Patent Publication Number: US-2019197071-A1

Title: System and method for evaluating nodes of funnel model

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The disclosure claims the benefits of priority to International Application Number PCT/CN2017/098752, filed Aug. 24, 2017, which claims priority to Chinese Application Number 201610798815.8, filed Aug. 31, 2016, both of which are incorporated herein by reference in their entireties. 
    
    
     BACKGROUND 
     In website operations, statistics on user access can be modeled as a funnel model. A funnel model typically includes at least two nodes (or levels), each node corresponding to a page (e.g., a web page). Parameters of the nodes of a funnel model can be acquired to evaluate the nodes of the funnel model. The parameters of a node can include, for example, the number of page views (PV) of the page corresponding to the node, the number of unique visitors (UV) of the page corresponding to the node, and the like. Node setup can be analyzed for nodes of the funnel model based on these parameters. 
     Conventionally, event tracking can be performed on a page corresponding to each node to collect a browsing record of each browsing action on the page. Each browsing action by a user for a page can generate a browsing record, and each browsing record can include a user identifier (e.g., cookie_ID) to differentiate different browsing users, a page number (page_ID), the page number of a navigation page for the current page (e.g., refer_page_ID), a web address of a browsed page (e.g., Uniform Resource Locator (URL)). The page number can be refreshed every time when the page is refreshed. The numbering of a navigation page for the current page is the page number of the page browsed prior to browsing the current page. 
     When each node of a funnel model is evaluated, parameters of each node of the funnel model can be acquired sequentially in an order of the nodes of the funnel model. 
     For example, parameters of a first node can be acquired. For example, a first set of browsing records having the same URL as the URL of the first node can be acquired. The number of browsing records in the first set can be the PV number of the first node, and the number of different cookie_IDs in the browsing records in the first set can be the UV number of the first node. 
     Then, parameters of a second node can be acquired. For example, a second set of browsing records having the same URL as the URL of the second node can be acquired. A third set of browsing records can be acquired from the second set, where each of the third set of browsing records has a refer_page_ID that is the same as a page_ID of a browsing record in the first set of browsing records. The number of browsing records in the third set is the PV number of the second node, and the number of different cookie_IDs in the browsing records in the third set is the UV number of the second node. 
     Then, parameters of subsequent nodes can be acquired. For example, parameters of a third node can be acquired. A fourth set of browsing records having the same URL as the URL of the third node can be acquired. A fifth set of browsing records can be acquired that from browsing records in the fourth set, where each of the fifth set of browsing records has a refer_page_ID that is the same as a page_ID of a browsing record in the third set of browsing records. The number of browsing records in the fifth set of browsing records is the PV number of the third node, and the number of different cookie_IDs in the browsing records in the fifth set of browsing records is the UV number of the third node. 
     The same process can be repeated until parameters of a last node of the funnel model are acquired. It can be seen that in the conventional method of evaluating nodes of a funnel model, parameters of each node of the funnel model are acquired sequentially in an order of the nodes of the funnel model, reducing the efficiency of evaluating nodes of a funnel model. 
     SUMMARY OF THE DISCLOSURE 
     The present disclosure provides a system, a method, and an apparatus for evaluating nodes of a funnel model, so as to improve the efficiency of evaluating nodes of a funnel model. 
