CN110675194A - Funnel analysis method, device, equipment and readable medium - Google Patents
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Abstract
The embodiment of the specification discloses a funnel analysis method, a funnel analysis device, funnel analysis equipment and a computer readable medium of the funnel analysis device. The funnel analysis method comprises the following steps: acquiring funnel analysis configuration information, wherein the funnel analysis configuration information comprises funnel step information and time range information; according to the funnel step information and the time range information, user behavior log information which corresponds to the funnel step information and meets the time range information is obtained, wherein the user behavior log information is stored in a column type storage table, each row of the column type storage table corresponds to one piece of user behavior log information, each column of the column type storage table corresponds to one parameter field in the user behavior log information, and the parameter fields corresponding to the columns are different; and calculating the access user amount of each funnel step based on the acquired user behavior log information.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a funnel analysis method, apparatus, device, and computer readable medium.
Background
Ordered funnel calculation is an important way to analyze user conversions, which is of great significance in addressing the relevant needs in product operation with respect to user conversions.
Currently, one way common in the industry is to perform funnel calculations by Left Join. Specifically, the user behavior log table needs to be screened according to conditions and left-connected, and multiple funnel steps mean multiple join operations. The disadvantage of this approach is that if the step preceding the funnel step uses a large log amount of buried points, such as those tied to a login button, those tied to a first page exposure, etc., the implementation of this method requires the connection of multiple large tables, so that the computation speed can be slow to the minute or even hour level, which is unacceptable in an analysis system where the user desires to obtain results instantly.
In addition, when there is a fixed conversion calculation requirement, the funnel model can also be built in the prior art by constructing Cube in advance. The method for constructing the Cube through the Apache Kylin is a non-real-time analysis method, a funnel step needs to be specified in advance, the Cube dimension is determined, and the effect of outputting a result quickly during query is achieved through pre-calculation. The disadvantage of this method is that, due to different operation requirements, the funnels that need to be constructed in practical applications have large differences in step selection, and funnel steps cannot be pre-solidified, which makes it impossible to calculate all funnel models in a pre-constructed manner, resulting in that diverse analysis requirements of users cannot be satisfied in an ad hoc query scenario.
Disclosure of Invention
In view of this, the embodiment of the present application provides a funnel analysis method, which is used to improve efficiency of obtaining an ordered funnel calculation result in an instant funnel query scene.
In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:
the funnel analysis method provided by the embodiment of the specification comprises the following steps: acquiring funnel analysis configuration information, wherein the funnel analysis configuration information comprises funnel step information and time range information, and the funnel step information comprises an event identifier to be analyzed of an event to be analyzed related to funnel analysis and an event expectation step to be analyzed; according to the funnel step information and the time range information, user behavior log information which corresponds to the funnel step information and meets the time range information is obtained, wherein the user behavior log information is stored in a column type storage table, each row of the column type storage table corresponds to one piece of user behavior log information, each column of the column type storage table corresponds to one parameter field in the user behavior log information, and the parameter fields corresponding to the columns are different; and calculating the access user amount of each funnel step based on the acquired user behavior log information.
The funnel analytical equipment that this specification embodiment provided includes: the funnel analysis system comprises a configuration information acquisition module, a time range acquisition module and a time range analysis module, wherein the configuration information acquisition module is used for acquiring funnel analysis configuration information, the funnel analysis configuration information comprises funnel step information and time range information, and the funnel step information comprises an event identifier to be analyzed of an event to be analyzed related to funnel analysis and an event expectation step to be analyzed; the log information acquisition module is used for acquiring user behavior log information which corresponds to the funnel step information and meets the time range information according to the funnel step information and the time range information, wherein the user behavior log information is stored in a column-type storage table, each row of the column-type storage table corresponds to one piece of user behavior log information, each column of the column-type storage table corresponds to one parameter field in the user behavior log information, and the parameter fields corresponding to the columns are different; and the calculating module is used for calculating the access user amount of each funnel step based on the acquired user behavior log information.
An embodiment of this specification provides a data analysis device, includes:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to: acquiring funnel analysis configuration information, wherein the funnel analysis configuration information comprises funnel step information and time range information, and the funnel step information comprises an event identifier to be analyzed of an event to be analyzed related to funnel analysis and an event expectation step to be analyzed; according to the funnel step information and the time range information, user behavior log information which corresponds to the funnel step information and meets the time range information is obtained, wherein the user behavior log information is stored in a column type storage table, each row of the column type storage table corresponds to one piece of user behavior log information, each column of the column type storage table corresponds to one parameter field in the user behavior log information, and the parameter fields corresponding to the columns are different; and calculating the access user amount of each funnel step based on the acquired user behavior log information.
Embodiments of the present specification provide a computer readable medium having stored thereon computer readable instructions executable by a processor to implement a funnel analysis method as described in embodiments of the present application.
The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects:
the embodiment of the application provides a funnel analysis method, based on acquired funnel analysis configuration information, user behavior log information to be analyzed, which accords with funnel step information and time range information, is acquired from a structured column storage table, and then a funnel analysis result is acquired based on the acquired information analysis.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic diagram of an application scenario of the funnel analysis method in the embodiment of the present specification.
Fig. 2 is a schematic flow chart of a funnel analysis method provided in an embodiment of the present disclosure;
FIG. 3 shows a computational flow diagram of a funnel analysis method according to an embodiment;
FIG. 4 is a schematic structural diagram of a funnel analysis device corresponding to FIG. 2 provided in an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a funnel analysis device provided in an embodiment of the present specification.
