CN108876479B - Channel attribution method and device for object entity - Google Patents
Channel attribution method and device for object entity Download PDFInfo
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Abstract
The invention discloses a channel attribution method and device for an object entity. The method comprises the following steps: collecting user flow log data according to pre-established buried points aiming at a plurality of object entities; acquiring user service behavior data according to the user flow log data, wherein the user service behavior data comprises at least one object entity corresponding to at least one user service behavior; the channel attribution logic analysis is carried out by utilizing the user flow log data and the user business behavior data, the channel attribution result of at least one object entity corresponding to at least one user business behavior is determined, and the channel attribution result of each object entity guided by which channel node can be accurately analyzed and determined, so that the marketing platform can conveniently measure the guiding effect of each channel node, the value of each channel node to a marketing platform merchant is measured, the marketing platform merchant can measure the drainage value of each channel node according to the determined channel attribution result, and the appropriate channel node is selected for popularization.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a channel attribution method and device for an object entity.
Background
With the development of internet technology, marketing platforms come up, the marketing platforms are provided with various channel nodes for pulling passenger flow, a plurality of merchants choose to set stores on the channel nodes of the marketing platforms and put in commodities or coupons (collectively referred to as object entities) so as to achieve the purposes of popularization and marketing, and channel attribution analysis needs to be carried out on the object entities in order to measure the value of the channel nodes for the marketing platforms and the merchants of the marketing platforms.
The existing channel attribution methods can only perform attribution analysis on one object entity, for example, application publication No. CN 107886382A discloses a method, a device and a system for analyzing the channel drainage effect in a website, which are used for analyzing the drainage effect of an important page, and specifically disclose: the method comprises the steps of counting the number of commodities added to an order and the number of commodities added to a shopping cart aiming at important pages, such as a home page, a search result page, a recommendation page or an activity page, calculating the drainage effect of the corresponding page according to the number of the commodities added to the order and the number of the commodities added to the shopping cart, only disclosing channel attribution of the commodities, and being incapable of realizing channel attribution analysis of a plurality of object entities.
Disclosure of Invention
In view of the above problems, the present invention has been made to provide a channel attribution method and apparatus for object entities that overcomes or at least partially solves the above problems.
According to an aspect of the present invention, there is provided a channel attribution method for an object entity, the method comprising:
collecting user flow log data according to pre-established buried points aiming at a plurality of object entities;
acquiring user service behavior data according to the user flow log data, wherein the user service behavior data comprises at least one object entity corresponding to at least one user service behavior;
and performing channel attribution logic analysis by using the user flow log data and the user business behavior data, and determining a channel attribution result of at least one object entity corresponding to at least one user business behavior.
Optionally, the obtaining the user service behavior data according to the user traffic log data further includes:
and acquiring user service behavior data belonging to the same preset time period according to the user flow log data belonging to the preset time period.
Optionally, the channel attribution logic analysis is performed by using the user traffic log data and the user business behavior data, and determining the channel attribution result of the at least one object entity corresponding to the at least one user business behavior further includes:
extracting channel node access data from user traffic log data, and establishing a channel node access time axis;
establishing a user service behavior time axis according to the user service behavior data;
determining the incidence relation between any user business behavior and any channel node based on a channel node access time axis and a user business behavior time axis;
and determining a channel attribution result of at least one object entity corresponding to at least one user business behavior according to the incidence relation between any user business behavior and any channel node.
Optionally, determining the association relationship between any user business behavior and any channel node based on the channel node access time axis and the user business behavior time axis further includes:
aiming at any user service behavior, acquiring a first time point of the user service behavior in a user service behavior time axis;
inquiring channel nodes, of which the corresponding time points in the access time axis are located before the first time point, of the channel nodes;
and determining at least one channel node from the queried channel nodes based on a preset attribution rule, and establishing an incidence relation between the user business behavior and the determined at least one channel node.
Optionally, determining at least one channel node from the queried channel nodes based on the preset attribution rule further comprises:
determining a channel node with a time point closest to a first time point from the queried channel nodes based on a preset proximity attribution rule;
and/or determining a channel node accessed for the first time in the channel node access time axis from the queried channel nodes based on a preset first attribution rule.
Optionally, after determining the association relationship between any user business behavior and any channel node, the method further includes:
judging whether an object entity corresponding to any user business behavior is an object entity directly related to any channel node;
if yes, further determining that the incidence relation between any user service behavior and any channel node is a direct incidence relation;
if not, further determining that the association relationship between any user business behavior and any channel node is an indirect association relationship.
Optionally, after collecting the user traffic log data, the method further includes:
judging whether the user traffic log data contains channel node access data with a backspacing mark;
if yes, extracting channel node access data from the user traffic log data specifically comprises: and extracting channel node access data from the user traffic log data, and filtering the channel node access data with the rollback mark.
Optionally, after determining the channel attribution result of the at least one object entity corresponding to the at least one user business behavior, the method further includes:
extracting user behavior data of a user access object entity from user flow log data;
and calculating the conversion rate of at least one object entity in each channel node according to the channel attribution result of at least one object entity corresponding to at least one user business behavior and the user behavior data.
Optionally, the data collected by the buried point definition comprises one or more of the following: channel node information, user behavior type, user access time information, object entities accessed by the user, and a backspacing mark.
Optionally, the channel node comprises: the channel page channel nodes, the activity channel nodes, the external online drainage channel nodes and the external offline drainage channel nodes.
Optionally, the object entity includes: merchandise, tickets, stores.
