Electronic apparatus for analyzing data and its operation method

Abstract

The data analysis method of an electronic device according to one embodiment of the present invention is characterized by including the following steps: obtaining a plurality of log data related to the use of the above-mentioned e-commerce service from a plurality of user terminals; generating behavioral tracking data of each user related to the use of the above-mentioned e-commerce service based on the plurality of log data; extracting data that satisfies a first condition from the behavioral tracking data of the plurality of users; and displaying a dashboard including the extracted data.

Description

Electronic device for analyzing data and operation method thereof The present invention relates to an electronic device for analyzing data and its operating method. More specifically, the present invention relates to an electronic device for collecting log data related to the use of an e-commerce service from a user terminal of a user, and analyzing the collected log data to obtain information related to the service or the user, and its operating method. As internet usage becomes more widespread, the e-commerce market is expanding. In particular, with the spread of infectious diseases, the proportion of people purchasing goods in physical stores is decreasing, while the proportion of people purchasing goods through e-commerce using computers or smartphones is rapidly increasing. With the expansion of the e-commerce market, collecting a variety of information, including user behavior patterns, shopping habits, and service preferences, is crucial. This data is generated millions of times, and log data from users visiting websites or using applications includes conversation information, click events, and purchase records. By efficiently collecting and analyzing this data, e-commerce service providers can identify the information they need to establish business strategies and improve service quality in real time. On the other hand, there are various methods for extracting and classifying the necessary information from large amounts of log data. To monitor user activity and make informed decisions, effective data analysis and application to business operations are essential. As data analysis becomes increasingly important, there is a need for a method and system that allows e-commerce service providers to extract core information from various log data and utilize it in real time to improve the quality of their e-commerce services. In this regard, reference may be made to previous literature such as KR 10-1743269 B1 and KR 10-2014-0055282 A. [Problems to be solved by the invention] The object of the present invention is to provide a method for collecting log data of users who utilize e-commerce services in real time and storing the log data in a database according to desired conditions. Furthermore, the present invention aims to provide a method for processing log data stored in a database and displaying information required to improve service quality on a dashboard. The technical issues to be achieved by the present invention are not limited to the technical issues mentioned above. Those with common knowledge in the technical field to which the present invention belongs can clearly understand other technical issues not mentioned based on the following description. [Technical Means for Solving the Problem] A data analysis method for an electronic device according to one embodiment of the present invention is characterized in that it includes the following steps: obtaining a plurality of log data related to the use of e-commerce services from a plurality of user terminals; generating behavior tracking data related to the use of the above-mentioned e-commerce services for each user based on the above-mentioned plurality of log data; extracting data that meets the first condition from the behavior tracking data of the plurality of users; and displaying a dashboard including the above-mentioned extracted data. In one embodiment, the data analysis method is characterized in that the behavior tracking data includes at least one of information related to visits to at least one page and information related to user input in at least one page. In one embodiment, the data analysis method is characterized in that the first condition includes at least one of a condition related to a date or a condition related to identifier information of a user terminal. In one embodiment, the data analysis method is characterized in that: the behavior tracking data includes information about the pages visited by the user terminal; and the extraction step includes the following steps: based on the behavior tracking data of the plurality of users, extracting data on the number of visits corresponding to the first date and the first page. In one embodiment, the data analysis method is characterized in that when the page views satisfy a preset condition, the dashboard further includes information related to the page views. In one embodiment, the data analysis method is characterized in that it further includes the following steps: when the first page is a page related to a store, generating an event related to the store; and transmitting information related to the event to the plurality of user terminals. In one embodiment, the data analysis method is characterized in that: the behavior tracking data includes information about the pages visited by the user terminal; and the extraction step further includes the following steps: based on the behavior tracking data of the plurality of users, extracting data on the conversion ratio between the plurality of pages related to the e-commerce service; and based on the conversion ratio, determining the depth of at least one of the plurality of pages within the service. In one embodiment, the data analysis method is characterized in that: the behavior tracking data includes information on search terms received from the user terminal; and the extraction step includes the following steps: based on the behavior tracking data of the multiple users, extracting data on the number of searches related to the first category in each time period; and the data analysis method further includes the following steps: when the number of searches related to the first category in the first time interval meets the preset conditions, the recommended store information related to the first category is transmitted to the multiple user terminals within the first time interval. In one embodiment, the data analysis method is characterized in that: the behavior tracking data includes information related to user input on a page of a user terminal; the extraction step includes the following steps: based on the behavior tracking data of the plurality of users, extracting data related to user input on a second page of a first user terminal; and the display step includes the following steps: when the information related to user input on the second page of the first user terminal meets a preset condition, displaying a message including the identifier information of the first user terminal and the information on the second page on the dashboard. In one embodiment, the data analysis method is characterized in that: the behavior tracking data further includes information on the time when the user terminal accesses the page; the extraction step includes the following steps: based on the behavior tracking data of the multiple users, extracting data on the average residence time corresponding to the third page; and the display step includes the following steps: when the average residence time corresponding to the third page meets the preset conditions, displaying the information of the third page and the information of the average residence time in the dashboard. In one embodiment, the data analysis method is characterized in that: the behavior tracking data includes platform information corresponding to the user terminal; and the step of displaying a dashboard including the extracted data includes the following steps: based on the platform information, the extracted data is displayed in a classified manner. In one embodiment, the data analysis method is characterized in that: the behavior tracking data includes information on pages visited by the user terminal and information on the loading time of the pages visited by the user terminal; and the extraction step includes the following steps: based on the behavior tracking data of the multiple users, extracting data on the loading time of the fourth page corresponding to the second user terminal; and the display step further