KR102642892B1 - System and method for analysis of stores using scraping and computer program for the same - Google Patents

Abstract

A store analysis system using scraping includes a communication module configured to receive user authentication information for one or more ordering platform servers from a user device; a scraping module configured to collect store information of a store corresponding to the user from the ordering platform server through a scraping method by accessing the one or more ordering platform servers using the authentication information; and an analysis module configured to generate analysis information related to sales of the store using the store information. Using the store analysis system, information is automatically collected from the platform through scraping using the authentication information of users who use the ordering platform, such as restaurant store owners, and is analyzed to analyze the store's sales from the big data provided by the platform. It has the advantage of automatically generating and providing various analysis information to increase store sales, such as location analysis, review analysis, and competitive store analysis.

Description

Store analysis system and method using scraping and computer program therefor {SYSTEM AND METHOD FOR ANALYSIS OF STORES USING SCRAPING AND COMPUTER PROGRAM FOR THE SAME}

Embodiments relate to a store analysis system and method using scraping and a computer program therefor. More specifically, the embodiments relate to technology that automatically scrapes store sales-related information using the business owner's member information on a delivery and ordering platform, and uses this to process and analyze data related to the store's sales and provide them. .

Due to the recent increase in single-person households and the increase in non-face-to-face demands due to the spread of coronavirus infection (COVID-19), more and more people are using food delivered to their homes or offices rather than visiting a store to eat. In particular, the use of platforms that allow ordering and payment for delivery food through websites or smartphone applications is becoming more active. According to Statistics Korea, the sales size of the delivery market in Korea will increase from KRW 5.2 trillion in 2018 to KRW 17 trillion in 2020. It increased to 300 billion won, and the usage rate of delivery apps is also rapidly increasing from 45.5% in 2018 to 57.4% in 2019 and 79.4% in 2020, taking chicken specialty restaurants as an example.

As business dependence on ordering and delivery platforms increases, the sales and profits of food store owners are greatly influenced by consumer reviews and ratings on the platforms. In addition, business owners who want to earn more profits are in an environment where they have no choice but to use paid advertising services provided by the platform to ensure that their stores are exposed to consumers at the top of the platform.

Accordingly, business owners who register their stores on ordering and delivery platforms need to closely manage consumer reviews and ratings on the platform. However, in the current environment where ordering and delivery platforms have sovereignty over order information, business owners have difficulty efficiently managing consumer ratings or sales details through the platform, and one store is registered on multiple platforms. There has been a limitation in that it is impossible to integrate and manage sales or ratings on each platform.

Public Patent Publication No. 10-2018-0034908

According to one aspect of the present invention, the system automatically scrapes information related to the store's business using the business owner's member information on the delivery and ordering platform, and uses this to process and analyze data related to the store's business. A store analysis system and method using scraping and a computer program for the same can be provided.

A store analysis system using scraping according to one aspect of the present invention includes a communication module configured to receive user authentication information for one or more ordering platform servers from a user device; a scraping module configured to collect store information of a store corresponding to the user from the ordering platform server through a scraping method by accessing the one or more ordering platform servers using the authentication information; and an analysis module configured to generate analysis information related to sales of the store using the store information.

In one embodiment, the store information includes one or more of store details including one or more of the category and location of the store, consumer reputation information for the store, sales information of the store, and advertisement usage information of the store. Includes.

In one embodiment, the analysis information is generated by classifying one or more of the store's reputation, sales, and competitive stores based on one or more of the store's category, location, date and time, and whether or not it advertises.

In one embodiment, the scraping module includes: a first scraping unit configured to scrape the store information by logging into the ordering platform server using the authentication information; and a second scraping unit configured to scrape platform information from the ordering platform server without logging in using the authentication information. At this time, the analysis module is further configured to generate the analysis information using the store information and the platform information.

In one embodiment, the scraping module is further configured to receive store information of the one or more affiliated stores using the one or more authentication information corresponding to the one or more affiliated stores. At this time, the analysis module is further configured to generate integrated analysis information related to the business of the franchise headquarters associated with the one or more affiliated stores using store information of the one or more affiliated stores.