According to another aspect of the present invention, there is provided a channel attribution apparatus for object entities, the apparatus including:
the acquisition module is suitable for acquiring user flow log data according to pre-established buried points aiming at a plurality of object entities;
the acquisition module is suitable for acquiring user service behavior data according to the user flow log data, wherein the user service behavior data comprises at least one object entity corresponding to at least one user service behavior;
and the channel attribution logic analysis module is suitable for performing channel attribution logic analysis by utilizing the user flow log data and the user business behavior data and determining a channel attribution result of at least one object entity corresponding to at least one user business behavior.
Optionally, the obtaining module is further adapted to: and acquiring user service behavior data belonging to the same preset time period according to the user flow log data belonging to the preset time period.
Optionally, the channel attribution logic analysis module further comprises:
the establishing unit is suitable for extracting channel node access data from user flow log data and establishing a channel node access time axis; establishing a user service behavior time axis according to the user service behavior data;
the first determining unit is suitable for determining the incidence relation between any user business behavior and any channel node based on a channel node access time axis and a user business behavior time axis;
and the second determining unit is suitable for determining a channel attribution result of at least one object entity corresponding to at least one user business behavior according to the incidence relation between any user business behavior and any channel node.
Optionally, the first determination unit is further adapted to: aiming at any user service behavior, acquiring a first time point of the user service behavior in a user service behavior time axis;
inquiring channel nodes, of which the corresponding time points in the access time axis are located before the first time point, of the channel nodes;
and determining at least one channel node from the queried channel nodes based on a preset attribution rule, and establishing an incidence relation between the user business behavior and the determined at least one channel node.
Optionally, the first determination unit is further adapted to: determining a channel node with a time point closest to a first time point from the queried channel nodes based on a preset proximity attribution rule;
and/or determining a channel node accessed for the first time in the channel node access time axis from the queried channel nodes based on a preset first attribution rule.
Optionally, the apparatus further comprises: the first judging module is suitable for judging whether an object entity corresponding to any user business behavior is an object entity directly related to any channel node;
the first determination unit is further adapted to: if the object entity corresponding to any user business behavior is the object entity directly related to any channel node, further determining the incidence relation between any user business behavior and any channel node as a direct incidence relation; and if the object entity corresponding to any user business behavior is not the object entity directly related to any channel node, further determining that the incidence relation between any user business behavior and any channel node is an indirect incidence relation.
Optionally, the apparatus further comprises: the second judgment module is suitable for judging whether the user traffic log data contains channel node access data with a backspacing mark;
the establishing unit is further adapted to: if the user traffic log data contains channel node access data with the backspacing marks, extracting the channel node access data from the user traffic log data, and filtering the channel node access data with the backspacing marks.
Optionally, the apparatus further comprises: the extraction module is suitable for extracting user behavior data of the user access object entity from the user flow log data;
and the calculation module is suitable for calculating the conversion rate of at least one object entity at each channel node according to the channel attribution result of at least one object entity corresponding to at least one user business behavior and the user behavior data.
Optionally, the data collected by the buried point definition comprises one or more of the following: channel node information, user behavior type, user access time information, object entities accessed by the user, and a backspacing mark.
Optionally, the channel node comprises: the channel page channel nodes, the activity channel nodes, the external online drainage channel nodes and the external offline drainage channel nodes.
Optionally, the object entity includes: merchandise, tickets, stores.
According to yet another aspect of the present invention, there is provided a computing device comprising: the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the channel attribution method of the object entity.
According to still another aspect of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to perform an operation corresponding to the channel attribution method for the object entity.
According to the scheme provided by the invention, user flow log data are collected according to pre-established buried points aiming at a plurality of object entities; acquiring user service behavior data according to the user flow log data, wherein the user service behavior data comprises at least one object entity corresponding to at least one user service behavior; and performing channel attribution logic analysis by using the user flow log data and the user business behavior data, and determining a channel attribution result of at least one object entity corresponding to at least one user business behavior. Based on the scheme provided by the invention, the channel node guiding each object entity can be analyzed and determined, so that the marketing platform can measure the guiding effect of each channel node and the value of each channel node to the marketing platform commercial tenant conveniently, and in addition, the marketing platform commercial tenant can measure the drainage value of each channel node according to the determined channel attribution result, so that a proper channel node is selected for popularization.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 shows a flow diagram of a channel attribution method for object entities, according to one embodiment of the present invention;
FIG. 2A is a flow diagram illustrating a channel attribution method for object entities, according to another embodiment of the present invention;
FIGS. 2B-2C are page views of channel nodes;
FIG. 2D is a schematic view of a page of a non-channel node;
fig. 2E is a schematic diagram of an established channel node access time axis and a user service behavior time axis;
FIG. 2F is a schematic diagram of channel attribution results of at least one object entity corresponding to at least one user business behavior;
FIG. 3 is a schematic structural diagram of a channel attribution apparatus for object entities according to one embodiment of the present invention;
FIG. 4 shows a schematic structural diagram of a computing device according to one embodiment of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
With the development of internet technology, marketing platforms come to the fore, and many merchants choose to open shops on the marketing platforms to put in commodities or coupons, so as to achieve the purposes of popularization and marketing. The marketing platform is provided with various channel nodes for pulling the passenger flow, wherein the channel nodes have the functions of bearing marketing platform flow guide and guiding the marketing platform merchants, the merchant content (such as stores, commodities or tickets) of the marketing platform merchants can be exposed in the pages corresponding to the channel nodes, and the marketing platform merchants can be guided to the private areas (store detail pages, commodity detail pages or ticket detail pages) of the merchants by one hop. The inventor of the present invention finds that the existing channel attribution analysis method can only perform attribution analysis on one object entity, and cannot realize attribution analysis of a plurality of object entities at a plurality of channel nodes, and for the technical defect, the inventor of the present invention provides a channel attribution method and device for an object entity, and the following describes in detail the channel attribution process for a plurality of object entities by combining with specific examples:
fig. 1 is a flowchart illustrating a channel attribution method of an object entity according to an embodiment of the present invention. As shown in fig. 1, the apparatus includes:
step S100, collecting user flow log data according to pre-established buried points aiming at a plurality of object entities.