     Embodiments of the disclosure provide a system for evaluating nodes of a funnel model. The system can include: a memory storing a set of instruction; and at least one processor configured to execute the set of instructions to cause the system to perform: acquiring browsing records of users, the browsing records including browsing paths of the users; and performing real-time evaluation on the nodes of the funnel model according to the acquired browsing paths of the users, wherein the acquired browsing paths are stored offline. 
     Embodiments of the disclosure also provide a method for evaluating nodes of a funnel model. The method can include: acquiring browsing records of users, the browsing records including browsing paths of the users; and performing real-time evaluation on the nodes of the funnel model according to the acquired browsing paths of the users, wherein the acquired browsing paths are stored offline. 
     Embodiments of the disclosure further provide a non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computer system to cause the computer system to perform a method for evaluating nodes of a funnel model. The method can include: acquiring browsing records of users, the browsing records including browsing paths of the users; and performing real-time evaluation on the nodes of the funnel model according to the acquired browsing paths of the users, wherein the acquired browsing paths are stored offline. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute part of this disclosure, together with the description, illustrate and serve to explain the principles of various example embodiments. 
         FIG. 1  illustrates a schematic diagram of a funnel model, according to embodiments of the present disclosure. 
         FIG. 2  illustrates a schematic diagram of a node definition, according to embodiments of the present disclosure. 
         FIG. 3A  illustrates a schematic diagram of an exemplary screened path, according to embodiments of the present disclosure. 
         FIG. 3B  illustrates a schematic diagram of another exemplary screened path, according to embodiments of the present disclosure. 
         FIG. 3C  illustrates a schematic diagram of yet another exemplary screened path, according to embodiments of the present disclosure. 
         FIG. 3D  illustrates a schematic diagram of still another exemplary screened path, according to embodiments of the present disclosure. 
         FIG. 4  illustrates a schematic diagram of a screened path, according to embodiments of the present disclosure. 
         FIG. 5  is a block diagram of an exemplary evaluation system for nodes of a funnel model, according to embodiments of the present disclosure. 
         FIG. 6  illustrates a flow chart of an exemplary method for acquiring a user browsing path, according to embodiments of the present disclosure. 
         FIG. 7  illustrates a flow chart of an exemplary method for acquiring parameters of nodes of a funnel model, according to embodiments of the present disclosure. 
         FIG. 8  illustrates a flow chart of an exemplary method for obtaining a conversion rate of a node of a funnel model, according to embodiments of the present disclosure. 
         FIG. 9  illustrates a flow chart of an exemplary method for evaluating a node of a funnel model, according to embodiments of the present disclosure. 
         FIG. 10  illustrates a block diagram an exemplary apparatus for acquiring a user browsing path, according to embodiments of the present disclosure. 
         FIG. 11  illustrates a block diagram of an exemplary apparatus for acquiring parameters of nodes of a funnel model, according to embodiments of the present disclosure. 
         FIG. 12  illustrates a block diagram of an exemplary apparatus for obtaining a conversion rate of a node of a funnel model, according to embodiments of the present disclosure. 
         FIG. 13  illustrates a block diagram of an apparatus for evaluating a node of a funnel model, according to embodiments of the present disclosure. 
         FIG. 14  illustrates a block diagram of another exemplary apparatus for acquiring a user browsing path, according to embodiments of the present disclosure. 
         FIG. 15  illustrates a block diagram of another exemplary apparatus for acquiring parameters of nodes of a funnel model, according to embodiments of the present disclosure. 