A store analysis method using scraping according to one aspect of the present invention includes the steps of a store analysis system receiving user authentication information for one or more ordering platform servers from a user device; Collecting, by the store analysis system, store information of the store corresponding to the user from the ordering platform server in a scraping manner by accessing the one or more ordering platform servers using the authentication information; and generating, by the store analysis system, analysis information related to sales of the store using the store information.

The store analysis method using scraping according to one embodiment includes, before generating the analysis information, the store analysis system scraping platform information from the ordering platform server without logging in using the authentication information. Includes more. Additionally, at this time, the step of generating the analysis information includes the step of the store analysis system generating the analysis information using the store information and the platform information.

In one embodiment, the step of collecting the store information by scraping includes the step of the store analysis system receiving store information of the one or more affiliated stores using one or more authentication information corresponding to one or more affiliated stores. do. In addition, at this time, the step of generating the analysis information includes the step of the store analysis system generating integrated analysis information related to the sales of the franchise headquarters associated with the one or more affiliated stores using the store information of the one or more affiliated stores. .

The computer program according to one aspect of the present invention is combined with hardware to execute the blockchain-based remittance method according to the above-described embodiments, and may be stored in a computer-readable medium.

According to a store analysis system and method using scraping according to an aspect of the present invention, authentication information of a user using an ordering platform, such as a restaurant store owner, is input and information is automatically collected from the platform through scraping using the same. , it has the advantage of being able to automatically generate various analytical information to increase store sales, such as the store's sales, location, reputation, and analysis data of competing stores, from the big data provided by the platform.

In addition, the store analysis system and method according to one aspect of the present invention are applied to franchise status management for franchise headquarters, including integrated sales management for one or more franchise stores belonging to the franchise headquarters, individual franchise sales management, franchise reputation management, There is an advantage that it can be used to analyze competitive franchises and establish marketing strategies for franchisees.

1 is a schematic block diagram showing the configuration of a store analysis system using scraping according to an embodiment.
Figure 2 is a flowchart showing each step of a store analysis method using scraping according to an embodiment.
Figure 3 is a flowchart showing each step of a store analysis method using scraping according to another embodiment.
Figure 4 is a conceptual diagram showing an exemplary form of analysis information provided by a store analysis method using scraping according to an embodiment.
Figure 5 is a conceptual diagram showing an exemplary form of reputation analysis data provided by a store analysis method using scraping according to an embodiment.
Figure 6 is a conceptual diagram showing an exemplary form of sales analysis data provided by a store analysis method using scraping according to an embodiment.
7 is a conceptual diagram illustrating an exemplary form of location analysis data provided by a store analysis method using scraping according to an embodiment.
8A and 8B are conceptual diagrams showing an exemplary form of competitive store analysis data provided by a store analysis method using scraping according to an embodiment.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

1 is a schematic block diagram showing the configuration of a store analysis system using scraping according to an embodiment.

Referring to FIG. 1, the store analysis system 3 according to this embodiment collects information by scraping from one or more order platform servers 2, and uses the collected information to generate analysis information related to store sales. It is configured to do so. For example, the store analysis system 3 receives authentication information including the user ID and password of the store owner who is a member of the ordering platform from the user device 1 of the store owner, and uses the authentication information to place the ordering platform By automatically connecting to the server 2 and performing user authentication, information related to the business owner's store can be received from the ordering platform server 2.

In this specification, the order platform server (2) refers to a computing device that registers one or more stores and their menus and implements a service platform that allows consumers to search stores or menus to place orders and make payments. do. In this specification, it is abbreviated as an ordering platform, but the service provided by the ordering platform may include the functions of the delivery service platform for delivering the menu ordered by the consumer to the consumer through delivery.

In addition, in the following specification, the operation of embodiments of the present invention will be described based on stores (i.e., restaurants) and menus in the restaurant industry, but the contents of products or services that can be ordered through the ordering platform server 2 are explained in the restaurant industry. It is not limited to field.

The store analysis system (3) can receive authentication information such as ID and password for logging into the ordering platform from the user device (1) of the business owner who is a member of the ordering platform by communication through a wired and/or wireless network. there is. In addition, the store analysis system 3 connects to the business owner's store from the order platform server 2 by accessing a web page or application service page provided by the order platform server 2 through a wired and/or wireless network. Store information related to sales can be scraped. Furthermore, the store analysis system 3 can generate analysis information by processing and classifying the scraped information and provide it to the user devices 1 and 4 of the business owner or other users.