includes the following steps: when the loading time of the fourth page meets the preset conditions, displaying the identifier information of the second user terminal and the information of the fourth page in the dashboard. A non-transitory storage medium according to an embodiment of the present invention is characterized in that it is a non-transitory computer-readable storage medium recording a program for executing the above-mentioned method in a computer. An electronic device for analyzing data according to an embodiment of the present invention is characterized by comprising: a transceiver for transmitting and receiving information to and from a plurality of user terminals; and a processor for obtaining a plurality of log data related to the use of an e-commerce service from the plurality of user terminals, generating behavior tracking data related to each user's use of the e-commerce service based on the plurality of log data, extracting data that satisfies a first condition from the plurality of user behavior tracking data, and displaying a dashboard including the extracted data. [Effects of the Invention] The present invention can analyze user characteristics by collecting and analyzing service utilization-related data of users utilizing e-commerce services in real time. The present invention can improve e-commerce services based on user characteristics, thereby enhancing user experience. The user characteristics are obtained by analyzing data related to the user's service utilization. Based on the collected data, the present invention can quickly sense and respond to service problems or obstacles, analyze user utilization patterns, and thus predict and prevent future obstacles. The effects that can be obtained by the present invention are not limited to the effects mentioned above. Those having common sense in the technical field to which the present invention belongs can clearly understand other effects not mentioned based on the following description. When describing the embodiments, descriptions of technical contents that are well known in the technical field to which the present invention belongs and are not directly related to the present invention are omitted. The reason for this is to clearly convey the main idea of ​​the present invention by omitting redundant descriptions and to avoid confusion about the main idea of ​​the present invention. For similar reasons, some components may be exaggerated, omitted, or schematically shown in the drawings. Furthermore, the dimensions of each component do not necessarily reflect the actual dimensions. In the drawings, identical or corresponding components are given the same reference numerals. The advantages and features of the present invention, as well as methods for achieving these advantages and features, will become apparent with reference to the accompanying drawings and the embodiments described in detail below. However, the present invention is not limited to the following embodiments and can be implemented in a variety of different forms. These embodiments are provided solely to fully disclose the present invention and to fully inform those skilled in the art of the present invention of the scope of the invention. The present invention is defined solely by the scope of the claims. Throughout this specification, identical reference numerals denote identical components. At this point, it should be understood that the combination of each block of the process flow chart and the flowchart can be implemented by computer program instructions. These computer program instructions can be loaded onto a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the instructions executed by the processor of the computer or other programmable data processing device produce a method for performing the functions described in the blocks of the flowchart. To achieve the functions in a specific manner, these computer program instructions can also be stored in a computer-usable or computer-readable memory that is readable by the computer or other programmable data processing device, so that the instructions stored in the computer-usable or computer-readable memory can also produce an article of manufacture that includes the command method for performing the functions described in the blocks of the flowchart. The computer program instructions can also be loaded onto a computer or other programmable data processing device, so that the computer or other programmable data processing device executes a series of action steps to produce a process executed by the computer, so that the instructions executed by the computer or other programmable data processing device can also provide steps for performing the functions described in the blocks of the flowchart. Furthermore, each block may represent a module, segment, or portion of code that includes one or more executable instructions for performing a specific logical function. Furthermore, it should be noted that in some alternative implementations, the functions described in the blocks may not occur in the order in which they are described. For example, two blocks shown in succession may be executed substantially simultaneously, or may occasionally be executed in the reverse order depending on the corresponding functions. As used in this embodiment, the term "component" refers to software or hardware components such as FPGAs (Field Programmable Gate Arrays) and ASICs (Application Specific Integrated Circuits), which perform certain functions. However, "component" is not limited to software or hardware. A "component" can be configured as a memory located on an addressable storage medium or as a representation of one or more processors. Therefore, as an example, "component" includes components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, routines, code snippets, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The components and the functions provided within the "component" can be combined into a smaller number of components and "components," or further separated into additional components and "components." In addition, the components and "~ parts" can also be implemented in a way that one or more CPUs (Central Processing Units) in the device or secure multimedia card are regenerated. The expression "at least one of a, b, and c" throughout the specification may include "a alone," "b alone," "c alone," "a and b," "a and c," "b and c," or "a, b, and c." The "terminal" mentioned below can be implemented as a computer or portable terminal that can connect to a server or other terminals via a network. Computers include, for example, notebook computers, desktops, and laptops equipped with web browsers. Portable terminals, which are wireless communication devices that ensure portability and mobility, include, for example, IMT (International Mobile Telecommunication), CDMA (Code Division Multiple Access), W-CDMA (W-Code Division Multiple Access), LTE (Long Term Evolution) communication terminals, as well as all types of handheld wireless communication devices such as smartphones and tablets. Hereinafter, with reference to the accompanying drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily implement the present invention. However, the present invention can be implemented in various forms and is not limited to the embodiments described herein. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. FIG1 is a diagram schematically showing the structure of a system for analyzing data according to various embodiments. Referring to Figure 1 , various embodiments of the data analysis system of the present invention may include an electronic device 100 and a user terminal 110 . The system shown in Figure 1 only illustrates components relevant to the embodiments of the present invention. Therefore, those skilled in the art of the present invention will understand that, in addition to the components shown in Figure 1 , other general components may be included. In one embodiment, a user terminal 110 corresponding to a user of an e-commerce service may be equipped with an application that provides the e-commerce service. The application accesses a server related to the e-commerce service under control of the application, and the e-commerce service can be utilized by exchanging information with the server. E-commerce applications may include applications for buyers, applications for sellers, applications for delivery personnel, and so on, depending on the identity of the application user. In other words, user terminal 110 can be used by various users, including customers who purchase items through the service, sellers who sell items through the service, and delivery personnel who deliver purchased items from sellers to customers. User terminal 110 can be a mobile device such as a smartphone or tablet, or a static device such as a desktop computer. Any device capable of hosting and executing an e-commerce or delivery intermediary service application can be used as user terminal 110 without restriction. In one embodiment, the user terminal 110 can exchange information with the electronic device 100, which includes a system for analyzing data. For example, the electronic device 100 can obtain log data from the user terminal 110. The electronic device 100 can store the log data obtained from the user terminal 110 in a database of the electronic device 100 and help improve the quality of e-commerce services by analyzing the stored data. For example, the electronic device 100 can provide personalized services or quickly detect system problems and take measures by collecting and analyzing log data from users who utilize e-commerce services. This can improve the user experience and enhance the efficiency of service operations. FIG. 2 is a diagram schematically showing the operation of a system for analyzing data according to an embodiment of the present invention. 