KR102588300B1 - Shopping mall product exposure information provision system and method - Google Patents

Abstract

A system and method for providing shopping mall product exposure information are provided. The shopping mall product exposure information providing system according to an embodiment of the present invention is connected to a user terminal and at least one shopping mall server to provide product ranking or marketing proposal information to the user terminal. In the shopping mall product exposure information providing system, a user information processing unit configured to receive and process input information input from the user terminal to generate user information; a shopping mall information processing unit configured to obtain and process shopping mall information from the at least one shopping mall server; an exposure algorithm prediction unit formed to predict an exposure algorithm used by each shopping mall server using the shopping mall information and to increase the accuracy of the exposure algorithm predicted through 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 the marketing proposal information 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 exposure information for products in a shopping mall. In particular, the product ranking allows users to check and track the ranking of their products registered in a shopping mall or obtain marketing suggestion information to increase the ranking of a product. It relates to a system and method for providing shopping mall product exposure information that can perform confirmation tracking and provision of marketing proposal information.

Recently, online shopping malls have become widely distributed due to the rapid development of wired and wireless communication technology and the expansion of the communication network on which it is based. An online shopping mall is a representative form of e-commerce using the Internet. Off-line stores are implemented as virtual stores on the Internet, and visitors (buyers) access the Internet and analyze various types of products displayed in the online store. It allows you to conveniently purchase the product you want through product evaluation and comparative quotes with the same product from other stores.

Meanwhile, from the seller's perspective, it is necessary to analyze the correlation between customer inflow and sales of their products in online shopping malls. Conventional online shopping mall sales analysis analyzes the number of visitors through log analysis of access to the store's homepage in the online shopping mall itself, analyzes the inflow path of visitors, and analyzes the correlation between inflow 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 within an online shopping mall, and the general manager (MD), who mainly manages the overall operation from product selection to display and sales, is responsible for determining product sales strategies. The analysis information of these products is used for this purpose. However, this analysis information is only used and exclusive to the online shopping mall and is not provided to the seller or manufacturer of the product.

In this situation, various online shopping malls are disclosing the indicators that determine the ranking of the seller's store homepage, and the seller can use the indicators to increase the ranking of the store's homepage. However, since the algorithm for processing the indicators released by online shopping malls is not made public, there is a problem that sellers may have to spend countless trials and errors and costs to expose their store homepages at the top of online shopping malls.

Korean Patent No. 10-1575640

In order to solve the problems of the prior art as described above, an embodiment of the present invention provides a ranking of products according to the user's request, tracks the ranking of the product for a preset period, or conducts marketing to increase the ranking of the product. We intend to provide a system and method for providing shopping mall product exposure information that can provide suggested information to users.

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

The shopping mall product exposure information providing system includes a user information processing unit configured to receive and process input information input from the user terminal to generate user information; a shopping mall information processing unit configured to obtain and process shopping mall information from the at least one shopping mall server; an exposure algorithm prediction unit formed to predict an exposure algorithm used by each shopping mall server using the shopping mall information and to increase the accuracy of the exposure algorithm predicted through 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 the marketing proposal information using the predicted product exposure algorithm.

The input information includes at least one of user request information, user unique information, and product information, and the user information processing unit is configured to check user request information, which is information that the user wishes to receive through the user terminal, among the input information. a user-requested information confirmation module; a user-specific information processing module configured to obtain and process user-specific information formed to distinguish a plurality of users from the input information; and a user market information processing module configured to obtain the user's market information including the corresponding product by acquiring 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 the ranking of a specific product registered by the user in at least one shopping mall. is a signal requesting, the product ranking tracking request signal is a signal requesting a cumulative record of tracking the ranking of a specific product registered by the user for more than a preset period, and the marketing proposal request signal is a specific product input through the user terminal. It may include a signal requesting marketing offer information to increase the product's ranking.

The shopping mall information includes one of a disclosure index for each shopping mall server or product information for each shopping mall server, and the shopping mall information processing unit is configured to obtain at least one disclosure index for each shopping mall server from the shopping mall information. indicator acquisition module; a shopping mall product ranking acquisition module configured to obtain at least one product ranking for each shopping mall server from the shopping mall information; and a shopping mall product ranking accumulation module that accumulates and stores the shopping mall product rankings.

The public indicator is at least one of suitability, popularity, and reliability, and the suitability is an indicator that determines whether the product is suitable for the consumer's search intention, and the popularity is an indicator that determines whether the product is frequently sought after by the consumer and is sold in large quantities. Reliability can be an indicator that determines whether product information is a trustworthy product.

The suitability is obtained using at least one of a 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 by using the product name. It can be obtained using at least one of SEO and shopping mall penalties.

The exposure algorithm prediction unit includes a shopping mall information accumulation module configured to accumulate the shopping mall information; a shopping mall product detailed information acquisition module configured to obtain product detailed information including detailed information about the product and information corresponding to a public indicator of the product from the product information; and a product exposure algorithm prediction module configured to predict the product exposure algorithm using changes in each product detail information and each product ranking, wherein the public indicator is at least one of suitability, popularity, and reliability. It could be one.

