KR20220163088A - Shopping mall product exposure information provision system and method - Google Patents

Abstract

A shopping mall product exposure information provision system and method are provided. The shopping mall product exposure information provision system according to an embodiment of the present invention is a shopping mall product exposure information provision system connected to a user terminal and at least one shopping mall server to provide product ranking or marketing suggestion information to the user terminal, comprising: a user information processing unit configured to generate user information by receiving and processing input information received from the user terminal; a shopping mall information processing unit configured to acquire shopping mall information from the at least one shopping mall server and process the same; an exposure algorithm prediction unit configured to predict an exposure algorithm used in each shopping mall server by using the shopping mall information and increase an accuracy of the exposure algorithm predicted by machine learning of the shopping mall information; and a marketing proposal unit configured to transmit a marketing method for a specific product to the user terminal as marketing proposal information by using the predicted product exposure algorithm.

Description

Shopping mall product exposure information provision system and method

The present invention relates to a system and method for providing product exposure information in a shopping mall, and more particularly, to enable a user to check and track the ranking of his or her product registered in a shopping mall, or obtain marketing proposal information for increasing the ranking of a product. A system and method for providing product exposure information in a shopping mall capable of tracking confirmation and providing marketing suggestion information.

In recent years, online shopping malls have been widely spread due to the rapid development of wired and wireless communication technologies and the expansion and spread of communication networks that are the basis thereof. An online shopping mall is a representative e-commerce transaction using the Internet. Offline stores are implemented as virtual stores on the Internet, and visitors (buyers) connect to the Internet and analyze various types of products displayed in online stores. It allows you to conveniently purchase the product you want through product evaluation and comparison with the same product in other stores.

On the other hand, from the seller's point of view, it is necessary to analyze the correlation between customer inflow and sales of his or her product in the online shopping mall. Conventional online shopping mall sales analysis analyzes the number of visitors through log analysis that accesses the store homepage in the online shopping mall itself, analyzes the inflow path of visitors, and analyzes the correlation between inflow amount and sales. However, this system can only check the inflow/sales of one online shopping mall, that is, its own shopping mall. For example, it is possible to analyze multiple brand products sold in one online shopping mall, and the general manager (Merchandiser: MD) who manages the overall operation from product selection to display and sale mainly manages product sales strategy. We will use the analysis information of these products for such purposes. However, this analysis information is used and monopolized only in the corresponding online shopping mall and is not provided to the seller or manufacturer of the product.

In this situation, various online shopping malls disclose indicators for determining the ranking of the seller's store homepage, and the seller can use the corresponding index to increase the ranking of the store homepage. However, since the algorithm for processing the indicators disclosed by online shopping malls is not open to the public, there is a problem in that countless trials and errors and costs may be consumed in order for sellers to expose their store homepages at the top of online shopping malls.

Korean Patent Registration No. 10-1575640

In order to solve the problems of the prior art as described above, an embodiment of the present invention is marketing for providing product rankings according to a user's request, tracking product rankings for a predetermined period of time, or increasing product rankings. An object of the present invention is to provide a shopping mall product exposure information providing system and method capable of providing suggestion information to a user.

According to one aspect of the present invention for solving the above problems, a shopping mall product exposure information providing system configured to provide product ranking or marketing suggestion information to the user terminal by being connected to a user terminal and at least one shopping mall server is provided. .

The shopping mall product exposure information providing system includes a user information processing unit configured to generate user information by receiving and processing input information received from the user terminal; a shopping mall information processor configured to acquire and process shopping mall information from the at least one shopping mall server; an exposure algorithm prediction unit configured to predict an exposure algorithm used in each shopping mall server using the shopping mall information and to increase accuracy of the exposure algorithm predicted through machine learning of the shopping mall information; and a marketing suggestion unit configured to transmit a marketing method for a specific product to the user terminal as the marketing suggestion information by using the predicted product exposure algorithm.

The input information includes at least one of user request information, user-specific information, and product information, and the user information processing unit is formed to check user request information, which is information that the user wants to be provided through the user terminal, among the input information. a user request information verification module; a user-specific information processing module configured to acquire and process user-specific information formed to distinguish a plurality of users from among the input information; and a user market information processing module configured to obtain the product information among the input information and obtain market information of the user including the corresponding product.

The user request information includes at least one of a product ranking confirmation request signal, a product ranking tracking request signal, and a marketing proposal request signal, and the product ranking confirmation request signal confirms a ranking of a specific product registered by the user in at least one shopping mall. The product rank tracking request signal is a signal requesting an accumulated record of tracking the rank of a specific product registered by the user for a predetermined period or more, and the marketing proposal request signal is a specific signal input through the user terminal. A signal requesting marketing proposal information for increasing the ranking of a product may be included.

The shopping mall information includes any one of a public index for each shopping mall server and product information for each shopping mall server, and the shopping mall information processing unit obtains at least one public index for each shopping mall server from the shopping mall information. indicator acquisition module; a shopping mall product ranking acquiring module configured to acquire at least one product ranking from the shopping mall information for each shopping mall server; and a shopping mall product ranking accumulation module for accumulating and storing the shopping mall product rankings.

The disclosure indicator is at least one of relevance, popularity, and reliability, the relevance is an indicator for determining whether a product is suitable for a consumer's search intention, and the popularity is an indicator for determining whether a product is sought and sold a lot by the consumers. Reliability may be an index for determining whether product information is a reliable product.

The suitability is obtained using at least one of product name, category, manufacturer brand, and attribute tag, the popularity is obtained using at least one of sales performance, number of reviews, recency, star rating, and number of clicks, and the reliability is obtained using product name It can be obtained using at least one of SEO and shopping mall penalty.

The exposure algorithm prediction unit may include a shopping mall information accumulation module configured to accumulate the shopping mall information; a shopping mall product detailed information acquiring module configured to acquire detailed product information including detailed information about a product and information corresponding to a disclosure indicator of the product from the product information; and a product exposure algorithm prediction module configured to predict the product exposure algorithm using each of the detailed product information and a change in each of the product rankings, wherein the disclosure indicator is at least one of relevance, popularity, and reliability. can be one

The detailed information on the product is product-related information exposed to the shopping mall server, and includes at least one of product description content, a representative product picture, and a product title, and the product exposure algorithm is obtained for each shopping mall server, and each The shopping mall server may be configured to obtain a reference value for each disclosure index necessary for changing the ranking of the product.

The product exposure algorithm prediction module may be configured to check a change in information corresponding to the disclosure indicator of the product or detailed information on the product, and learn a change in product rank according to the confirmed change.