2 , the electronic device 100 can obtain log data from the user terminal 110. For example, the electronic device 100 can use Lumberjack to obtain log data from the user terminal 110 in real time, as shown in FIG2 . The log data obtained from the user terminal 110 may include page information (page 1, page 2, page 3), identifier information (schemaid) corresponding to each page in the service, and identifier information (pcid) of the user terminal. In addition, the obtained log data may further include information about actions performed by the user terminal 110 in the e-commerce service, such as entering a page, logging in, entering a search term, and purchasing an item. Thereafter, the electronic device 100 can process the log data obtained in real time and store it in the first database 101. The log data obtained in real time can be processed using Flink, and this process is called Flink Streaming. At this time, the data stored in the first database 101 can be data obtained in real time from the user terminal 110 and classified according to each date and user. In addition, the data stored in the first database 101 can be behavior tracking data in the form of user behavior information of a specific user terminal in a service on a specific date listed in sequence. For example, as shown in Figure 2, Flink can be used to convert the log data collected in real time in the form of <schemaid, page, pcid, …> into the form of <day, pcid1, <page 1, time1>, <page 2, time2>, …>, the latter refers to a series of user behavior information in the service of the user corresponding to pcid1, and the converted data is stored in the first database 101. Furthermore, the electronic device 100 can reprocess the data stored in the first database 101 to obtain the desired information, thereby extracting data in a new format. For example, since the data stored in the first database 101 sequentially lists user behavior information through a specific user terminal on a specific date, if this data is used, the number of visits (uv) of a specific page on a specific date can be understood. In addition, based on the types of information related to the use of the service included in each data stored in the first database 101, data such as the average dwell time of a specific page, the conversion rate between pages provided in the service, and the popularity category of a specific time period can be extracted from the data stored in the first database 101. As described above, the new format of data obtained by reprocessing the data stored in the first database 101 can be stored in the second database 102. For example, as shown in Figure 2, the data stored in the second database 102 can be data in the format of <day, page, time, uv>, which includes information on the daily number of visits to a specific page. Furthermore, the electronic device 100 can visually provide information related to the service and the user utilizing the service by displaying a dashboard 103. The dashboard 103 includes a portion of the data stored in the first database 101 or the second database 102. The following describes in detail the method by which the electronic device 100 collects and analyzes log data related to service utilization, as described above, with reference to FIG. FIG. 3 is a flow chart showing the operation of an electronic device for executing a method for analyzing data according to various embodiments of the present invention. 3 , in step S310 , the electronic device 100 may obtain a plurality of log data related to the use of e-commerce services from a plurality of user terminals 110 . The electronic device 100 can obtain log data from the user terminal 110 corresponding to each user for all users who utilize the e-commerce service. In one embodiment, the log data obtained from the user terminal 110 may further include date information, identifier information of the user terminal, IP address corresponding to the user terminal, location information corresponding to the user terminal, platform information corresponding to the user terminal, information of pages visited by the user terminal, information related to user input (for example, inputting a search term, clicking a button) within the page of the user terminal, and at least one of information on the time when the user input occurred. On the other hand, the information included in the log data collected by the electronic device 100 from the user terminal 110 is not limited to the above examples, and may include any information that the e-commerce service provider considers necessary to analyze matters related to the use of the service. In addition, the log data obtained from the user terminal 110 can be obtained using any method for collecting log data. In step S320 , the electronic device 100 may generate behavior tracking data related to each user's use of the e-commerce service based on the plurality of log data. In one embodiment, the electronic device 100 may store the behavior tracking data generated in step S320 in the first database 101 . Furthermore, if the log data obtained in real time from the user terminals 110 corresponding to the majority of users utilizing the service is not categorized by user and is stored in the database, a tedious process is required to subsequently identify the log data of a specific user. Specifically, the log data stored in the database must be retrieved to identify the user terminal identifier information included in the log data and the log data that matches the identifier information of the user terminal corresponding to the specific user. As the amount of data stored in the database increases, the time required to retrieve the desired log data increases. To address this issue, the real-time log data is categorized by user based on the identifier information of the user terminal included in each log data, generating behavior tracking data including information on the user's behavior within the service and storing it in the first database 101. At this point, the generated behavior tracking data for each user can be based on the information included in the log data obtained in step S310, including at least one of information related to visits to at least one page and information related to user input on at least one page. For example, information related to visits to at least one page can include page identification information of the page visited by the user, information on the time of page access, and other information, and information related to user input on at least one page can include page identification information of the page related to the user input, information on the type of user input, and information on the time when the user input occurred. In addition, the behavior tracking data can also include, within the information included in the log data obtained in step S310, any data necessary to analyze user behavior related to the use of the service. When generating user behavior tracking data based on the collected log data described above and storing it in a database, to identify a specific user's log data, it is sufficient to retrieve data from the storage space corresponding to that specific user within the database, without having to search all log data stored in the database. This method allows for efficient management of the data stored in the database and quick retrieval of desired data. Therefore, even if the