CN114092138A - User behavior analysis method, device, equipment and storage medium - Google Patents
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Abstract
The application provides a user behavior analysis method, a user behavior analysis device, user behavior analysis equipment and a storage medium, and relates to the technical field of data analysis. The method comprises the steps of obtaining a data analysis request, wherein the data analysis request comprises a user behavior analysis category; determining each process node corresponding to the user behavior analysis category according to a preset user link; acquiring node quantitative data of each process node in each process node corresponding to the user behavior analysis category; and generating user behavior analysis data according to the acquired quantitative data of each node. By adopting the technical scheme, the problem that the user operation data on part of the pages of the product is incomplete can be solved, the workload of re-constructing the pages by developers can be reduced, and the accuracy of user behavior analysis data can be improved.
Description
Technical Field
The present application relates to the field of data analysis technologies, and in particular, to a method, an apparatus, a device, and a storage medium for user behavior analysis.
Background
Currently, after the user operates the corresponding page for each product of the marketing campaign, the user operation information of the user for each product of the marketing campaign is obtained, for example, the user operation information includes user click information, user browsing information, and user purchase information. It is necessary to obtain a user behavior analysis result of the user based on the user operation information, for example, to obtain a user conversion rate.
In the prior art, after a user operates a page, user operation data of the user on a part of pages of a product is obtained, and then user behavior analysis data is obtained based on the user operation data of the user on the part of pages of the product.
However, in the prior art, only user operation data of a user on a part of pages of a product can be obtained, the obtained user operation data is not comprehensive, and further the obtained user behavior analysis data is not accurate; in addition, in the prior art, if more user operation data need to be acquired, modeling needs to be performed again to analyze and acquire the operation data of the user on each page, so that the development cost is high.
Disclosure of Invention
The application provides a user behavior analysis method, a device, equipment and a storage medium, which are used for solving the problem that user operation data on partial pages of a product are incomplete, reducing the workload of a developer for rebuilding the pages and improving the accuracy of user behavior analysis data.
In a first aspect, the present application provides a user behavior analysis method, including:
acquiring a data analysis request, wherein the data analysis request comprises a user behavior analysis category;
determining each process node corresponding to the user behavior analysis category according to a preset user link; the user link comprises a plurality of process nodes, and the plurality of process nodes comprise at least one main node and at least one branch node; the preorder node and the successor node of each branch node are both main nodes, and each branch node represents a jump page between the preorder node and the successor node corresponding to each branch node; each process node in the user link has an execution order, each process node in the user link has node quantization data, and the node quantization data of each process node in the user link is generated after the user link is executed;
acquiring node quantization data of each flow node in each flow node corresponding to the user behavior analysis category;
and generating user behavior analysis data according to the acquired quantitative data of each node.
In a second aspect, the present application provides a user behavior analysis apparatus, the apparatus comprising:
the data analysis request acquisition module is used for acquiring a data analysis request, wherein the data analysis request comprises a user behavior analysis category;
the determining module is used for determining each process node corresponding to the user behavior analysis category according to a preset user link; the user link comprises a plurality of process nodes, and the plurality of process nodes comprise at least one main node and at least one branch node; the preorder node and the successor node of each branch node are both main nodes, and each branch node represents a jump page between the preorder node and the successor node corresponding to each branch node; each process node in the user link has an execution order, each process node in the user link has node quantization data, and the node quantization data of each process node in the user link is generated after the user link is executed;
a node quantized data obtaining module, configured to obtain node quantized data of each flow node in each flow node corresponding to the user behavior analysis category;
and the generating module is used for generating user behavior analysis data according to the acquired quantized data of each node.
In a third aspect, the present application provides a computer device comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the method of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon computer-executable instructions for implementing the method according to the first aspect when executed by a processor.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the method according to the first aspect.