In this embodiment, the object entity is an object part of channel attribution logic analysis, specifically, the object entity may be a store opened by a merchant on a marketing platform or a released commodity or coupon, the marketing platform is provided with a plurality of channel nodes for pulling a passenger flow volume, and the merchant of the marketing platform can promote on the plurality of channel nodes, so that it is mainly determined which object entity is guided by which channel node; the embedded points are key parts of data acquisition, data to be acquired are predefined through the embedded points, and specifically, the embedded points can be set in a channel page of a marketing platform by adopting code embedded points, visual embedded points and non-embedded point technologies.
After the embedded points aiming at the object entities are created, the operation behaviors of the user corresponding to the embedded points are required to be detected in real time, and after the operation behaviors of the user corresponding to the embedded points are detected, user flow log data are collected according to the embedded points aiming at the object entities which are created in advance.
Step S101, obtaining user service behavior data according to user flow log data.
The user traffic log data is data generated by a user aiming at the operation of a channel page of the marketing platform, and the user traffic log data reflects the operation condition of the user on the channel page and cannot reflect which channel the object entity corresponding to the user business behavior is guided by, so that the user business behavior data needs to be acquired after the user traffic log data is collected.
In this step, the user service behavior data is not acquired at will, but the user service behavior data related to the user traffic log data needs to be acquired, and specifically, the user service behavior data may be acquired according to the user traffic log data, where the user service behavior data includes at least one object entity corresponding to at least one user service behavior, and the user service behavior is specifically a behavior generated for the object entity, for example, for a commodity, the user service behavior may be a purchase; for coupons, the user business activity may be picking; for a store, the user business activity may be a transaction, such as purchasing item a, picking up coupon C, buy order transaction F, etc.
Step S102, channel attribution logic analysis is carried out by utilizing the user flow log data and the user business behavior data, and a channel attribution result of at least one object entity corresponding to at least one user business behavior is determined.
The channel attribution logic analysis specifically refers to a process of analyzing which channel node an object entity corresponding to a user business behavior is guided by, and associating the object entity with the channel node, specifically, after collecting user traffic log data according to step S100 and acquiring the user business behavior data according to step S101, analyzing the user traffic log data and the user business behavior data by using a channel attribution logic analysis algorithm, and determining a channel attribution result of at least one object entity corresponding to at least one user business behavior through analysis, wherein the channel attribution result may be represented in the following form: object entity-channel node, e.g., commodity a-channel node a.
According to the method provided by the embodiment of the invention, user flow log data is collected according to pre-established buried points aiming at a plurality of object entities; acquiring user service behavior data according to the user flow log data, wherein the user service behavior data comprises at least one object entity corresponding to at least one user service behavior; and performing channel attribution logic analysis by using the user flow log data and the user business behavior data, and determining a channel attribution result of at least one object entity corresponding to at least one user business behavior. Based on the scheme provided by the invention, the channel node guiding each object entity can be analyzed and determined, so that the marketing platform can measure the guiding effect of each channel node and the value of each channel node to the marketing platform commercial tenant conveniently, and in addition, the marketing platform commercial tenant can measure the drainage value of each channel node according to the determined channel attribution result, so that a proper channel node is selected for popularization.
Fig. 2A is a flowchart illustrating a channel attribution method of an object entity according to another embodiment of the present invention. As shown in fig. 2A, the apparatus includes:
step S200, collecting user flow log data according to pre-established buried points aiming at a plurality of object entities.
Wherein the collected data defined by the buried point comprises one or more of the following data: channel node information, user information (e.g., user identification), user behavior type (e.g., click, exposure, access), user access time information, object entities accessed by the user, fallback flags.
In this embodiment, the channel node includes: fig. 2B-2D schematically show page schematic diagrams of channel nodes and non-channel nodes, where fig. 2B-2C are page schematic diagrams of channel nodes and fig. 2D are page schematic diagrams of non-channel nodes.
Because the marketing platform provides a plurality of channel nodes, during browsing, a user is likely to continuously browse commodity detail pages or ticket detail pages or store detail pages under the plurality of channel nodes, find that a browsed certain commodity or ticket or store is better, and then return to the commodity detail page of a certain commodity or ticket detail page of a certain ticket or store detail page of a certain store under the browsed channel node, for such return behavior, called rollback, in order to be able to more accurately determine which channel node the target entity is guided by, and improve channel attribution accuracy, a rollback mark needs to be defined when a point is buried, wherein the rollback mark marks whether the user returns to the behavior, and in case of the return behavior, a mark is recorded; in case no return action occurs, no rollback flag is recorded.
Step S201, judging whether the user traffic log data contains channel node access data with a backspacing mark, if so, executing step S202; if not, go to step S203.
In order to accurately determine the effect of each object entity caused by which channel node and improve channel attribution accuracy, after user traffic log data are collected, whether the user traffic log data contain channel node access data with a backspacing mark or not needs to be judged, and if the user traffic log data contain the channel node access data with the backspacing mark, the channel node access data with the backspacing mark needs to be filtered when channel attribution logic analysis is carried out; if the user traffic log data does not contain the data with the backspacing mark, channel attribution logic analysis can be performed by using channel node access data, wherein the channel node access data comprises: channel node information, user access time information, and the channel node access data may further include: and marking the rollback.