For the above operations, the store analysis system 3 according to embodiments is configured to be communicatively connected to one or more user devices 1 and 4 and one or more ordering platform servers 2 through a wired and/or wireless network. do. The communication method of this specification may include all communication methods that enable object-to-object networking through a wired and/or wireless network, and is not limited to wired communication, wireless communication, 3G, 4G, or other methods. .

For example, wired and/or wireless communications networks include Local Area Network (LAN), Metropolitan Area Network (MAN), Global System for Mobile Network (GSM), Enhanced Data GSM Environment (EDGE), and High Speed Downlink Packet Access (HSDPA). ), W-CDMA (Wideband Code Division Multiple Access), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), Bluetooth, Zigbee, Wi-Fi, VoIP ( Voice over Internet Protocol), LTE Advanced, IEEE802.16m, WirelessMAN-Advanced, HSPA+, 3GPP Long Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), UMB (formerly EV-DO Rev. C), Flash-OFDM, Refers to a communication network using one or more communication methods selected from the group consisting of iBurst and MBWA (IEEE 802.20) systems, HIPERMAN, Beam-Division Multiple Access (BDMA), Wi-MAX (World Interoperability for Microwave Access), and ultrasonic communication. It can be done, but it is not limited to this.

In one embodiment, store analytics system 3 includes a communication module 31, a scraping module 32, and an analysis module 34. Also, in one embodiment, the store analysis system 3 may further include a data processing module 33. Each unit constituting the store analysis system 3 according to embodiments may be entirely hardware, or may have aspects that are partly hardware and partly software. For example, the communication module 31, scraping module 32, data processing module 33, and analysis module 34 of the store analysis system 3 process data of a specific format and content or/or use electronic communication. It can collectively refer to hardware for sending and receiving and software related to it.

In this specification, terms such as “unit,” “module,” “device,” “terminal,” “server,” or “system” are intended to refer to a combination of hardware and software driven by the hardware. For example, hardware may be a data processing device that includes a CPU or other processor. Additionally, software driven by hardware may refer to a running process, object, executable, thread of execution, program, etc.

Additionally, each element constituting the store analysis system 3 is not necessarily intended to refer to a separate device that is physically distinct from each other. That is, the communication module 31, scraping module 32, data processing module 33, and analysis module 34 shown in FIG. 1 are the hardware constituting the store analysis system 3 and the operations performed by the hardware. It is only functionally divided according to the function, and each part does not necessarily have to be provided independently from each other. Of course, depending on the embodiment, one or more of the communication module 31, scraping module 32, data processing module 33, and analysis module 34 may be implemented as separate devices that are physically separated from each other.

The communication module 31 is a part that provides a user interface (UI) for users who use the store analysis system 3, for example, store owners. For example, the communication module 31 may receive authentication information (ordering platform ID, password, etc.) for user authentication on the ordering platform from the user device 1. Additionally, the communication module 31 may provide analysis information generated through the store analysis system 3 to the user devices 1 and 4 of users using the store analysis system 3.

For example, the store analysis system 3 is a web server that provides a web page that can be viewed using a web browser running on the user devices 1 and 4, or an application running on the user devices 1 and 4 (or, It may be an application service server that communicates with the application to enable operation of the app. In addition, at this time, the user devices 1 and 4 are mobile communication terminals such as smartphones, personal computers, laptop computers, personal digital assistants (PDAs), tablets, and IPTV (Internet devices). It may be any computing device such as a set-top box for Protocol Television, etc.

In the embodiments shown herein, the store analytics system 3 is depicted in the form of a server that communicates with the user devices 1 , 4 and the ordering platform server 2 . However, in other embodiments, the store analysis system 3 may be implemented in the form of a user device rather than a server.

In other words, the store analysis system 3 may be implemented in the form of a user device (e.g., a smartphone or PC) on which a computer program or application for scraping and analyzing information from the order platform is executed, in which case the store analysis system (3) may receive authentication information by direct input using an input device (not shown) provided in the store analysis system (3). Additionally, the store analysis system 3 may display analysis information generated using information scraped from the order platform server 2 on an output device (not shown) provided in the store analysis system 3.