         FIG. 16  illustrates a block diagram of another exemplary apparatus for obtaining a conversion rate of a node of a funnel model, according to embodiments of the present disclosure. 
         FIG. 17  illustrates a block diagram of another exemplary apparatus for evaluating a node of a funnel model, according to embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure provides a system, a method, and an apparatus for evaluating nodes of a funnel model. Techniques of the disclosure can acquire browsing paths of users and store browsing paths offline, so that screened paths of nodes of the funnel model can be screened in parallel when performing online calculation of parameters of nodes of the funnel model to determine parameters of all nodes. Moreover, the screening operation has high operation efficiency, thereby improving the efficiency of evaluating nodes of a funnel model. 
     Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise represented. The implementations set forth in the following description of exemplary embodiments do not represent all implementations consistent with the disclosure. Instead, they are merely examples of apparatuses and methods consistent with aspects related to the disclosure as recited in the appended claims. 
     Terms of “first,” “second,” “third,” “fourth,” and the like in the specification, claims, and accompanying drawings of the present disclosure are used to differentiate similar objects, rather than describing a particular sequence or order. It should be understood that embodiments of the present disclosure described herein can be implemented in a sequence other than those illustrated or described herein. In addition, tennis of “comprising” and “including,” as well as any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or apparatus comprising a series of steps or units may not necessarily be limited to those clearly listed steps or units, but may comprise other steps or units that are not clearly listed or are internal to the process, method, product, or apparatus. 
       FIG. 1  illustrates a schematic diagram of a funnel model  100 , according to embodiments of the disclosure. The funnel model in  FIG. 1  includes five nodes. 
     Node  1  can be associated with a “Homepage.” For example, node  1  can be associated with a page with a URL containing “home.” 
     Node  2  can be associated with a “Product List Page.” For example, node  2  can be associated with a page directed from the node  1 , and with a URL containing “list.” 
     Node  3  can be associated with a “Product Details Page.” For example, node  3  can be associated with a page directed from the node  1  and the node  2 , and with a URL containing “detail.” 
     Node  4  can be associated with “Product Order Page.” For example, node  4  can be associated with a page directed from the node  1 , the node  2 , and the node  3 , and with a URL containing “order.” 
     Node  5  can be associated with a “Product Pay Page.” For example, node  5  can be associated with a page directed from the node  1 , the node  2 , the node  3 , and the node  4 , and with a URL containing “pay.” 
     In this example, the browsing sequence of the nodes in the funnel model  100  is the node  1 , the node  2 , the node  3 , the node  4 , and the node  5 . 
       FIG. 2  is a schematic diagram of a node  200 , according to embodiments of the disclosure. Node  200  of the funnel model (e.g., funnel model  100  of  FIG. 1 ) can include a node name, a node field, a field operator, and a field input, as shown in  FIG. 2 . The node name can be used to differentiate different nodes. The node name can include, for example, node  1 , node  2 , node  3 , node  4 , node  5 , and the like. The node field can be used to designate the field(s), e.g., “URL,” in a browsing record as being associated with the node. The field operator can be used to select an operator for a field associated with a node, such as “=,” “&gt;,” “&lt;,” “&gt;=,” “&lt;=,” “!=,” “like,” “not like.” A user can select one of the operators for one field. The field input can be used to designate an operation object of a field (e.g., the object right to an operator. An exemplary node is shown in Table 1 below. 
     