Step S202, channel node access data are extracted from user traffic log data, filtering processing is carried out on the channel node access data with the backspacing marks, and a channel node access time axis is established.
The user traffic log data is collected according to a pre-created buried point, wherein the collected data defined by the buried point comprises one or more of the following data: the method comprises the steps of obtaining user flow log data, channel node information, user behavior types, user access time information, object entities accessed by users and backspacing marks, wherein the user flow log data comprise one or more kinds of data, extracting the channel node access data from the user flow log data under the condition that the user flow log data comprise the channel node access data with the backspacing marks, filtering the channel node access data with the backspacing marks, and then establishing a channel node access time axis according to the filtered channel node access data.
The following is illustrated with reference to specific examples:
the marketing platform is provided with the following channel nodes: the method comprises the following steps of quick large-brand robbing, special preference, guessing of you by shop pages, searching of result pages, and code scanning under line, wherein the sequence of browsing channel node pages of a user on a marketing platform is as follows: the method comprises the following steps that a big card is quickly robbed, a commodity A, an exclusive coupon C, a shop page guessing you like, a coupon G is found, after a coupon detail page of the coupon G in which the shop page guessing you like is seen, the coupon C in the exclusive coupon is found to be better, the coupon C can be returned to the coupon detail page of the coupon C, and the coupon C is received, and for the situations, channel node access data are as follows: quick-robbing big card, 7 months and 10 days in 2018 at a ratio of 11:30: 00; exclusive benefit, 11:31:00 in 7/10/7/2018; guessing your favorite shop page, 7 months and 10 days in 2018 at a ratio of 11:32:00, and returning; and then filtering the channel node access data with the rollback flag, and establishing a channel node access time axis, which is only schematically illustrated here, and may also be marked in a 0-1 manner, for example, 0 indicates unretired, 1 indicates rollback, and when a return action occurs, may be recorded as 1.
Step S203, channel node access data are extracted from the user traffic log data, and a channel node access time axis is established.
Under the condition that the user traffic log data does not contain the channel node access data with the backspacing mark according to the step S201, extracting the channel node access data from the user traffic log data, wherein the channel node access data comprises: and then, establishing a channel node access time axis according to the channel node information and the user access time information, wherein the channel node access time axis is obtained by connecting the channel nodes in series according to the user access time sequence, as shown in fig. 2E.
Step S204, according to the user flow log data belonging to the preset time period, obtaining the user service behavior data belonging to the same preset time period.
In this step, the user service behavior data is not acquired at will, but the user service behavior data related to the user traffic log data needs to be acquired, and the acquired user service behavior data should satisfy the following two conditions: firstly, the user information is the same as the user flow log data; secondly, the user traffic behavior data belonging to the same time period as the user traffic log data can be used for analyzing the channel attribution of the object entity, wherein the user traffic behavior data comprises: and the object entity corresponding to the user service behavior and the user service behavior time.
For example, if the time period of the user traffic log data is 7 months and 10 days in 2018 and the user information is user 1, in this step, according to the time period belonging to the preset time period: and acquiring user flow log data in 7, 10 and 2018, wherein the user flow log data belong to the same preset time period: in 2018, 7, month and 10, the user information is the user service behavior data of the user 1, for example, the obtained user service behavior data is: purchasing a commodity A, 2018-7-1011:30: 07; purchasing a commodity B, 2018-7-1011:30: 15; getting coupon C, 2018-7-1012: 33: 37; picking up the coupon D, 2018-7-1012:34: 57; purchasing commodity E, 2018-7-1015:33: 00; buy order transaction F, 2018-7-1017:40: 00.
In this embodiment, the execution sequence of step S204 and step S202 is not limited, and step S204 may be executed first and then step S202 may be executed; alternatively, step S202 and step S204 are performed simultaneously.
Step S205, according to the user service behavior data, establishing a user service behavior time axis.
After the user service behavior data belonging to the same preset time period is obtained according to step S204, a user service behavior time axis is established according to the object entities corresponding to the user service behaviors in the user service behavior data and the user service behavior time, where the user service behavior time axis is obtained by connecting the object entities corresponding to the user service behaviors in series according to the user service behavior time sequence, as shown in fig. 2E.
After the channel node access time axis and the user service behavior time axis are established, the association relationship between any user service behavior and any channel node may be determined based on the channel node access time axis and the user service behavior time axis, and specifically, the association relationship between any user service behavior and any channel node may be determined by the method in step S206 to step S208:
step S206, aiming at any user service behavior, acquiring a first time point of the user service behavior in a user service behavior time axis.
The purpose of this embodiment is to determine which channel node the object entity corresponding to the user service behavior in one session is guided from, where a sign of one session ending is that the corresponding user service behavior occurs, for example, a user browses a commodity a in a big-brand quick contest and a coupon C in an exclusive discount, and generates the user service behavior: and if the coupon C is received, the process of receiving the coupon C from the commodity A in the big-brand fast-robbery, the coupon C in the special discount, and the coupon C is considered to be a session, and the user browses the commodity E in which the shop page guesses you like and the commodity B in the big-brand fast-robbery in the subsequent process, so that the user business behavior is generated: when a commodity B is purchased, the process of guessing the commodity B in the favorite commodity E-big brand quick robbery-purchasing the commodity B by the shop page is considered as a session, so that the time point of the user business behavior can be used as a determination basis, specifically, the time axis of the user business behavior is inquired for any user business behavior, and the first time point of the user business behavior in the time axis of the user business behavior is obtained, for example, the first time point of purchasing the commodity A is 2018-7-1011:30:07, and the first time point of purchasing the commodity B is 2018-7-1011:30: 15; the first time point of picking up the coupon C is 2018-7-1012: 33: 37; the first time point of picking up the ticket D is 2018-7-1012:34: 57; the first time point for purchasing commodity E is 2018-7-1015:33: 00; the first time point for the purchase order transaction F is 2018-7-1017:40: 00.
