CN114629946B - Cross-platform user analysis method, device, system and medium - Google Patents
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- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
- H04N21/44213—Monitoring of end-user related data
- H04N21/44222—Analytics of user selections, e.g. selection of programs or purchase activity
- H04N21/44224—Monitoring of user activity on external systems, e.g. Internet browsing
Abstract
The invention discloses a cross-platform user analysis method, a device, a system and a medium, wherein first interaction data of a user and a first account number are acquired, and the first account number is a registered account number of an enterprise on a first platform; when the fact that the user pays attention to a second account number of the enterprise on a second platform is detected, combining second interaction data of the user on the second platform with the first interaction data to obtain cross-platform interaction data; performing liveness analysis on the behavior of the user according to the cross-platform interaction data; and distributing corresponding grouping labels for the users according to the activity analysis result. Through gathering interactive data on the different platforms and merging, and then obtain cross-platform interactive data and further analyze user liveness, realized the medium linkage comprehensive analysis between the multiple platforms, avoid the deviation that single platform data analysis caused, effectively improved user analysis's comprehensiveness, accuracy, be favorable to promoting user conversion rate.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a cross-platform user analysis method, apparatus, system, and medium.
Background
Currently, people are increasingly used to fast food enjoyment to cause short video burst, a large amount of flow is rushed into a short video platform, such as sound shaking, fast handholding and the like, and rushing into of a large number of users also provides more customer conversion sources for merchants.
However, in the existing user analysis and conversion, only background data of a short video platform can be analyzed, and single platform information affects the accuracy of analyzing and identifying potential clients in a large number of users, so that the user analysis accuracy and conversion rate are reduced.
Disclosure of Invention
In view of the shortcomings of the prior art, the invention aims to provide a cross-platform user analysis method, device, system and medium, which aim to improve the accuracy of platform user analysis and further improve the user conversion rate.
The technical scheme of the invention is as follows:
a cross-platform user analysis method, comprising:
collecting first interaction data of a user and a first account, wherein the first account is a registered account of an enterprise on a first platform;
when the fact that the user pays attention to a second account number of the enterprise on a second platform is detected, combining second interaction data of the user on the second platform with the first interaction data to obtain cross-platform interaction data;
performing liveness analysis on the behavior of the user according to the cross-platform interaction data;
and distributing corresponding grouping labels for the users according to the activity analysis result.
In one embodiment, the collecting first interaction data of the user and the first account, where the first account is a registered account of the enterprise on the first platform specifically includes:
and collecting private information data, message data, browsing data and telephone data of the user and the first account number on the first platform to obtain the first interaction data.
In one embodiment, when detecting that the user has focused on the second account of the enterprise on the second platform, combining the second interaction data of the user on the second platform with the first interaction data to obtain cross-platform interaction data, including:
detecting whether the second account number exists in the attention list of the second platform or not, if yes, acquiring common identification of the user on the first platform and the second platform;
and combining the second interaction data of the user on the second platform with the first interaction data according to the common identification to obtain cross-platform interaction data.
In one embodiment, the merging, according to the common identifier, the second interaction data of the user on the second platform with the first interaction data to obtain cross-platform interaction data includes:
collecting second interaction data of the user on a second platform according to the common identifier;
and carrying out data combination according to the time axis of the first interaction data and the second interaction data to obtain cross-platform interaction data of the user between the first platform and the second platform.
In one embodiment, the analyzing the activity of the user according to the cross-platform interaction data includes:
according to the cross-platform interaction data, active behavior screening is carried out, wherein the active behavior is an interaction behavior meeting a first preset condition on a first platform, and/or an interaction behavior meeting a second preset condition on a second platform, and/or a cross-platform interaction behavior meeting a third preset condition between the first platform and the second platform;
and counting the times and time of the active behaviors to obtain a corresponding activity analysis result.
In one embodiment, the allocating a corresponding packet label to the user according to the activity analysis result specifically includes:
when the number of the active behaviors is greater than a preset number, an active label is distributed to the user;
a corresponding time period stamp is assigned according to the time of the active behavior of the user with the active stamp.