The marketing suggestion unit may include a selected keyword acquisition module configured to obtain a selected keyword selected by the user from the user terminal; a necessary index calculation module configured to analyze the selected keyword, calculate a necessary index for a specific product requested by the user using the product exposure algorithm, and generate a selected keyword analysis result; a calculation result output module configured to calculate a required value of the specific product to reach a reference value of the required indicator and output a calculation result including a result of analyzing the selected keyword; And a marketing information suggestion module configured to generate marketing information, which is a marketing method for the specific product, including the selected keyword analysis result and the calculation result, and deliver it to the user terminal, wherein the necessary indicator is, The current information corresponding to the disclosure indicator of the specific product may be a disclosure indicator less than a reference value obtained using the product exposure algorithm.

According to an aspect of the present invention, there is provided a method for providing product exposure information in a shopping mall connected to a user terminal and at least one shopping mall server to provide product ranking or marketing suggestion information to the user terminal. The method of providing product exposure information in the shopping mall may include generating user information by receiving and processing input information received from the user terminal using a user information processing unit; obtaining and processing shopping mall information from the at least one shopping mall server using a shopping mall information processing unit; predicting an exposure algorithm used in each shopping mall server using the shopping mall information through an exposure algorithm predictor, and increasing accuracy of the exposure algorithm predicted through machine learning of the shopping mall information; and transmitting a marketing method for a specific product as the marketing proposal information to the user terminal by using the product exposure algorithm predicted through the marketing proposal unit.

The input information includes at least one of user request information, user-specific information, and product information, and the generating of the user information includes user request information, which is information that the user wants to be provided through the user terminal, among the input information. checking; obtaining and processing user-specific information formed to distinguish a plurality of users from among the input information; and obtaining market information of the user including the corresponding product by obtaining the product information among the input information.

The user request information includes at least one of a product ranking confirmation request signal, a product ranking tracking request signal, and a marketing proposal request signal, and the product ranking confirmation request signal confirms a ranking of a specific product registered by the user in at least one shopping mall. The product rank tracking request signal is a signal requesting an accumulated record of tracking the rank of a specific product registered by the user for a predetermined period or more, and the marketing proposal request signal is a specific signal input through the user terminal. A signal requesting marketing proposal information for increasing the ranking of a product may be included.

The shopping mall information includes either a public index for each shopping mall server or product information for each shopping mall server, and the acquiring and processing the shopping mall information includes obtaining at least one public index for each shopping mall server from the shopping mall information. doing; acquiring at least one product rank for each shopping mall server from the shopping mall information; and accumulating and storing the shopping mall product rankings.

The disclosure indicator is at least one of relevance, popularity, and reliability, the relevance is an indicator for determining whether a product is suitable for a consumer's search intention, and the popularity is an indicator for determining whether a product is sought and sold a lot by the consumers. Reliability may be an index for determining whether product information is a reliable product.

The suitability is obtained using at least one of product name, category, manufacturer brand, and attribute tag, the popularity is obtained using at least one of sales performance, number of reviews, recency, star rating, and number of clicks, and the reliability is obtained using product name It can be obtained using at least one of SEO and shopping mall penalty.

Predicting the exposure algorithm and increasing the accuracy of the exposure algorithm predicted through machine learning of the shopping mall information may include accumulating the shopping mall information; acquiring detailed product information including detailed information about a product and information corresponding to the disclosure indicator of the product from product information; and predicting the product exposure algorithm using each of the detailed product information and a change in each of the product rankings, wherein the disclosure index may be at least one of relevance, popularity, and reliability.

The detailed information on the product is product-related information exposed to the shopping mall server, and includes at least one of product description content, a representative product picture, and a product title, and the product exposure algorithm is obtained for each shopping mall server, and each The shopping mall server may be configured to obtain a reference value for each disclosure index necessary for changing the ranking of the product.

The step of predicting the product exposure algorithm may be configured to identify a change in information corresponding to the disclosure indicator of the product or detailed information about the product, and to learn a change in product rank according to the confirmed change.

The transmitting of the marketing suggestion information to the user terminal may include: obtaining a selection keyword selected by the user from the user terminal; analyzing the selected keyword, calculating a required index for a specific product requested by the user using the product exposure algorithm, and generating a selected keyword analysis result; calculating a required value of the specific product to reach a reference value of the required index, and outputting a calculation result including a result of analyzing the selected keyword; and generating marketing information, which is a marketing method for the specific product, including the result of analyzing the selected keyword and the result of the calculation, and transmitting the information to the user terminal. The current information corresponding to the disclosure indicator may be a disclosure indicator that is less than a reference value obtained using the product exposure algorithm.

A system and method for providing product exposure information in a shopping mall according to an embodiment of the present invention provides marketing proposal information for providing a product ranking according to a user's request, tracking a product ranking for a predetermined period of time, or increasing a product ranking. There is an effect that can provide to the user.

1 is a block diagram simply illustrating a shopping mall product exposure information providing system according to an embodiment of the present invention.
FIG. 2 is a block diagram illustrating a user information processing unit of FIG. 1 .
FIG. 3 is a block diagram illustrating a shopping mall information processing unit of FIG. 1 .
FIG. 4 is a block diagram illustrating an exposure algorithm prediction unit of FIG. 1 .
FIG. 5 is a block diagram illustrating a marketing suggestion unit of FIG. 1 .
6 is a flowchart illustrating a method of providing exposure information for shopping mall products according to an embodiment of the present invention.
7 is a flowchart illustrating step S111 of FIG. 6 .
8 is a flowchart illustrating step S113 of FIG. 6 .
9 is a flowchart illustrating step S115 of FIG. 9 .
10 is a flowchart illustrating step S117 of FIG. 6 .

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 carry out the present invention. This invention may be embodied in many different forms and is not limited to the embodiments set forth herein. In order to clearly describe the present invention in the drawings, parts irrelevant to the description are omitted, and the same reference numerals are assigned to the same or similar components throughout the specification.

1 is a simple block diagram showing a shopping mall product exposure information providing system according to an embodiment of the present invention, FIG. 2 is a block diagram showing the user information processing unit of FIG. 1, and FIG. 3 is a block diagram showing the shopping mall information processing unit of FIG. FIG. 4 is a block diagram showing the exposure algorithm prediction unit of FIG. 1 , and FIG. 5 is a block diagram showing the marketing proposal unit of FIG. 1 . Hereinafter, a shopping mall product exposure information providing system according to an embodiment of the present invention will be described in more detail using FIGS. 1 to 5 .