According to the user behavior analysis method, the device, the equipment and the storage medium, a data analysis request is obtained, wherein the data analysis request comprises a user behavior analysis category; determining each process node corresponding to the user behavior analysis category according to a preset user link; the user link comprises a plurality of process nodes, and the plurality of process nodes comprise at least one main node and at least one branch node; the preorder node and the successor node of each branch node are both main nodes, and each branch node represents a jump page between the preorder node and the successor node corresponding to each branch node; each process node in the user link has an execution order, each process node in the user link has node quantization data, and the node quantization data of each process node in the user link is generated after the user link is executed; acquiring node quantization data of each flow node in each flow node corresponding to the user behavior analysis category; and generating user behavior analysis data according to the acquired quantitative data of each node. By adopting the technical scheme, the problem that the user operation data on part of the pages of the product is incomplete can be solved, the workload of re-constructing the pages by developers can be reduced, and the accuracy of user behavior analysis data is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is an architecture diagram of a user behavior analysis applicable to a plurality of scenarios according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a user behavior analysis method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of a user behavior analysis method according to a second embodiment of the present application;
fig. 4 is a schematic flowchart of a user behavior analysis method according to a third embodiment of the present application;
fig. 5 is a schematic diagram of a user behavior analysis apparatus according to a fourth embodiment of the present application;
fig. 6 is a schematic diagram of a user behavior analysis apparatus according to a fifth embodiment of the present application;
FIG. 7 is a block diagram illustrating a computer device in accordance with an example embodiment.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is an architecture diagram of a user behavior analysis applicable to multiple scenarios, according to an embodiment of the present application, where data acquisition, storage, use, processing, and the like in all embodiments of the present application conform to relevant regulations of national laws and regulations. Specifically, different data analysis requests are sent to the same user link, different user behavior data results can be obtained by the user link, and the method is suitable for different scenes, so that different people can use the same user link without building the user link again. For example, referring to 3 data analysis requests in fig. 1, which are a data analysis request 1, a data analysis request 2, and a data analysis request 3, where user analysis categories included in each data analysis request are different, the 3 data analysis requests are sent to the same user link, and the user link obtains 3 data results according to the user analysis categories included in the data requests, where the 3 data results can be used for analysis of 3 scenarios. Specifically, the analysis scenario may be a product operator analysis scenario, an activity operator analysis scenario, and a website customer manager analysis scenario.
Fig. 2 is a schematic flowchart of a user behavior analysis method according to an embodiment of the present application. All the embodiments of the present application conform to the relevant regulations of national laws and regulations for data acquisition, storage, use, processing, etc. The first embodiment comprises the following steps:
s201, obtaining a data analysis request, wherein the data analysis request comprises a user behavior analysis category.
Illustratively, the data analysis request is an instruction message for analyzing specified data, and the data analysis request can be created by a user at the use interface and acquired by the computer device through the use interface.
The data analysis request may be a series of code instructions, which is not limited herein. In this embodiment, the user behavior analysis category refers to classifying the user behavior and summarizing the characteristics of the classified user behavior.
Wherein the user behavior analysis categories may include: the user arrives at the activity head, the user arrives at the registration page, the user registration success page, the user arrives at the task A page, the user views the financial product A, the user arrives at the payment page, and the user successfully purchases the financial product A. It should be noted that the types of the user behavior analysis categories are not limited to the above, and are only examples.
S202, determining each process node corresponding to the user behavior analysis category according to a preset user link; the user link comprises a plurality of process nodes, and the plurality of process nodes comprise at least one main node and at least one branch node; the preorder node and the postorder node of each branch node are main nodes, and each branch node represents a jump page between the preorder node and the postorder node corresponding to each branch node; each process node in the user link has an execution order, each process node in the user link has node quantization data, and the node quantization data of each process node in the user link is generated after the user link is executed.
Illustratively, the preset user link refers to a built architecture, and the user link is composed of a plurality of process nodes, and each process node corresponds to a user behavior. The number of the flow nodes is not limited, and the flow nodes may include a plurality of main nodes and a plurality of branch nodes.
Wherein, a branch node can be created between different main nodes, i.e. the branch node is used for connecting different main nodes. For example, the user link includes 4 main nodes and 3 branch nodes, wherein the 4 main nodes respectively provide the user with an activity home page, the user successfully registers, the user views the financial product a and the user successfully purchases the financial product a. The 3 branching nodes are the user arrival registration page, the user arrival task a page, and the user arrival payment page.
Furthermore, the branch node user reaches the preorder node of the registration page to indicate that the user reaches the active home page, and the branch node user reaches the postorder node of the registration page to indicate that the user is successfully registered.