       
         
           
               
               
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Node Name 
                 Node Field 
                 Field Operator 
                 Field Input 
               
               
                   
                   
               
             
            
               
                   
                 node 1 
                 URL 
                 “like” 
                 detail 
               
               
                   
                   
               
            
           
         
       
     
     Table 1 indicates that a user creates a “node  1 ” and the “node  1 ” is associated with all webpages in browsing records that have a “URL” field containing “detail.” 
     A funnel model can include a starting node (e.g., node  1  of  FIG. 1 ) and a target node. And a screened path corresponding to the target node refers to a path from the starting node to the target node of the funnel model.  FIG. 3A  illustrates a screened path corresponding to node  2  of  FIG. 1 , including node  1  and node  2 .  FIG. 3B  illustrates a screened path corresponding to node  3  of  FIG. 1 , including node  1 , node  2 , and node  3 .  FIG. 3C  illustrates a screened path corresponding to node  4  of  FIG. 1 , including node  1 , node  2 , node  3 , and node  4 .  FIG. 3D  illustrates a screened path corresponding to node  5  of  FIG. 1 , including node  1 , node  2 , node  3 , node  4 , and node  5 . 
     A browsing path can include a screened path corresponding to a node. In some implementations, the screened path is a part of the browsing path.  FIG. 4  illustrates a schematic diagram of a browsing path  400 , according to embodiments of the disclosure. Browsing path  400  can include a screened path of the node  2  and a screened path of the node  3 . In this example, browsing path  400  does not include screened paths of the node  4  or the node  5 . 
       FIG. 5  illustrates a block diagram of an exemplary evaluation system  500  for nodes of a funnel model, according to embodiments of the disclosure. System  500  can include: a client terminal  502 , an offline computation node  504 , and a real-time computation node  506 . Client terminal  502  can be connected with real-time computation node  506 , and can be configured to define information of the funnel model and send the information of the funnel model to real-time computation node  506 . Offline computation node  504  can be configured to acquire and store browsing paths of users. Real-time computation node  506  can be connected with offline computation node  504  and client terminal  502 , and configured to receive the information of the funnel model from client terminal  502 , and evaluate nodes of the funnel model according to the information of the funnel model and the browsing paths stored in the offline computation node. 
     In system  500 , offline computation node  504  can be configured to acquire and store browsing paths of users. For example, offline computation node  504  can acquire, according to a user identifier in browsing records, browsing records for each user. The browsing records can include a user identifier, a numbering of a current page, a numbering of a navigation page for the current page, and a page identifier. Offline computation node  504  can also acquire an order of numberings of browsed pages by the user according to the numberings of the current pages and the numberings of navigation pages for the current pages, and acquire a browsing path of the user according to a correspondence relationship between page identifiers and nodes and the order of numberings of browsed pages by the user. 
     In some embodiments, if the numbering of the current page in a first browsing record of a user is the same as the numbering of a navigation page for the current page in a second browsing record of the user, the offline computation node  504  may determine that the numbering of the current page in the first browsing record is sequentially before the numbering of the current page in the second browsing record. 
     In some embodiments, real-time computation node  506  is further configured to acquire a screened path corresponding to a target node of the funnel model. The screened path corresponding to the target node is a path from a starting node of the funnel model to the target node. Real-time computation node  506  is also configured to evaluate the target node corresponding to the screened path according to the number of first browsing paths among the browsing paths of all users, where each of the first browsing paths includes the screened path. 
     In some embodiments, to evaluate the target node corresponding to the screened path, real-time computation node  506  can be configured to determine, according to the number of different users corresponding to the first browsing paths, the number of UV of the target node corresponding to the screened path. In some embodiments, the real-time computation node  506  can be configured to determine, according to the number of the first browsing paths, the number of PV of a browsed page of the target node corresponding to the screened path. 
     In the system  500 , the offline computation node acquires and stores offline browsing paths of users. Upon receiving the information of the funnel model from the client terminal, the real-time computation node evaluates nodes of the funnel model according to the information of the funnel model and the browsing paths of users stored in the offline computation node. Because the browsing paths of users have been acquired and stored offline, when performing online calculation of parameters of each node of the funnel model, screened paths of nodes of the same funnel model and screened paths of nodes of different funnel models can be determined in parallel. In the system  500 , when acquiring parameters of nodes of funnel models, the browsing paths of users stored offline can be used repeatedly to determine parameters of each node. Moreover, the screening operation is relatively simple and with high processing efficiency, thereby improving the efficiency of evaluating nodes of a funnel model. 
     In some embodiments, real-time computation node  506  can be further configured to feed a real-time evaluation result of the funnel model back to the client terminal. 
       FIG. 6  is a flow chart of a method  600  for acquiring a user browsing path, according to embodiments of the disclosure. For example, method  600  can be implemented by, e.g., system  500  of  FIG. 5 . The method  600  includes steps S 601 -S 604 . 
     In step S 601 , browsing records of users can be acquired. The browsing records can include user identifier, a numbering of a current page, a numbering of a navigation page for the current page, and page identifier. The user identifier can be identified by “cookie_ID,” the numbering of the current page can be identified by “page_ID,” the numbering of a navigation page for the current page can be identified by “refer_page_ID,” and the webpage identifier can be identified by a URL. 
     For example, the browsing records of User A and User B are listed in Table 2 below. 
     
       
         
           
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                   
                 Pages 
               
               
                 User 
                 (format: page_ID#refer_page_ID#URL, and pages are 
               
               
                 identifier 
                 separated by a comma) 
               
               
                   
               
             
            
               
                 A 
                 1#-1#home, 3#2#list, 2#1#list 
               
               
                 B 
                 3#-1#home, 4#3#list, 8#12#pay, 12#4#detail 
               
               
                   
               
            
           
         
       
     