The detailed information about the product is product-related information exposed to the shopping mall server and includes at least one of a product description, a representative product photo, and a product title. 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 public indicator necessary to change the ranking of the product.

The product exposure algorithm prediction module may be configured to check changes in information corresponding to the public index of the product or detailed information about the product, and learn changes in product rankings according to the confirmed changes.

The marketing proposal unit includes a selection keyword acquisition module configured to obtain a selection keyword selected by the user from the user terminal; a necessary indicator calculation module configured to analyze the selected keyword, calculate a necessary indicator 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 the reference value of the necessary index and output a calculation result including the selected keyword analysis result; and a marketing information proposal 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 index of the specific product may be a disclosure index that is less than a reference value obtained using the product exposure algorithm.

According to one aspect of the present invention, a method of providing exposure information for shopping mall products is provided, which is connected to a user terminal and at least one shopping mall server to provide product ranking or marketing proposal information to the user terminal. The method of providing exposure information on shopping mall products includes the steps of receiving and processing input information input from the user terminal using a user information processing unit to generate user information; 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 by each shopping mall server using the shopping mall information through an exposure algorithm prediction unit, and increasing the accuracy of the exposure algorithm predicted through machine learning of the shopping mall information; and transmitting a marketing method for a specific product to the user terminal as the marketing proposal information using the product exposure algorithm predicted through the marketing proposal unit.

The input information includes at least one of user request information, user unique information, and product information, and the step of generating the user information includes selecting user request information, which is information that the user wishes to receive through the user terminal, from among the input information. Confirmation steps; Obtaining and processing user-specific information formed to distinguish a plurality of users from the input information; and obtaining the user's market information including the corresponding product by acquiring 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 the ranking of a specific product registered by the user in at least one shopping mall. is a signal requesting, the product ranking tracking request signal is a signal requesting a cumulative record of tracking the ranking of a specific product registered by the user for more than a preset period, and the marketing proposal request signal is a specific product input through the user terminal. It may include a signal requesting marketing offer information to increase the product's ranking.

The shopping mall information includes either a public index for each shopping mall server or product information for each shopping mall server, and the step of obtaining and processing the shopping mall information includes obtaining at least one public index for each shopping mall server from the shopping mall information. steps; Obtaining at least one product ranking for each shopping mall server from the shopping mall information; and accumulating and storing the shopping mall product rankings.

The public indicator is at least one of suitability, popularity, and reliability, and the suitability is an indicator that determines whether the product is suitable for the consumer's search intention, and the popularity is an indicator that determines whether the product is frequently sought after by the consumer and is sold in large quantities. Reliability can be an indicator that determines whether product information is a trustworthy product.

The suitability is obtained using at least one of a 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 by using the product name. It can be obtained using at least one of SEO and shopping mall penalties.

Predicting the exposure algorithm and increasing the accuracy of the exposure algorithm predicted through machine learning of the shopping mall information includes accumulating the shopping mall information; Obtaining detailed product information including detailed information about the product and information corresponding to the public indicator of the product from product information; And predicting the product exposure algorithm using the detailed information of each product and the change in the ranking of each product, wherein the public indicator may be at least one of suitability, popularity, and reliability.

The detailed information about the product is product-related information exposed to the shopping mall server and includes at least one of a product description, a representative product photo, and a product title. 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 public indicator necessary to change the ranking of the product.

The step of predicting the product exposure algorithm may be configured to check changes in information corresponding to the public index of the product or detailed information about the product, and learn changes in product rankings according to the confirmed changes.

The step of delivering the marketing proposal information to the user terminal includes: acquiring a selection keyword selected by the user from the user terminal; Analyzing the selected keywords, calculating a necessary 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 for the specific product to reach the reference value of the necessary index, and outputting a calculation result including a result of analyzing the selected keywords; 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 the marketing information to the user terminal, wherein the necessary indicator is, The current information corresponding to the public index may be a public index that is less than the reference value obtained using the product exposure algorithm.

A system and method for providing exposure information for shopping mall products according to an embodiment of the present invention provides product rankings according to user requests, tracks product rankings for a preset period, or provides marketing proposal information to increase product rankings. There is an effect of providing this to the user.

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

Hereinafter, with reference to the attached drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily implement the present invention. The present invention may be implemented in many different forms and is not limited to the embodiments described herein. In order to clearly explain the present invention in the drawings, parts not related to the description are omitted, and identical or similar components are given the same reference numerals throughout the specification.

FIG. 1 is a block diagram briefly showing a system for providing exposure information for shopping mall products 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. 1. 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 system for providing exposure information for shopping mall products according to an embodiment of the present invention will be described in more detail using FIGS. 1 to 5.

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

The shopping mall product exposure information providing system 110 receives request information from the user terminal 120, provides results corresponding to the requested information to the user terminal 120, and receives 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. It can be formed.