Referring to FIG. 1 , a shopping mall product exposure information providing system 110 according to an embodiment of the present invention is formed to be connected to a user terminal 120 and a shopping mall server 130 using a wired or wireless communication network. At this time, a plurality of the user terminal 120 and the shopping mall server 130 may be connected. Here, the user terminal 120 may be a terminal owned by a business operator that generally operates an Internet market, and the shopping mall server 130 may be a shopping mall server in which Internet markets such as Amazon, Gmarket, 11th Street, and Coupang are available.

The shopping mall product exposure information providing system 110 receives request information from the user terminal 120, provides a result corresponding to the requested information to the user terminal 120, and provides related information from a plurality of shopping mall servers 130. Formed to acquire and process. To this end, the shopping mall product exposure information providing system 110 according to an embodiment of the present invention includes a user information processing unit 111, a shopping mall information processing unit 113, an exposure algorithm prediction unit 115, and a marketing proposal unit 117. can be formed by

The user information processing unit 111 according to an embodiment of the present invention is formed to generate user information by receiving and processing input information received from the user terminal 120 . Here, the input information may include user request information, user-specific information, and product information. The user information processing unit 111 includes a user request information confirmation module 211, a user-specific information processing module 212, and a user market information processing module 213 as shown in FIG. 2 to process input information. It can be.

The user-requested information check module 211 is configured to check user-requested information, which is information that the user wants to receive through the user terminal 120 among input information. Here, the user request information may be any one of a product ranking confirmation request signal, a product ranking tracking request signal, and a marketing proposal request signal.

The product ranking confirmation request signal may be a signal requesting that at least one shopping mall server 130 check the ranking of a specific product registered in the Internet market by the user.

The product ranking tracking request signal may be a signal requesting a record accumulated by tracking the ranking of a product requested by a user among products registered in an Internet market by a user over a preset period of time.

Also, the marketing proposal request signal may be a signal requesting marketing proposal information for increasing the rank of a product requested by the user among products registered in the Internet market by the user.

That is, the shopping mall product exposure information providing system 110 of the present invention may receive requested information from the user terminal 120 through the user request information confirmation module 211, and through which information the user wants to be provided is can check whether it is

Next, the user-specific information processing module 212 may be configured to acquire and process user-specific information formed to distinguish a plurality of users from among input information.

The shopping mall product exposure information providing system 110 of the present invention is configured to manage product information of a plurality of users. Therefore, the shopping mall product exposure information providing system 110 matches unique information such as an ID that can distinguish users for each user terminal 120 and analyzes the input information received from the user terminal 120 to obtain user-specific information. can do.

The user market information processing module 213 is configured to acquire product information among the input information and obtain user market information including the corresponding product. Products registered by the user in the shopping mall server 130 may differ for each shopping mall or for each market of the user.

For example, when user A sells moisture cream product B, user A may create a plurality of markets in one shopping mall server 130 and register B moisture cream in all of the plurality of markets, and a plurality of shopping mall servers ( 130), it is also possible to select specific markets among the markets created respectively and register B moisture cream products only in the selected markets. Accordingly, the user market information processing module 213 may be configured to receive user input information, obtain product information included in the received input information, and acquire user market information including the obtained product.

When the input information input from the user terminal 120 is processed by the user information processing unit 111 to obtain the user who delivered the input information, the user's request information, and the product information and market information included in the user's request information, this The shopping mall information processing unit 113 according to an embodiment of the present invention is configured to acquire and process shopping mall information from at least one shopping mall server 130 . Here, the shopping mall information includes public indicators for each shopping mall server 130 and product information for each shopping mall server 130. 232) and a shopping mall product ranking accumulation module 233.

The shopping mall public index acquisition module 231 is configured to obtain a public index for each at least one shopping mall server 130 from shopping mall information. Here, the disclosure indicators are indicators used in a product or market ranking arrangement algorithm disclosed by each shopping mall server 130, which may differ for each shopping mall server 130.

The shopping mall public index acquisition module 231 according to an embodiment of the present invention is configured to obtain relevance, popularity, and reliability as public indexes. Here, suitability, popularity, and reliability are groups of detailed indicators for convenience of explanation of the present invention, and detailed indicators described later may be grouped in a different way from the embodiments of the present invention, but these may also be included in the concept of the present invention. It is self-evident that there is

Relevance is an indicator to determine whether a product is suitable for the consumer's search intention, popularity is an indicator to determine whether a product is sought and sold a lot by consumers, and reliability is an indicator to determine whether product information registered in a shopping mall is reliable product information. may be an indicator of

More specifically, the suitability includes at least one of product name, category, manufacturer brand, and attribute tags, the popularity includes at least one of sales performance, number of reviews, recency, star rating, and number of clicks, and reliability includes product name SEO and Includes at least one of the shopping mall penalties. Here, the shopping mall penalty may be interpreted as meaning a penalty applied to a corresponding market or a corresponding product in a shopping mall.

The shopping mall product rank obtaining module 232 is configured to acquire at least one product rank for each shopping mall server 130 from shopping mall information. The shopping mall product ranking acquisition module 232 receives the user's input information analysis result from the user information processing unit 111, and if the requested information is a product ranking confirmation request signal according to the received user's input information analysis result, the corresponding product information It is possible to obtain the ranking of the corresponding product from the shopping mall information of the shopping mall server 130 in which the corresponding product is registered.

That is, when user A requests product ranking information of a moisturizing cream product B, the shopping mall product ranking acquisition module 232 first obtains a shopping mall in which user A has entered the Internet market, and each of them from the acquired shopping mall server 130. The acquired shopping mall information may be analyzed to obtain a current ranking of a moisture cream product B registered by user A. Here, the current ranking may be, for example, an order in which B moisture cream products are searched in each shopping mall in a search result.

The shopping mall product ranking accumulation module 233 is configured to accumulate and store shopping mall product rankings. The shopping mall product ranking accumulation module 233 receives the user's input information analysis result from the user information processing unit 111, and if the requested information is a product ranking tracking request signal according to the received user's input information analysis result, the product is assigned to the corresponding product. In order to track product rankings for products, the shopping mall product rankings may be accumulated and stored. At this time, in an embodiment of the present invention, when the shopping mall product ranking accumulation module 233 provides the existing cumulative ranking information from the shopping mall server 130, by obtaining and storing the corresponding cumulative ranking information from the shopping mall server 130, the user It is also possible to provide a cumulative ranking prior to the request of, and, if necessary, a cumulative ranking for each product that can be pre-stored according to settings in a database (not shown) that can be further included in the system 110 is obtained. The cumulative ranking prior to the user's request may also be provided.

Meanwhile, in another embodiment of the present invention, the shopping mall product ranking accumulation module 233 may also store changes in product information input by the user in the process of accumulating the user's shopping mall product ranking. More easily explained, when the user changes the product information of the market, the shopping mall product rank accumulation module 233 also stores the corresponding product information change data, and when providing the rank accumulation information to the user terminal 120, the corresponding information By providing together, it is possible for the user to easily determine whether the ranking has changed according to the user's change in product information.