The scraping module 32 connects to the ordering platform server 2 using the authentication information of the user (e.g., the store owner) and performs user authentication, thereby scraping store information corresponding to the user from the ordering platform server. It can be collected. The process of receiving store information may be performed through a screen scraping method in which a computing device automatically accesses a web page, etc., causes the information to appear on the screen, and then collects the information displayed on the screen, but is not limited to this. No, other different methods may be used for the system to automatically connect to the server and perform user authentication on behalf of the user to receive information.

In one embodiment, the store information that the scraping module 32 receives from the order platform server 2 includes store details including the category and/or location of the store, consumer reputation information about the store, and store sales information. , and/or store advertisement usage information, etc. How each information is used will be described in detail later.

In one embodiment, the scraping module 32 is configured to scrape information that requires member authentication from the ordering platform using the user's authentication information, and to further scrape information that can be viewed without a user authentication process from the ordering platform. It may be possible. In this specification, public information that can be received from the ordering platform server 2 without user authentication is referred to as platform information, which includes information on stores registered on the ordering platform corresponding to a specific category and/or location, and the ordering platform. It may include information on stores that use the provided advertising service, order history and reputation for these stores (eg, ratings and/or reviews, etc.).

For the above operation, in one embodiment, the scraping module 32 uses the user's authentication information to scrape store information from the ordering platform server 2 and the first scraping unit 321 for scraping store information from the ordering platform server 2. It may include a second scraping unit 322 for scraping public platform information.

The data processing module 33 is a part for performing data mining on scraped data. For example, the data processing module 33 may perform classification and filtering of scraped data, morphological analysis of text data, and sentiment analysis of user reviews. In the data mining process, known libraries such as MeCap and preset emotional dictionaries may be used, but are not limited to these.

The analysis module 34 may generate analysis information related to store operations using store information scraped by the first scraping unit 321 of the scraping module 32. Additionally, in one embodiment, the analysis module 34 may further use the platform information scraped by the second scraping unit 322 of the scraping module 32 to generate analysis information.

In one embodiment, the analysis module 34 includes a sales analysis unit 341 for generating analysis information related to the store's sales from the scraped information. For example, the analysis information generated by the sales analysis unit 341 classifies data related to the store's reputation, sales, and/or competitive stores according to preset criteria such as the store's category, location, date and time, and/or whether or not there is an advertisement. It may have been created by doing so.

In this case, the category refers to the standard for classifying stores based on industry, menu, store type, etc. For example, in the case of the restaurant industry, stores are categorized based on the type of food, such as Korean food, Western food, chicken, Chinese food, pig's trotters/bossam, snack foods, etc., or stores or sales types such as restaurants, cafes/desserts, lunch boxes, etc. Stores can be classified into categories based on this standard, but this is an example and the store classification method is not limited to this.

In one embodiment, the analysis module 34 includes a competitor analysis unit 342 for generating analysis information about competitive stores by comparing analysis information about the store corresponding to the user with analysis information about other stores in the ordering platform. can do. In addition, in one embodiment, the analysis module 34 further includes a franchise analysis unit 343 for generating integrated analysis information related to the business of the franchise using analysis information about one or more franchises belonging to the same franchise. It can be included. The specific form of the analysis information will be described in detail later.

Figure 2 is a flowchart showing each step of a store analysis method using scraping according to an embodiment. The store analysis method using scraping according to this embodiment can be performed using a computing device, that is, a user device or a server-type store analysis system.

Referring to FIG. 2, the store analysis system 3 may first receive authentication information, which is the user's membership registration information (ID, password, etc.) for the ordering platform, from the user device 1 (S11). For example, the store analysis system 3 may receive authentication information entered by the user into his or her user device 1 through communication over a network. However, this is an example, and in other embodiments, the store analysis system 3 itself may be implemented as a user device, in which case step S11 may be omitted.

Next, the store analysis system 3 may receive store information by scraping from one or more order platform servers 21 and 22 using the user's authentication information (S12, S13). At this time, if there are multiple ordering platforms where the user has registered his or her store, the user's authentication information includes authentication information for each of the plurality of ordering platforms, and the store analysis system 3 uses authentication information corresponding to each ordering platform. Thus, store information can be received from each of the plurality of ordering platform servers 21 and 22.