Step S207, inquiring channel nodes which are positioned before the first time point corresponding to the time point in the channel node access time axis.
After the first time point of the user business behavior in the user business behavior time axis is determined, the channel node access time axis needs to be queried, the channel node in the channel node access time axis, which is located before the first time point at the corresponding time point, is determined, which is described with reference to fig. 2E, and at the first time point of purchasing the commodity a: the channel nodes before 2018-7-1011:30:07 are: quickly robbing the big card; first point in time for purchase of item B: the channel nodes before 2018-7-1011:30: 15 are: quickly robbing the big card; first time point of ticket C pickup: the channel nodes before 2018-7-1012: 33:37 are: a special offer; first time point of ticket D: the channel nodes before 2018-7-1012:34:57 are: a special offer; first point in time for purchase of item E: the channel nodes before 2018-7-1015:33:00 are: guessing you like at the shop page; first point in time of the purchase order transaction F: the channel nodes before 2018-7-1017:40:00 are: search results page, line sweep code.
Step S208, based on the preset attribution rule, at least one channel node is determined from the queried channel nodes, and the incidence relation between the user business behavior and the determined at least one channel node is established.
After it is determined that the channel node accesses a channel node of the time axis whose corresponding time point is before the first time point, at least one channel node may be determined from the queried channel nodes based on preset attribution rules, for example, a preset nearby attribution rule and a preset first-time attribution rule.
Specifically, a channel node whose time point is closest to the first time point may be determined from the queried channel nodes based on the preset proximity attribution rule, which is exemplified below with reference to the channel node determined in step S207:
for example, a first point in time from the purchase of item a is determined: 2018-7-1011:30:07 the nearest channel node is: quickly robbing the big card; distance from the first point in time at which item B was purchased: 2018-7-1011:30: 15 the nearest channel node is: quickly robbing the big card; first time point from ticket C: 2018-7-1012: 33:37 the nearest channel node is: a special offer; first time point from ticket D: 2018-7-1012:34:57 the nearest channel node is: a special offer; distance from the first point in time at which item E was purchased: 2018-7-1015:33:00 the nearest channel node is: guessing you like at the shop page; first point in time from the purchase order transaction F: 2018-7-1017:40:00 the nearest channel node is: scanning the code under the line;
and/or, a channel node to be accessed for the first time in the channel node access time axis may be determined from the queried channel nodes based on the preset first-time attribution rule, and the channel node determined based on the preset first-time attribution rule is exemplified below with reference to the channel node determined in step S207:
for example, for user traffic behavior: and purchasing the commodity A, wherein the channel nodes accessed for the first time are as follows: quickly robbing the big card; for user traffic behavior: and purchasing a commodity B, wherein the channel nodes accessed for the first time are as follows: quickly robbing the big card; for user traffic behavior: and (3) getting the coupon C, wherein the channel nodes accessed for the first time are as follows: a special offer; for user traffic behavior: and (3) getting the coupon D, wherein the channel nodes accessed for the first time are as follows: a special offer; for user traffic behavior: and purchasing a commodity E, wherein the channel nodes accessed for the first time are as follows: guessing you like at the shop page; for user traffic behavior: the purchase order transaction F, the channel node of first visit is: a search results page.
After determining at least one channel node from the queried channel nodes based on the preset attribution rule, establishing an association relationship between the user business behavior and the determined at least one channel node, for example, purchasing commodity A-big card for quick snatching.
Step S209, judging whether the object entity corresponding to the user business behavior is the object entity directly related to the determined at least one channel node; if yes, go to step S210; if not, step S211 is executed.
In this embodiment, it may be determined not only by which channel node the object entity is guided, but also whether the object entity is guided directly or indirectly by the channel node, and specifically, the following method may be adopted for determination: judging whether the object entity corresponding to the user business behavior is an object entity directly related to the determined at least one channel node, wherein the object entity directly related to the channel node refers to an object entity which can be landed to a detail page after the channel node jumps once, and judging by adopting a tracing means, for example, tracing back from the detail page of the object entity corresponding to the user business behavior forwards, and if the object entity can be traced back to the page of the channel node once, the object entity directly related to the determined at least one channel node can be considered; otherwise, it is not an object entity directly related to the determined at least one channel node.
Step S210, determining that the incidence relation between the user business behavior and the determined at least one channel node is a direct incidence relation.
In the case that it is determined that the object entity corresponding to the user business behavior is an object entity directly related to the determined at least one channel node according to step S209, it may be determined that the association relationship between the user business behavior and the determined at least one channel node is a direct association relationship, where the direct association relationship may be understood in a colloquial manner as: the object entities (e.g., goods, tickets, stores) that are clicked on the page of the channel node are the same as the goods purchased or the tickets received or the trading stores.
Step S211, determining that the association relationship between the user service behavior and the determined at least one channel node is an indirect association relationship.