In one embodiment, when the second account number of the enterprise on the second platform is detected, the second interaction data of the user on the second platform is combined with the first interaction data, so as to obtain cross-platform interaction data, and the method further includes:
when receiving the message sent by the user on the first platform, the corresponding reply message is sent to the user through the first platform and/or the second platform.
A cross-platform user analysis device, comprising:
the system comprises an acquisition module, a first interaction module and a second interaction module, wherein the acquisition module is used for acquiring first interaction data of a user and a first account, and the first account is a registered account of an enterprise on a first platform;
the merging module is used for merging second interaction data of the user on the second platform with the first interaction data to obtain cross-platform interaction data when the fact that the user pays attention to the second account of the enterprise on the second platform is detected;
the analysis module is used for analyzing the activity of the user according to the cross-platform interaction data;
and the grouping module is used for distributing corresponding grouping labels for the users according to the activity analysis result.
A cross-platform user analysis system, the system comprising at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the cross-platform user analysis method described above.
A non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the cross-platform user analysis method described above.
The beneficial effects are that: compared with the prior art, the embodiment of the invention acquires and merges the interactive data on different platforms, so as to further analyze the user activity by obtaining the cross-platform interactive data, realize the medium linkage comprehensive analysis among multiple platforms, avoid the deviation caused by the data analysis of a single platform, effectively improve the comprehensiveness and the accuracy of the user analysis and be beneficial to improving the conversion rate of the user.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flowchart of a cross-platform user analysis method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a functional module of a cross-platform user analysis device according to an embodiment of the present invention;
fig. 3 is a schematic hardware structure of a cross-platform user analysis system according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail below in order to make the objects, technical solutions and effects of the present invention more clear and distinct. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. Embodiments of the present invention are described below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flowchart of an embodiment of a cross-platform user analysis method provided by the present invention. The cross-platform user analysis method provided by the embodiment is suitable for the situation of analyzing multi-platform user data, and is particularly applied to a system comprising terminal equipment, a network and a server, wherein the network is a medium for directly providing a communication link between the terminal equipment and the server, and can comprise various connection types, such as a wired communication link, a wireless communication link or an optical fiber cable and the like; the operating system on the terminal device may include a handheld device operating system (iPhone operating system, iOS system), an android system, or other operating system, and the terminal device may be connected to a server via a network to interact to receive or transmit data, etc., and may specifically be various electronic devices that have a display screen and support web browsing, including but not limited to smartphones, tablet computers, portable computers, desktop servers, etc. As shown in fig. 1, the method specifically includes the following steps:
s100, collecting first interaction data of a user and a first account, wherein the first account is a registered account of an enterprise on a first platform.
In this embodiment, when an authorization instruction of a user is received, data of the user on a first platform may be collected to ensure data privacy of the user, and for corresponding data collection for different enterprises, first interaction data of the user and the first account is collected, where the first account is a registered account of the enterprise on the first platform, specifically, the first platform may be a short video media platform, for example, a tremble platform, a fast-handy platform, a micro-vision platform, etc., the enterprise may register an account, that is, the first account, on the short video media platform in advance, so as to issue a corresponding short video through the first account to perform enterprise operation, etc., when the user browses a video issued by the first account, the user may interact with the first account by sending a private message to the first account, leaving a message below the video, dialing a homepage phone of the first account, etc., that is, the collected first interaction data may include private message data, browsing data, phone data of the user and the first account, so as to embody interaction behavior between the user and the enterprise.
And S200, when the fact that the user pays attention to the second account number of the enterprise on the second platform is detected, combining second interaction data of the user on the second platform with the first interaction data to obtain cross-platform interaction data.