Meanwhile, the exposure algorithm predictor 115 according to an embodiment of the present invention predicts the exposure algorithm used in each shopping mall server 130 using shopping mall information, and the exposure algorithm predicted through machine learning of the shopping mall information. It can be formed to increase the accuracy of. To this end, the exposure algorithm prediction unit 115 may be formed to include a shopping mall information accumulation module 251, a shopping mall product detail information acquisition module 252, and a product exposure algorithm prediction module 253, as shown in FIG. .

The shopping mall information accumulation module 251 is configured to accumulate shopping mall information. As described above, the shopping mall information includes public indicators for each shopping mall server 130 and product information for each shopping mall server 130. is forwarded to

The shopping mall product detailed information acquisition module 252 is configured to acquire product detailed information by receiving accumulated shopping mall information. The detailed product information may include detailed information about a product from product information among shopping mall information and information corresponding to a disclosure index of the corresponding product. In addition, the detailed product information is product-related information exposed to the shopping mall server 130, and may include at least one of product description content, product representative photos, product categories, and product titles.

The product exposure algorithm prediction module 253 is configured to predict and generate a product exposure algorithm using the obtained product detailed information and product level change. The product exposure algorithm may be obtained for each shopping mall server 130 . This is because indicators disclosed for each shopping mall server 130 may be different. In addition, the product exposure algorithm may be configured to obtain a reference value for each disclosure index necessary for changing the ranking of products for each shopping mall server 130 .

The product exposure algorithm can present quantitative reference values for quantifiable disclosure indicators, and analyze and acquire commonalities that positively affect ranking changes for disclosure indicators that are difficult to quantify. At this time, the disclosure indicators that are difficult to quantify may be product-related information such as product description content, representative product photos, and product categories. Such product-related information is keywords included in the information through keyword extraction in the product exposure algorithm prediction module 253. It is also possible to extract factors related to ranking change through methods such as extracting and comparing with other product-related information and applying weights for each keyword.

Meanwhile, in another embodiment of the present invention, the exposure algorithm prediction unit 115 may be formed to further include a product exposure algorithm enhancement module (not shown). A product exposure algorithm enhancement module may also be included to enhance the product exposure algorithm using, for example, a Generative Adversarial Network (GAN). The product exposure algorithm is an algorithm that is predicted in the system of the present invention using public indicators because an accurate internal algorithm of each shopping mall server 130 cannot be obtained. Therefore, in order to increase the degree of consistency between the predicted product exposure algorithm and the internal algorithm of the undisclosed shopping mall, the product exposure algorithm enhancement module may be configured to generate various virtual market and product data through a generative adversarial neural network.

In this case, the virtual market product data obtained through the product exposure algorithm is separated from the shopping mall server 130 and stored together with the actual market and product data through an internal database that can be included in the system of the present invention, and product exposure The algorithm enhancement module creates a virtual product ranking for each shopping mall using data included in the internal database. Subsequently, when the product exposure algorithm enhancement module confirms that the same information as the virtual market product data is generated through the shopping mall information, the ranking of the actual market and product data is compared with the ranking of the virtual market product data, and the comparison result is displayed. Analysis can also be used to supplement and enhance product exposure algorithms.

Finally, the marketing proposal unit 117 of the shopping mall product exposure information providing system 110 according to an embodiment of the present invention transmits a marketing method for a specific product to the user terminal as marketing proposal information using a product exposure algorithm. can be formed When the user request information confirmed by the user request information confirmation module 211 is a marketing proposal request signal, the marketing proposal unit 117 selects a marketing method for a specific product requested by the user, generates marketing proposal information, and generates marketing proposal information. It may be configured to deliver marketing suggestion information to the user terminal 120 . For example, the marketing suggestion unit 117 may be configured to acquire keywords selected by the user, generate marketing information by analyzing the acquired keywords, and then propose the marketing information to the user. To this end, the marketing suggestion unit 117 according to an embodiment of the present invention includes a selected keyword acquisition module 271, a necessary index calculation module 272, a calculation result output module 273, and a marketing information suggestion module 274. can be formed by

The selected keyword acquisition module 271 is configured to acquire keywords or tags from user terminals. The selected keyword acquisition module 271 may recommend keywords satisfying preset conditions to the user in order to suggest marketing information to the user in the marketing suggestion unit 117, and may obtain a keyword selected by the user from among the recommended keywords. . Here, the keywords recommended to the user may be customized, and may be, for example, keywords related to the user's market information and product information registered in the corresponding market.

The recommended keyword transmitted from the selected keyword acquisition module 271 to the user terminal 120 is transmitted as recommended keyword information, and the recommended keyword information may include a keyword and marketing supplementary information of the keyword. Marketing auxiliary information may include the search volume of the keyword, the number of products containing the keyword, the level of competition of the keyword, and the average number of reviews per page of the product including the keyword, etc. Through this, users can analyze recommended keywords. Thus, a desired keyword can be selected through the user terminal 120, and the selected keyword acquisition module 271 can acquire the keyword selected by the user.

Here, the recommended keywords under preset conditions may be core keywords with a certain number of hits or more, small and medium-sized keywords with a product number of less than a certain number, and tags used by products whose popularity in the corresponding shopping mall is higher than a certain standard.

The necessary index calculation module 272 receives the selected keywords selected by the user, applies the selected keywords to each shopping mall to obtain selected keyword search results, analyzes the acquired selected keyword search results, and calculates the selected keyword analysis results. After acquiring, it is configured to calculate a necessary index corresponding to the acquired selection keyword analysis result. When receiving the specific product information requested by the user and the selected keyword selected by the user, the necessary index calculation module 272 obtains the selected keyword search result from each shopping mall, and then analyzes the top exposure and keyword analysis of the obtained selected keyword search result. It is possible to obtain a selected keyword analysis result by performing the selected keyword analysis, and calculate a required index for the corresponding product by using a product exposure algorithm based on the obtained selected keyword analysis result.

Here, the top exposure analysis may include the average monthly sales number of 1st page 40th rank, the average price of 1st page 40th rank, the average sales of 1st page 40th rank, the average monthly sales number of 1st page 10th rank, average sales and expected sales of 1st page 10th rank, etc. , Keyword analysis can include competitive strength, brand relevance, monthly search volume (by device, age, gender), content volume (blog, cafe, intellectual), search placement, and related search words. The various analysis contents described above are specific analysis contents presented for the example of the present invention, and the present invention is not limited to the above-described analysis contents, and may be configured to obtain a selected keyword analysis result using various analysis contents according to user settings. have.