     It can be seen from Table 2 that User A has 3 browsing records, including “1#−1#home,” “3#2#list,” and “2#1#list.” 
     The browsing record “1#−1#home” can indicate that the numbering of the current page is 1, the numbering of a navigation page for the current page is “−1,” indicating no navigation page, and the URL contains “home.” 
     The browsing record “3#2#list” can indicate that the numbering of the current page is 3, the numbering of a navigation page for the current page is 2, and the URL contains “list.” 
     The browsing record “2#1#list” can indicate that the numbering of the current page is 2, the numbering of a navigation page for the current page is 1, and the URL contains “list.” 
     As shown in Table 2, User B has 4 browsing records. The browsing record “3#−1#home” indicates that the numbering of the current page is 3, the numbering of a navigation page for the current page is −1, and URL contains “home.” The browsing record “4#3#list” indicates that the numbering of the current page is 4, the numbering of a navigation page for the current page is 3, and URL contains “list.” The browsing record “8#12#pay” indicates that the numbering of the current page is 8, the numbering of a navigation page for the current page is 12, and URL contains “pay.” 
     In step S 602 , according to user identifiers in the browsing records, browsing records of a same user can be acquired. For example, the browsing records of the same user can be identified by the same user identifier. 
     In step S 603 , an order of numberings of browsed pages by the same user can be acquired according to the numberings of the current pages and the numberings of navigation pages for the current pages in the browsing records of the same user. 
     For example, if the numbering of the current page in a first browsing record is the same as the numbering of a navigation page for the current page in a second browsing record, it can be determined that the numbering of the current page in the first browsing record is sequentially before the numbering of the current page in the second browsing record. 
     With reference back to Table 2, it can be determined that the order of numberings of browsed pages by User A is 1, 2, 3. Similarly, it can be determined that the order of numberings of browsed pages by User B is 3, 4, 12, and 8. 
     In step S 604 , a browsing path of the same user can be acquired and stored, according to a correspondence relationship between page identifiers and nodes and the order of numberings of browsed pages by the user. For example, the browsing path can be stored offline. 
     With reference back to  FIG. 1 , it can be seen that node  1  corresponds to a page with a page identifier of “home,” node  2  corresponds to a page with a page identifier of “list,” node  3  corresponds to a page with a page identifier of “detail,” and the node  5  corresponds to a page with a page identifier of “pay.” 
     Therefore, it can be seen that the browsing path of User A is “homepage-list-list,” i.e., “node  1 →node  2 →node  2 .” And the browsing path of User B is “homepage-list-details-pay,” i.e., “node  1 →node  2 →node  3 →node  5 .” 
     By acquiring browsing records of a user, an order of numberings of browsed pages by the user can be acquired according to the numberings of the current pages and the numberings of navigation pages for the current pages in the browsing records. Therefore, a browsing path of the user can be acquired according to a correspondence relationship between page identifiers and nodes and the order of numberings of browsed pages, thereby acquiring a browsing path of each user. 
     Acquiring a browsing path of a user described in  FIG. 6  can be achieved offline and the acquired results can be stored offline, thereby reducing the load of online calculation. Moreover, parallel processing can be performed not only for evaluating different nodes of the same funnel model, but also for evaluating nodes of different funnel models. 
       FIG. 7  is a flow chart a method  700  for acquiring parameters of nodes of a funnel model, according to embodiments of the disclosure. For example, method  700  can be implemented by system  500  of  FIG. 5 . The method  700  may include the following steps. 
     In step S 701 , browsing paths of users can be acquired and stored. For example, the browsing paths can be stored offline. 
     In some embodiments, event tracking can be performed on webpages to collect a browsing record corresponding to each browsing action by a user on the page. Each browsing action by the user for a page can generate a browsing record, and a browsing path of the user is acquired according to the browsing records. In some embodiments, a browsing path of a user can be directly tracked and stored offline. In some embodiments, a browsing path of a user according to an access log of the user can be acquired. It is appreciated that embodiments of acquiring the browsing path are not limited by the disclosure. 
     In step S 702 , a screened path corresponding to a target node of the funnel model can be acquired. The target node can be any node in the funnel model, and the screened path corresponding to the target node can be a path from the starting node of the funnel model to the target node. 