Because enterprises often carry out network marketing through multiple platforms, besides a short video media platform, the enterprises often carry out enterprise operation through instant messaging such as a WeChat platform, a microblog platform, a payment treasured platform and the like and a social platform, on the platforms, the enterprises often issue corresponding marketing contents including characters, pictures, videos and the like in a mode of operating public account numbers (WeChat public numbers, microblog account numbers and payment treasured life numbers), and if a user pays attention to a certain public account number, the message of the public account number is pushed to the user.
Therefore, on the basis of the first interaction data, if it is detected that the user has focused on the second account number of the enterprise on the second platform (the public account number of the enterprise on the second platform), for example, focused on the WeChat public number of the enterprise, the microblog account number of the enterprise, etc., the second interaction data of the user and the second account number on the second platform are combined with the first interaction data, where the second interaction data may include private letter data, comment data, praise data, browsing data, click skip data, etc., and on the second platform, the instant messaging and social platform may also skip to the relevant page of the first platform by, for example, clicking link, copying password, etc., so that after the first interaction data and the second interaction data are combined, cross-platform interaction data between different platforms of the user may be obtained, so as to implement more comprehensive and accurate analysis of user behavior.
In one embodiment, step S200 includes:
detecting whether the second account number exists in the attention list of the second platform or not, if yes, acquiring common identification of the user on the first platform and the second platform;
and combining the second interaction data of the user on the second platform with the first interaction data according to the common identifier to obtain cross-platform interaction data.
In this embodiment, whether the user pays attention to the enterprise is determined by detecting whether a second account registered by the enterprise exists in the attention list of the second platform, if so, the user is an intended client, and if so, the client conversion success rate is higher compared with the remaining guests who do not pay attention, so that further cross-platform comprehensive analysis is performed on the users who pay attention to the second account, and the pertinence of the user analysis is improved to improve the conversion rate.
For the user focusing on the second account, the common identifier of the user on the first platform and the second platform is obtained as the super ID, for example, registration information of the user on different platforms is obtained, unique and common identifiers are extracted to be used as the common identifiers, for example, mobile phone numbers, micro signals, microblog accounts, panning accounts and the like, and the user accounts in multiple channels are combined through the common identifiers, so that the second interaction data and the first interaction data are combined, the multi-dimensional multi-channel cross-platform interaction data are obtained, the generation of complete customer views is facilitated, and the accuracy of user analysis is improved.
In one embodiment, according to the common identifier, combining the second interaction data of the user on the second platform with the first interaction data to obtain cross-platform interaction data includes:
collecting second interaction data of the user on a second platform according to the common identifier;
and carrying out data combination according to the time axis of the first interaction data and the second interaction data to obtain cross-platform interaction data of the user between the first platform and the second platform.
In this embodiment, when any interaction behavior occurs between the user and the second account, second interaction data of the user on the second platform is collected according to the extracted common identifier, a cross-platform user portrait is established through the first interaction data and the second interaction data, so as to record and analyze the behavior of the user, the first interaction data and the second interaction data are combined according to time axes, that is, the interaction behaviors on different platforms are arranged according to time sequences to generate a cross-platform time axis interaction list, the time axis interaction list includes interaction time and specific interaction behaviors of the interaction platforms, so that cross-platform interaction data of the user on multiple platforms are obtained, no matter which platform interacts with the corresponding account of the enterprise, the cross-platform interaction data can be recorded and stored in the time axis interaction list in time, so that complete and multi-azimuth user behavior analysis data can be obtained, and an accurate data basis is used for subsequent user behavior analysis.
S300, analyzing the activity of the user according to the cross-platform interaction data.
Through combining the obtained cross-platform interaction data, comprehensive liveness analysis can be performed on behaviors of users on different platforms and joint interaction among different platforms, the problem that the workload is large and data analysis deviation is caused by independently performing user analysis on the data of different platforms is avoided, the fragmented behaviors of the users on the different platforms are integrated and analyzed to obtain comprehensive global behaviors, accurate analysis results are improved for follow-up accurate marketing, and the conversion rate of clients is improved.