Detailed Description
In the field of big data, a mode of querying data in a data warehouse is called ad hoc query (ad hoc), when ad hoc query is performed, a user flexibly selects query conditions according to own requirements, and a system can generate a corresponding statistical report according to the selection of the user. The biggest difference between the ad hoc query and the common application query is that the common application query is developed in a customized manner, and the ad hoc query is defined by a user according to query conditions. The general queries are known at system design and implementation, so they can be optimized by building indexes, partitions, etc. at system implementation, thereby increasing the efficiency of the queries. While ad hoc queries are generated temporarily by the user at the time of use, the system cannot optimize these queries in advance.
When the ordered funnel calculation is carried out in the instant query scene, on one hand, because the bottom data of the funnel is the behavior log data of the user, the quantity of the bottom data is in the hundred million level every day, and the operations of sequencing, aggregation and the like are inevitably required in the funnel calculation process; on the other hand, due to different operation requirements, the funnels that need to be constructed each time have large differences in step selection, which makes it impossible to calculate all funnel models in a pre-constructed manner. In such a background, it is desirable to provide a method for quickly outputting funnel calculation results of instant query under a large number of backgrounds and under the condition that the funnel steps cannot be pre-constructed and optimized in advance.
Therefore, it is desirable to provide a funnel analysis method, which can quickly obtain ordered funnel calculation results in an instant funnel query scene.
In view of the above, an embodiment of the present application provides a funnel analysis method, where based on acquired funnel analysis configuration information, user behavior log information to be analyzed, which conforms to funnel step information and time range information, is acquired from a structured column-type storage table, and then a funnel analysis result is obtained based on the acquired information analysis, in this process, because the user behavior log information is stored in the column-type storage table, each column in the column-type storage table corresponds to one parameter field in the user behavior log information, therefore, when acquiring the user behavior log information, a large number of JSON analysis operations are not required, which reduces consumption of CPU resources, reduces time spent on data screening and aggregation, can implement second-level data aggregation and screening requirements, and improve efficiency of funnel analysis.
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an application scenario of the funnel analysis method in the embodiment of the present specification.
As shown in fig. 1, in the embodiment of the present application, the behavior analysis system is a system for performing user behavior analysis based on user behavior data, where the essence of the user behavior analysis is to perform screening, aggregation, and calculation on user behavior log data through written SQL statements, and finally obtain a target result.
The behavior analysis system comprises a data storage module and a data analysis module.
A data storage module in the behavior analysis system stores user behavior log information in a columnar structure. Specifically, the data storage module may convert the user behavior log data in the text format reported by the user side into a columnar storage format, for example, convert the data in the JSON format into a queue format. The reported user behavior log data can be reported by a preset buried point. The type and setting method of the buried point are not limited, and for example, the buried point may be a buried point bound to a certain button (e.g., a register button, a pay button) or a buried point bound to a display of a certain page (e.g., an advertisement page exposure). The data storage module can determine a disassembly mode of the textual user behavior log data reported by the client based on the buried point configuration information, namely determine a structure of a behavior analysis table used for user behavior analysis, and complete conversion of the user behavior data from unstructured text data to a columnar storage format. When updating the buried point configuration information, such as adding, deleting, or changing buried points, the column-wise stored behavior analysis table may increase or decrease the columns accordingly.
The data analysis module in the behavior analysis system may analyze data analysis indexes (for example, page visitation amount, number of independent visitors, and the like) related to each buried point based on a behavior analysis table stored in a column in the data storage module, or may construct a funnel model and analyze the number of visitors of each step event in the funnel model, thereby calculating a conversion rate of each step in the funnel model. In the embodiments of the present application, the funnel analysis method is described with emphasis.
Fig. 2 is a schematic flow chart of a funnel analysis method provided in an embodiment of the present disclosure. From the viewpoint of the program, the execution subject of the flow may be a program installed in the server for executing data analysis.
As shown in fig. 2, the process may include the following steps:
s210: the method comprises the steps of obtaining funnel analysis configuration information, wherein the funnel analysis configuration information comprises funnel step information and time range information, and the funnel step information comprises to-be-analyzed event identification and to-be-analyzed event expectation steps of to-be-analyzed events related to funnel analysis.
Wherein the time range information represents a date interval of occurrence of an event to be analyzed related to the funnel analysis.
The funnel analysis configuration information may be information configured by an analyst at the front end of the behavior analysis system when a funnel model is used for data analysis (e.g., when user behavior analysis is performed).
The funnel model is a set of flow-type funnel analysis method, can scientifically reflect the behavior state of a user and the user conversion rate condition in each stage from a starting point to an end point, and is an important data analysis model. The funnel analysis model is widely applied to daily data operation and data analysis work such as flow monitoring of website and APP user behavior analysis, CRM (customer relationship management) system, SEO (search optimization), product marketing and sales and the like. The funnel model can be mainly used for decomposing and quantifying each link in the flow, and helps to effectively find problems and optimize the problems, so that the operation efficiency is integrally improved. The classification of the funnel model may be any kind of funnel model, such as an AIDA model, an AIDMA model, an AISAS model, an AARRR model, and the like.
Typical cases compared using the funnel model include conversions of the e-commerce web site. The most common indicators used for funnel analysis include two complementary indicators of conversion and loss. For example, suppose that 100 people visit a certain e-commerce website, 30 people click to register, and 10 people successfully register. The process has three steps, wherein the conversion rate from the first step to the second step is 30%, the loss rate is 70%, the conversion rate from the second step to the third step is 33%, and the loss rate is 67%; the conversion rate in the whole process is 10%, and the loss rate is 90%.