     For example, for the funnel model shown in  FIG. 1 , the screened paths corresponding to the nodes are shown in  FIG. 2  to  FIG. 6 . 
     In step S 703 , parameters of the target node corresponding to the screened path can be determined according to the number of first browsing paths in the browsing paths of the users, where each of the first browsing paths includes the screened path. For example, the first browsing path can be a full screened path, or the screened path is a part of the first browsing path. 
     The parameters can include the number of UV of users and the number of PV of a browsed page. The number of UV of users and the number of PV of a browsed page of each node of the funnel model are acquired according to the browsing paths of users. For example, according to a number of different users corresponding to the first browsing paths in the browsing paths of all users, the number of UV of the target node corresponding to the screened path can be determined. And according to the number of the first browsing paths in the browsing paths of all, the number of PV of a browsed page of the target node corresponding to the screened path can be determined. 
     With reference to the browsing paths of User A and User B in Table 2 and the funnel model in  FIG. 1 , it can be seen that the node  1  has a PV of 2 and a UV of 2, the node  2  has a PV of 2 and a UV of 2, the node  3  has a PV of 1 and a UV of 1, the node  4  has a PV of 0 and a UV of 0, and the node  5  has a PV of 0 and a UV of 0. 
     In some embodiments, prior to S 701 , method  700  can further include: receiving, via a real-time computation node, the information of the funnel model from the client terminal, and constructing the funnel model according to the information of the funnel model. 
     Embodiments of the disclosure acquire browsing paths of users and store the browsing paths offline, acquire a screened path corresponding to a target node of the funnel model, and determine, according to the number of first browsing paths in the browsing paths of users, parameters of the target node corresponding to the screened path. Because the screening of first browsing paths from browsing paths can be performed in parallel for different nodes of the funnel model, and the screening operation is relatively simple with high operation efficiency, the efficiency of acquiring parameters of nodes of a funnel model can be improved. 
       FIG. 8  is a flow chart of a method  800  for obtaining a conversion rate of a node of a funnel model, according to embodiments of the disclosure. For example, method  800  can be implemented by system  500  of  FIG. 5 . The method  800  may include the following steps. 
     In step S 801 , browsing paths of users can be acquired and stored. For example, the browsing paths can be stored offline. 
     In step S 802 , the number of UV of users and the number of PV of a browsed page of each node of the funnel model can be acquired according to the browsing paths of the users. 
     Detailed description of steps S 801  and S 802  is provided above in connection with  FIG. 7 , and will not be repeated herein. 
     In step S 803 , a conversion rate of each node of the funnel model can be determined according to the number of UV of users and the number of PV of a browsed page of the corresponding node. The conversion rate can include a UV conversion rate and a PV conversion rate. The UV conversion rate can be a ratio of the UV value of the current node to the UV value of the previous node, and the PV conversion rate can be a conversion rate between the PV value of the current node and the PV value of the previous node. 
     Embodiment of the disclosure can determine a conversion rate of each node of the funnel model according to the UV number of users and the PV number of a browsed page of the each node. When acquiring the UV number of users and the PV number of a browsed page of each node of the funnel model according to the browsing paths of users, the UV number of users and the PV number of browsed pages of nodes can be acquired in parallel in a path screening manner. Moreover, the screening operation is relatively simple with high operation efficiency. Therefore, the efficiency of acquiring the UV number of users and the PV number of a browsed page of each node can be improved, thereby improving the efficiency of obtaining a conversion rate of nodes of a funnel model. 
       FIG. 9  is a flow chart of a method  900  for evaluating a node of a funnel model, according to embodiments of the disclosure. For example, method  900  can be implemented by system  500  of  FIG. 5 . The method  900  includes the following steps. 
     In step S 901 , browsing paths of users can be acquired and stored. For example, the browsing paths can be stored offline. 
     In step S 902 , a conversion rate of each node of the funnel model can be acquired according to the browsing paths of the users. 
     Detailed description of steps S 901  and S 902  is provided above in connection with  FIG. 7 , and will not be repeated herein. 