In one embodiment, step S300 includes:
according to the cross-platform interaction data, active behavior screening is carried out, wherein the active behavior is an interaction behavior meeting a first preset condition on a first platform, and/or an interaction behavior meeting a second preset condition on a second platform, and/or a cross-platform interaction behavior meeting a third preset condition between the first platform and the second platform;
and counting the times and time of the active behaviors to obtain a corresponding activity analysis result.
In this embodiment, for the combined cross-platform data of the user, active behavior screening is performed based on the interactive behaviors of the user on different platforms, so as to screen out the interactive behaviors of the user on the first platform, where the interactive behaviors of the user meet a first preset condition, and the first preset condition may be that the number of times of sending private messages to the first account is greater than or equal to 2 times, or that the number of times of sending messages to the first account is greater than or equal to 2 times, or that the number of times of browsing videos issued by the first account is greater than or equal to 10 times, and so on; and/or screening out an interaction behavior of the user on the second platform, where the second preset condition may be that the number of times of sending private messages to the second account is greater than or equal to 2 times, or that the number of times of approving the second account to issue content is greater than or equal to 2 times, or that the number of times of browsing the content issued by the second account is greater than or equal to 10 times, and the like, and when the specific implementation is performed, the first preset condition and the second preset condition may be set to the same condition or different conditions, which is not limited in this embodiment; and/or screening out cross-platform interaction behaviors of the user between the first platform and the second platform, wherein the cross-platform interaction behaviors meet a third preset condition, and the third preset condition can be that the number of times of jumping between the cross-platforms is greater than or equal to 2 times, or the number of times of cross-platform content sharing is greater than or equal to 2ci, and the like.
Through presetting corresponding screening conditions, active behaviors of a user in multiple platforms and among the multiple platforms are screened in cross-platform interaction data, statistics is carried out on the screened active behaviors to obtain the times and time of the active behaviors, and the platform types of the active behaviors can be further counted, namely, each time the active behaviors occur on a first platform, a second platform or the cross-platform, so that further inclined analysis is carried out on the active behaviors of an intended client, a more comprehensive and complete user behavior analysis result is obtained through the interaction behaviors of the multiple platforms, more refined and targeted accurate marketing information pushing is facilitated, and user viscosity and conversion rate are improved.
S400, distributing corresponding grouping labels for users according to the activity analysis result.
Based on the activity analysis result, corresponding grouping labels are allocated to the users, so that different users can be automatically grouped according to the interaction behaviors of the users on multiple platforms and across platforms, the specific grouping labels can be one or more, for example, the grouping labels can be independently performed according to the number of activities or the grouping labels can be independently performed according to the time period of the activity, and the grouping labels can be performed according to the number of activities and the time period of the activity at the same time, namely, one or more labels can be added to the same user.
Preferably, particularly when the grouping labels are distributed, when the number of times of the active actions is larger than a preset number of times, the active labels are distributed to users so as to indicate that the users in the group are all users with higher activity, and the users can be tracked and maintained in a key way; and users with active labels are further grouped according to time, namely, corresponding time interval labels are distributed to the users according to time intervals in which the time with active behaviors of the users with active labels falls, so that the users with higher activity can be further distinguished according to the preferential active time of the users, when marketing information is pushed, the users can push in the preferential active time interval of each user based on different time interval labels, the probability of reading the marketing information by the users is improved as much as possible, and the effectiveness of information pushing is improved.
Of course, in other embodiments, the platforms where the active behaviors of the user are located may be grouped, and corresponding platform labels may be allocated, for example, when the number of times of the active behaviors of the user on the first platform is greater than the number of times of the active behaviors of the user on the second platform, the first platform label is allocated to the user, otherwise, the second platform label is allocated, so that the preference of the user for the marketing channel may be further known, when the accurate pushing of the marketing information is performed, the information may be preferentially sent through the platform identified by the user platform label, and if the user has read, no other platform sends the marketing information, thereby avoiding trouble to the user caused by excessive marketing information, and improving the accuracy and efficiency of the message delivery.