According to an embodiment, the funnel step information may be ordered funnel step information. Specifically, the information of the funnel analysis step may include a user behavior path to be monitored, which is focused by an analyst. Alternatively, each funnel step may correspond to a single buried point, or may correspond to a virtual buried point consisting of a plurality of buried points.
Regarding the virtual buried point, for example, for an electronic book presentation page, a buried point "ev 1" may be bound on the "listening trial" button, and a buried point "ev 2" may be bound on the "reading trial" button, so that user behavior data reported by the buried point "ev 1" and the buried point "ev 2" may be analyzed respectively to obtain information related to a user listening trial situation and information related to a user viewing trial situation; the two can also be used as a virtual buried point to be analyzed, and the trial situation information of the user can be obtained integrally. Given here is only one example of a virtual buried point, and any plurality of buried points may be set as one virtual buried point as needed.
According to the embodiment, the identifier of the event to be analyzed may be specifically an identifier of an event reported by a single buried point or a virtual buried point corresponding to the funnel step. A piece of user behavior log information may mean that an event corresponding to the buried point has occurred.
Funnel analysis was divided into disordered and ordered funnels. In the out-of-order funnel, the occurrence of the events before and after can be ordered arbitrarily, such as the user switching freely between pages and returning to the homepage operation, and is not restricted by the logic sequence between steps. While there are strict order constraints between each step of an ordered funnel, the events of the second step must occur after the first step. For example, in the payment process, the article needs to be selected first to enter the corresponding payment operation. The ordered funnel is widely applied to path behaviors with strict logic level requirements, such as shopping payment and information registration. Compared with an unordered funnel with extremely low user path coincidence rate, the data research of the ordered funnel is more valuable. In an embodiment of the application, the funnel analysis is an ordered funnel analysis.
According to an embodiment, in the ordered funnel analysis, an event expected step to be analyzed can be set to define the occurrence sequence of each step in the ordered funnel, wherein the event expected step to be analyzed comprises an initial step and a subsequent step.
Optionally, when the user behavior analysis is performed, the user behavior in a certain time period may be focused, and therefore, the user behavior data that conforms to the preset time range information may be acquired for analysis. The timeframe information may include a date interval of the funnel query configured by the analyst, which affects the query range of the buried point log data.
Optionally, the funnel analysis configuration information may further include user attribute information, for example, user equipment information (e.g., IOS, Android), application software version information, user age information, user location information, and the like, where the user attribute information may be used as grouping information for grouping and contrasting funnel analysis results. Specifically, when the user attribute information is set, the funnel analysis may be performed in groups based on the specific contents of the set user attribute information. For example, when an operating system (specifically, including IOS and Android) is set as the user attribute information for grouping, two funnel analysis results may be obtained as comparison, one being a funnel analysis result based on the user behavior information statistics of which the operating system is IOS, and the other being a funnel analysis result based on the user behavior information statistics of which the operating system is Android. Optionally, a funnel analysis result of user behavior information statistics of all users (the operating systems are Android or IOS) may also be included. Thus, the user attribute information can provide a more detailed and contrastable query analysis result meeting the needs of analysts.
S220: according to the funnel step information and the time range information, user behavior log information which corresponds to the funnel step information and meets the time range information is obtained, wherein the user behavior log information is stored in a column type storage table, each row of the column type storage table corresponds to one piece of user behavior log information, each column of the column type storage table corresponds to one parameter field in the user behavior log information, and the parameter fields corresponding to the columns are different.
The list-type storage table may be obtained by transferring user behavior log data reported by the user side to a list of a list-type structure. According to a non-limiting embodiment, the user behavior log data reported by the user side may be data in an unformatted text format, such as JSON; the format of the columnar storage table may be in partial format. The columnar store table may be stored on a Hadoop Distributed File System (HDFS).
The format of the columnar storage table corresponds to parameter fields contained in the user behavior log information, specifically, each column in the columnar storage table corresponds to one parameter field in the user behavior log information, and each row in the columnar storage table corresponds to one piece of user behavior log data.
To more clearly illustrate the structure of the columnar storage table, the following is exemplified:
it is assumed that when the reported user behavior log data in JSON format is as follows,
log 1 reported by the user:
log 2 reported by the user:
the format after being transferred to the columnar storage table is as follows.
| ev | uid | P1 | P2 |
| ev1 | 1234 | param1 | |
| ev2 | 1234 | param1 | param2 |
| … | … | … | … |
According to the embodiment of the application, the fields corresponding to the columns in the columnar storage table may include an event identification field, a user identification field, a timestamp field, a device identification field, and the like, and are not limited to the examples given herein. The format of the columnar storage table may be as follows.
| Event identification | User identification | Time stamp | Device identification | …… |
| …… | …… | …… | …… | …… |
Wherein the event identifier is a preset unique identifier corresponding to a predefined event. For example, an event corresponding to a login button, a payment button, and the like, an event corresponding to page exposure. The user behavior log information corresponding to the event may be buried point log data reported by a buried point bound to a page or a page element, or the like.
Wherein the user identification field may be a unique identification for indicating the identity of the user.
Wherein, the timestamp field can record the specific time of the event.
The device identification field may be a number of a device of the terminal where the event occurs, and may be, for example, an International Mobile Equipment Identity (IMEI) or the like.
In an embodiment of the present application, before S220, the method may further include collecting data through a message middleware (e.g., kafka), storing the collected data in a column-wise storage table (e.g., a column-wise storage table in a partial format) on the HDFS, and performing data query and analysis from the column-wise storage table by using a data query engine. The data storage process is only an example given here, and is not limited to the present application as long as the user behavior log data can be stored in a columnar storage table structure that meets the requirements.
