10所示方法实施例的技术方案，其实现原理和技术效果类似，此处不再赘述。
图19为本申请漏斗模型的节点的转化率获取装置实施例二的结构示意图，本实施例的装置包括处理器1901和存储器1902，其中，处理器1901用于获取所有用户的浏览路径；存储器1902存储器耦合至处理器，用于离线存储所有用户的浏览路径；处理器1901还用于根据所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。处理器1901还用于根据各节点的用户uv数量和浏览页面pv次数，确定漏斗模型各节点的转化率。
本实施例的装置，对应的可用于执行图11所示方法实施例的技术方案，其实现原理和技术效果类似，此处不再赘述。
图20为本申请漏斗模型的节点的评估装置实施例二的结构示意图，本实施例的装置包括处理器2001和存储器2002，其中，处理器2001用于获取所有用户的浏览路径；存储器2002用于离线存储所有用户的浏览路径；处理器2001还用于根据所有用户的浏览路径，获取漏斗模型各节点的转化率；处理器2001还用于根据各节点的转化率，评估漏斗模型各节点设置是否合理。
本实施例的装置，对应的可用于执行图12所示方法实施例的技术方案，其实现原理和技术效果类似，此处不再赘述。
本领域普通技术人员可以理解：实现上述各方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成。前述的程序可以存储于一计算机可读取存储介质中。该程序在执行时，执行包括上述各方法实施例的步骤；而前述的存储介质包括：ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。
最后应说明的是：以上各实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述各实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围

Claims (26)

  1. 一种漏斗模型的节点的评估系统，其特征在于，包括：
    客户端、离线计算节点和实时计算节点，其中，所述客户端与所述实时计算节点连接，用于定义漏斗模型的信息，并向所述实时计算节点发送所述漏斗模型的信息；
    所述离线计算节点用于获取并存储所有用户的浏览路径；
    所述实时计算节点与所述离线计算节点和所述客户端连接，用于接收所述客户端发送的漏斗模型的信息，根据所述漏斗模型的信息和所述离线计算节点中存储的所有用户的浏览路径，对所述漏斗模型的各节点进行实时评估。
  2. 根据权利要求1所述的系统，其特征在于，所述离线计算节点具体用于针对每个用户，根据浏览记录中的用户标识，获取同一用户的浏览记录，其中，所述浏览记录中包括：所述用户标识、当前页面的编号、当前页面的引导页面编号和页面的标识；根据所述同一用户的浏览记录中的所述当前页面的编号和所述当前页面的引导页面的编号，获取所述同一用户的浏览页面编号的顺序；根据所述页面的标识和节点对应关系，以及所述用户的浏览页面编号的顺序，获取所述同一用户的浏览路径。
  3. 根据权利要求2所述的系统，其特征在于，所述离线计算节点具体用于针对同一用户的浏览记录，若第一浏览记录中当前页面的编号和第二浏览记录中当前页面的引导页面的编号相同，则确定第一浏览记录中的当前页面的编号的顺序在所述第二浏览记录中的当前页面的编号之前。
  4. 根据权利要求1-3任一项所述的系统，其特征在于，所述实时计算节点具体用于获取所述漏斗模型的目标节点对应的筛选路径，所述目标节点为所述漏斗模型中的任一节点，所述目标节点对应的筛选路径为所述漏斗模型的第一个节点到所述目标节点的路径；根据所述所有用户的浏览路径中包含的第一浏览路径的数量，评估所述筛选路径对应的目标节点，其中，所述第一浏览路径指包含所述筛选路径的浏览路径。
  5. 根据权利要求4所述的系统，其特征在于，所述实时计算节点具体用于根据所述所有用户的浏览路径中第一浏览路径对应的不同用户的数量，确定所述筛选路径对应的目标节点的用户uv数量；根据所述所有用户的浏览路径中第一浏览路径的数量，确定所述筛选路径对应的目标节点的浏览页面pv次数。
  6. 根据权利要求1-3任一项所述的系统，其特征在于，所述实时计算节点还用于将所述漏斗模型的实时评估结果反馈给所述客户端。
  7. 一种用户浏览路径的获取方法，其特征在于，包括：
    获取所有用户的浏览记录，所述浏览记录中包括：用户标识、当前页面的编号、当前页面的引导页面编号和页面的标识；
    针对每个用户，根据所述浏览记录中的用户标识，获取同一用户的浏览记录；
    根据所述同一用户的浏览记录中的所述当前页面的编号和所述当前页面的引导页面的编号，获取所述同一用户的浏览页面编号的顺序；