The user information processing unit 111 according to an embodiment of the present invention is configured to receive and process input information received from the user terminal 120 to generate user information. 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 unique 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 confirmation module 211 is configured to check user-requested information, which is information that the user wishes to receive through the user terminal 120, among input information. Here, the user requested 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 confirm 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 an accumulated record by tracking the ranking of the product requested by the user among products registered in the Internet market for a preset period or more.

Additionally, the marketing proposal request signal may be a signal requesting marketing proposal information to increase the ranking of the product requested by the user among products registered by the user in the Internet market.

In other words, the shopping mall product exposure information providing system 110 of the present invention can receive requested information from the user terminal 120 through the user request information confirmation module 211, and through this, determine what information the user wants to receive. You can check if it is.

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

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

The user market information processing module 213 is configured to obtain product information from input information and obtain user market information including the product. Products registered by the user on the shopping mall server 130 may differ depending on the shopping mall or the user's market.

As an example, if user A sells B moisture cream product, user A may create multiple markets on one shopping mall server 130 and register B moisture cream in all of the plurality of markets, and multiple shopping mall servers ( You can also select specific markets among the markets created in 130) and register the B moisture cream product only in the selected markets. Accordingly, the user market information processing module 213 may be configured to receive the user's input information, obtain product information included in the received input information, and obtain the user's market information including the acquired 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 of one embodiment of the invention is configured to obtain and process shopping mall information from at least one shopping mall server 130. Here, the shopping mall information includes a public index for each shopping mall server 130 and product information for each shopping mall server 130. To this end, the shopping mall information processing unit 113 includes a shopping mall public index acquisition module 231 and a shopping mall product ranking acquisition module ( 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 at least one shopping mall server 130 from shopping mall information. Here, the public indicators are indicators used in the product or market ranking algorithm disclosed by each shopping mall server 130, and differences may exist for each shopping mall server 130.

The shopping mall public indicator acquisition module 231 according to an embodiment of the present invention is configured to obtain suitability, popularity, and reliability as public indicators. Here, suitability, popularity, and reliability are groupings of detailed indicators for the convenience of explaining the present invention, and the detailed indicators described later may be grouped in a different way from the embodiments of the present invention, but may also be included in the concept of the present invention. It is obvious that it exists.

Suitability is an indicator that determines whether the product is suitable for the consumer's search intent, popularity is an indicator that determines whether the product is frequently sought after by consumers and sold in large quantities, and reliability is an indicator that determines whether the product information registered in the shopping mall is trustworthy. It may be an indicator that

In more detail, relevance includes at least one of product name, category, manufacturer brand, and attribute tags, popularity includes at least one of sales performance, number of reviews, recency, star rating, and number of clicks, and trustworthiness includes product name SEO and Includes at least one of the shopping mall penalties. Here, the shopping mall penalty can be interpreted to mean a penalty applied to the relevant market or product by the shopping mall.

The shopping mall product ranking acquisition module 232 is configured to obtain at least one product ranking for each shopping mall server 130 from shopping mall information. The shopping mall product ranking acquisition module 232 receives the user 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 input information analysis result, the corresponding product information The ranking of the product can be obtained from the shopping mall information of the shopping mall server 130 where the product is registered.

That is, when user A requests product ranking information for moisturizing cream product B, the shopping mall product ranking acquisition module 232 first obtains the shopping mall where user A has entered the Internet market and retrieves the information from the obtained shopping mall server 130, respectively. By analyzing the acquired shopping mall information, the current ranking of moisture cream product B registered by user A can be obtained. Here, as an example, the current ranking may be the order in which the B moisture cream product appears in the search results of each shopping mall.

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 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 input information analysis result, the corresponding product is To track product rankings, shopping mall product rankings can be accumulated and stored. At this time, in one embodiment of the present invention, when the shopping mall server 130 provides existing cumulative ranking information, the shopping mall product ranking accumulating module 233 obtains and stores the cumulative ranking from the shopping mall server 130, so that the user Cumulative rankings prior to the request can also be provided, and if necessary, cumulative rankings for each product that can be stored in a database (not shown) that can be further included in the system 110 are obtained according to settings. Cumulative rankings prior to the user's request can 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 during the user's shopping mall product ranking accumulation process. To put it more simply, when a user changes product information in the market, the shopping mall product ranking accumulation module 233 also stores the corresponding product information change data, and when providing the ranking accumulation information to the user terminal 120, the corresponding information is stored. By providing it together, users can easily determine whether the ranking has changed as the user changes product information.

Meanwhile, the exposure algorithm prediction unit 115 according to an embodiment of the present invention predicts the exposure algorithm used by each shopping mall server 130 using shopping mall information, and uses 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 detailed information acquisition module 252, and a product exposure algorithm prediction module 253, as shown in FIG. 4. .

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, and the shopping mall information accumulated and stored in the shopping mall information accumulation module 251 is stored in the shopping mall product detailed information acquisition module 252. is passed on.