In addition, in one embodiment, the store analysis system 3 may further receive platform information, which is public information that can be obtained without user authentication, from one or more order platform servers 21 and 22 through scraping (S14, S15) ).

For example, in this specification, store information is member data that can be obtained after performing a member login to the ordering platform, including store details (category, location, etc.), sales information (order history, payment history, etc.), and reputation information (reviews). history, comment history, ratings, etc.) and/or advertisement usage information (whether paid advertisements are used and usage history, etc.). Additionally, platform information is public data available through the ordering platform and may include a list of stores across the entire platform, the number of orders for each store, ratings, review lists, and location information for each store.

Next, the store analysis system 3 can generate analysis information related to store sales through data classification (S16), text data analysis (S17), etc. for scraped store information and/or platform information (S18) ).

The data classification process (S16) may refer to dividing the scraped data into numerical data related to sales or ratings, and text data related to user reviews, store introduction, location, etc., depending on the data type. Additionally, the text data analysis process (S17) may mean classifying the content of text data or analyzing its frequency or amount using a morphological analysis library such as MeCap or a preset emotional dictionary. At this time, the emotional dictionary may refer to a database that pre-classifies consumers' evaluations revealing the keywords into positive and negative for each keyword that can be included in a consumer review.

The store analysis system 3 can provide the analysis information generated as described above to users who use the store analysis system 3 (S19). For example, a store owner using the store analysis system (3) enters his or her authentication information into an application that operates while communicating with the store analysis system (3) and analyzes the scraped information in the store using his or her authentication information. The analysis information generated by analysis in the system (3) can be viewed through the application. Alternatively, the store owner may enter authentication information and receive analysis information through a web page provided by the store analysis system 3.

The user who receives analysis information about the store may be the store owner, but may also be another user who uses the store analysis system 3. For example, in one embodiment, the store analysis system 3 receives authentication information from the business owners of one or more affiliated stores belonging to the same franchise headquarters, scrapes the store information of each affiliated store from the order platform, and stores the scraped stores of each affiliated store. The analysis information generated using the information may be provided to the manager of the franchise headquarters in addition to the business owner of each franchise store.

Figure 3 is a flowchart showing each step of the store analysis method using scraping according to this embodiment.

Referring to FIG. 3, in this embodiment, the store analysis system 3 receives authentication information from one or more user devices 11 and 12 (S21 and S22), where each user device 11 and 12 uses the same It may correspond to one or more franchise stores belonging to the franchisor. For example, the owner of each affiliated store can enter his or her authentication information for the ordering platform into the store analysis system 3.

In the store analysis system 3, the store information of each affiliated store can be scraped from the ordering platform server 2 using the received authentication information of the owner of the affiliated store. In the embodiment of FIG. 3, for convenience of explanation, first store information is scraped using authentication information received from the user device 11 of the first affiliated store (S23), and information is scraped from the user device 12 of the second affiliated store. This will be described as scraping the second store information using the received authentication information (S24). However, this is an example, and the number of store information scraped is determined depending on the number of affiliated stores and may be different from the example shown in the drawing.

In addition, in FIG. 3, for convenience of explanation, it is explained that the store information of each affiliated store is scraped from one ordering platform server 2. However, as described above with reference to FIG. 2, each store is registered in one or more ordering platforms. Or/Also, each store may be registered with a different ordering platform, and in this case, the store analysis system 3 may use one or more different ordering platform servers ( 2) You can also receive store information from affiliated stores.

Next, the store analysis system 3 can generate analysis information for the franchise headquarters (S27) through data mining using the store information of the received affiliated stores (S16). The analysis information provided in this embodiment is for the franchisor and is targeted at one store in that it includes analysis data on one or more franchises related to the franchisor and/or the franchisor's competitors (e.g., competing franchises). It is different from the analysis information. For example, in this embodiment, the analysis information includes the ordering platform subscription status of franchisees belonging to the franchise headquarters, analysis data such as sales, reputation, location, and advertisements analyzed individually or collectively for each franchise, and competitors' information. It may include, but is not limited to, analysis data about reputation and/or location.