According to an embodiment, the operation of obtaining the corresponding data from the column-wise storage table in S220 may be implemented by an SQL statement.
According to an optional embodiment, the obtaining, according to the funnel step information and the time range information, user behavior log information that corresponds to the funnel step information and satisfies the time range information may specifically include: screening user behavior log information in a data partition corresponding to the time range information according to the time range information; and according to the event identifier to be analyzed, acquiring the user behavior log information in the data partition corresponding to the event identifier to be analyzed from the user behavior log information in the data partition corresponding to the time range information.
According to an embodiment, the user behavior log information may be stored in a multi-level partition. Specifically, the user behavior log information may be stored in a first-level partition according to a date and a second-level partition according to an event identifier. The date information is analyzed from the time stamp included in the user behavior log information.
In practical application, for a selected event to be analyzed and a time range to be analyzed, user behavior log information under a primary partition corresponding to the time range to be analyzed and under a secondary partition corresponding to the event to be analyzed can be directly obtained, so that the user behavior log information is obtained based on data stored in the partitions, file data under a specified partition is read only each time, the data screening speed is increased, and the funnel analysis efficiency is improved.
According to an alternative embodiment, the funnel analysis configuration information in S210 may further include a buried point parameter filter condition selected from fields in the columnar storage table. For example, the buried point parameter filter condition set for one or more buried points in the funnel step may be that '"device id' equals '123456789'", and then only user behavior log information having a device identification field value equal to "123456789" is obtained when data is obtained from the columnar storage table. In the process, the acquired data come from the columnar storage format and each column of the columnar storage table corresponds to the non-repeated buried point parameters in the user behavior log information, so that the efficiency of the screening process of the user behavior log information meeting the buried point parameter filtering condition is improved.
According to the embodiment, under the condition that the user behavior log information is stored in the partition, based on the set time range, the set event to be analyzed and the set buried point parameter filtering condition, the user behavior log information meeting the buried point parameter filtering condition can be acquired under the primary partition corresponding to the time range to be analyzed and under the secondary partition corresponding to the event to be analyzed.
S230: and calculating the access user amount of each funnel step based on the acquired user behavior log information.
Specifically, after the corresponding user behavior log information is obtained according to the preset screening conditions in the funnel analysis configuration information, the access user amount of each funnel step in the funnel analysis model can be further obtained through aggregation and calculation.
According to an optional embodiment, when the fields corresponding to the columns in the tabular storage table may include a user identification field, an event identification field, and a timestamp field, the calculating, based on the obtained user behavior log information, the amount of users visited in each funnel step may specifically include: calculating the longest behavior path of each user meeting the expected steps of the event to be analyzed based on the user identification, the event identification and the timestamp in the acquired user behavior log information; and calculating the access user amount of each funnel step based on the longest behavior path of each user.
According to an optional embodiment, the calculating, based on the user identifier, the event identifier, and the timestamp in the user behavior log information, a longest behavior path of each user that satisfies the expected step of the event to be analyzed may specifically include: classifying the acquired user behavior log information according to the user identification based on the user identification in the acquired user behavior log information to obtain a user behavior log information set corresponding to each user; based on the time stamps in the user behavior log information, sorting the user behavior log information sets corresponding to the users to obtain sorted user behavior log information sets of the users; and respectively obtaining the longest behavior path of each user according with the expected steps of the event to be analyzed based on the sequenced user behavior log information sets of each user.
According to the embodiment, the operation of sorting the user behavior log information sets corresponding to the users can be realized by SQL statements.
According to an embodiment, the calculation of the ordered funnel requires, in addition to the interval and the sequence of the events, a time window constraint that all events have to occur within the window period, i.e. only the funnel steps specified in the sequence within the time window can be calculated as a successful transformation.
According to an optional embodiment, the funnel analysis configuration information further includes time window information, where the time window information is used to determine whether a subsequent event in the funnel step belongs to an event in the longest behavior path of the user by determining whether a difference between a time stamp of the subsequent event in the funnel step and a time stamp of the start event in the funnel step satisfies the time window information. The more subsequent steps within the time window that are eligible, the deeper the hierarchy of the funnel is illustrated. The granularity of the time window can be set as required, for example, 1 day, 12 hours, 1 hour, 30 minutes, and the like.
According to an embodiment, the obtaining the longest behavior path of each user according with the expected step of the event to be analyzed based on the sorted user behavior log information sets of each user may specifically include: and for any user, based on the sorted user behavior log information set of each user, adopting a maximum subsequence algorithm to respectively obtain the user maximum behavior path of each user, which accords with the funnel generation step and meets the time window.
According to an embodiment, the obtaining the longest behavior path of each user according with the expected step of the event to be analyzed based on the sorted user behavior log information sets of each user may specifically include: for any user in the users, traversing the sorted user behavior log information set of the user according to a time sequence, and determining an event meeting the funnel step information as an initial event in a user behavior path; continuously traversing the sorted user behavior log information set of any user, and sequentially determining events which meet the funnel step information and meet the time window information compared with the initial event as corresponding subsequent events in the user behavior path; and obtaining an event list of the longest behavior path of any user based on the starting event and the corresponding subsequent event.
According to an embodiment, the funnel analysis method further comprises: and calculating the conversion rate and/or the attrition rate of each funnel step based on the visiting user amount of each funnel step.