In the case that it is determined that the object entity corresponding to the user business behavior is not an object entity directly related to the determined at least one channel node according to step S209, it may be determined that the association relationship between the user business behavior and the determined at least one channel node is an indirect association relationship, where the indirect association relationship may be understood in a colloquial manner as: the object entities (e.g., goods, coupons, stores) that are clicked on the page of the channel node are different from the goods purchased or coupons retrieved or the trading stores.
Step S212, according to the incidence relation between any user business behavior and any channel node, determining the channel attribution result of at least one object entity corresponding to at least one user business behavior.
The channel attribution result is the result of the channel attribution logic analysis, and represents whether the object entity is guided by which channel node, and is directly guided or indirectly guided.
Specifically, after the association relationship between the user business behavior and the determined at least one channel node is established according to step S208, and after the association relationship between the user business behavior and the determined at least one channel node is determined as the direct association relationship or the indirect association relationship according to steps S209 to S211, the channel attribution result of the at least one object entity corresponding to the at least one user business behavior may be determined according to the association relationship between any user business behavior and any channel node, as shown in fig. 2F.
Step S213, extracting the user behavior data of the user access object entity from the user traffic log data.
In this embodiment, not only the channel attribution result can be determined, but also the conversion effect of each object entity at the channel node can be calculated, for example, the conversion effect of the commodity a at the channel node can be calculated: the conversion rate of the big card quick robbery is specifically that user behavior data of user access object entities, such as user behavior types and user access object entities, are extracted from user traffic log data.
Step S214, calculating the conversion rate of at least one object entity at each channel node according to the channel attribution result of at least one object entity corresponding to at least one user business behavior and the user behavior data.
In this step, the number of times of purchase/pickup/transaction of the object entity can be determined according to the channel attribution result of the at least one object entity corresponding to the at least one user business behavior, and the total number of times of click/exposure/access of the object entity accessed by the user can be counted according to the user behavior data, so that the conversion rate of the at least one object entity in each channel node can be calculated.
In addition, the conversion rate of the commodity detail page/ticket detail page can be calculated according to the exposure times and the access times.
In this embodiment, a channel node may include a secondary channel node or a tertiary channel node, and the channel attribution method using the object entity may be attributed to the secondary channel node or the tertiary channel node in priority, and in case that the channel node cannot be attributed to a subdivided channel node (e.g., the secondary channel node or the tertiary channel node), the channel node may be attributed to the primary channel node.
According to the method provided by the embodiment of the invention, by setting the backspacing mark, the guiding effect of each object entity from which channel node is guided can be accurately analyzed and determined, the channel attribution accuracy is improved, the guiding effect of each channel node can be conveniently and intuitively measured by the marketing platform by calculating the conversion effect of each object entity in the channel node, the value of each channel node to a marketing platform commercial tenant can be measured, in addition, the drainage value of each channel node can be measured by the marketing platform commercial tenant according to the conversion effect, so that a proper channel node can be selected for popularization, and the popularization of the marketing platform is facilitated.
Fig. 3 is a schematic structural diagram illustrating a channel attribution apparatus of an object entity according to an embodiment of the present invention. As shown in fig. 3, the apparatus includes: acquisition module 300, acquisition module 310, channel attribution logic analysis module 320.
The collection module 300 is adapted to collect user traffic log data according to pre-created burial points for a plurality of object entities.
The obtaining module 310 is adapted to obtain user service behavior data according to the user traffic log data, where the user service behavior data includes at least one object entity corresponding to at least one user service behavior.
The channel attribution logic analysis module 320 is adapted to perform channel attribution logic analysis by using the user traffic log data and the user business behavior data, and determine a channel attribution result of at least one object entity corresponding to at least one user business behavior.
Optionally, the obtaining module 310 is further adapted to: and acquiring user service behavior data belonging to the same preset time period according to the user flow log data belonging to the preset time period.
Optionally, the channel attribution logic analysis module 320 further comprises: the establishing unit 321 is adapted to extract channel node access data from the user traffic log data, and establish a channel node access time axis; establishing a user service behavior time axis according to the user service behavior data;
a first determining unit 322, adapted to determine an association relationship between any user service behavior and any channel node based on a channel node access time axis and a user service behavior time axis;
the second determining unit 323 is adapted to determine, according to the association relationship between any user business behavior and any channel node, a channel attribution result of at least one object entity corresponding to at least one user business behavior.
Optionally, the first determining unit 322 is further adapted to: aiming at any user service behavior, acquiring a first time point of the user service behavior in a user service behavior time axis;
inquiring channel nodes, of which the corresponding time points in the access time axis are located before the first time point, of the channel nodes;
and determining at least one channel node from the queried channel nodes based on a preset attribution rule, and establishing an incidence relation between the user business behavior and the determined at least one channel node.
Optionally, the first determining unit 322 is further adapted to: determining a channel node with a time point closest to a first time point from the queried channel nodes based on a preset proximity attribution rule;
and/or determining a channel node accessed for the first time in the channel node access time axis from the queried channel nodes based on a preset first attribution rule.
Optionally, the apparatus further comprises: the first judging module 330 is adapted to judge whether an object entity corresponding to any user business behavior is an object entity directly related to any channel node;
the first determination unit 322 is further adapted to: if the object entity corresponding to any user business behavior is the object entity directly related to any channel node, further determining the incidence relation between any user business behavior and any channel node as a direct incidence relation; and if the object entity corresponding to any user business behavior is not the object entity directly related to any channel node, further determining that the incidence relation between any user business behavior and any channel node is an indirect incidence relation.