    根据所述页面的标识和节点对应关系，以及所述用户的浏览页面编号的顺序，获取并离线存储所述同一用户的浏览路径。
  8. 根据权利要求7所述的方法，其特征在于，所述根据所述同一用户的浏览记录中的所述当前页面的编号和所述当前页面的引导页面的编号，获取所述同一用户的浏览页面编号的顺序，包括：
    若第一浏览记录中当前页面的编号和第二浏览记录中当前页面的引导页面的编号相同，则确定第一浏览记录中的当前页面的编号的顺序在所述第二浏览记录中的当前页面的编号之前。
  9. 根据权利要求7或8所述的方法，其特征在于，所述根据所述页面的标识和节点对应关系，以及所述用户的浏览页面编号的顺序，获取所述同一用户的浏览路径之后，包括：
    获取漏斗模型的目标节点对应的筛选路径，所述目标节点为所述漏斗模型中的任一节点，所述目标节点对应的筛选路径为所述漏斗模型的第一个节点到所述目标节点的路径；
    根据所有用户的浏览路径中包含的第一浏览路径的数量，确定所述筛选路径对应的目标节点的参数，其中，所述第一浏览路径指包含所述筛选路径的浏览路径。
  10. 根据权利要求9所述的方法，其特征在于，所述根据所有用户的浏览路径中包含的第一浏览路径的数量，确定所述筛选路径对应的目标节点的参数，包括：
    根据所有用户的浏览路径中第一浏览路径对应的不同用户的数量，确定所述筛选路径对应的目标节点的用户uv数量；根据所述所有用户的浏览路径中第一浏览路径的数量，确定所述筛选路径对应的目标节点的浏览页面pv次数。
  11. 根据权利要求10所述的方法，其特征在于，还包括：
    根据所述筛选路径对应的目标节点的用户uv数量和所述筛选路径对应的目标节点的浏览页面pv次数，确定所述目标节点的转化率。
  12. 根据权利要求11所述的方法，其特征在于，还包括：
    根据所述目标节点的转化率，评估所述目标节点设置是否合理。
  13. 根据权利要求9所述的方法，其特征在于，所述获取所述漏斗模型的目标节点对应的筛选路径之前，还包括：
    接收客户端发送的漏斗模型的信息，根据所述漏斗模型的信息，构建所述漏斗模型。
  14. 一种漏斗模型的节点的参数获取方法，其特征在于，包括：
    获取并离线存储所有用户的浏览路径；
    根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。
  15. 根据权利要求14所述的方法，其特征在于，所述根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数，包括：
    获取所述漏斗模型的目标节点对应的筛选路径，所述目标节点为所述漏斗模型中的任一节点，所述目标节点对应的筛选路径为所述漏斗模型的第一个节点到所述目标节点的路径；
    根据所有用户的浏览路径中第一浏览路径对应的不同用户的数量，确定所述筛选路径对应的目标节点的用户uv数量；根据所述所有用户的浏览路径中第一浏览路径的数量，确定所述筛选路径对应的目标节点的浏览页面pv次数。
  16. 根据权利要求14所述的方法，其特征在于，所述获取并离线存储所有用户的浏览路径，包括：
    从离线计算节点中获取所述所有用户的浏览路径。
  17. 一种漏斗模型的节点的转化率获取方法，其特征在于，包括：
    获取并离线存储所有用户的浏览路径；
    根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数；
    根据所述各节点的用户uv数量和浏览页面pv次数，确定所述漏斗模型各节点的转化率。
  18. 一种漏斗模型的节点的评估方法，其特征在于，包括：
    获取并离线存储所有用户的浏览路径；
    根据所述所有用户的浏览路径，获取漏斗模型各节点的转化率；
    根据所述各节点的转化率，评估所述漏斗模型各节点设置是否合理。
  19. 一种用户浏览路径的获取装置，其特征在于，包括：
    处理模块，用于获取所有用户的浏览记录，所述浏览记录中包括：用户标识、当前页面的编号、当前页面的引导页面编号和页面的标识；
    所述处理模块，还用于针对每个用户，根据所述浏览记录中的用户标识，获取同一用户的浏览记录；
    所述处理模块还用于根据所述同一用户的浏览记录中的所述当前页面的编号和所述当前页面的引导页面的编号，获取所述同一用户的浏览页面编号的顺序；
    所述处理模块，还用于根据所述页面的标识和节点对应关系，以及所述用户的浏览页面编号的顺序，获取所述同一用户的浏览路径；
    存储模块，用于离线存储所述同一用户的浏览路径。
  20. 一种漏斗模型的节点的参数获取装置，其特征在于，包括：
    处理模块，用于获取所有用户的浏览路径；
    存储模块，用于离线存储所述所有用户的浏览路径；
    所述处理模块，还用于根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。