The store analysis system (3) can provide the analysis information for the franchise created as above to the manager device (4) (S28), and at this time, the manager device (4) is for the purpose of managing affiliated stores at the franchise headquarters. It may refer to a server used as a server or a user device used by an administrator with the job of managing affiliated stores. However, in another embodiment, the store analysis system 3 may provide analysis information for the franchise to the user devices of franchise store owners belonging to the franchise.

In one embodiment, the store analysis system 3 receives information on franchises belonging to the franchise by communicating with the franchise server (5; Figure 1), and based on this, collects the store information of the corresponding franchises to organize the franchise. It is also possible to generate analysis information for For example, the franchise headquarters server (5) stores store details such as names and locations of affiliated franchises, and the store analysis system (3) can receive the store details of affiliated stores from the franchise headquarters server (5). . Additionally, the store analysis system 3 may store store information received through scraping using a big data method using authentication information of users using the store analysis system 3.

At this time, the analysis module 33 of the store analysis system 3 compares the name and location of the store details of the franchisees received from the franchise server 5 with the big data stored in the store analysis system 3, thereby Stores corresponding to affiliated stores of the server 5 can be automatically identified, and analysis information for the franchise headquarters can be generated using the store information of the stores. In addition, as a result of comparing the store details of affiliated stores with big data, if there is an affiliated store whose authentication information is not stored in the store analysis system (3), the analysis module (33) registers members and inputs authentication information for the unregistered affiliated store. A notification requesting may also be transmitted to the manager device 4 of the affiliated store.

Figure 4 is a conceptual diagram showing an exemplary form of analysis information provided by a store analysis method using scraping according to an embodiment.

Analysis information provided through the store analysis method according to embodiments may be generated by classifying data on the store's reputation, sales, and/or competitive stores based on the store's category, location, date, advertisement status, etc. there is. For example, the analysis information may include reputation analysis data 401, sales analysis data 402, location analysis data 403, and competitive store analysis data 404, as shown in FIG. 4. It is not limited.

Figure 5 is a conceptual diagram showing an exemplary form of reputation analysis data provided by a store analysis method using scraping according to an embodiment.

Referring to FIG. 5, the reputation analysis data among the analysis information includes reputation (e.g., ratings, comments and/or reviews) status for each store (501), frequency analysis of keywords included in reviews (502), and keyword analysis by rating (503). ), etc., but is not limited thereto. For example, in addition to the data shown in the drawing, reputation analysis data may include the status of new reviews by unit period (e.g., by day), positive and negative keyword analysis results based on text analysis, or other types of analysis results. there is.

Additionally, in one embodiment, the analysis results of reputation analysis data may comprehensively represent data collected from multiple ordering platforms, and through this, the store owner can efficiently manage the store's reputation across multiple ordering platforms. there is.

Figure 6 is a conceptual diagram showing an exemplary form of sales analysis data provided by a store analysis method using scraping according to an embodiment.

Referring to FIG. 6, sales data among the analysis information may include sales status (601, 602) by unit period (e.g., hourly, daily, weekly, etc.), sales status by menu and/or location (603), etc. , but is not limited to this. Additionally, this sales status can be processed in the form of a sales amount that aggregates the order amount, or in the form of a sales ranking that sorts the sales amount based on time/menu/location, etc. Additionally, in one embodiment, the sales amount of the sales analysis data may comprehensively represent data collected from multiple ordering platforms, and through this, the store owner can integrate and manage sales across multiple ordering platforms.

Figure 7 is a conceptual diagram showing an example form of location analysis data provided by a store analysis method using scraping according to an embodiment.

Among the analysis information, location analysis data is a compilation of the number of nearby stores located within a preset radius based on a specific location, the number of orders for nearby stores, and the rating status of nearby stores based on information registered in the ordering platform. May contain information. At this time, the specific location may be the location of the store of the business owner using the store analysis system, and the store analysis system can extract the location of the store from the store details registered in the ordering platform and automatically set the reference location. Alternatively, in addition to the business owner's store location, it is also possible to count the number of nearby stores, number of orders, rating status, etc. based on a specific location designated by the user.