The shopping mall product detailed information acquisition module 252 is configured to obtain detailed product information by receiving accumulated shopping mall information. Product details may include detailed information about the product from product information among shopping mall information and information corresponding to the public indicator of the product. Additionally, product details are product-related information exposed to the shopping mall server 130 and may include at least one of product description content, product representative photo, product category, and product title.

The product exposure algorithm prediction module 253 is configured to predict and generate a product exposure algorithm using the obtained product details and changes in product level. The product exposure algorithm can be obtained for each shopping mall server 130. This is because the indicators disclosed by each shopping mall server 130 may be different. Additionally, the product exposure algorithm may be formed to obtain a reference value for each public index necessary for changing the ranking of a product for each shopping mall server 130.

The product exposure algorithm can present quantitative standard values for quantifiable public indicators, and can obtain public indicators that are difficult to quantify by analyzing commonalities that have a positive effect on ranking changes. At this time, public indicators that are difficult to quantify may be product-related information such as product description content, product representative photos, and product categories, and 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 changes 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). The product exposure algorithm strengthening module may be included to strengthen the product exposure algorithm using, for example, a generative adversarial network (GAN). The product exposure algorithm is an algorithm predicted by the system of the present invention using published indicators because the accurate internal algorithm of each shopping mall server 130 cannot be obtained. Therefore, in order to increase the consistency between the predicted product exposure algorithm and the internal algorithm of the undisclosed shopping mall, the product exposure algorithm enhancement module can be formed 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 not linked to the shopping mall server 130, but is separated 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 uses data contained in the internal database to create a virtual product ranking for each shopping mall. Afterwards, when the product exposure algorithm enhancement module confirms that the same information as the virtual market product data has been created through the shopping mall information, it compares the ranking of the actual market and product data with the ranking of the virtual market product data and reports the comparison result. Through analysis, product exposure algorithms can be supplemented and strengthened.

Finally, the marketing proposal unit 117 of the shopping mall product exposure information provision system 110 according to an embodiment of the present invention uses a product exposure algorithm to deliver a marketing method for a specific product to the user terminal as marketing proposal information. can be formed. If the user request information confirmed in the user request information confirmation module 211 is a marketing proposal request signal, the marketing proposal unit 117 selects a marketing method for the specific product requested by the user to generate marketing proposal information, and generates the marketing proposal information. It may be configured to transmit marketing offer information to the user terminal 120. As an example, the marketing suggestion unit 117 may be configured to acquire keywords selected by the user, analyze the obtained keywords, generate marketing information, and then suggest the keywords to the user. To this end, the marketing proposal unit 117 according to an embodiment of the present invention includes a selection keyword acquisition module 271, a necessary index calculation module 272, a calculation result output module 273, and a marketing information proposal module 274. It can be formed.

The selection keyword acquisition module 271 is configured to acquire keywords or tags from the user terminal. The selected keyword acquisition module 271 may recommend keywords that satisfy preset conditions to the user in order to suggest marketing information to the user in the marketing suggestion unit 117, and obtain the keyword selected by the user from among the recommended keywords. . Here, keywords recommended to the user may be customized to the user, and for example, may be keywords related to the user's market information and product information registered in the 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 the keyword and marketing auxiliary information for the keyword. Marketing assistance information may include the search volume of the keyword, the number of products containing the keyword, the intensity of competition for the keyword, and the average number of reviews on page 1 for products containing the keyword, which allows users to 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, recommended keywords with preset conditions may be core keywords with a certain number of views or more, small and medium-sized keywords with a certain number of products or less, and tags used by products with a popularity of the corresponding shopping mall above a certain standard.

The necessary index calculation module 272 receives the selected keywords selected by the user, receives the selected keywords, applies the selected keywords to each shopping mall to obtain selected keyword search results, and analyzes the obtained selected keyword search results to provide the selected keyword analysis results. After being obtained, it is formed to calculate necessary indicators corresponding to the obtained selected keyword analysis results. 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 performs top exposure analysis and keyword analysis of the obtained selected keyword search result. By performing this, the selected keyword analysis results are obtained, and the necessary indicators for the product can be calculated by using the product exposure algorithm on the obtained selected keyword analysis results.

Here, the top exposure analysis may include the average monthly sales at rank 40 on page 1, average price at rank 40 on page 1, average sales at rank 40 on page 1, average monthly sales at rank 10 on page 1, average sales at rank 10 on page 1, and expected sales. , Keyword analysis may include competition intensity, 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 as examples of the present invention, and the present invention is not limited to the above-described analysis contents and can be formed to obtain a selection keyword analysis result using various analysis contents according to the user's settings. there is.

In addition, a necessary indicator refers to an indicator in which the current information of the product corresponding to the public indicator is less than the standard value obtained through the product exposure algorithm.