  21. 一种漏斗模型的节点的转化率获取装置，其特征在于，包括：
    处理模块，用于获取所有用户的浏览路径；
    存储模块，用于离线存储所述所有用户的浏览路径；
    所述处理模块，还用于根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数；
    所述处理模块，还用于根据所述各节点的用户uv数量和浏览页面pv次数，确定所述漏斗模型各节点的转化率。
  22. 一种漏斗模型的节点的评估装置，其特征在于，包括：
    处理模块，用于获取所有用户的浏览路径；
    存储模块，用于离线存储所述所有用户的浏览路径；
    所述处理模块，还用于根据所述所有用户的浏览路径，获取漏斗模型各节点的转化率；
    所述处理模块，还用于根据所述各节点的转化率，评估所述漏斗模型各节点设置是否合理。
  23. 一种用户的浏览路径获取装置，其特征在于，包括：
    处理器，用于获取所有用户的浏览记录，所述浏览记录中包括：用户标识、当前页 面的编号、当前页面的引导页面编号和页面的标识；
    所述处理器还用于针对每个用户，根据所述浏览记录中的用户标识，获取同一用户的浏览记录；
    所述处理器还用于根据所述同一用户的浏览记录中的所述当前页面的编号和所述当前页面的引导页面的编号，获取所述同一用户的浏览页面编号的顺序；
    所述处理器还用于根据所述页面的标识和节点对应关系，以及所述用户的浏览页面编号的顺序，获取所述同一用户的浏览路径；
    存储器，所述存储器耦合至所述处理器，用于离线存储所述同一用户的浏览路径。
  24. 一种漏斗模型的节点的参数获取装置，其特征在于，包括：
    处理器，用于获取所有用户的浏览路径；
    存储器，所述存储器耦合至所述处理器，用于离线存储所述所有用户的浏览路径；
    所述处理器，还用于根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数。
  25. 一种漏斗模型的节点的转化率获取装置，其特征在于，包括：
    处理器，用于获取所有用户的浏览路径；
    存储器，所述存储器耦合至所述处理器，用于离线存储所述所有用户的浏览路径；
    所述处理器，还用于根据所述所有用户的浏览路径，获取漏斗模型的各节点的用户uv数量和浏览页面pv次数；
    所述处理器还用于根据所述各节点的用户uv数量和浏览页面pv次数，确定所述漏斗模型各节点的转化率。
  26. 一种漏斗模型的节点的评估装置，其特征在于，包括：
    处理器，用于获取所有用户的浏览路径；
    存储器，用于离线存储所述所有用户的浏览路径；
    所述处理器，还用于根据所述所有用户的浏览路径，获取漏斗模型各节点的转化率；
    所述处理器，还用于根据所述各节点的转化率，评估所述漏斗模型各节点设置是否合理。
PCT/CN2017/098752 2016-08-31 2017-08-24 漏斗模型的节点的评估系统、方法和装置 Ceased WO2018040997A1 (zh)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104462156A (zh) * 2013-09-25 2015-03-25 阿里巴巴集团控股有限公司 一种基于用户行为的特征提取、个性化推荐方法和系统
CN104484479A (zh) * 2014-12-31 2015-04-01 小米科技有限责任公司 网页浏览处理方法及装置
CN105550356A (zh) * 2015-12-28 2016-05-04 魅族科技（中国）有限公司 一种浏览内容预加载方法及终端

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101114287B (zh) * 2006-07-27 2011-06-15 国际商业机器公司 为数据生成浏览路径的方法和装置及浏览数据的方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104462156A (zh) * 2013-09-25 2015-03-25 阿里巴巴集团控股有限公司 一种基于用户行为的特征提取、个性化推荐方法和系统
CN104484479A (zh) * 2014-12-31 2015-04-01 小米科技有限责任公司 网页浏览处理方法及装置
CN105550356A (zh) * 2015-12-28 2016-05-04 魅族科技（中国）有限公司 一种浏览内容预加载方法及终端

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