In addition, the required index refers to an index in which the current information of the product corresponding to the disclosure index is less than the reference value obtained through the product exposure algorithm.

In other words, to explain more easily, the required indicator calculation module 272 applies user A's B moisture cream product to the product exposure algorithm, and the application result is 35 purchases, 20 text reviews, 15 photo reviews, and 10 steamed products. , If it appears like two Q&As (example), each value can be selected as the standard value for each necessary indicator.

In addition, in the case of a title or category that is not a quantitative result, the result applied to the product exposure algorithm in the necessary indicator calculation module 272 is selected as a keyword and category to be included or not to be included as a standard value of the required indicator. can

The calculation result output module 273 is configured to calculate the required value of a specific product to reach the reference value of the required index. The required index and reference values obtained in the required index calculation module 272 are transferred to the calculation result output module 273, and the calculation result output module 273 compares the required index and reference value with the current state of a specific product, As a result of the comparison, the required figures and conditions to be achieved by a specific product can be output as calculation results.

In addition, when the calculation result output module 273 receives the selected keyword analysis result, it may be configured to further output a calculation result for higher exposure using the received selected keyword analysis result and a necessary index. Here, the calculation result may include the number of sales, the number of reviews, the number of wishes, and the number of Q&As required for the top exposure of the user's product by using the necessary index, the selected keyword analysis result, and the reference value. It can be a desired ranking, or it can basically mean being exposed on the first page of search results.

The marketing information suggestion module 274 is configured to generate marketing information including selected keyword analysis results and calculation results and deliver it to the user terminal. Here, the selected keyword analysis result included in the marketing information may be the analysis result of the product group including the user's product, shopping competitiveness index, average number of sales, average price, and average sales, and the calculation result is the number of sales, which is a required item for top exposure. , the number of text reviews, the number of photo reviews, the number of steams, and the number of Q&A. In addition, the marketing information may further include a summary result summarizing the two results, and the summary result may include an estimated cost, an estimated period, and the total number of products in progress.

Meanwhile, FIGS. 6 to 10 show a method of providing exposure information for shopping mall products according to an embodiment of the present invention.

6 is a flowchart showing a method of providing exposure information for shopping mall products according to an embodiment of the present invention, FIG. 7 is a flowchart showing step S111 of FIG. 6, FIG. 8 is a flowchart showing step S113 of FIG. 6, and FIG. 9 is a flow chart showing step S115, and FIG. 10 is a flow chart showing step S117 of FIG.

Hereinafter, for convenience of description, an embodiment of the present invention will be described using the system shown in FIGS. 1 to 5 described above, but the present invention is not necessarily limited thereto.

The shopping mall product exposure information providing method 600 is configured to receive request information from a user terminal, provide a result corresponding to the requested information to the user terminal, and obtain and process related information from a plurality of shopping mall servers. To this end, the method 600 of providing product exposure information in a shopping mall according to an embodiment of the present invention includes processing user information (S111), processing shopping mall information (S113), and predicting a product exposure algorithm (S115). and providing marketing proposal information to the user (S117).

In step S111 of processing user information according to an embodiment of the present invention, input information received from a user terminal is received and processed using a user information processing unit to generate user information. Here, the input information may include user request information, user-specific information, and product information. Step S111 includes checking user request information as shown in FIG. 10 to process input information (S211), processing user-specific information (S212), and processing user market information (S213). can be formed by

The step of checking the user request information (S211) is formed to check the user request information, which is information that the user wants to be provided through the user terminal among the input information. Here, the user request information may be any one of a product ranking confirmation request signal, a product ranking tracking request signal, and a marketing proposal request signal.

The product ranking confirmation request signal may be a signal requesting that at least one shopping mall server check the ranking of a specific product registered by the user in the Internet market.

The product ranking tracking request signal may be a signal requesting a record accumulated by tracking the ranking of a product requested by a user among products registered in an Internet market by a user over a preset period of time.

Also, the marketing proposal request signal may be a signal requesting marketing proposal information for increasing the rank of a product requested by the user among products registered in the Internet market by the user.

That is, in the method 600 of providing exposure information for shopping mall products according to the present invention, request information may be received from the user terminal through step S211, and through this, it may be possible to determine what kind of information the user wants to be provided.

Next, processing the user-specific information (S212) may be formed to acquire and process user-specific information formed to distinguish a plurality of users from among the input information.

The shopping mall product exposure information providing method 600 of the present invention is configured to manage product information of a plurality of users. Accordingly, the method 600 for providing product exposure information in a shopping mall may obtain user-specific information by matching unique information such as an ID capable of distinguishing users for each user terminal and analyzing input information received from the user terminal.

The step of processing user market information (S213) is formed to obtain product information among the input information and obtain user market information including the corresponding product. Products registered by the user in the shopping mall server may differ for each shopping mall or for each market of the user.

For example, when user A sells moisture cream product B, user A may create a plurality of markets in one shopping mall server and register B moisture cream in all of the plurality of markets. B moisture cream products may be registered only in the selected markets by selecting specific markets among the markets. Accordingly, step S213 may be configured to receive user input information, obtain product information included in the received input information, and obtain user market information including the obtained product.

When the input information input from the user terminal is processed in step S111 to obtain the user who delivered the input information, the user's request information, and the product information and market information included in the user's request information, shopping mall information according to an embodiment of the present invention. The step of processing (S113) is configured to obtain and process shopping mall information from at least one shopping mall server using the shopping mall information processing unit. Here, the shopping mall information includes public indicators for each shopping mall server and product information for each shopping mall server. (S232) and accumulating shopping mall product rankings (S233).

Acquiring a shopping mall public index (S231) is configured to obtain a public index for each shopping mall server from shopping mall information. Here, the disclosure indicators are indicators used in a product or market ranking arrangement algorithm disclosed by each shopping mall server, and there may be differences between each shopping mall server.

Step S231 according to an embodiment of the present invention is formed to obtain relevance, popularity, and reliability as public indicators. Here, suitability, popularity, and reliability are groups of detailed indicators for convenience of explanation of the present invention, and detailed indicators described later may be grouped in a different way from the embodiments of the present invention, but these may also be included in the concept of the present invention. It is self-evident that there is

Relevance is an indicator to determine whether a product is suitable for the consumer's search intention, popularity is an indicator to determine whether a product is sought and sold a lot by consumers, and reliability is an indicator to determine whether product information registered in a shopping mall is reliable product information. may be an indicator of

More specifically, the suitability includes at least one of product name, category, manufacturer brand, and attribute tags, the popularity includes at least one of sales performance, number of reviews, recency, star rating, and number of clicks, and reliability includes product name SEO and Includes at least one of the shopping mall penalties. Here, the shopping mall penalty may be interpreted as meaning a penalty applied to a corresponding market or a corresponding product in a shopping mall.