amount of data stored in the database increases, the time required to retrieve the desired data can be reduced. In step S330 , the electronic device 100 may extract data that meets the first condition from the behavior tracking data of a plurality of users. In one embodiment, the first condition may include at least one of a condition related to a date or a condition related to identifier information of the user terminal. This first condition may be determined by selecting an e-commerce service provider on a dashboard provided on the electronic device 100 and may be used to retrieve data on a specific date or data on activities of a specific user. For example, an e-commerce service provider may want to analyze data related to a specific date, such as a product launch or a special event. In this case, by setting the specific date to be analyzed as the first condition, the first database 101 can be used to retrieve the behavior tracking data of multiple users corresponding to that date. Alternatively, an e-commerce service provider may want to analyze data such as the behavior patterns or purchase history of a specific user to provide personalized services. In this case, by setting the identifier information of the user terminal corresponding to the specific user to be analyzed as the first condition, the first database 101 can be used to retrieve the behavior tracking data corresponding to that user. Furthermore, when an e-commerce service provider receives a complaint from a specific user regarding service use, it may want to analyze data related to the specific user's service use on a specific date. In this case, by setting both the specific date to be analyzed and the identifier information of the user terminal corresponding to the specific user as the first condition, the behavior tracking data of a specific user on the specific date can be retrieved. In one embodiment, the first condition can be a condition for extracting new data by reprocessing the behavior tracking data stored in the first database 101. For example, to determine the number of visits to a specific page within a specific period, data stored in the first database 101 that contains date information corresponding to the specific period and the identification information of the page visited by the user matches the identification information of the specific page satisfies the first condition. The first condition can vary depending on the information the service provider wishes to determine from the behavior tracking data. Various examples related to the first condition are described below with reference to Figures 5a and 5b. In step S340 , the electronic device 100 may display a dashboard including the data extracted in step S330 . In one embodiment, the electronic device 100 can display a dashboard containing information analyzing the service and / or users utilizing the service based on the extracted data. As described above, the dashboard provides important business intelligence to e-commerce service providers and provides a function for visually confirming various information. Such a dashboard can include a variety of information. In one embodiment, the dashboard may include data such as the number of orders, sales, user activity patterns, or purchase history of a specific user on a specific date. E-commerce service providers can set a time range on the dashboard to view data within a specific period or analyze the activities of a specific user. For example, visual elements such as graphs or icons can be used to display the reasons why a specific item category sold more frequently on a specific date, or analyze the purchasing patterns of a specific user group. In one embodiment, the e-commerce service provider can change the first condition to the second condition on the dashboard, whereby the electronic device 100 can extract data that meets the changed second condition from the behavior tracking data stored in the first database 101 based on the changed second condition, and display a dashboard including the extracted data. As described above, the e-commerce service provider can use the dashboard to dynamically confirm the required information according to various scenarios, monitor business trends in real time, and help make important decisions. The dashboard function described above can support data-based decision-making and help improve business results. Below, with reference to Figures 4 to 5b, an example of a dashboard including various information related to service utilization is described. FIG4 is a diagram showing an instrument panel according to an embodiment of the present invention. Referring to Figure 4 , dashboard 400 may include area 410 and area 420 . In one embodiment, area 410 may include area 411 for selecting a specific date and area 412 for selecting a specific user. Area 411 may allow service providers to select a specific date within the dashboard. Service providers may use a calendar or date selection tool to specify a desired date. Service providers may visually confirm the behavior tracking data for the selected date or period. In one embodiment, area 412 allows service providers to select or filter specific users within the dashboard. Service providers can enter identifier information corresponding to a specific user's terminal or select a user from a drop-down list to identify that user's behavior tracking data. Furthermore, based on that user's behavior tracking data, detailed analysis of the user's purchase history, activity patterns, and other characteristics can be performed to provide personalized services or more quickly identify and address errors that occur during user service usage. In one embodiment, area 420 may include behavior tracking data extracted from the behavior tracking data of multiple users based on the information selected in area 410. The behavior tracking data of the multiple users is stored in the first database 101 of the electronic device 100. Based on the information selected in area 410, area 420 may display behavior tracking data for a specific date, behavior tracking data for a specific user, or behavior tracking data for a specific user on a specific date. The behavior tracking data displayed in area 420 may include all information included in the collected behavior tracking data, or may include only information selected by the service provider from the information included in the behavior tracking data. The example shown in FIG4 shows a dashboard including the following data: date information from the information included in the behavior tracking data, identifier information of the user terminal, page URL (Uniform Resource Locator) information, access time information for the page, and behavior information related to the page. In one embodiment, at the service provider's option, the behavior tracking data displayed in area 420 can be sorted based on specific information within the information included in the behavior tracking data. For example, the behavior tracking data displayed in area 420 can be sorted based on access time, and the service provider can use the behavior tracking data displayed in area 420 to understand the user's behavior path within the service. For another example, the behavior tracking data displayed in area 420 can be sorted based on page URLs, and the service provider can use the behavior tracking data displayed in area 420 to collect information about the number of visits to a specific page and the user terminals that visited the page. As described above, service providers can arrange and analyze the behavior tracking data displayed in area 420 based on various criteria to gain a detailed understanding of user behavior within the service, page usage statistics, or results within a specific period. This allows e-commerce service providers to ensure efficient data management and real-time business intelligence. However, as shown in Figure 4, simply displaying the behavior tracking data collected for a specific date or specific user on the dashboard makes it difficult to clearly grasp information about the user or service. Therefore, it is necessary to analyze the desired information based on the behavior tracking data stored in the first database 101 and visually display the analyzed information (e.g., daily visits, hourly orders, search term rankings, etc.). The following describes a dashboard that includes information analyzed based on the behavior tracking data stored in the first database 101 based on Figures 5a and 5b. 5a and 5b are diagrams showing instrument panels according to various embodiments of the present invention. First, referring to FIG. 5 