Optionally, the apparatus further comprises: the second judging module 340 is adapted to judge whether the user traffic log data includes channel node access data with a fallback flag;
the establishing unit 321 is further adapted to: if the user traffic log data contains channel node access data with the backspacing marks, extracting the channel node access data from the user traffic log data, and filtering the channel node access data with the backspacing marks.
Optionally, the apparatus further comprises: an extracting module 350, adapted to extract user behavior data of the user access object entity from the user traffic log data;
the calculating module 360 is adapted to calculate a conversion rate of at least one object entity at each channel node according to the channel attribution result of at least one object entity corresponding to at least one user business behavior and the user behavior data.
Optionally, the data collected by the buried point definition comprises one or more of the following: channel node information, user behavior type, user access time information, object entities accessed by the user, and a backspacing mark.
Optionally, the channel node comprises: the channel page channel nodes, the activity channel nodes, the external online drainage channel nodes and the external offline drainage channel nodes.
Optionally, the object entity includes: merchandise, tickets, stores.
According to the device provided by the embodiment of the invention, user flow log data is collected according to pre-established buried points aiming at a plurality of object entities; acquiring user service behavior data according to the user flow log data, wherein the user service behavior data comprises at least one object entity corresponding to at least one user service behavior; and performing channel attribution logic analysis by using the user flow log data and the user business behavior data, and determining a channel attribution result of at least one object entity corresponding to at least one user business behavior. Based on the scheme provided by the invention, the channel node guiding each object entity can be analyzed and determined, so that the marketing platform can measure the guiding effect of each channel node and the value of each channel node to the marketing platform commercial tenant conveniently, and in addition, the marketing platform commercial tenant can measure the drainage value of each channel node according to the determined channel attribution result, so that a proper channel node is selected for popularization.
The embodiment of the present application further provides a non-volatile computer storage medium, where the computer storage medium stores at least one executable instruction, and the computer executable instruction may execute the channel attribution method of the object entity in any of the above method embodiments.
Fig. 4 is a schematic structural diagram of a computing device according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.
As shown in fig. 4, the computing device may include: a processor (processor)402, a Communications Interface 404, a memory 406, and a Communications bus 408.
Wherein:
the processor 402, communication interface 404, and memory 406 communicate with each other via a communication bus 408.
A communication interface 404 for communicating with network elements of other devices, such as clients or other servers.
The processor 402 is configured to execute the program 410, and may specifically execute the relevant steps in the embodiment of the channel attribution method for an object entity.
In particular, program 410 may include program code comprising computer operating instructions.
The processor 402 may be a central processing unit CPU, or an application specific Integrated circuit asic, or one or more Integrated circuits configured to implement an embodiment of the present invention. The computing device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 406 for storing a program 410. Memory 406 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 410 may specifically be adapted to cause the processor 402 to execute the channel attribution method for the object entity in any of the above-described method embodiments. For specific implementation of each step in the program 410, reference may be made to corresponding steps and corresponding descriptions in units in the channel attribution embodiment of the object entity, which are not described herein again. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described devices and modules may refer to the corresponding process descriptions in the foregoing method embodiments, and are not described herein again.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components of the channel attribution equipment of the subject entities according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (22)
1. A channel attribution method for an object entity, the method comprising:
collecting user flow log data according to pre-established buried points aiming at a plurality of object entities;
acquiring user service behavior data belonging to the same preset time period according to user flow log data belonging to the preset time period, wherein the user service behavior data comprises at least one object entity corresponding to at least one user service behavior;
respectively establishing a channel node access time axis and a user service behavior time axis according to user traffic log data belonging to a preset time period and user service behavior data belonging to the same preset time period;
and establishing an incidence relation between the user business behavior and the determined at least one channel node according to a channel node access time axis and a user business behavior time axis, and determining a channel attribution result of at least one object entity corresponding to the at least one user business behavior according to the incidence relation.
2. The method of claim 1, wherein the establishing a channel node access timeline and a user traffic behavior timeline respectively according to user traffic log data belonging to a preset time period and user traffic behavior data belonging to the same preset time period further comprises:
channel node access data are extracted from the user flow log data, and a channel node access time axis is established;
and establishing a user service behavior time axis according to the user service behavior data.
3. The method of claim 1, wherein establishing an association of the user traffic behavior with the determined at least one channel node according to a channel node access timeline and a user traffic behavior timeline further comprises:
aiming at any user service behavior, acquiring a first time point of the user service behavior in a user service behavior time axis;
inquiring channel nodes of which the corresponding time points in the channel node access time shaft are located before the first time point;
and determining at least one channel node from the queried channel nodes based on a preset attribution rule, and establishing an incidence relation between the user business behavior and the determined at least one channel node.
4. The method of claim 3, wherein the determining at least one channel node from the queried channel nodes based on a preset attribution rule further comprises:
determining a channel node with a time point closest to the first time point from the queried channel nodes based on a preset proximity attribution rule;
and/or determining a channel node accessed for the first time in the channel node access time axis from the queried channel nodes based on a preset first attribution rule.
5. The method according to any of claims 1-4, wherein after establishing the association of the user traffic behavior with the determined at least one channel node, the method further comprises:
judging whether an object entity corresponding to any user business behavior is an object entity directly related to any channel node;
if yes, further determining that the association relationship between any user business behavior and any channel node is a direct association relationship;
if not, further determining that the association relationship between any user business behavior and any channel node is an indirect association relationship.
6. The method of claim 2, wherein after said collecting user traffic log data, the method further comprises:
judging whether the user traffic log data contains channel node access data with a backspacing mark;
if yes, the channel node access data extracted from the user traffic log data specifically comprises: and extracting channel node access data from the user traffic log data, and filtering the channel node access data with the rollback mark.