In other words, to explain it more simply, the necessary index calculation module 272 applies user A's moisture cream product B to the product exposure algorithm, and the result of the application is 35 purchases, 20 text reviews, 15 photo reviews, and 10 favorites. , Q&A 2 (example), each value can be selected as a standard value for each required indicator.

In addition, in the case of a title or category that is not a quantitative result, the results applied to the product exposure algorithm in the necessary indicator calculation module 272 may be selected as keywords and categories that should be included as the standard value of the necessary indicator or keywords and categories that should not be included. You 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 necessary indicators and reference values obtained from the necessary indicator calculation module 272 are transmitted to the calculation result output module 273, and the calculation result output module 273 uses the necessary indicators and reference values to compare them with the current state of a specific product. As a result of the comparison, the required values and conditions that a specific product must achieve can be output as calculation results.

In addition, when the calculation result output module 273 receives the selected keyword analysis result, it can be configured to further output the calculation result for top exposure using the received selected keyword analysis result and necessary indicators. Here, the calculation results may include the number of sales, reviews, favorites, and Q&A required for top exposure of the user's product using necessary indicators, selected keyword analysis results, and standard values. As an example, top exposure is the number of It may be a desired ranking, or it may basically mean being exposed on page 1 of search results.

The marketing information proposal 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 results included in the marketing information may be the analysis results of the product group containing the user's product, shopping competitiveness index, average number of sales, average price, and average sales, and the calculation result may be the number of sales, which is a required item for top exposure. , can include the number of text reviews, photo reviews, favorites, and Q&A. In addition, the marketing information may further include a summary result summarizing the two results, and the summary result may include the expected cost, the expected period, and the total number of products in progress.

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

FIG. 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 This is a flowchart showing step S115 in FIG. 9, and FIG. 10 is a flowchart showing step S117 in FIG. 6.

Hereinafter, for convenience of explanation, 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 method 600 of providing exposure information for shopping mall products is configured to receive request information from a user terminal, provide results 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 exposure information for shopping mall products according to an embodiment of the present invention includes a step of processing user information (S111), a step of processing shopping mall information (S113), and a step of predicting a product exposure algorithm (S115). and providing marketing proposal information to the user (S117).

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

The step of checking user requested information (S211) is designed to check user requested information, which is information that the user wishes to receive through the user terminal, among input information. Here, the user requested 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 confirm 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 an accumulated record by tracking the ranking of the product requested by the user among products registered in the Internet market for a preset period or longer.

Additionally, the marketing proposal request signal may be a signal requesting marketing proposal information to increase the ranking of the product requested by the user among products registered by the user in the Internet market.

That is, the method 600 of the present invention for providing exposure information for shopping mall products can receive requested information from the user terminal through step S211, and through this, the user can confirm what information the user wants to receive.

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

The method 600 of the present invention for providing exposure information on shopping mall products is designed to manage product information for a plurality of users. Accordingly, the method 600 of providing exposure information for shopping mall products matches unique information such as an ID that can identify users for each user terminal, and obtains unique user information by analyzing input information received from the user terminal.

The user market information processing step (S213) is formed to obtain product information from input information and obtain user market information including the product. Products registered by the user on the shopping mall server may differ depending on the shopping mall or the user's market.

For example, if user A sells B moisturizing cream product, user A may create multiple markets on one shopping mall server and register B moisturizing cream in all of the multiple markets, and may register B moisturizing cream in all of the multiple markets, and create multiple markets in each shopping mall server. You can select specific markets among the markets and register the B moisture cream product only in the selected markets. Accordingly, step S213 may be configured to receive the user's input information, obtain product information included in the received input information, and obtain the user's market information including the acquired 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 product information and market information included in the user's request information, shopping mall information in an embodiment of the present invention The processing step (S113) is configured to obtain and process shopping mall information from at least one shopping mall server using a shopping mall information processing unit. Here, the shopping mall information includes a public index for each shopping mall server and product information for each shopping mall server, and for this purpose, step S113 includes a step of obtaining a shopping mall public index (S231) and a step of obtaining a shopping mall product ranking as shown in FIG. 11. (S232) and accumulating shopping mall product rankings (S233).

The step of obtaining a shopping mall public index (S231) is configured to obtain a public index for at least one shopping mall server from shopping mall information. Here, public indicators are indicators used in the product or market ranking algorithm disclosed by each shopping mall server, and differences may exist for each shopping mall server.

Step S231 according to an embodiment of the present invention is formed to obtain suitability, popularity, and reliability as public indicators. Here, suitability, popularity, and reliability are groupings of detailed indicators for the convenience of explaining the present invention, and the detailed indicators described later may be grouped in a different way from the embodiments of the present invention, but may also be included in the concept of the present invention. It is obvious that it exists.

Suitability is an indicator that determines whether the product is suitable for the consumer's search intent, popularity is an indicator that determines whether the product is frequently sought after by consumers and sold in large quantities, and reliability is an indicator that determines whether the product information registered in the shopping mall is trustworthy. It may be an indicator that

In more detail, relevance includes at least one of product name, category, manufacturer brand, and attribute tags, popularity includes at least one of sales performance, number of reviews, recency, star rating, and number of clicks, and trustworthiness includes product name SEO and Includes at least one of the shopping mall penalties. Here, the shopping mall penalty can be interpreted to mean a penalty applied to the relevant market or product by the shopping mall.