a , a dashboard 500 may include an area 510 and an area 520 . In one embodiment, area 510 may be area 410 in FIG. 4 and may include area 511 for selecting a date and area 512 for selecting identifier information corresponding to the user terminal. The service provider may select a specific date or a specific user using areas 511 and 512. If no specific date or user is selected, the service provider may base the data on all dates or all users. Similar to area 420 in FIG. 4 , area 520 may include data based on behavior tracking data stored in first database 101 of electronic device 100 that meets the condition selected in area 510 . However, unlike area 420 , area 520 may extract and display data that meets the first condition from a plurality of behavior tracking data, rather than directly displaying a portion of the behavior tracking data extracted from the plurality of behavior tracking data stored in first database 101 of electronic device 100 . In this case, the data that meets the first condition may include data from the plurality of behavior tracking data that analyzes characteristics of a service or user. For example, dashboard 500 in Figure 5a is an example of a dashboard that displays information about the conversion ratios between service-related pages for all service users between January 1 and January 10, 2024. Service providers can change the analysis period using area 511 and analyze page conversion ratios for a subset of users using area 512. Area 520 visually displays information about the conversion ratios between pages provided within the service. The page conversion ratio refers to the rate at which users who visited a specific page converted from that page to other pages, and is an important indicator for understanding and improving user behavior within a service. In this regard, the electronic device 100 can extract data from the behavior tracking data stored in the first database 101, including data from a specific period (in this example, January 1 to January 10, 2024), and data whose identification information of pages accessed by the user terminal is consistent with the identification information of a specific page (e.g., page 1), as data that meets the first condition. Furthermore, for the extracted data, data of pages accessed after the specific page can also be confirmed. As described above, the electronic device 100 can confirm the number of visits and page entry paths for each page from the behavior tracking data stored in the first database 101, and display them in area 520. For example, in the example shown in Figure 5a, it can be seen that between January 1 and January 10, 2024, Page 1 was visited 10,000 times, of which 7,000 times were visited after Page 1, 2,500 times were visited after Page 1, and 500 times were visited after Page 1. Similarly, it can be seen how many times a visitor entered Page 4 or Page 5 from Page 2, or left Page 4, and how many times a visitor entered Page 6, Page 7, or Page 8 from Page 3, or left Page 8. As described above, the conversion ratios between multiple pages provided in the service can also be determined from the number of visits to each page and the data on the page entry paths. For example, it can be determined that the ratio of users who entered Page 1 to those who entered Page 2, the ratio of users who entered Page 2 to those who entered Page 4, and the ratio of users who entered Page 1 to those who entered Page 4 are 7 / 10, 3 / 7, and 3 / 10, respectively. Based on the page conversion rate information within the service described above, the service provider can determine the depth of a page within the multiple pages provided within the service, thereby optimizing the order of access. For example, if page 4 provides store-related event information and the service provider has established a business goal of displaying the event information to at least 50% of users who visit page 1, the service provider can use the conversion rate information between page 1 and page 4, as determined from the information provided in area 520, to determine that the event exposure rate is not meeting the target. In this case, the service provider can increase the probability of page 4 being displayed to users by changing the depth of page 4 so that users can directly access page 4 from page 1, or modify the display of store-related event information to be provided via page 2 or page 1. As described above, based on the page conversion rate information displayed in area 520 of dashboard 500, the service provider can clearly identify the relationships and conversion rates between the various pages within the service. Based on this, the service provider can optimize the connections between the various pages or the depth of the pages to enhance the user experience and help achieve the desired goals. Furthermore, when the number of visits to a specific page of the electronic device 100 meets a preset condition, the dashboard displayed may further include information related to the number of visits. For example, when the number of visits to page 1 on a specific day increases or decreases by more than a fixed level compared to the daily average number of visits to page 1, a message informing that the number of visits to page 1 has increased or decreased may be displayed on the dashboard. As described above, when information on the increase or decrease in the number of visits is displayed on the dashboard, the service provider can establish a business strategy based on this. For example, when the number of visits to pages related to a specific store increases after an event related to the store is set, the service provider can determine that the event has effectively attracted users, and can set similar events for other stores based on this. For another example, when the number of visits to a page related to a specific store decreases, it can be confirmed whether an error has occurred on the page, and an event related to the store can be set to increase customer inflow, and information related to the event can be transmitted to multiple user terminals using the service. For another example, dashboard 500 in FIG5 b is an example of a dashboard including various information related to a service. In one embodiment, area 510 includes, in addition to area 511 for selecting a date and area 512 for selecting identifier information corresponding to the user terminal, area 513 for selecting a page and area 514 for selecting a time period. As described above, service providers can use areas 511, 512, 513, and 514 to analyze service and user characteristics based solely on behavior tracking data corresponding to a specific date, specific user, specific page, and specific time period. If no specific values ​​are selected, the analysis can be based on the entire range of behavior tracking data. Similar to Figure 5a, the data displayed in area 520 of Figure 5b may be the result of extracting data that meets the first condition from the behavior tracking data stored in the first database 101. Depending on the information included in the behavior tracking data stored in the first database 101, the information that can be extracted or analyzed based on the behavior tracking data may vary, and therefore the data displayed on the dashboard may vary. For example, the behavior tracking data may include information on search terms received from the user terminal. In the case where the behavior tracking data includes information on search terms received from the user terminal, the electronic device 100 may reprocess the behavior tracking data of multiple users and extract data on the number of searches for the search terms in each time period. In this regard, although the number of searches for the search terms themselves can be confirmed, in order to more easily grasp the interest tendencies corresponding to the time periods of users utilizing the service, the number of searches for categories corresponding to the search terms may also be confirmed. As described above, the electronic device 100 may reprocess the behavior tracking data of multiple users stored in the first database 101, extract data on the number of searches for each category corresponding to the time periods, and may store the reprocessed data in the second database 102. The electronic device 100 can extract data that satisfies the period selected in area 511 and the time period selected in area 514 from the search count data for each category corresponding to the time period stored in the second database 102, and display relevant information on the dashboard. The data displayed on the dashboard can be the search count for