7. The method of any of claims 1-4, wherein after determining the channel attribution results for the at least one subject entity to which the at least one user business behavior corresponds, the method further comprises:
extracting user behavior data of a user access object entity from the user flow log data;
and calculating the conversion rate of at least one object entity in each channel node according to the channel attribution result of at least one object entity corresponding to at least one user business behavior and the user behavior data.
8. The method of any one of claims 1 to 4, wherein the acquisition data defined by the buried site comprises one or more of the following: channel node information, user behavior type, user access time information, object entities accessed by the user, and a backspacing mark.
9. The method of any of claims 1-4, wherein the channel node comprises: the channel page channel nodes, the activity channel nodes, the external online drainage channel nodes and the external offline drainage channel nodes.
10. The method of any one of claims 1-4, wherein the subject entity includes: merchandise, tickets, stores.
11. An apparatus for channel attribution of object entities, the apparatus comprising:
the acquisition module is suitable for acquiring user flow log data according to pre-established buried points aiming at a plurality of object entities;
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is suitable for acquiring user service behavior data belonging to the same preset time period according to user flow log data belonging to the preset time period, and the user service behavior data comprises at least one object entity corresponding to at least one user service behavior;
the channel attribution logic analysis module comprises:
the establishing unit is suitable for respectively establishing a channel node access time axis and a user service behavior time axis according to user flow log data belonging to a preset time period and user service behavior data belonging to the same preset time period;
the first determining unit is suitable for establishing an incidence relation between the user business behavior and the determined at least one channel node according to a channel node access time axis and a user business behavior time axis;
and the second determining unit is suitable for determining a channel attribution result of at least one object entity corresponding to at least one user business behavior according to the incidence relation.
12. The apparatus according to claim 11, wherein the establishing unit is further adapted to: channel node access data are extracted from the user flow log data, and a channel node access time axis is established; and establishing a user service behavior time axis according to the user service behavior data.
13. The apparatus of claim 11, wherein the first determining unit is further adapted to:
aiming at any user service behavior, acquiring a first time point of the user service behavior in a user service behavior time axis;
inquiring channel nodes of which the corresponding time points in the channel node access time shaft are located before the first time point;
and determining at least one channel node from the queried channel nodes based on a preset attribution rule, and establishing an incidence relation between the user business behavior and the determined at least one channel node.
14. The apparatus of claim 13, wherein the first determining unit is further adapted to:
determining a channel node with a time point closest to the first time point from the queried channel nodes based on a preset proximity attribution rule;
and/or determining a channel node accessed for the first time in the channel node access time axis from the queried channel nodes based on a preset first attribution rule.
15. The apparatus of any one of claims 11-14, wherein the apparatus further comprises:
the first judging module is suitable for judging whether an object entity corresponding to any user business behavior is an object entity directly related to any channel node;
the first determination unit is further adapted to: if the object entity corresponding to any user business behavior is the object entity directly related to any channel node, further determining that the incidence relation between any user business behavior and any channel node is a direct incidence relation; and if the object entity corresponding to any user business behavior is not the object entity directly related to any channel node, further determining that the association relationship between any user business behavior and any channel node is an indirect association relationship.
16. The apparatus of any one of claims 12-14, wherein the apparatus further comprises:
the second judging module is suitable for judging whether the user traffic log data contains channel node access data with a backspacing mark;
the establishing unit is further adapted to: if the user traffic log data contains channel node access data with the backspacing marks, extracting the channel node access data from the user traffic log data, and filtering the channel node access data with the backspacing marks.
17. The apparatus of any one of claims 11-14, wherein the apparatus further comprises:
the extraction module is suitable for extracting user behavior data of a user access object entity from the user flow log data;
and the calculation module is suitable for calculating the conversion rate of at least one object entity at each channel node according to the channel attribution result of at least one object entity corresponding to at least one user business behavior and the user behavior data.
18. The apparatus of any one of claims 11-14, wherein the acquisition data defined by the buried point comprises one or more of: channel node information, user behavior type, user access time information, object entities accessed by the user, and a backspacing mark.
19. The apparatus of any of claims 11-14, wherein the channel node comprises: the channel page channel nodes, the activity channel nodes, the external online drainage channel nodes and the external offline drainage channel nodes.
20. The apparatus of any of claims 11-14, wherein the subject entity comprises: merchandise, tickets, stores.
21. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform operations corresponding to the channel attribution method of the subject entity according to any one of claims 1-10.
22. A computer storage medium having stored therein at least one executable instruction to cause a processor to perform operations corresponding to the channel attribution method of an object entity of any one of claims 1-10.
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| CN111310061B (en) * | 2018-11-27 | 2023-12-15 | 百度在线网络技术（北京）有限公司 | Full-link multi-channel attribution method, device, server and storage medium |
| CN110033315A (en) * | 2019-03-18 | 2019-07-19 | 北京品友互动信息技术股份公司 | The attribution method and device of advertising information conversion, storage medium, electronic device |
| CN110995524B (en) * | 2019-10-28 | 2022-06-14 | 北京三快在线科技有限公司 | Flow data monitoring method and device, electronic equipment and computer readable medium |
| CN112200618B (en) * | 2020-10-29 | 2022-05-17 | 度小满科技(北京)有限公司 | Message channel attribution method, device and system |
| CN114140031A (en) * | 2022-01-28 | 2022-03-04 | 支付宝(杭州)信息技术有限公司 | Method and device for attribution analysis of user behaviors |
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