The step of obtaining a shopping mall product ranking (S232) is performed to obtain at least one product ranking 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 request information is a product ranking confirmation request signal according to the user's input information analysis result, the shopping mall server where the product is registered obtains the product information. You can obtain the ranking of the product from the shopping mall information.

That is, when user A requests product ranking information for moisturizing cream product B, step S232 first obtains the shopping mall where user A has entered the Internet market, and analyzes the shopping mall information obtained from each of the obtained shopping mall servers to determine user A's You can obtain the current ranking of B moisturizing cream products registered by . Here, as an example, the current ranking may be the order in which the B moisture cream product appears in the search results of each shopping mall.

The step of 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 ranking tracking request signal according to the received user input information analysis result, the shopping mall product ranking is used to track the product ranking for the corresponding product. can be stored cumulatively. At this time, in one embodiment of the present invention, in step S233, when the shopping mall server provides existing cumulative ranking information, the cumulative ranking at the time before the user's request may also be provided by obtaining and storing the cumulative ranking from the shopping mall server. In addition, if necessary, a cumulative ranking for each product that can be stored in a database (not shown) that can be further included in the method 600 is obtained according to the settings, and the cumulative ranking at the time before the user's request is also provided. It may be possible.

Meanwhile, in another embodiment of the present invention, step S233 may also store changes in product information input by the user during the user's shopping mall product ranking accumulation process. To explain more simply, when the user changes product information in the market, step S233 also stores the corresponding product information change data and provides the corresponding information when providing ranking cumulative information to the user terminal, thereby changing the user's product information. It is also possible for users to easily determine whether the ranking has changed.

Meanwhile, in the step (S115) of predicting the product exposure algorithm according to an embodiment of the present invention, the exposure algorithm used by each shopping mall server is predicted using shopping mall information through the 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 a step of accumulating shopping mall information (S251), obtaining detailed information on shopping mall products (S252), and predicting a product exposure algorithm (S253), as shown in FIG. 12.

The step of accumulating shopping mall information (S251) is designed 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 shopping mall information accumulated in step S251 is transferred to step S252.

The step of acquiring detailed information on shopping mall products (S252) is configured to obtain detailed product information by receiving accumulated shopping mall information. Product details may include detailed information about the product from product information among shopping mall information and information corresponding to the public indicator of the product. Additionally, product details are product-related information exposed on the shopping mall server and may include at least one of product description content, product representative photo, product category, and product title.

The step of predicting the product exposure algorithm (S253) is formed to predict and generate a product exposure algorithm using the obtained product details and changes in product level. The product exposure algorithm can be obtained for each shopping mall server. This is because the indicators released by each shopping mall server may be different. Additionally, the product exposure algorithm can be formed to obtain a reference value for each public index necessary for changing the ranking of products for each shopping mall server.

The product exposure algorithm can present quantitative standard values for quantifiable public indicators, and can obtain public indicators that are difficult to quantify by analyzing commonalities that have a positive effect on ranking changes. At this time, public indicators that are difficult to quantify may be product-related information such as product description content, product representative photos, and product categories, and such product-related information is extracted from keywords included in the information through keyword extraction in step S253, and other products Factors related to changes in rankings can also be extracted through methods such as comparing relevant information and applying weights to each keyword.

Meanwhile, in another embodiment of the present invention, the step of predicting the product exposure algorithm (S115) may further include a step of strengthening the product exposure algorithm (not shown). The step of performing strengthening of the product exposure algorithm may be included to strengthen the product exposure algorithm, for example, using a generative adversarial network (GAN). Since the product exposure algorithm cannot obtain the exact internal algorithm of each shopping mall server, it is an algorithm predicted by the method of the present invention using published indicators. Therefore, the step of strengthening the product exposure algorithm to increase the consistency between the predicted product exposure algorithm and the internal algorithm of the undisclosed shopping mall can be formed to generate various virtual market and product data through a generative adversarial neural network. there is.

In this case, the virtual market product data obtained through the product exposure algorithm is not linked to the shopping mall server, but is separated and stored with the actual market and product data through an internal database, and the step of strengthening the product exposure algorithm is performed in the internal database. Using the included data, a virtual product ranking is created for each shopping mall. Thereafter, in the step of strengthening the product exposure algorithm, when it is confirmed that the same information as the virtual market product data has been created 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. , 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), the marketing method for a specific product is delivered to the user terminal as marketing proposal information using a product exposure algorithm through the marketing proposal unit. can be formed. In step S117, if 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 the specific product requested by the user, and the generated marketing proposal information is transmitted to the user terminal. It can be. As an example, step S117 may be configured to obtain a keyword selected by the user, analyze the obtained keyword, generate marketing information, and then suggest 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 index (S272), outputting the calculation result (S273), and proposing marketing information ( It can be formed including S274).