And the branch node user arrives at the preorder node of the task A page to register the user successfully, and the branch node user arrives at the posterior node of the task A page to check the financial product A for the user. The branch node user arrives at the preorder node of the payment page to check the financial product A for the user, and the branch node user arrives at the subsequent node of the payment page to successfully purchase the financial product A for the user.
In this embodiment, after the user link is executed, the node quantization data of the flow node is generated, specifically, the execution record and the use condition of the flow node are recorded after the flow node is executed, and the data in the execution record and the use condition of the flow node generates the node quantization data.
S203, obtaining node quantization data of each flow node in each flow node corresponding to the user behavior analysis type.
For example, the node quantization data stored in each flow node in the flow nodes corresponding to the user behavior analysis category is different, and specifically, the node quantization data of the flow node may be read from each flow node, and the read node quantization data may be subjected to mathematical computation processing.
And S204, generating user behavior analysis data according to the acquired quantized data of each node.
In this embodiment, the user behavior analysis data may characterize the user behavior attributes. For example, the node quantization data obtained from the successful registration of the host node user is analyzed to obtain the number of users who successfully register the user, and the number of users who successfully register the user is compared with a preset value. If the value exceeds the preset value, the item can be shown to achieve the expected effect.
According to the user behavior analysis method, the device, the equipment and the storage medium, the data analysis request is obtained, wherein the data analysis request comprises the user behavior analysis category; determining each process node corresponding to the user behavior analysis category according to a preset user link; the user link comprises a plurality of process nodes, and the plurality of process nodes comprise at least one main node and at least one branch node; the preorder node and the postorder node of each branch node are main nodes, and each branch node represents a jump page between the preorder node and the postorder node corresponding to each branch node; each process node in the user link has an execution sequence, each process node in the user link has node quantization data, and the node quantization data of each process node in the user link is generated after the user link is executed; acquiring node quantitative data of each process node in each process node corresponding to the user behavior analysis category; and generating user behavior analysis data according to the acquired quantitative data of each node. By adopting the technical scheme, the problem that the user operation data on part of the pages of the product are incomplete can be solved, the workload of re-constructing the pages by developers can be reduced, and the accuracy of user behavior analysis data can be improved.
Fig. 3 is a schematic flowchart of a user behavior analysis method according to a second embodiment of the present application. All the embodiments of the present application conform to the relevant regulations of national laws and regulations for data acquisition, storage, use, processing, etc. The second embodiment comprises the following steps:
s301, a data analysis request is obtained, wherein the data analysis request comprises a user behavior analysis category.
For example, the explanation of this step can refer to the content of S101, which is not described herein.
S302, identifying keywords in the user behavior analysis category.
In this embodiment, the keywords may be identified through the fields, and a specific algorithm is not limited herein. The keywords may be nouns or verbs.
S303, determining the flow nodes which are the same as the keywords and the preorder nodes and the postorder nodes of the flow nodes in the preset user link.
In this embodiment, the name of a flow node that is the same as the keyword is searched for in a flow node of a preset user link, the flow node is determined, and a preamble node and a subsequent node of the flow node are determined according to a connection sequence of the flow node.
For example, if the keyword is "arrival registration", the name of the flow node matched with "arrival registration" is determined in the preset user link, and it may be determined that the branch node user arrives at the registration page, and it is determined that the preceding node of the branch node is the user arrival activity home page according to the connection sequence of the preset user link, and the succeeding node of the branch node user arriving at the registration page is successful in user registration.
S304, reading the node quantization data of the flow node in the preset user link, which is the same as the keyword, the node quantization data of the preorder node of the flow node and the node quantization data of the subsequent node.
In this embodiment, if the process node that is the same as the keyword is that the user arrives at the registration page, the node quantization data that the user arrives at the registration page is obtained, where the node quantization data includes: the number of node users, the node conversion rate and the node loss rate.
Furthermore, the node quantized data of the preamble node user reaching the active home page and the node quantized data of the subsequent node user successfully registered can be obtained.
And S305, generating user behavior analysis data according to the acquired node quantization data.
In this embodiment, according to the obtained quantized data of each node, a difference value of the quantized data of the node between different nodes or a ratio of the quantized data of the node is calculated, and then some numerical values are obtained and analyzed, and a specific analysis manner may be a table or a diagram.