each category corresponding to the period and time period selected by the service provider, or it can be a ranking of search terms based on the search count for each category. Furthermore, the dashboard can display information on increases or decreases in ranking compared to the ranking for the period or time period immediately preceding the period and time period selected by the service provider. As described above, by displaying the search count or ranking for each category on the dashboard, service providers can easily understand user demand at different times or time periods, and use this information to develop business strategies. For example, if the number of searches for a specific category during a specific time period meets pre-set conditions, a recommended list of stores belonging to that category can be provided during that time period. This allows users to easily access their desired stores, improving the user experience and encouraging them to purchase items, thereby increasing the service provider's revenue. For another example, behavior tracking data may include information related to user input within a page. In this case, information related to user input may include information about user actions performed within the page, such as the user clicking buttons (e.g., login buttons, page navigation buttons, product purchase buttons, refresh buttons), clicking links (e.g., hyperlinks), entering data into forms, mouse movements, and touch actions. The electronic device 100 may reprocess the behavior tracking data including information related to user input to extract information related to user input within a specific page for a specific user. The service provider can analyze the behavior of a user based on information related to user input within a specific page of a specific user, and can quickly sense and respond to problems that occur to the user during the use of the service. For example, the electronic device 100 can sense that the user has repeatedly clicked the refresh button or other buttons provided on the page more than a preset number of times based on information related to user input within a specific page of a specific user. When the user continues to click the same button, the reason may be that the screen cannot be converted as the user expected or the page cannot be loaded normally. In this case, the electronic device 100 can display the information of the user and the page on the dashboard, and the service provider can use this to quickly confirm whether there is a problem with the user or the page and whether there is a problem and respond accordingly, thereby improving the user experience. For another example, the behavior tracking data may include information about the time the user terminal accesses a page. The electronic device 100 may reprocess the behavior tracking data including information about the time the user terminal accesses a page, and extract information about the average time the user stays on each page. For example, the time the user terminal stays on a specific page can be determined by calculating the difference between the time the user terminal accesses a specific page and the time the user terminal accesses the next page, and the average time the user stays on each page can be determined by confirming the time the user stays on the page based on the behavior tracking data stored in the first database 101. Furthermore, the electronic device 100 may display the average time the user stays on each page on the dashboard. In this case, it may not be effective to display the average time the user stays on each page for all pages. Therefore, only when the average time the user stays on a page meets a preset condition, the page and the information about the average time the user stays on the page can be displayed. For example, the preset condition can be set to display the page and the information about the time the user stays on the page when the average time the user stays on the page exceeds a preset time range. Furthermore, the preset time range can be set differently according to the characteristics of the page. For example, on pages containing product catalogs, the average dwell time is relatively long due to the time required to search for products. Conversely, on pages where users can select products and then proceed to purchase, the average dwell time is relatively short due to the time required to search for information within the page. Therefore, if we consider the characteristics of the page and determine the time range, we can extract more meaningful data. Service providers can use this information on the average dwell time of each page to establish business strategies. For example, pages with longer dwell time may be pages that users are particularly interested in. Therefore, service providers can optimize the results of the page by strengthening the content of the page or improving marketing strategies. In addition, pages with shorter dwell time may mean that the user has no information of interest or is not suitable for the user. Therefore, service providers can improve the content of the page to attract the user's interest, or provide more content on the page to provide users with various options. Alternatively, in order to enable users to easily find the desired information on the page, the page structure can be modified or the search function can be enhanced. Feedback can also be collected directly from users to understand and reflect the user's requirements for the page. For another example, the behavior tracking data may include information about the pages accessed by the user terminal and information about the loading time of the page. In this case, the electronic device 100 may extract information about the loading time of a specific page of a specific user based on the behavior tracking data. Furthermore, the electronic device 100 may display information about the loading time of a specific page of a specific user on the dashboard. The electronic device 100 may display information about the user and the page on the dashboard only when the loading time of the specific page of the specific user meets the preset conditions. As described above, the electronic device 100 may continuously monitor the loading time based on the behavior tracking data, and when the loading time exceeds the preset critical value, it may be regarded as an abnormal loading time and relevant information may be recorded. If abnormal loading times are detected, the service provider can quickly identify which page the user is experiencing loading issues on and which device they are using based on the user and page information displayed on the dashboard. Furthermore, by identifying the corresponding page on the dashboard, the service provider can verify information about other users currently using that page and, based on this information, determine whether the issue is a user-specific issue or a page-specific issue. Thus, by confirming loading time information on the dashboard, user issues can be quickly identified and addressed. For another example, the behavior tracking data may include platform information corresponding to the user terminal. In this case, the electronic device 100 can extract only the platform information corresponding to the user terminal and the data corresponding to the specific platform based on the behavior tracking data, and confirm the information related to the service and the user based on the extracted data. In addition, the electronic device 100 can display the confirmed information related to the service and the user according to the platform classification. In addition, as a result of the analysis based on the data classified by platform, when it is confirmed that an error has occurred in a specific platform, the platform information and error information can be displayed on the dashboard. As described above, by displaying information related to services and users according to the platform classification, the characteristics and trends of the corresponding platform can be easily analyzed, and the parts required to improve the service can be quickly identified to enhance the user experience or quickly solve problems occurring in a specific platform, thereby improving the quality of the service. As mentioned above, dashboards that include visually presented information are intuitive and easy for users or service providers to understand, allowing them to monitor business trends in real time and help make important decisions. Furthermore, dashboard functionality can make log data stored in databases more useful, helping e-commerce service providers