Acquiring shopping mall product ranks (S232) is configured to obtain at least one product rank for each shopping mall server from shopping mall information. Step S232 receives the user's input information analysis result from step S111, and if the requested information is a product ranking confirmation request signal according to the received user's input information analysis result, the corresponding product information is obtained to the shopping mall server where the corresponding product is registered. It is possible to obtain the ranking of the corresponding product from shopping mall information of .

That is, when user A requests product ranking information for a moisturizing cream product B, in step S232, user A first obtains a shopping mall in which user A has entered the Internet market, analyzes the obtained shopping mall information from the acquired shopping mall server, and then user A It is possible to obtain the current ranking of the registered B moisture cream product. Here, the current ranking may be, for example, an order in which B moisture cream products are searched in each shopping mall in a search result.

Accumulating shopping mall product rankings (S233) is configured to accumulate and store shopping mall product rankings. Step S233 receives the user's input information analysis result from step S111, and if the requested information is a product rank tracking request signal according to the received user's input information analysis result, the shopping mall product rank to track the product rank for the corresponding product. can be accumulated and stored. At this time, in an embodiment of the present invention, in step S233, when the shopping mall server provides the existing cumulative ranking information, the cumulative ranking may be obtained from the shopping mall server and stored, so that the cumulative ranking prior to the user's request may also be provided. And, if necessary, obtain a cumulative ranking for each product that can be pre-stored according to settings in a database (not shown) that can be further included in the method 600, and also provide a cumulative ranking prior to the user's request. may be

Meanwhile, in another embodiment of the present invention, in step S233, changes in product information input by the user in the process of accumulating product rankings of the user's shopping mall may also be stored. To put it more simply, when the user changes the product information of the market, in step S233, the corresponding product information change data is also stored, and the user's product information is changed by providing the corresponding information together when the ranking cumulative information is provided to the user terminal. It is also possible for the user to easily determine whether or not the ranking has changed according to the above.

Meanwhile, in the step of predicting a product exposure algorithm according to an embodiment of the present invention (S115), an exposure algorithm used in each shopping mall server is predicted using shopping mall information through an exposure algorithm prediction unit, and machine learning of the shopping mall information is performed. It can be formed to increase the accuracy of the exposure algorithm predicted through To this end, step S115 is formed to include accumulating shopping mall information (S251), acquiring shopping mall product details (S252), and predicting a product exposure algorithm (S253) as shown in FIG. 12.

Accumulating shopping mall information (S251) is configured to accumulate shopping mall information. As described above, the shopping mall information includes public indicators for each shopping mall server and product information for each shopping mall server, and the accumulated shopping mall information in step S251 is transmitted to step S252.

Acquiring shopping mall product details (S252) is configured to receive accumulated shopping mall information and acquire detailed product information. The detailed product information may include detailed information about a product from product information among shopping mall information and information corresponding to a disclosure index of the corresponding product. In addition, the detailed product information is product-related information exposed to the shopping mall server, and may include at least one of product description content, product representative photos, product categories, and product titles.

The step of predicting the product exposure algorithm (S253) is configured to predict and generate the product exposure algorithm using the obtained product detailed information and product level change. The product exposure algorithm may be obtained for each shopping mall server. This is because indicators disclosed by each shopping mall server may be different. In addition, the product exposure algorithm may be configured to obtain a reference value for each disclosure index necessary for changing the ranking of products for each shopping mall server.

The product exposure algorithm can present quantitative reference values for quantifiable disclosure indicators, and analyze and acquire commonalities that positively affect ranking changes for disclosure indicators that are difficult to quantify. At this time, the disclosure indicators that are difficult to quantify may be product-related information such as product description content, product representative photos, and product categories. For such product-related information, keywords included in the information are extracted through keyword extraction in step S253, and other products Factors related to ranking change may be extracted through methods such as applying a weight for each keyword in comparison with related information.

Meanwhile, in another embodiment of the present invention, the step of predicting the product exposure algorithm (S115) may be formed to further include a step (not shown) of performing reinforcement of the product exposure algorithm. The step of performing reinforcement of the product exposure algorithm may be included to strengthen the product exposure algorithm using, for example, a Generative Adversarial Network (GAN). The product exposure algorithm is an algorithm that is predicted in the method of the present invention using public indicators because it is impossible to obtain an accurate internal algorithm of each shopping mall server. Therefore, the step of strengthening the product exposure algorithm in order to increase the consistency between the predicted product exposure algorithm and the internal algorithm of the undisclosed shopping mall may be formed to generate various virtual market and product data through a generative adversarial neural network. have.

In this case, the virtual market product data obtained through the product exposure algorithm is separated from the shopping mall server and stored together with the actual market and product data through the internal database, and the step of strengthening the product exposure algorithm is stored in the internal database. A virtual product ranking is created for each shopping mall using the included data. Then, in the step of strengthening the product exposure algorithm, if it is confirmed that the same information as the virtual market product data is generated through the shopping mall information, the ranks of the actual market and product data are compared with the ranks of the virtual market product data However, the product exposure algorithm can be supplemented and strengthened by analyzing the comparison results.

Finally, in the step of providing marketing proposal information to the user according to an embodiment of the present invention (S117), a marketing method for a specific product is transmitted to the user terminal as marketing proposal information using a product exposure algorithm through the marketing proposal unit. can be formed In step S117, when the user request information confirmed in step S211 is a marketing proposal request signal, marketing proposal information is generated by selecting a marketing method for a specific product requested by the user, and the generated marketing proposal information is transmitted to the user terminal. It can be. For example, step S117 may be configured to obtain a keyword selected by the user, analyze the obtained keyword, generate marketing information, and then propose it to the user. To this end, step S117 according to an embodiment of the present invention includes obtaining a selected keyword (S271), calculating a necessary indicator (S272), outputting the calculation result (S273), and suggesting marketing information (S273). S274) may be formed.

Acquiring a selected keyword (S271) is configured to obtain a keyword or tag from a user terminal. In step S271, in order to suggest marketing information to the user in step S117, keywords satisfying preset conditions may be recommended to the user, and keywords selected by the user may be obtained from among the recommended keywords. Here, the keywords recommended to the user may be customized, and may be, for example, keywords related to the user's market information and product information registered in the corresponding market.