According to the user behavior analysis method, the device, the equipment and the storage medium, the data analysis request is obtained, wherein the data analysis request comprises the user behavior analysis category; identifying keywords in a user behavior analysis category; determining a flow node which is the same as the keyword and a preorder node and a postorder node of the flow node in a preset user link; and reading the node quantized data of the flow node which is the same as the keyword in a preset user link, the node quantized data of the preorder node of the flow node and the node quantized data of the postorder node. By adopting the technical scheme, the process node corresponding to the user behavior to be analyzed and the preorder node and the postorder node corresponding to the process node can be determined, so that the node quantitative data of different process nodes can be obtained, and the final user behavior analysis data is more accurate.
Fig. 4 is a schematic flowchart of a user behavior analysis method according to a third embodiment of the present application. All the embodiments of the present application conform to the relevant regulations of national laws and regulations for data acquisition, storage, use, processing, etc. The third embodiment comprises the following steps:
s401, responding to the user trigger request, executing a page jump process corresponding to the user trigger request according to the user trigger request, and obtaining a user link.
In this embodiment, the user trigger request is sent by the user on the interface, specifically, the user trigger request may be a message instruction created by the user, or may be triggered by the user through a preset instruction. For example, the user may create the message instruction to analyze the number of users arriving at the registration page, or may select a preset instruction to trigger from a plurality of triggering instructions. Illustratively, a single click is to analyze the number of users that the user has reached the registration page, and a double click is to analyze the number of users that the user has successfully registered.
And skipping to each process node according to the user trigger request, and recording each process node so as to generate a user link.
S402, obtaining a data analysis request, wherein the data analysis request comprises a user behavior analysis category.
For example, the explanation of this step can refer to the content of step S101, which is not described herein again.
S403, determining each process node corresponding to the user behavior analysis category according to a preset user link; the user link comprises a plurality of process nodes, and the plurality of process nodes comprise at least one main node and at least one branch node; the preorder node and the postorder node of each branch node are main nodes, and each branch node represents a jump page between the preorder node and the postorder node corresponding to each branch node; each process node in the user link has an execution order, each process node in the user link has node quantization data, and the node quantization data of each process node in the user link is generated after the user link is executed.
For example, the explanation of this step can refer to the content of step S102, which is not described herein again.
S404, obtaining node quantization data of each flow node in each flow node corresponding to the user behavior analysis type.
For example, the explanation of this step can refer to the content of step S103, which is not described herein again.
S405, if the fact that the node quantization data of one process node is abnormal in each process node is detected, the corresponding process node is readjusted.
For example, the current node quantized data of the flow node may be compared with the historical node quantized data of the flow node, and if the difference between the current node quantized data and the historical node quantized data of the flow node exceeds a threshold, it indicates that the flow node has a problem in the bottom layer design or the skip design, and the flow node may be redesigned.
And S406, generating user behavior analysis data according to the acquired quantized data of each node.
In this embodiment, optionally, the user behavior analysis data includes rationality data of process node design, rationality data of product operation, rationality data of product sales, and a user representation. Wherein the rationality data for the design of a flow node includes whether the flow node should exist and whether the flow node should be refined into a plurality of flow nodes. The rationality data of the product operation can be obtained by comparing the design of the whole user link with the user participation degree of the historical user link design.
Further, the user representation includes user behaviors that may represent personal characteristics of the user, such as the age of the user, the occupation of the user, the home environment of the user, and the preference of the user, and is not limited herein.
According to the user behavior analysis method, the user behavior analysis device, the user behavior analysis equipment and the storage medium, a user link is obtained by responding to a user trigger request and executing a page jump flow corresponding to the user trigger request according to the user trigger request; acquiring a data analysis request, wherein the data analysis request comprises a user behavior analysis category; determining each process node corresponding to the user behavior analysis category according to a preset user link; acquiring node quantitative data of each process node in each process node corresponding to the user behavior analysis category; and generating user behavior analysis data according to the acquired quantitative data of each node. By adopting the technical scheme, the node quantization data of the corresponding process node can be obtained according to the trigger request of the user and the trigger request, and the user behavior analysis data is generated, so that different user behavior analysis data can be obtained according to different trigger requests, and the method has better universality.