deliver better services and improve business results. FIG. 6 is a diagram schematically showing various components of an electronic device according to an embodiment of the present invention. Referring to Figure 6 , electronic device 600 may include a transceiver 610, a processor 620, and a memory 630. The electronic device 600 shown in Figure 6 only illustrates the components relevant to this embodiment. Therefore, those skilled in the art will appreciate that the electronic device 600 may include other common components in addition to the components shown in Figure 6 . Because the electronic device 600 in Figure 6 can be included in the electronic device 100 in Figures 1 and 2 , the description of the content that overlaps with Figures 1 to 5 b is omitted. The transceiver 610 can communicate with other devices. Therefore, the electronic device 600 can transmit and receive information with other devices via the transceiver. For example, the electronic device 600 can communicate with the user terminal 110 or other devices via the transceiver. Here, communication, i.e., the transmission and reception of data, can be achieved in a wired or wireless manner. To this end, the transceiver 610 may include a wired communication module connected to the Internet via a LAN (Local Area Network), a mobile communication module connected to a mobile communication network via a mobile communication base station to transmit and receive data, a short-range communication module utilizing a WLAN (Wireless Local Area Network) series communication method such as Wi-Fi or a WPAN (Wireless Personal Area Network) series communication method such as Bluetooth or Zigbee, a satellite communication module utilizing a GNSS (Global Navigation Satellite System) such as GPS (Global Positioning System), or a combination thereof. The processor 620 controls the overall operation of the electronic device 600. To this end, the processor 620 can perform various calculations and processes on information. For example, the processor 620 can perform various calculations and processes based on log data received from the user terminal 110 via the transceiver 610. The processor 620 can be implemented by a computer or a similar device based on hardware, software, or a combination of hardware and software. In terms of hardware, the processor 620 can be implemented in the form of a circuit that processes electrical signals to perform control functions. In terms of software, it can be implemented in the form of a program that drives the hardware processor 620. On the other hand, in situations not specifically mentioned in the above description, the actions of the electronic device can be interpreted as being performed by the control of the processor 620. That is, in the case of executing a module implemented in a system for analyzing the above data, the module can be interpreted as being controlled in the manner in which the processor 620 performs the above actions of the electronic device. For example, the processor 620 can obtain a plurality of log data related to the use of e-commerce services from a plurality of user terminals through the transceiver 610, and generate behavior tracking data related to the use of e-commerce services for each user based on the obtained plurality of log data. Furthermore, the processor 620 may extract data satisfying the first condition from the behavior tracking data of a plurality of users, and display a dashboard including the extracted data via a display included in the electronic device 600 or a separate display connected to the electronic device 600 . Memory 630 can store various types of information. Memory 630 can store data temporarily or semi-permanently. For example, memory 630 of electronic device 600 can store the operating system (OS) used to drive electronic device 500, data used to host a website, or programs used to generate Braille or data related to applications (e.g., web applications). Furthermore, memory 630 can store modules in the form of computer code, as described above. Examples of the memory 630 include a hard disk drive (HDD), a solid state drive (SSD), a flash memory, a read-only memory (ROM), a random access memory (RAM), etc. The memory 630 can be provided as a built-in or removable type. In short, various embodiments can be implemented by various mechanisms, such as hardware, firmware, software, or a combination thereof. When implemented by hardware, the methods of various embodiments can be implemented by one or more ASICs, DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs, processors, controllers, microcontrollers, microprocessors, etc. When implemented via firmware or software, the methods of various embodiments can be implemented in the form of modules, programs, or functions that perform the functions or actions described below. For example, software code can be stored in memory and driven by a processor. The memory can be located internally or externally to the processor and can transmit and receive data with the processor using various well-known methods. The electronic device or terminal in the above-described embodiment may include a processor, memory for storing and executing program data, permanent storage such as a disk drive, a communication port for communicating with external devices, and a user interface device such as a touch panel, keys, or buttons. The method implemented by the software module or algorithm may be stored on a computer-readable recording medium as computer-readable code or program commands executable on the processor. Examples of computer-readable recording media include magnetic storage media (e.g., ROM (read-only memory), RAM (random-access memory), floppy disks, hard disks, etc.) and optical readable media (e.g., CD-ROMs and DVDs). The computer-readable recording medium is distributed among computer systems connected to a network, enabling the storage and execution of computer-readable code in a distributed manner. The medium can be read by a computer, stored in memory, and executed by the processor. This embodiment can be represented by functional blocks and various processing steps. These functional blocks can be implemented by hardware and / or software components of varying numbers that perform specific functions. For example, the embodiment can utilize integrated circuits capable of performing various functions through the control of one or more microprocessors or other control devices, such as memory, processing, logic, and lookup tables. Components can be implemented by software programs or software components. Similarly, this embodiment includes algorithms implemented as a combination of data structures, programs, routines, or other program components. Therefore, they can be implemented using programming or scripting languages ​​such as C, C++, Java, and assemblers. Functionally, they can be implemented by algorithms executed in one or more processors. Furthermore, this embodiment can utilize prior art technologies for electronic environment settings, signal processing, and / or data processing. Terms such as "mechanism," "component," "mechanism," and "component" are used broadly and are not limited to mechanical or physical components. The above terms may be associated with a processor and the like and include the meaning of a series of software processes (routines). The above embodiment is merely an example, and other embodiments may be implemented within the scope of the invention patent application described below. 100: electronic device 101: first database 102: second database 103: dashboard 110: user terminal 400: dashboard 410: area 411: area 412: area 420: area 500: dashboard 510: area 511: area 512: area 513: area 514: area 520: area 600: electronic device 610: transceiver 620: processor 630: memory S310: step S320: step S330: step S340: step FIG1 schematically illustrates the configuration of a data analysis system according to various embodiments. FIG2 schematically illustrates the operation of a data analysis system according to an embodiment of the present invention. FIG3 is a flow chart illustrating the operation of an electronic device for executing a data analysis method according to various embodiments of the present invention. FIG4 schematically illustrates an instrument panel according to an embodiment of the present invention. FIG5a and FIG5b schematically illustrate instrument panels according to various embodiments of the present invention. FIG6 schematically illustrates the various configurations of an electronic device according to an embodiment of the present invention. S310: Step S320: Step S330: Step S340: Steps