In one embodiment, after step S200, the method further comprises:
when receiving the message sent by the user on the first platform, the corresponding reply message is sent to the user through the first platform and/or the second platform.
In this embodiment, in order to achieve accurate and automatic marketing touch, when a user sends a message on a first platform to perform consultation, the user may easily ignore the reply message of an enterprise because of the influence of watching video on a short video media platform such as the first platform, so that the problem that the marketing message touch channel is single and not accurate enough may be caused.
Another embodiment of the present invention provides a cross-platform user analysis apparatus, including:
the collection module 11 is configured to collect first interaction data of a user and a first account, where the first account is a registered account of an enterprise on a first platform;
the merging module 12 is configured to merge second interaction data of the user on the second platform with the first interaction data to obtain cross-platform interaction data when detecting that the user has focused on the second account of the enterprise on the second platform;
the analysis module 13 is used for performing liveness analysis on the behavior of the user according to the cross-platform interaction data;
and the grouping module 14 is used for distributing corresponding grouping labels to the users according to the activity analysis result.
The acquisition module 11, the merging module 12, the analysis module 13 and the grouping module 14 are sequentially connected, and the modules referred to in the invention refer to a series of computer program instruction sections capable of completing specific functions, and are more suitable for describing the execution process of cross-platform user analysis than programs, and specific implementation manners of each module are referred to the corresponding method embodiments and are not repeated herein.
Another embodiment of the present invention provides a cross-platform user analysis system, as shown in fig. 3, the system 10 includes:
one or more processors 110 and a memory 120, one processor 110 being illustrated in fig. 3, the processors 110 and the memory 120 being coupled via a bus or other means, the bus coupling being illustrated in fig. 3.
The memory 120 is used as a non-volatile computer readable storage medium for storing non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions corresponding to the cross-platform user analysis method in the embodiment of the present invention. Processor 110 executes various functional applications of system 10 and data processing, i.e., implements the cross-platform user analysis method of the method embodiments described above, by running non-volatile software programs, instructions, and units stored in memory 120.
One or more units are stored in memory 120 that, when executed by one or more processors 110, perform the cross-platform user analysis method in any of the method embodiments described above, e.g., perform method steps S100 through S400 in fig. 1 described above.
Embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer-executable instructions for execution by one or more processors, e.g., to perform the method steps S100-S400 of fig. 1 described above.
By way of example, nonvolatile storage media can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM may be available in many forms such as Synchronous RAM (SRAM), dynamic RAM, (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The disclosed memory components or memories of the operating environments described herein are intended to comprise one or more of these and/or any other suitable types of memory.
In summary, in the cross-platform user analysis method, device, system and medium disclosed by the invention, the method is implemented by collecting first interaction data of a user and a first account, wherein the first account is a registered account of an enterprise on a first platform; when the fact that the user pays attention to a second account number of the enterprise on a second platform is detected, combining second interaction data of the user on the second platform with the first interaction data to obtain cross-platform interaction data; performing liveness analysis on the behavior of the user according to the cross-platform interaction data; and distributing corresponding grouping labels for the users according to the activity analysis result. Through gathering interactive data on the different platforms and merging, and then obtain cross-platform interactive data and further analyze user liveness, realized the medium linkage comprehensive analysis between the multiple platforms, avoid the deviation that single platform data analysis caused, effectively improved user analysis's comprehensiveness, accuracy, be favorable to promoting user conversion rate.
Of course, those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-volatile computer readable storage medium, which when executed may comprise the steps of the above described method embodiments, to instruct related hardware (e.g., processors, controllers, etc.). The storage medium may be a memory, a magnetic disk, a floppy disk, a flash memory, an optical memory, etc.
It is to be understood that the invention is not limited in its application to the examples described above, but is capable of modification and variation in light of the above teachings by those skilled in the art, and that all such modifications and variations are intended to be included within the scope of the appended claims.