In the embodiments of the present application, in order to solve the problems that JSON operations need to be analyzed multiple times in the funnel data analysis process, a large amount of computing resources are consumed, a large amount of computing time is spent, and requirements for data aggregation and screening at the level of seconds cannot be met in ad hoc funnel query, the present application proposes a method for obtaining user behavior log data for funnel analysis by using a structured column-type storage table, instead of directly from unstructured log information in a textual JSON format. Because each column of the non-repetitive multi-column storage format corresponds to one parameter field, the method has the advantage of high reading rate, so that the query process based on the column-type storage table is quickly carried out, JSON (Java Server object notation) operation does not need to be analyzed for many times during data query, the consumed computing resources are less, the spent computing time is short, and the requirements of second-level data aggregation and screening during ad hoc query can be met.
In the above embodiment, after obtaining the user behavior log information (including the user identifier, the timestamp, and the event identifier) satisfying the funnel analysis configuration information, aggregating according to the user identifier, a table below may be obtained, where the uid is located in the column to store the user identifier, and the time _ event _ list is located in the column to store the (time, buried point) pair. Here, the event1, the event2, and the event3 are buried point identifiers. The method has the advantages that data exchange among nodes is only performed once, data of each user are gathered on the same node by using the aggregation operation after the query engine is optimized, and path calculation of the user-defined function for the same user is facilitated.
In practical application, a query engine such as Impala may be used, the data in the time _ event _ list column of the same user is sorted according to time by using a custom function, the sorted data is traversed, and the longest path of the user behavior is selected by using a sliding window according to the time window information, where the longest path is calculated by using an algorithm of searching for the largest subsequence from back to front, and the query time may be shortened. Then, the total number of the converted people in each funnel step can be counted after aggregation according to the longest path of each user.
The Impala query engine is a data query tool constructed on Hadoop, and can provide a tool for carrying out SQL query on HDFS data and directly carry out data query on the HDFS. Impala directly implements queries on data blocks without any framework, so the delay of the query is very low, usually in milliseconds. The Impala query engine has the advantages of good dynamic expansion support for the columnar storage table, high reading speed and low operation cost.
The custom function refers to a user-defined function (UDF) in the HIVE. The UDF operation acts on a single data line and produces one data line as output.
To more clearly illustrate the above method for obtaining the longest behavior path by using the algorithm of searching the maximum subsequence from back to front, the following example is given:
example 1: assuming that the ordered funnel constraint specifies an expected step of A, B, C, D, E … …, etc., a user has sequentially occurred events E, A, B, C, E, D, E in a time sequence within a preset time frame (e.g., 10:00-11:00 for 9, 11, 2019).
Sequentially traversing the events in time sequence:
reading an event E, and not recording because the event E is not an initial step in the ordered funnel;
then reading an event A, recording the event A in a user behavior list as A is an initial step in the ordered funnel, and recording a timestamp a;
then reading an event B, updating a record in a user behavior list to be an event A + B and recording a timestamp a as the event B is an event which is immediately behind the event A in the ordered funnel and the difference between the time B of the event B and the time a of the initial event A meets a time window m;
then reading an event C, and updating a record in a user behavior list to be an event A + B + C and recording a timestamp a as the event C is an event which is next to the event B in the ordered funnel and the difference between the time C of the event C and the time a of the initial event A meets a time window m;
then reading the event E, and not recording because the event E is not the event which is next to the event C in the ordered funnel;
then reading an event D, updating a record in a user behavior list to be an event A + B + C + D and recording a timestamp a as the event D is an event which is next to the event C in the ordered funnel and the difference between the time D of the event D and the time a of the initial event A meets a time window m;
event E is then read, which is the event in the ordered funnel immediately after event D, but is not recorded because the difference between the time of event D and time a from the start event a does not satisfy time window m.
At this point, after the traversal completes the event satisfying the time range, it is known that the longest behavior path of the user is a + B + C + D.
Example 1 shows a case where an initial event occurs once in the acquired buried point information, and the same applies in a case where a plurality of initial events occur. The following example 2 is specifically given for explanation.
Example 2: assume that user 1 has completed events A, B, A, B, C in chronological order within a time frame, as described in the table below.
| Uid | Ev | Timestamp |
| 1 | |
1 |
| 1 | |
2 |
| 1 | A | 3 |
| 1 | |
4 |
| 1 | |
5 |
The data in the upper graph is traversed sequentially in time order, assuming a time window of 3.
Traversal to the first row: the list of saved final results is as follows:
| | Timestamp |
| A | |
| 1 |
traversal to the second row: the actual time difference between event B and event a is 2, which is less than time window 3. The list of final results is thus saved as follows:
traverse to the third row: the list of saved final results is as follows:
| Ev | Timestamp |
| A | 3 (time of update event A) |
| |
1 |
Go to the fourth row: the list of saved final results is as follows:
| Ev | Timestamp |
| A | 3 |
| B | 3 (time of event A updated to save) |
Go to the fifth row: the list of saved final results is as follows:
| Ev | Timestamp |
| A | 3 |
| B | 3 (time of update saved event A) |
| C | 3 (time of occurrence of event A) |
Then, the result list is traversed from back to front, and the last step in the list is known as event C. Thus, the longest behavior path of the user 1 in the selected time range is A + B + C.
It should be added that, in the process of acquiring the most frequent behavior path of the user, the described data tables are for illustration purposes, and the data tables do not have to be generated and stored.
To more clearly illustrate the embodiments of the ordered funnel analysis method described above, fig. 3 is provided as an example, and fig. 3 shows a computational flow diagram of a funnel analysis method according to an embodiment. It should be noted that, after the user behavior log information satisfying the filtering condition is obtained and the log data of each user is sorted according to time, although some tables are shown in fig. 3 to explain the calculation flow, these tables are only for illustrative purposes and do not have to be generated and stored.