     In step S 903 , the setup of each node of the funnel model can be evaluated according to the conversion rate of the corresponding node. When the conversion rate is higher than a predetermined threshold, the setup of a node can be evaluated to be appropriate. And when the conversion rate is less than the predetermined threshold, the setup of the node can be evaluated to be not appropriate and can be improved. 
     In some embodiments, method  900  can further include feeding, via a real-time computation node, a real-time evaluation result of the funnel model back to the client terminal. 
     In some embodiments, system  500  can employ a Spark computing framework. The Spark computing framework has a strong capability for memory operations, which can therefore further improve the computing efficiency. 
     Conventionally, in an example, with 700 million pieces of PV data, it may take two hours to evaluate a 6-node funnel model and 200 hours to evaluate 100 6-node funnel models when a conventional computation method is used. By the technical solution of the disclosure, with the same 700 million pieces of PV data, it may take 15 minutes for the offline calculation, one minute for online evaluate of a 6-node funnel model or 100 minutes for online evaluate of 100 6-node funnel models. Therefore, the use of the technical solution according to embodiments of the disclosure can improve the efficiency of evaluating nodes of a funnel model. 
     Embodiment of the disclosure can evaluate whether the setup of each node of the funnel model is appropriate according to the conversion rate of the each node. Because the step of acquiring a conversion rate of each node of the funnel model according to the browsing paths of users can be performed with a high efficiency, the efficiency of evaluating nodes of a funnel model can be improved. 
       FIG. 10  illustrates a block diagram of an exemplary apparatus  1000  for acquiring a user browsing path, according to embodiments of the disclosure. Apparatus  1000  can include a processing module  1001  and a storing module  1002 . Processing module  1001  is configured to acquire browsing records of users. The browsing records can include a user identifier, a numbering of the current page, a numbering of a navigation page for the current page, and a page identifier. Processing module  1001  is further configured to acquire, according to user identifiers in the browsing records, browsing records of each user. Processing module  1001  is further configured to acquire an order of numberings of browsed pages by a user according to the numberings of the current pages and the numberings of navigation pages for the current pages in the browsing records of the user. Processing module  1001  is further configured to acquire a browsing path of a user according to a correspondence relationship between page identifiers and nodes and the order of numbers of browsed pages by the user. Storing module  1002  is configured to store offline the browsing path of the same user. 
     Apparatus  1000  can be used to implement the method embodiments described above in connection with  FIG. 6 , which will not be repeated herein. 
       FIG. 11  is a block diagram of an apparatus  1100  for acquiring parameters of nodes of a funnel model, according to embodiments of the disclosure. Apparatus  1100  can include a processing module  1101  and a storing module  1102 . Processing module  1101  is configured to acquire browsing paths of users (e.g., all users), storing module  1102  is configured to store the browsing paths of the users (e.g., offline storing). Processing module  1101  is further configured to acquire the number of UV of users and the number of PV of a browsed page of each node of the funnel model according to the browsing paths of the users. 
     Apparatus  1100  can be used to implement the method embodiments described above in connection with  FIG. 7 , which will not be repeated herein. 
       FIG. 12  is a block diagram of an apparatus  1200  for obtaining a conversion rate of a node of a funnel model, according to embodiments of the disclosure. Apparatus  1200  can include a processing module  1201  and a storing module  1202 . Processing module  1201  is configured to acquire browsing paths of users (e.g., all users), and storing module  1202  is configured to store the browsing paths of the users (e.g., offline storing). Processing module  1201  is further configured to acquire the number of UV of users and the number of PV of a browsed page of each node of the funnel model, according to the browsing paths of users, and determine a conversion rate of each node of the funnel model according to the number of UV of users and the number of PV of a browsed page of the corresponding node. 
     The apparatus  1200  can be used to implement the method embodiments described above in connection with  FIG. 8 , which will not be repeated herein. 
       FIG. 13  is a block diagram of an apparatus  1300  for evaluating a node of a funnel model, according to embodiments of the disclosure. Apparatus  1300  can include a processing module  1301  and a storing module  1302 . Processing module  1301  is configured to acquire browsing paths of users (e.g., all users), and storing module  1302  is configured to store the browsing paths of the users (e.g., offline storing). Processing module  1301  is further configured to acquire a conversion rate of each node of the funnel model according to the browsing paths of users, and evaluate whether the setup of each node of the funnel model is appropriate according to the conversion rate of the each node. 