The recommended keyword transmitted to the user terminal in step S271 is transmitted as recommended keyword information, and the recommended keyword information may include a keyword and marketing supplementary information of the keyword. Marketing auxiliary information may include the search volume of the keyword, the number of products containing the keyword, the level of competition of the keyword, and the average number of reviews per page of the product including the keyword, etc. Through this, users can analyze recommended keywords. Thus, a desired keyword can be selected through the user terminal, and the keyword selected by the user can be acquired in step S271.

Here, the recommended keywords under preset conditions may be core keywords with a certain number of hits or more, small and medium-sized keywords with a product number of less than a certain number, and tags used by products whose popularity in the corresponding shopping mall is higher than a certain standard.

In the step of calculating the required index (S272), the selected keyword is received and the selected keyword is received, the selected keyword is applied to each shopping mall to obtain the selected keyword search result, and the selected keyword search result is analyzed to analyze the selected keyword. After obtaining the result, it is configured to calculate a necessary indicator corresponding to the obtained result of analysis of the selected keyword. In step S272, upon receiving the specific product information requested by the user and the selected keyword selected by the user, the selected keyword analysis is performed by obtaining the selected keyword search result from each shopping mall and performing keyword analysis and upper exposure analysis of the obtained selected keyword search result. A result is obtained, and a necessary index for the corresponding product may be calculated using a product exposure algorithm based on the acquired selected keyword analysis result.

Here, the top exposure analysis may include the average monthly sales number of 1st page 40th rank, the average price of 1st page 40th rank, the average sales of 1st page 40th rank, the average monthly sales number of 1st page 10th rank, average sales and expected sales of 1st page 10th rank, etc. , Keyword analysis can include competitive strength, brand relevance, monthly search volume (by device, age, gender), content volume (blog, cafe, intellectual), search placement, and related search words. The various analysis contents described above are specific analysis contents presented for the example of the present invention, and the present invention is not limited to the above-described analysis contents, and may be configured to obtain a selected keyword analysis result using various analysis contents according to user settings. have.

In addition, the required index refers to an index in which the current information of the product corresponding to the disclosure index is less than the reference value obtained through the product exposure algorithm.

In other words, in step S272, user A's B moisture cream product is applied to the product exposure algorithm, and the application result is 35 purchases, 20 text reviews, 15 photo reviews, 10 steamed products, and 2 Q&A ( Example), each value can be selected as the reference value for each required indicator.

In addition, in the case of a title or category that is not a quantitative result, the result applied to the product exposure algorithm in step S272 may be selected as a keyword and category to be included or not to be included as a reference value of a necessary indicator.

The step of outputting the calculation result (S273) is formed to calculate the required value of a specific product to reach the reference value of the necessary index. The necessary indicators and reference values obtained in step S272 are transferred to step S273, and step S273 compares the current state of a specific product using the necessary indicators and reference values, and as a result of the comparison, the required values and conditions to be achieved by the specific product are calculated. can be output as

Further, in step S273, upon receiving the selected keyword analysis result, a calculation result for higher exposure may be further output using the received selected keyword analysis result and a necessary indicator. Here, the calculation result may include the number of sales, the number of reviews, the number of wishes, and the number of Q&As required for the top exposure of the user's product by using the necessary index, the selected keyword analysis result, and the reference value. It can be a desired ranking, or it can basically mean being exposed on the first page of search results.

In the step of proposing marketing information (S274), marketing information including selected keyword analysis results and calculation results is generated and transmitted to the user terminal. Here, the selected keyword analysis result included in the marketing information may be the analysis result of the product group including the user's product, shopping competitiveness index, average sales number, average price, and average sales, and the calculation result is the number of sales, which is a required item for top exposure. , the number of text reviews, the number of photo reviews, the number of steams, and the number of Q&A. In addition, the marketing information may further include a summary result summarizing the two results, and the summary result may include an estimated cost, an estimated period, and the total number of products in progress.

Although one embodiment of the present invention has been described above, the spirit of the present invention is not limited to the embodiments presented herein, and those skilled in the art who understand the spirit of the present invention may add elements within the scope of the same spirit. However, other embodiments can be easily proposed by means of changes, deletions, additions, etc., but these will also fall within the scope of the present invention.

110: shopping mall product exposure information providing system
111: user information processing unit 113: shopping mall information processing unit
115: exposure algorithm prediction unit 117: marketing proposal unit
120: user terminal 130: shopping mall server
211: user request information verification module 212: user-specific information processing module
213: User market information processing module 231: Shopping mall disclosure index acquisition module
232: shopping mall product ranking acquisition module 233: shopping mall product ranking accumulation module
251: shopping mall information accumulation module
252: shopping mall product details acquisition module
253: product exposure algorithm prediction module
271: Selected keyword acquisition module 272: Required indicator calculation module
273: calculation result output module 274: marketing information suggestion module

Claims (20)