Fig. 5 is a schematic diagram of a user behavior analysis apparatus according to a fourth embodiment of the present application. All the embodiments of the present application conform to the relevant regulations of national laws and regulations for data acquisition, storage, use, processing, etc. The apparatus 50 according to the fourth embodiment, comprising:
a data analysis request obtaining module 501, configured to obtain a data analysis request, where the data analysis request includes a user behavior analysis category.
A determining module 502, configured to determine, according to a preset user link, each flow node corresponding to a user behavior analysis category; the user link comprises a plurality of process nodes, and the plurality of process nodes comprise at least one main node and at least one branch node; the preorder node and the postorder node of each branch node are main nodes, and each branch node represents a jump page between the preorder node and the postorder node corresponding to each branch node; each process node in the user link has an execution order, each process node in the user link has node quantization data, and the node quantization data of each process node in the user link is generated after the user link is executed.
The node quantized data obtaining module 503 is configured to obtain node quantized data of each flow node in the flow nodes corresponding to the user behavior analysis category.
A generating module 504, configured to generate user behavior analysis data according to the obtained quantized data of each node.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the above-described apparatus may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
Fig. 6 is a schematic diagram of a user behavior analysis apparatus according to a fifth embodiment of the present application. All the embodiments of the present application conform to the relevant regulations of national laws and regulations for data acquisition, storage, use, processing, etc. The apparatus 60 of the fifth embodiment, comprising:
the data analysis request obtaining module 601 is configured to obtain a data analysis request, where the data analysis request includes a user behavior analysis category.
A determining module 602, configured to determine, according to a preset user link, each process node corresponding to a user behavior analysis category; the user link comprises a plurality of process nodes, and the plurality of process nodes comprise at least one main node and at least one branch node; the preorder node and the postorder node of each branch node are main nodes, and each branch node represents a jump page between the preorder node and the postorder node corresponding to each branch node; each process node in the user link has an execution order, each process node in the user link has node quantization data, and the node quantization data of each process node in the user link is generated after the user link is executed.
The node quantized data obtaining module 603 is configured to obtain node quantized data of each flow node in the flow nodes corresponding to the user behavior analysis category.
The generating module 604 is configured to generate user behavior analysis data according to the obtained quantized data of each node.
Optionally, the determining module 602 includes:
a recognition unit 6021 for recognizing the keywords in the user behavior analysis category.
A determining unit 6022, configured to determine, in a preset user link, a flow node that is the same as the keyword, and a preceding node and a subsequent node of the flow node.
Optionally, the node quantized data obtaining module 603 includes:
a reading unit 6031 configured to read node quantized data of a flow node that is the same as the keyword in a preset user link, and node quantized data of a preamble node and node quantized data of a subsequent node of the flow node.
Optionally, the node quantizes data, including:
the number of node users, the node conversion rate and the node loss rate.
The apparatus 60, further comprising:
the adjusting module 605 is configured to readjust the corresponding process node if it is detected that the node quantization data of one process node is abnormal in each process node.
The apparatus 60, further comprising:
and the response module 606 is configured to respond to the user trigger request, and execute a page jump procedure corresponding to the user trigger request according to the user trigger request to obtain a user link.
Optionally, the user behavior analysis data includes rationality data of process node design, rationality data of product operation, rationality data of product sales, and user representation.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the above-described apparatus may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
FIG. 7 is a block diagram illustrating a computer device, which may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like, according to an exemplary embodiment.
The apparatus 700 may include one or more of the following components: a processing component 702, a memory 704, a power component 706, a multimedia component 708, an audio component 710, an input/output (I/O) interface 712, a sensor component 714, and a communication component 716.
The processing component 702 generally controls overall operation of the device 700, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 702 may include one or more processors 720 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 702 may include one or more modules that facilitate interaction between the processing component 702 and other components. For example, the processing component 702 may include a multimedia module to facilitate interaction between the multimedia component 708 and the processing component 702.
The memory 704 is configured to store various types of data to support operations at the apparatus 700. Examples of such data include instructions for any application or method operating on device 700, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 704 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 706 provides power to the various components of the device 700. The power components 706 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the apparatus 700.