     Apparatus  1300  can be used to implement the method embodiments described above in connection with  FIG. 9 , which will not be repeated herein. 
       FIG. 14  is a block diagram of an apparatus  1400  for acquiring a user browsing path, according to embodiments of the disclosure. Apparatus  1400  can include a processor  1401  and a memory  1402 . Processor  1401  is configured to acquire browsing records of users. The browsing records can include a user identifier, a numbering of the current page, a numbering of a navigation page for the current page, and a page identifier. Processor  1401  is further configured to acquire, according to user identifiers in the browsing records, browsing records of a user. Processor  1401  is further configured to acquire an order of numberings of browsed pages by the user according to the numberings of the current pages and the numberings of navigation pages for the current pages in the browsing records of the user. Processor  1401  is further configured to acquire a browsing path of the user according to a correspondence relationship between page identifiers and nodes and the order of numberings of browsed pages by the user. Memory  1402  is coupled to the processor and configured to store the browsing path of the user. For example, the browsing path can be stored offline. 
     The apparatus  1400  can be used to implement the method embodiments described above in connection with  FIG. 6 , which will not be repeated herein. 
       FIG. 15  is a block diagram of an apparatus  1500  for acquiring parameters of nodes of a funnel model, according to embodiments of the disclosure. Apparatus  1500  includes a processor  1501  and a memory  1502 . Processor  1501  is configured to acquire browsing paths of users (e.g., all users), memory  1502  is coupled to processor  1501  and configured to store the browsing paths of the users (e.g., offline storing). Processor  1501  is further configured to acquire the number of UV of users and the number of PV a browsed page of each node of the funnel model according to the browsing paths of users. 
     Apparatus  1500  can be used to implement the method embodiments described above in connection with  FIG. 7 , which will not be repeated herein. 
       FIG. 16  is a block diagram of an apparatus  1600  for obtaining a conversion rate of a node of a funnel model, according to embodiments of the disclosure. Apparatus  1600  includes a processor  1601  and a memory  1602 . Processor  1601  is configured to acquire browsing paths of users (e.g., all users), and memory  1602  is coupled to processor  1601  and configured to store the browsing paths of the users (e.g., offline storing). Processor  1601  is further configured to acquire the number of UV of users and the number of PV of a browsed page of each node of the funnel model according to the browsing paths of users, and determine a conversion rate of each node of the funnel model according to the UV number of users and the PV number of a browsed page of the each node. 
     Apparatus  1600  can be used to implement the method embodiments described above in connection with  FIG. 8 , which will not be repeated herein. 
       FIG. 17  is a block diagram of an apparatus  1700  for evaluating a node of a funnel model, according to embodiments of the disclosure. Apparatus  1700  can include a processor  1701  and a memory  1702 . Processor  1701  is configured to acquire browsing paths of users (e.g., all users), and memory  1702  is configured to store the browsing paths of the users (e.g., offline storing). Processor  1701  is further configured to acquire a conversion rate of each node of the funnel model according to the browsing paths of all users, and evaluate whether the setup of each node of the funnel model is appropriate according to the conversion rate of the corresponding node. 
     Apparatus  1700  can be used to implement the method embodiments described above in connection with  FIG. 9 , which will not be repeated herein. 
     It is appreciated that all or some steps of the above method embodiments can be implemented by a program instructing relevant hardware. The above program can be stored in a computer readable storage medium. When the program is executed, the steps of the above method embodiments are executed, while the storage medium comprises various media capable of storing program codes, such as ROM, RAM, a magnetic disc, an optical disc, and the like. 
     It is also appreciated that the above embodiments are only used to describe the technical solutions of the present disclosure, rather than limiting the present disclosure. Although the present disclosure has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions according to the above embodiments can still be amended, or equivalent substitution can be performed on some or all of the technical features thereof, while these amendments or substitutions do not cause the essence of corresponding technical solutions to depart from the scope of the technical solutions of the embodiments of the present disclosure.