Claims (9)
1. A cross-platform user analysis method, comprising:
collecting first interaction data of a user and a first account, wherein the first account is a registered account of an enterprise on a first platform;
when the fact that the user pays attention to a second account number of the enterprise on a second platform is detected, combining second interaction data of the user on the second platform with the first interaction data to obtain cross-platform interaction data;
performing liveness analysis on the behavior of the user according to the cross-platform interaction data;
distributing corresponding grouping labels for users according to the activity analysis result;
the activity analysis of the behavior of the user according to the cross-platform interaction data comprises the following steps:
according to the cross-platform interaction data, active behavior screening is carried out, wherein the active behavior is an interaction behavior meeting a first preset condition on a first platform, and/or an interaction behavior meeting a second preset condition on a second platform, and/or a cross-platform interaction behavior meeting a third preset condition between the first platform and the second platform;
and counting the times and time of the active behaviors to obtain a corresponding activity analysis result.
2. The method for analyzing the cross-platform user according to claim 1, wherein the collecting the first interaction data of the user and the first account, the first account being a registered account of the enterprise on the first platform, specifically includes:
and collecting private information data, message data, browsing data and telephone data of the user and the first account number on the first platform to obtain the first interaction data.
3. The method for cross-platform user analysis according to claim 1, wherein when detecting that the user has focused on the second account of the enterprise on the second platform, merging the second interaction data of the user on the second platform with the first interaction data to obtain the cross-platform interaction data, comprises:
detecting whether the second account number exists in the attention list of the second platform or not, if yes, acquiring common identification of the user on the first platform and the second platform;
and combining the second interaction data of the user on the second platform with the first interaction data according to the common identifier to obtain cross-platform interaction data.
4. The method for analyzing a cross-platform user according to claim 3, wherein the merging the second interaction data of the user on the second platform with the first interaction data according to the common identifier to obtain the cross-platform interaction data includes:
collecting second interaction data of the user on a second platform according to the common identifier;
and carrying out data combination according to the time axis of the first interaction data and the second interaction data to obtain cross-platform interaction data of the user between the first platform and the second platform.
5. The cross-platform user analysis method according to claim 1, wherein the allocating a corresponding grouping tag to the user according to the activity analysis result specifically comprises:
when the number of the active behaviors is greater than a preset number, an active label is distributed to the user;
a corresponding time period stamp is assigned according to the time of the active behavior of the user with the active stamp.
6. The method for cross-platform user analysis according to claim 1, wherein when detecting that the user has focused on the second account of the enterprise on the second platform, combining the second interaction data of the user on the second platform with the first interaction data to obtain the cross-platform interaction data, the method further comprises:
when receiving the message sent by the user on the first platform, the corresponding reply message is sent to the user through the first platform and/or the second platform.
7. A cross-platform user analysis device, comprising:
the system comprises an acquisition module, a first interaction module and a second interaction module, wherein the acquisition module is used for acquiring first interaction data of a user and a first account, and the first account is a registered account of an enterprise on a first platform;
the merging module is used for merging second interaction data of the user on the second platform with the first interaction data to obtain cross-platform interaction data when the fact that the user pays attention to the second account of the enterprise on the second platform is detected;
the analysis module is used for analyzing the activity of the user according to the cross-platform interaction data;
the grouping module is used for distributing corresponding grouping labels for users according to the activity analysis result;
the activity analysis of the behavior of the user according to the cross-platform interaction data comprises the following steps:
according to the cross-platform interaction data, active behavior screening is carried out, wherein the active behavior is an interaction behavior meeting a first preset condition on a first platform, and/or an interaction behavior meeting a second preset condition on a second platform, and/or a cross-platform interaction behavior meeting a third preset condition between the first platform and the second platform;
and counting the times and time of the active behaviors to obtain a corresponding activity analysis result.
8. A cross-platform user analysis system, the system comprising at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the cross-platform user analysis method of any one of claims 1-6.
9. A non-transitory computer-readable storage medium storing computer-executable instructions which, when executed by one or more processors, cause the one or more processors to perform the cross-platform user analysis method of any one of claims 1-6.
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