The step of acquiring the selected keyword (S271) is designed to obtain a keyword or tag from the user terminal. Step S271 may recommend keywords that satisfy preset conditions to the user in order to suggest marketing information to the user in step S117, and obtain the keyword selected by the user from among the recommended keywords. Here, keywords recommended to the user may be customized to the user, and for example, may be keywords related to the user's market information and product information registered in the 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 the keyword and marketing auxiliary information for the keyword. Marketing assistance information may include the search volume of the keyword, the number of products containing the keyword, the intensity of competition for the keyword, and the average number of reviews on page 1 for products containing the keyword, which allows users to analyze recommended keywords. Thus, the desired keyword can be selected through the user terminal, and the keyword selected by the user can be obtained in step S271.

Here, recommended keywords with preset conditions may be core keywords with a certain number of views or more, small and medium-sized keywords with a certain number of products or less, and tags used by products with a popularity of the corresponding shopping mall above a certain standard.

In the step of calculating the necessary index (S272), the selected keywords selected by the user are received, the selected keywords are received, the selected keywords are applied to each shopping mall to obtain selected keyword search results, and the obtained selected keyword search results are analyzed to analyze the selected keywords. After obtaining the results, it is configured to calculate necessary indicators corresponding to the obtained selected keyword analysis results. In step S272, when receiving the specific product information requested by the user and the selected keyword selected by the user, the selected keyword search result is obtained from each shopping mall, and then the selected keyword is analyzed by performing top exposure analysis and keyword analysis of the obtained selected keyword search result. The results are obtained, and the necessary indicators for the product can be calculated using the product exposure algorithm based on the obtained selected keyword analysis results.

Here, the top exposure analysis may include the average monthly sales at rank 40 on page 1, average price at rank 40 on page 1, average sales at rank 40 on page 1, average monthly sales at rank 10 on page 1, average sales at rank 10 on page 1, and expected sales. , Keyword analysis may include competition intensity, 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 as examples of the present invention, and the present invention is not limited to the above-described analysis contents and can be formed to obtain a selection keyword analysis result using various analysis contents according to the user's settings. there is.

In addition, a necessary indicator refers to an indicator in which the current information of the product corresponding to the public indicator is less than the standard value obtained through the product exposure algorithm.

In other words, to explain it more easily, step S272 applies user A's B moisture cream product to the product exposure algorithm, and the result of the application is 35 purchases, 20 text reviews, 15 photo reviews, 10 favorites, and 2 Q&As ( Example), each value can be selected as a reference value for each necessary indicator.

Additionally, in the case of a title or category that is not a quantitative result, the results applied to the product exposure algorithm in step S272 may be selected as keywords and categories that should be included or keywords and categories that should not be included as the standard value of the necessary index.

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

In addition, step S273 may be configured to further output a calculation result for top exposure using the selected keyword analysis result and necessary indicators upon receiving the selected keyword analysis result. Here, the calculation results may include the number of sales, reviews, favorites, and Q&A required for top exposure of the user's product using necessary indicators, selected keyword analysis results, and standard values. As an example, top exposure is the number of It may be a desired ranking, or it may basically mean being exposed on page 1 of search results.

The marketing information proposing step (S274) is configured to generate marketing information including selected keyword analysis results and calculation results and transmit it to the user terminal. Here, the selected keyword analysis results included in the marketing information may be the analysis results of the product group containing the user's product, shopping competitiveness index, average number of sales, average price, and average sales, and the calculation result may be the number of sales, which is a required item for top exposure. , can include the number of text reviews, photo reviews, favorites, and Q&A. In addition, the marketing information may further include a summary result summarizing the two results, and the summary result may include the expected cost, the expected 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 embodiment presented in the present specification, and those skilled in the art who understand the spirit of the present invention can add components within the scope of the same spirit. , other embodiments can be easily proposed by change, deletion, addition, etc., but this will also be said to be within the scope of the present invention.