The multimedia component 708 includes a screen that provides an output interface between the device 700 and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 708 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 700 is in an operation mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 710 is configured to output and/or input audio signals. For example, audio component 710 includes a Microphone (MIC) configured to receive external audio signals when apparatus 700 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in the memory 704 or transmitted via the communication component 716. In some embodiments, audio component 710 also includes a speaker for outputting audio signals.
The I/O interface 712 provides an interface between the processing component 702 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 714 includes one or more sensors for providing status assessment of various aspects of the apparatus 700. For example, sensor assembly 714 may detect an open/closed state of device 700, the relative positioning of components, such as a display and keypad of device 700, sensor assembly 714 may also detect a change in position of device 700 or a component of device 700, the presence or absence of user contact with device 700, orientation or acceleration/deceleration of device 700, and a change in temperature of device 700. The sensor assembly 714 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 714 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 714 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 716 is configured to facilitate wired or wireless communication between the apparatus 700 and other devices. The apparatus 700 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 716 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 716 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer readable storage medium comprising instructions, such as the memory 704 comprising instructions, executable by the processor 720 of the device 700 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
A non-transitory computer-readable storage medium, wherein instructions of the storage medium, when executed by a processor of a computer device, enable the computer device to perform the user behavior analysis method of the computer device.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (11)
1. A method for analyzing user behavior, the method comprising:
acquiring a data analysis request; wherein the data analysis request comprises a user behavior analysis category;
determining each process node corresponding to the user behavior analysis category according to a preset user link; the user link comprises a plurality of process nodes, and the plurality of process nodes comprise at least one main node and at least one branch node; the preorder node and the successor node of each branch node are both main nodes, and each branch node represents a jump page between the preorder node and the successor node corresponding to each branch node; each process node in the user link has an execution order, each process node in the user link has node quantization data, and the node quantization data of each process node in the user link is generated after the user link is executed;
acquiring node quantization data of each flow node in each flow node corresponding to the user behavior analysis category;
and generating user behavior analysis data according to the acquired quantitative data of each node.
2. The method of claim 1, wherein determining, according to a preset user link, each flow node corresponding to the user behavior analysis category comprises:
and identifying keywords in the user behavior analysis category, and determining the flow nodes which are the same as the keywords and the preorder nodes and the postorder nodes of the flow nodes in the preset user link.
3. The method of claim 2, wherein obtaining node quantization data for each of the process nodes corresponding to the user behavior analysis category comprises:
and reading the node quantization data of the flow node in the preset user link, which is the same as the keyword, and the node quantization data of the preorder node and the node quantization data of the subsequent node of the flow node.
4. The method of claim 1, wherein the node quantizing the data comprises:
the number of node users, the node conversion rate and the node loss rate.
5. The method of claim 1, wherein before generating the user behavior analysis data based on the obtained quantized data of each node, the method comprises:
and if the abnormal node quantization data of one process node in each process node is detected, readjusting the corresponding process node.
6. The method according to any one of claims 1-5, further comprising:
responding to a user trigger request, and executing a page jump flow corresponding to the user trigger request according to the user trigger request to obtain the user link.
7. A method according to any one of claims 1 to 5, wherein the user behavioural analysis data comprises process node design rationality data, product operation rationality data, product sales rationality data and user profiles.
8. A user behavior analysis apparatus, characterized in that the apparatus comprises:
the data analysis request acquisition module is used for acquiring a data analysis request; wherein the data analysis request comprises a user behavior analysis category;
the determining module is used for determining each process node corresponding to the user behavior analysis category according to a preset user link; the user link comprises a plurality of process nodes, and the plurality of process nodes comprise at least one main node and at least one branch node; the preorder node and the successor node of each branch node are both main nodes, and each branch node represents a jump page between the preorder node and the successor node corresponding to each branch node; each process node in the user link has an execution order, each process node in the user link has node quantization data, and the node quantization data of each process node in the user link is generated after the user link is executed;
a node quantized data obtaining module, configured to obtain node quantized data of each flow node in each flow node corresponding to the user behavior analysis category;
and the generating module is used for generating user behavior analysis data according to the acquired quantized data of each node.
9. A computer device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the method of any of claims 1-7.
10. A computer-readable storage medium having computer-executable instructions stored therein, which when executed by a processor, are configured to implement the method of any one of claims 1-7.
11. A computer program product, comprising a computer program which, when executed by a processor, implements the method of any one of claims 1-7.
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