A shopping mall product exposure information providing system connected to a user terminal and at least one shopping mall server to provide product ranking or marketing suggestion information to the user terminal,
a user information processor configured to generate user information by receiving and processing input information received from the user terminal;
a shopping mall information processor configured to acquire and process shopping mall information from the at least one shopping mall server;
an exposure algorithm prediction unit configured to predict an exposure algorithm used in each shopping mall server using the shopping mall information and to increase accuracy of the exposure algorithm predicted through machine learning of the shopping mall information; and
A shopping mall product exposure information providing system comprising: a marketing suggestion unit configured to transmit a marketing method for a specific product to the user terminal as the marketing proposal information by using the predicted product exposure algorithm. According to claim 1,
The input information includes at least one of user request information, user-specific information, and product information,
The user information processing unit,
a user-requested information check module configured to check user-requested information, which is information that a user desires to be provided through the user terminal, among the input information;
a user-specific information processing module configured to acquire and process user-specific information formed to distinguish a plurality of users from among the input information; and
A shopping mall product exposure information providing system formed to include; a user market information processing module configured to obtain the product information among the input information and obtain market information of the user including the corresponding product. According to claim 2,
The user request information includes at least one of a product rank confirmation request signal, a product rank tracking request signal, and a marketing proposal request signal;
The product ranking confirmation request signal is a signal for requesting ranking confirmation in at least one shopping mall of a specific product registered by the user,
The product ranking tracking request signal is a signal requesting an accumulated record of tracking the ranking of a specific product registered by the user for a predetermined period or more,
The marketing proposal request signal includes a signal requesting marketing proposal information for increasing the ranking of a specific product input through the user terminal. According to claim 1,
The shopping mall information includes either a public indicator for each shopping mall server or product information for each shopping mall server;
The shopping mall information processing unit,
a shopping mall public index acquisition module configured to obtain at least one public index for each shopping mall server from the shopping mall information;
a shopping mall product ranking acquiring module configured to acquire at least one product ranking from the shopping mall information for each shopping mall server; and
A shopping mall product ranking accumulation module configured to accumulate and store the shopping mall product rankings. According to claim 4,
The disclosure index is at least one of suitability, popularity, and reliability;
The suitability is an index for determining whether a product is suitable for the consumer's search intention,
The popularity is an index for determining whether the product is sought and sold a lot by the consumers,
The reliability is an indicator for determining whether product information is a reliable product. According to claim 5,
The suitability is obtained using at least one of a product name, a category, a manufacturer brand, and an attribute tag,
The popularity is obtained using at least one of sales performance, number of reviews, recency, star rating, and number of clicks,
The reliability is obtained by using at least one of product name SEO and shopping mall penalty. According to claim 1,
The exposure algorithm prediction unit,
a shopping mall information accumulating module configured to accumulate the shopping mall information;
a shopping mall product detailed information acquiring module configured to acquire detailed product information including detailed information about a product and information corresponding to a disclosure indicator of the product from the product information; and
A product exposure algorithm prediction module configured to predict the product exposure algorithm using each of the product details and a change in each of the product rankings;
The disclosure index is at least one of suitability, popularity, and reliability. According to claim 7,
The detailed information on the product is product-related information exposed on the shopping mall server, and includes at least one of product description content, a representative product picture, and a product title;
The product exposure algorithm is acquired for each shopping mall server, and is formed to obtain a reference value for each disclosure index necessary for changing the rank of the product in each shopping mall server. According to claim 8,
The product exposure algorithm prediction module,
A shopping mall product exposure information providing system formed to check a change in information corresponding to the disclosure indicator of the product or detailed information on the product, and to learn a change in product rank according to the checked change. According to claim 1,
The marketing proposal unit,
a selection keyword acquisition module configured to obtain a selection keyword selected by the user from the user terminal;
a necessary index calculation module configured to analyze the selected keyword, calculate a necessary index for a specific product requested by the user using the product exposure algorithm, and generate a selected keyword analysis result;
a calculation result output module configured to calculate a required value of the specific product to reach a reference value of the required indicator and output a calculation result including a result of analyzing the selected keyword; and
It is formed to include; a marketing information suggestion module configured to generate marketing information that is a marketing method for the specific product including the selected keyword analysis result and the calculation result and deliver it to the user terminal,
The necessary indicators are,
The shopping mall product exposure information providing system of claim 1 , wherein the current information corresponding to the disclosure indicator of the specific product is a disclosure indicator that is less than a reference value obtained using the product exposure algorithm. A method for providing product exposure information in a shopping mall connected to a user terminal and at least one shopping mall server to provide product ranking or marketing suggestion information to the user terminal,
generating user information by receiving and processing input information received from the user terminal using a user information processing unit;
obtaining and processing shopping mall information from the at least one shopping mall server using a shopping mall information processing unit;
predicting an exposure algorithm used in each shopping mall server using the shopping mall information through an exposure algorithm predictor, and increasing accuracy of the exposure algorithm predicted through machine learning of the shopping mall information; and
and transmitting a marketing method for a specific product to the user terminal as the marketing proposal information by using the product exposure algorithm predicted through a marketing suggestion unit. According to claim 11,
The input information includes at least one of user request information, user-specific information, and product information,
Generating the user information,
checking user request information, which is information that a user desires to be provided through the user terminal, among the input information;
obtaining and processing user-specific information formed to distinguish a plurality of users from among the input information; and
Acquiring the product information among the input information and acquiring market information of the user including the corresponding product; According to claim 12,
The user request information includes at least one of a product rank confirmation request signal, a product rank tracking request signal, and a marketing proposal request signal;
The product ranking confirmation request signal is a signal for requesting ranking confirmation in at least one shopping mall of a specific product registered by the user,
The product ranking tracking request signal is a signal requesting an accumulated record of tracking the ranking of a specific product registered by the user for a predetermined period or more,
The marketing proposal request signal includes a signal requesting marketing proposal information for increasing the ranking of a specific product input through the user terminal. According to claim 11,
The shopping mall information includes either a public indicator for each shopping mall server or product information for each shopping mall server;
The step of obtaining and processing the shopping mall information,
obtaining at least one disclosure indicator for each shopping mall server from the shopping mall information;
acquiring at least one product rank for each shopping mall server from the shopping mall information; and
Accumulating and storing the shopping mall product rankings; According to claim 14,
The disclosure index is at least one of suitability, popularity, and reliability;
The suitability is an index for determining whether a product is suitable for the consumer's search intention,
The popularity is an index for determining whether the product is sought and sold a lot by the consumers,
The method of providing shopping mall product exposure information, wherein the reliability is an index for determining whether product information is a reliable product. According to claim 15,
The suitability is obtained using at least one of a product name, a category, a manufacturer brand, and an attribute tag,
The popularity is obtained using at least one of sales performance, number of reviews, recency, star rating, and number of clicks,
The method of providing shopping mall product exposure information, wherein the reliability is obtained using at least one of product name SEO and shopping mall penalty. According to claim 11,
Predicting the exposure algorithm and increasing the accuracy of the exposure algorithm predicted through machine learning of the shopping mall information,
accumulating the shopping mall information;
acquiring detailed product information including detailed information about a product and information corresponding to the disclosure indicator of the product from product information; and
predicting the product exposure algorithm using each of the product details and each change in the product ranking;
The disclosure index is at least one of suitability, popularity, and reliability. According to claim 17,
The detailed information on the product is product-related information exposed on the shopping mall server, and includes at least one of product description content, a representative product picture, and a product title;
wherein the product exposure algorithm is obtained for each shopping mall server and is formed to obtain a reference value for each disclosure index necessary for changing the rank of the product in each shopping mall server. According to claim 18,
Predicting the product exposure algorithm,
A method of providing product exposure information in a shopping mall configured to check a change in information corresponding to the disclosure index of the product or detailed information on the product, and to learn a change in product rank according to the checked change. According to claim 19,
The step of transmitting the marketing proposal information to the user terminal,
obtaining a selection keyword selected by the user from the user terminal;
analyzing the selected keyword, calculating a required index for a specific product requested by the user using the product exposure algorithm, and generating a selected keyword analysis result;
calculating a required value of the specific product to reach a reference value of the required index, and outputting a calculation result including a result of analyzing the selected keyword; and
Generating marketing information, which is a marketing method for the specific product, including the selected keyword analysis result and the calculation result, and transmitting it to the user terminal;
The necessary indicators are,
The method of providing shopping mall product exposure information, wherein the current information corresponding to the disclosure index of the specific product is a disclosure index that is less than a reference value obtained using the product exposure algorithm.

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