Claims

1. A data analysis method performed via an electronic device, comprising the following steps: obtaining a plurality of log data related to the use of e-commerce services from a plurality of user terminals; generating behavioral tracking data for each user related to the use of the e-commerce services based on the plurality of log data; extracting data satisfying a first condition from the behavioral tracking data of the plurality of users; and displaying a dashboard including the extracted data; wherein, The aforementioned behavioral tracking data includes information on the pages accessed by the user's terminal; and the aforementioned extraction steps include extracting access data corresponding to the first date and the first page based on the behavioral tracking data of the aforementioned plurality of users. The aforementioned data analysis method further includes: generating an event related to the aforementioned store when the aforementioned first page is a store-related page, and when the value of the aforementioned visit volume is equal to or less than a preset ratio compared to the average daily visit volume of the aforementioned first page; and transmitting information related to the aforementioned event to the aforementioned plurality of user terminals.

2. The data analysis method of Request 1, wherein the aforementioned behavioral tracking data includes at least one of information related to access to at least one page and information related to user input on at least one page.

3. The data analysis method as described in Request 1, wherein the first condition includes at least one of a condition related to the date or a condition related to the identifier information of the user terminal.

4. As in the data analysis method of request item 1, when the above-mentioned visit volume meets the preset conditions, the dashboard further includes information related to the visit volume.

5. The data analysis method of Request 1, wherein the aforementioned behavior tracking data includes information on pages accessed by user terminals; and the aforementioned extraction step further includes the following steps: based on the aforementioned behavior tracking data of the plurality of users, extracting data on the conversion ratio between the plurality of pages related to the aforementioned e-commerce service; and based on the aforementioned conversion ratio, determining the depth of at least one of the plurality of pages within the aforementioned service.

6. The data analysis method of Request 1, wherein the aforementioned behavior tracking data includes information on search terms received from user terminals; and the aforementioned extraction steps include the following steps: Based on the behavior tracking data of the aforementioned plurality of users, extracting data on the number of searches related to the first category in each time period; and the aforementioned data analysis method further includes the following steps: When the number of searches related to the aforementioned first category in the first time interval meets a preset condition, transmitting recommended store information related to the aforementioned first category to the aforementioned plurality of user terminals within the aforementioned first time interval.

7. The data analysis method of Request 1, wherein the aforementioned behavior tracking data includes information related to user input within the user terminal's page; the aforementioned extraction steps include the following steps: Based on the aforementioned behavior tracking data of the plurality of users, extracting information related to user input within the second page of the first user terminal; and the aforementioned display steps include the following steps: When the information related to user input on the aforementioned second page of the first user terminal meets preset conditions, displaying a message including the identifier information of the first user terminal and the information of the aforementioned second page in the aforementioned dashboard.

8. The data analysis method of Request 1, wherein the aforementioned behavior tracking data further includes information on the time when the user terminal accesses the page; the aforementioned extraction steps include the following steps: Based on the aforementioned behavior tracking data of the plurality of users, extract the data on the average dwell time corresponding to the third page; and the aforementioned display steps include the following steps: When the average dwell time corresponding to the aforementioned third page meets the preset conditions, display the information of the aforementioned third page and the information of the aforementioned average dwell time in the aforementioned dashboard.

9. The data analysis method of Request 1, wherein the aforementioned behavior tracking data includes platform information corresponding to the user terminal; and the step of displaying a dashboard including the aforementioned extracted data includes the following steps: based on the aforementioned platform information, classifying and displaying the aforementioned extracted data.

10. The data analysis method of Request 1, wherein the aforementioned behavior tracking data includes information on pages accessed by the user terminal and information on the loading time of the aforementioned pages accessed by the user terminal; and the aforementioned extraction step includes the following steps: Based on the aforementioned behavior tracking data of the plurality of users, extracting data on the loading time of the fourth page corresponding to the second user terminal; and the aforementioned display step further includes the following steps: When the loading time of the aforementioned fourth page meets a preset condition, displaying the identifier information of the aforementioned second user terminal and the information of the aforementioned fourth page in the aforementioned dashboard.

11. A non-transitory computer-readable storage medium that records a program for performing the method of request item 1 on a computer.

12. An electronic device for analyzing data, comprising: transceiver It sends and receives information with multiple user terminals; The processor obtains a plurality of log data related to the use of e-commerce services from the plurality of user terminals, generates behavioral tracking data related to the use of e-commerce services for each user based on the plurality of log data, extracts data that meets the first condition from the behavioral tracking data of the plurality of users, and displays a dashboard including the extracted data; wherein the behavioral tracking data includes information on the pages accessed by the user terminals; and the extraction step includes extracting access data corresponding to the first date and the first page based on the behavioral tracking data of the plurality of users; the processor further executes: when the first page is a page related to the store, and when the value of the access volume is equal to or less than a preset ratio compared to the daily average access volume of the first page, an event related to the store is generated; and information related to the event is transmitted to the plurality of user terminals.

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