In the embodiment shown in FIG. 7, the user selects a specific store or a category of the store to be viewed through the UI element 701, and a preset radius 702 (e.g., 1 km) from the reference location. The locations of stores corresponding to the selected store or category were displayed on the map. Additionally, information such as the number of stores, number of orders, number of reviews, average rating, etc. collected from the ordering platform in relation to main stores displayed on the map may be displayed through the UI element 703 as location analysis data.

However, the display form of the location analysis data shown in FIG. 7 is merely exemplary, and the display form of the analysis information provided through the store analysis method according to the embodiments is not limited to that shown in the drawings of this specification.

8A and 8B are conceptual diagrams showing an exemplary form of competitive store analysis data provided by a store analysis method using scraping according to an embodiment.

As shown in FIG. 8A, in one embodiment, competitive store analysis data refers to competitive store status 802, such as the number of stores, number of orders, and reputation (e.g., number of ratings and/or reviews) of other stores registered on the ordering platform. It may be compared with the analysis result 801 of the user store. At this time, the competitive store status 802 may be generated for each individual store or for each category to which the store belongs. In addition, the analysis result of the user store may be, for example, an analysis result of the user store's reputation, such as the number of orders, ratings, number of reviews, number of replies, etc., but is not limited thereto. For example, in another embodiment, competitive store analysis data may be generated by comparing the user store with other stores using sales analysis results instead of reputation.

Additionally, referring to FIG. 8B, competitive store analysis data may include reputation analysis data 804 and 805 for stores belonging to a specific category. At this time, the specific category may refer to the same category as the category of the user's store, or may refer to a specific category selected by the user using the UI element 803, etc. The reputation analysis data of stores belonging to a specific category includes individual store data 804 listing the stores based on ratings, number of reviews, number of comments, and/or number of orders, and/or ratings and reviews of all stores in the category. It may include category aggregated data 805 for number of comments, number of comments and/or number of orders, etc.

In addition, analysis information provided by the store analysis method using scraping according to one embodiment may include advertising analysis data.

In this embodiment, advertising analysis data refers to the target of paid advertising for analyzing the effect of paid advertising being implemented by the user or recommending to the user through the advertising status of the user store and/or competing stores registered on the ordering platform. For example, it refers to data that defines a target area. In one embodiment, the advertising analysis data may include evaluation information on the appropriateness of paid advertising implemented by the store owner, advertising location recommendation information based on information registered in the ordering platform, etc.

For example, evaluation of the appropriateness of paid advertising among advertising analysis data can be done in the following manner.

Paid advertising on an ordering platform is usually a service that, when a business owner selects a specific area, the business owner's store or menu is preferentially exposed within the ordering platform to customers who access the area. At this time, the store analysis system according to this embodiment scrapes the store information of the business owner to specify the region where the business owner is conducting paid advertising, and sales data (e.g., order history) in that region is not implemented. Advertising adequacy can be evaluated by comparing sales data from other regions.

For example, if the order details in the advertised area have a value greater than a certain level compared to the order details in the area where the advertisement was not implemented, the advertisement can be considered appropriate, but the difference in order details in the two areas does not exceed a certain threshold. If it is less than or the order details in the advertising area are less than the order details in the non-advertising area, advertising implementation may be determined to be inappropriate. At this time, the threshold for determining whether advertising execution is appropriate may be determined in various ways depending on the embodiment, or the user may be configured to set the threshold directly.

Alternatively, in one embodiment, the store analysis system collects the amount of advertising cost that the user spends on paid advertising by scraping from the ordering platform, and adjusts the size of the threshold for determining the appropriateness of advertising execution in proportion to the amount of advertising cost spent. It may be configured. For example, if the amount of advertising expenses is relatively small, advertising execution may be considered appropriate even if there is a small difference in order details between the advertising area and the non-advertising area. However, if the advertising expenditure amount is relatively large, advertising execution may be considered appropriate between the advertising area and the non-advertising area. The size of the threshold can be dynamically changed so that the threshold increases as the amount of advertising expenditure increases so that advertisement execution is judged to be appropriate only when the difference in order details in the region exceeds a certain level.

As another example, recommendation of advertisement location among advertisement analysis data can be made in the following manner.