In the funnel analysis method based on the buried points, the bottom data of the funnel is behavior log data of a user, the quantity of the data is in the hundred million level every day, operations such as sequencing and aggregation are inevitably needed in the funnel calculation process, and under the condition of large data volume, it is very important to ensure that a result can be output by high-speed operation in an instant query scene. The existing JOIN-based calculation mode has low efficiency under the condition of large data volume and cannot meet the requirement of outputting results in real time.
In order to solve the problems existing in the funnel analysis method based on the buried point and improve the efficiency of funnel analysis, the embodiment is provided, on one hand, by converting the user behavior log information from unformatted text data into a column type storage table format, the query speed during screening the user behavior log data according to fields in the funnel step can be improved; on the other hand, JOIN among multiple tables is converted into the problem of maximum subsequence query through a self-defined function, and the funnel analysis speed is improved and calculated through optimization in an algorithm. In practical application, the funnel calculation speed under complex logic is increased, the analysis requirements of product operators for inquiring the conversion rate and the jump rate of users by customizing the funnel steps are met, and the second-level output of the analysis results is realized.
Based on the same idea, the embodiment of the present specification further provides a device corresponding to the above method. Fig. 4 is a schematic structural diagram of a funnel analysis device corresponding to fig. 2 provided in an embodiment of the present disclosure.
As shown in fig. 4, the apparatus may include:
a configuration information obtaining module 410, configured to obtain funnel analysis configuration information, where the funnel analysis configuration information includes funnel step information and time range information, and the funnel step information includes an event identifier to be analyzed of an event to be analyzed related to funnel analysis and an event expectation step to be analyzed;
a log information obtaining module 420, configured to obtain, according to the funnel step information and the time range information, user behavior log information that corresponds to the funnel step information and satisfies the time range information, where the user behavior log information is stored in a column-type storage table, each row of the column-type storage table corresponds to one piece of user behavior log information, and each column of the column-type storage table corresponds to one parameter field in the user behavior log information and the parameter fields corresponding to the columns are different;
and a calculating module 430, configured to calculate, based on the obtained user behavior log information, an access user amount of each funnel step.
According to an embodiment, the log information obtaining module 420 is specifically configured to: screening user behavior log information in a data partition corresponding to the time range information according to the time range information; and according to the event identifier to be analyzed, acquiring the user behavior log information in the data partition corresponding to the event identifier to be analyzed from the user behavior log information in the data partition corresponding to the time range information.
According to an embodiment, the fields corresponding to the columns in the columnar storage table include a user identification field, an event identification field, and a timestamp field, and the calculation module 430 specifically includes:
the longest path determining unit is used for calculating the longest behavior path of each user meeting the expected step of the event to be analyzed based on the user identifier, the event identifier and the timestamp in the acquired user behavior log information;
and the calculating unit is used for calculating the access user amount of each funnel step based on the longest behavior path of each user.
According to an embodiment, the calculating, based on the user identifier, the event identifier, and the timestamp in the user behavior log information, a longest behavior path of each user that satisfies the expected step of the event to be analyzed includes:
the classification subunit is configured to classify the acquired user behavior log information according to the user identifier based on the user identifier in the acquired user behavior log information, so as to obtain a user behavior log information set corresponding to each user;
the sorting subunit is configured to sort, based on the timestamp in the user behavior log information, the user behavior log information sets corresponding to the users to obtain sorted user behavior log information sets of the users;
and the path determining subunit is used for respectively obtaining the longest behavior path of each user according with the expected step of the event to be analyzed based on the sorted user behavior log information sets of each user.
According to an embodiment, the funnel analysis configuration information further includes time window information, wherein the time window information is used to determine whether the subsequent event belongs to an event in the longest behavior path of the user by determining whether a difference between a time stamp of the subsequent event in the funnel step and a time stamp of the start event in the funnel step satisfies the time window information. The path determining subunit is specifically configured to: for any user in the users, traversing the sorted user behavior log information set of the user according to a time sequence, and determining an event meeting the funnel step information as an initial event in a user behavior path; continuously traversing the sorted user behavior log information set of any user, and sequentially determining events which meet the funnel step information and meet the time window information compared with the initial event as corresponding subsequent events in the user behavior path; and obtaining an event list of the longest behavior path of any user based on the starting event and the corresponding subsequent event.
It will be appreciated that the modules and/or units described above refer to computer programs or program segments for performing a certain function or functions. In addition, the distinction between the above-described modules does not mean that the actual program code must also be separated.
Fig. 5 is a schematic structural diagram of a funnel analysis device provided in an embodiment of the present specification. As shown in fig. 5, the apparatus 1000 may include:
at least one processor 1010; and the number of the first and second groups,
a memory 1030 communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory 1030 stores instructions 1020 executable by the at least one processor 1010 to enable the at least one processor 1010 to:
acquiring funnel analysis configuration information, wherein the funnel analysis configuration information comprises funnel step information and time range information, and the funnel step information comprises an event identifier to be analyzed of an event to be analyzed related to funnel analysis and an event expectation step to be analyzed;
according to the funnel step information and the time range information, user behavior log information which corresponds to the funnel step information and meets the time range information is obtained, and the user behavior log information is stored in a column type storage table, wherein each row of the column type storage table corresponds to one piece of user behavior log information, each column of the column type storage table corresponds to one parameter field in the user behavior log information, and the parameter fields corresponding to the columns are different;
and calculating the access user amount of each funnel step based on the acquired user behavior log information.
Based on the same idea, the embodiments of the present specification further provide a computer-readable medium corresponding to the above method, where the computer-readable medium has stored thereon computer-readable instructions, and the computer-readable instructions are executable by a processor to implement the funnel analysis method described in any of the above embodiments.
While particular embodiments of the present specification have been described above, in some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the device and apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points.