In this embodiment, the store analysis system can classify the order status for the business owner's store by location from the scraped store information. At this time, the store analysis system divides the location around the store using a preset unit size (e.g., building) and uses advertising analysis data to recommend paid advertising for the section with the fewest orders based on order status. can be created. Alternatively, depending on the sales strategy, it may be recommended to conduct paid advertising targeting areas with relatively more orders than other areas.

As another example, the store analysis system can classify other stores belonging to the same category as the business owner's store by location section using platform information that can be collected from the ordering platform. Next, the store analysis system can generate advertising analysis data to recommend that paid advertising be carried out in a section where there are relatively many other stores belonging to the same category as the business owner's store. Alternatively, depending on the sales strategy, it may be recommended to conduct paid advertising targeting areas with relatively few competing stores in the same category.

The operation of the store analysis method using scraping according to the embodiments described above may be at least partially implemented as a computer program and recorded on a computer-readable recording medium. Computer-readable recording media on which programs for implementing operations by the store analysis method using scraping according to embodiments are recorded include all types of recording devices that store data that can be read by a computer. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. Additionally, computer-readable recording media may be distributed across computer systems connected to a network, and computer-readable codes may be stored and executed in a distributed manner. Additionally, functional programs, codes, and code segments for implementing this embodiment can be easily understood by those skilled in the art to which this embodiment belongs.

The present invention discussed above has been described with reference to the embodiments shown in the drawings, but these are merely illustrative examples, and those skilled in the art will understand that various modifications and modifications of the embodiments are possible therefrom. However, such modifications should be considered within the technical protection scope of the present invention. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the attached claims.

Claims (9)

a communication module configured to receive the user's authentication information to one or more ordering platform servers from the user device;
a scraping module configured to collect store information of a store corresponding to the user from the ordering platform server through a scraping method by accessing the one or more ordering platform servers using the authentication information; and
Includes an analysis module configured to generate analysis information related to sales of the store using the store information,
The scraping module is,
a first scraping unit configured to scrape the store information by logging into the ordering platform server using the authentication information; and
It includes a second scraping unit configured to scrape platform information from the ordering platform server without performing login using the authentication information,
The analysis module is further configured to generate the analysis information using the store information and the platform information.
According to paragraph 1,
The store information includes one or more of store details including one or more of the category and location of the store, consumer reputation information about the store, sales information of the store, and advertisement usage information of the store,
The analysis information is a store analysis system using scraping that is generated by classifying one or more of the store's reputation, sales, and competitive stores based on one or more of the store's category, location, date, and advertisement status.
delete According to paragraph 1,
The scraping module is further configured to receive store information of the one or more affiliated stores using one or more authentication information corresponding to the one or more affiliated stores,
The analysis module is a store analysis system using scraping further configured to generate integrated analysis information related to the business of the franchise headquarters associated with the one or more affiliated stores using store information of the one or more affiliated stores.
The store analytics system receiving the user's authentication information for one or more ordering platform servers from the user device;
Collecting, by the store analysis system, store information of the store corresponding to the user from the ordering platform server in a scraping manner by accessing the one or more ordering platform servers using the authentication information; and
The store analysis system includes generating analysis information related to sales of the store using the store information,
Before generating the analysis information, the store analysis system further includes the step of scraping platform information from the ordering platform server without logging in using the authentication information,
The step of generating the analysis information includes the step of the store analysis system generating the analysis information using the store information and the platform information.
According to clause 5,
The store information includes one or more of store details including one or more of the category and location of the store, consumer reputation information about the store, sales information of the store, and advertisement usage information of the store,
The analysis information is a store analysis method using scraping that is generated by classifying one or more of the store's reputation, sales, and competitive stores based on one or more of the store's category, location, date, and advertisement status.
delete According to clause 5,
The step of collecting the store information using a scraping method includes the step of the store analysis system receiving store information of the one or more affiliated stores using one or more authentication information corresponding to the one or more affiliated stores,
The step of generating the analysis information includes the step of the store analysis system generating integrated analysis information related to the sales of the franchise headquarters associated with the one or more affiliated stores using store information of the one or more affiliated stores using scraping. Store analysis method.
A computer program stored in a computer-readable recording medium coupled with hardware to execute the store analysis method using scraping according to any one of claims 5, 6, and 8.

Images