The apparatus, the device, and the method provided in the embodiments of the present specification are corresponding, and therefore, the apparatus and the device also have beneficial technical effects similar to those of the corresponding method, and since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus and device are not described again here.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A funnel analysis method, comprising:
acquiring funnel analysis configuration information, wherein the funnel analysis configuration information comprises funnel step information and time range information, and the funnel step information comprises an event identifier to be analyzed of an event to be analyzed related to funnel analysis and an event expectation step to be analyzed;
according to the funnel step information and the time range information, user behavior log information which corresponds to the funnel step information and meets the time range information is obtained, wherein the user behavior log information is stored in a column type storage table, each row of the column type storage table corresponds to one piece of user behavior log information, each column of the column type storage table corresponds to one parameter field in the user behavior log information, and the parameter fields corresponding to the columns are different;
and calculating the access user amount of each funnel step based on the acquired user behavior log information.
2. The method according to claim 1, wherein the obtaining, according to the funnel step information and the time range information, user behavior log information that corresponds to the funnel step information and satisfies the time range information specifically includes:
screening user behavior log information in a data partition corresponding to the time range information according to the time range information;
and according to the event identifier to be analyzed, acquiring the user behavior log information in the data partition corresponding to the event identifier to be analyzed from the user behavior log information in the data partition corresponding to the time range information.
3. The method of claim 2, wherein the fields corresponding to the columns in the columnar storage table include a user identification field, an event identification field, and a timestamp field,
the calculating, based on the obtained user behavior log information, the access user amount of each funnel step specifically includes:
calculating the longest behavior path of each user meeting the expected steps of the event to be analyzed based on the user identification, the event identification and the timestamp in the acquired user behavior log information;
and calculating the access user amount of each funnel step based on the longest behavior path of each user.
4. The method according to claim 3, wherein the calculating a longest behavior path of each user that satisfies the expected step of the event to be analyzed based on the user identifier, the event identifier, and the timestamp in the user behavior log information specifically includes:
classifying the acquired user behavior log information according to the user identification based on the user identification in the acquired user behavior log information to obtain a user behavior log information set corresponding to each user;
based on the time stamps in the user behavior log information, sorting the user behavior log information sets corresponding to the users to obtain sorted user behavior log information sets of the users;
and respectively obtaining the longest behavior path of each user according with the expected steps of the event to be analyzed based on the sequenced user behavior log information sets of each user.
5. The method of claim 4, wherein the funnel analysis configuration information further comprises time window information, wherein the time window information is used for determining whether the subsequent event belongs to the event in the longest action path of the user by determining whether a difference between a time stamp of the subsequent event in the funnel step and a time stamp of the start event in the funnel step satisfies the time window information,
the obtaining, based on the sorted user behavior log information sets of the users, the longest behavior path of each user that meets the expected step of the event to be analyzed includes:
for any user in the users, traversing the sorted user behavior log information set of the user according to a time sequence, and determining an event meeting the funnel step information as an initial event in a user behavior path;
continuously traversing the sorted user behavior log information set of any user, and sequentially determining events which meet the funnel step information and meet the time window information compared with the initial event as corresponding subsequent events in the user behavior path;
and obtaining an event list of the longest behavior path of any user based on the starting event and the corresponding subsequent event.
6. A funnel analysis device, the device comprising:
the funnel analysis system comprises a configuration information acquisition module, a time range acquisition module and a time range analysis module, wherein the configuration information acquisition module is used for acquiring funnel analysis configuration information, the funnel analysis configuration information comprises funnel step information and time range information, and the funnel step information comprises an event identifier to be analyzed of an event to be analyzed related to funnel analysis and an event expectation step to be analyzed;
the log information acquisition module is used for acquiring user behavior log information which corresponds to the funnel step information and meets the time range information according to the funnel step information and the time range information, wherein the user behavior log information is stored in a column-type storage table, each row of the column-type storage table corresponds to one piece of user behavior log information, each column of the column-type storage table corresponds to one parameter field in the user behavior log information, and the parameter fields corresponding to the columns are different;
and the calculating module is used for calculating the access user amount of each funnel step based on the acquired user behavior log information.
7. The apparatus according to claim 6, wherein the computing module specifically comprises:
the longest path determining unit is used for calculating the longest behavior path of each user meeting the expected step of the event to be analyzed based on the user identifier, the event identifier and the timestamp in the user behavior log information;
and the calculating unit is used for calculating the access user amount of each funnel step based on the longest behavior path of each user.
8. The apparatus according to claim 7, wherein the longest path determining unit specifically includes:
the classification subunit is configured to classify the user behavior log information according to the user identifier based on the user identifier in the user behavior log information to obtain a user behavior log information set corresponding to each user;
the sorting subunit is configured to sort, based on the timestamp in the user behavior log information, the user behavior log information sets corresponding to the users to obtain sorted user behavior log information sets of the users;
and the path determining subunit is used for respectively obtaining the longest behavior path of each user according with the expected step of the event to be analyzed based on the sorted user behavior log information sets of each user.
9. A funnel analysis apparatus, characterized in that the apparatus comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring funnel analysis configuration information, wherein the funnel analysis configuration information comprises funnel step information and time range information, and the funnel step information comprises an event identifier to be analyzed of an event to be analyzed related to funnel analysis and an event expectation step to be analyzed;
according to the funnel step information and the time range information, user behavior log information which corresponds to the funnel step information and meets the time range information is obtained, wherein the user behavior log information is stored in a column type storage table, each row of the column type storage table corresponds to one piece of user behavior log information, each column of the column type storage table corresponds to one parameter field in the user behavior log information, and the parameter fields corresponding to the columns are different;
and calculating the access user amount of each funnel step based on the acquired user behavior log information.
10. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the funnel analysis method of any of claims 1 to 5.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111488261A (en) * | 2020-03-11 | 2020-08-04 | 北京健康之家科技有限公司 | User behavior analysis system, method, storage medium and computing device |
| CN111652299A (en) * | 2020-05-26 | 2020-09-11 | 泰康保险集团股份有限公司 | Method and equipment for automatically matching service data |
| CN112579408A (en) * | 2020-10-29 | 2021-03-30 | 上海钱拓网络技术有限公司 | Classification method of embedded point information |
| CN112650743A (en) * | 2020-12-30 | 2021-04-13 | 咪咕文化科技有限公司 | Funnel data analysis method and system, electronic device and storage medium |
| CN113176980A (en) * | 2021-05-25 | 2021-07-27 | 医声医事(北京)科技有限公司 | Dynamic construction method and system of flow funnel |
| CN113609188A (en) * | 2021-07-23 | 2021-11-05 | 恩亿科(北京)数据科技有限公司 | Funnel analysis method, system, device and medium for user behavior steps |
| CN114092138A (en) * | 2021-11-10 | 2022-02-25 | 建信金融科技有限责任公司 | User behavior analysis method, device, equipment and storage medium |
| CN114429360A (en) * | 2021-12-29 | 2022-05-03 | 神策网络科技（北京）有限公司 | Conversion rate determination method, device, electronic equipment and computer readable storage medium |
| CN114925099A (en) * | 2022-06-08 | 2022-08-19 | 北京自如信息科技有限公司 | Funnel data query method and device and electronic equipment |
| CN116431366A (en) * | 2023-06-07 | 2023-07-14 | 北京集度科技有限公司 | Behavior path analysis method, system, storage terminal, server terminal and client terminal |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103916293A (en) * | 2014-04-15 | 2014-07-09 | 浪潮软件股份有限公司 | Method for monitoring and analyzing website user behaviors |
| CN106294559A (en) * | 2016-07-26 | 2017-01-04 | 北京三快在线科技有限公司 | A kind of application traffic analysis method and device |
| CN106484709A (en) * | 2015-08-26 | 2017-03-08 | 北京神州泰岳软件股份有限公司 | A kind of auditing method of daily record data and audit device |
| CN107609040A (en) * | 2017-08-16 | 2018-01-19 | 深信服科技股份有限公司 | Data analysing method, data processing equipment and computer-readable recording medium |
| CN109242528A (en) * | 2018-07-26 | 2019-01-18 | 焦点科技股份有限公司 | A kind of the funnel analysis method and device in the customized path of electric business platform |
-
2019
- 2019-09-29 CN CN201910930131.2A patent/CN110675194A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103916293A (en) * | 2014-04-15 | 2014-07-09 | 浪潮软件股份有限公司 | Method for monitoring and analyzing website user behaviors |
| CN106484709A (en) * | 2015-08-26 | 2017-03-08 | 北京神州泰岳软件股份有限公司 | A kind of auditing method of daily record data and audit device |
| CN106294559A (en) * | 2016-07-26 | 2017-01-04 | 北京三快在线科技有限公司 | A kind of application traffic analysis method and device |
| CN107609040A (en) * | 2017-08-16 | 2018-01-19 | 深信服科技股份有限公司 | Data analysing method, data processing equipment and computer-readable recording medium |
| CN109242528A (en) * | 2018-07-26 | 2019-01-18 | 焦点科技股份有限公司 | A kind of the funnel analysis method and device in the customized path of electric business platform |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111488261A (en) * | 2020-03-11 | 2020-08-04 | 北京健康之家科技有限公司 | User behavior analysis system, method, storage medium and computing device |
| CN111652299A (en) * | 2020-05-26 | 2020-09-11 | 泰康保险集团股份有限公司 | Method and equipment for automatically matching service data |
| CN112579408A (en) * | 2020-10-29 | 2021-03-30 | 上海钱拓网络技术有限公司 | Classification method of embedded point information |
| CN112650743A (en) * | 2020-12-30 | 2021-04-13 | 咪咕文化科技有限公司 | Funnel data analysis method and system, electronic device and storage medium |
| CN113176980A (en) * | 2021-05-25 | 2021-07-27 | 医声医事(北京)科技有限公司 | Dynamic construction method and system of flow funnel |
| CN113176980B (en) * | 2021-05-25 | 2023-09-12 | 医声医事(北京)科技有限公司 | Dynamic construction method and system of flow hopper |
| CN113609188A (en) * | 2021-07-23 | 2021-11-05 | 恩亿科(北京)数据科技有限公司 | Funnel analysis method, system, device and medium for user behavior steps |
| CN114092138A (en) * | 2021-11-10 | 2022-02-25 | 建信金融科技有限责任公司 | User behavior analysis method, device, equipment and storage medium |
| CN114429360A (en) * | 2021-12-29 | 2022-05-03 | 神策网络科技（北京）有限公司 | Conversion rate determination method, device, electronic equipment and computer readable storage medium |
| CN114925099A (en) * | 2022-06-08 | 2022-08-19 | 北京自如信息科技有限公司 | Funnel data query method and device and electronic equipment |
| CN116431366A (en) * | 2023-06-07 | 2023-07-14 | 北京集度科技有限公司 | Behavior path analysis method, system, storage terminal, server terminal and client terminal |
| CN116431366B (en) * | 2023-06-07 | 2023-10-31 | 北京集度科技有限公司 | Behavior path analysis method, system, storage terminal, server terminal and client terminal |
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