110: Shopping mall product exposure information provision 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 confirmation module 212: User unique information processing module
213: User market information processing module 231: Shopping mall public indicator 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 index calculation module
273: Calculation result output module 274: Marketing information proposal 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 proposal information to the user terminal,
a user information processing unit configured to receive and process input information input from the user terminal to generate user information;
a shopping mall information processing unit configured to obtain and process shopping mall information from the at least one shopping mall server;
an exposure algorithm prediction unit formed to predict an exposure algorithm used by each shopping mall server using the shopping mall information and to increase the accuracy of the exposure algorithm predicted through 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 the marketing proposal information using the predicted exposure algorithm,
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 confirmation module configured to check user-requested information, which is information that the user wishes to receive through the user terminal, among the input information;
a user-specific information processing module configured to obtain and process user-specific information formed to distinguish a plurality of users from the input information; and
It includes a user market information processing module configured to obtain the user's market information including the product by acquiring the product information among the input information,
Shopping mall information includes either public indicators 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 a public index for at least one shopping mall server from the shopping mall information;
a shopping mall product ranking acquisition module configured to obtain at least one shopping mall product ranking for each shopping mall server from the shopping mall information; and
It includes a shopping mall product ranking accumulation module that accumulates and stores the shopping mall product rankings,
Here, the public indicator is at least one of suitability, popularity, and reliability, and the suitability is obtained using at least one of product name, category, manufacturer brand, and attribute tag, and the popularity is sales performance, number of reviews, recency, and star rating. and the number of clicks, and the reliability is obtained using at least one of product name SEO and shopping mall penalty. delete According to clause 1,
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,
The product ranking confirmation request signal is a signal requesting confirmation of the ranking of a specific product registered by the user in at least one shopping mall,
The product ranking tracking request signal is a signal requesting a cumulative record of tracking the ranking of a specific product registered by the user for more than a preset period,
The shopping mall product exposure information providing system wherein the marketing proposal request signal includes a signal requesting marketing proposal information to increase the ranking of a specific product input through the user terminal. delete According to clause 1,
The public indicator is at least one of suitability, popularity, and reliability.
The suitability is an indicator that determines whether the product meets the consumer's search intention,
The popularity is an indicator that determines whether the product is frequently sought after by consumers and sold a lot,
The reliability is an indicator for determining whether product information is a trustworthy product. A shopping mall product exposure information providing system. delete According to clause 1,
The exposure algorithm prediction unit,
a shopping mall information accumulation module configured to accumulate the shopping mall information;
a shopping mall product detailed information acquisition module configured to obtain product detailed information including detailed information about the product and information corresponding to a public indicator of the product from the product information; and
A product exposure algorithm prediction module configured to predict the exposure algorithm using changes in each product detail information and each product ranking,
A system for providing exposure information for shopping mall products in which the public indicator is at least one of suitability, popularity, and reliability. According to clause 7,
The detailed information about the product is product-related information exposed on the shopping mall server and includes at least one of product description content, product representative photo, and product title,
The exposure algorithm is obtained for each shopping mall server, and is formed to obtain a reference value for each public index necessary for changing the ranking of the product in each shopping mall server. According to clause 8,
The product exposure algorithm prediction module is,
A shopping mall product exposure information providing system configured to check changes in information corresponding to the public index of the product or detailed information about the product, and learn changes in product rankings according to the confirmed changes. According to clause 1,
The marketing proposal department,
a selection keyword acquisition module configured to obtain a selection keyword selected by the user from the user terminal;
a necessary indicator calculation module configured to analyze the selected keyword, calculate a necessary indicator for a specific product requested by the user using the 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 the reference value of the necessary index and output a calculation result including the selected keyword analysis result; and
A marketing information proposal 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,
The above necessary indicators are:
A system for providing exposure information for shopping mall products in which 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 exposure algorithm. A method for providing exposure information on shopping mall products, which is connected to a user terminal and at least one shopping mall server to provide product ranking or marketing proposal information to the user terminal,
A user information processing unit receiving and processing input information received from the user terminal to generate user information;
A shopping mall information processing unit obtaining and processing shopping mall information from the at least one shopping mall server;
a step of an exposure algorithm prediction unit predicting an exposure algorithm used by each shopping mall server using the shopping mall information, and increasing the accuracy of the exposure algorithm predicted through machine learning of the shopping mall information; and
A step of transmitting a marketing method for a specific product to the user terminal as the marketing proposal information using the exposure algorithm predicted by the marketing proposal unit,
The input information includes at least one of user request information, user-specific information, and product information,
The step of generating the user information is,
Confirming user requested information, which is information that the user wishes to receive through the user terminal, among the input information;
Obtaining and processing user-specific information formed to distinguish a plurality of users from the input information; and
Obtaining the product information from the input information to obtain the user's market information including the product,
The shopping mall information includes either public indicators for each shopping mall server or product information for each shopping mall server,
The step of obtaining and processing the shopping mall information is,
Obtaining a disclosure index for at least one shopping mall server from the shopping mall information;
Obtaining at least one shopping mall product ranking for each shopping mall server from the shopping mall information; and
Comprising: accumulating and storing the shopping mall product rankings,
Here, the public indicator is at least one of suitability, popularity, and reliability, and the suitability is obtained using at least one of product name, category, manufacturer brand, and attribute tag, and the popularity is sales performance, number of reviews, recency, and star rating. and the number of clicks, and the reliability is obtained using at least one of product name SEO and shopping mall penalty. delete According to clause 11,
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,
The product ranking confirmation request signal is a signal requesting confirmation of the ranking of a specific product registered by the user in at least one shopping mall,
The product ranking tracking request signal is a signal requesting a cumulative record of tracking the ranking of a specific product registered by the user for more than a preset period,
The marketing proposal request signal includes a signal requesting marketing proposal information to increase the ranking of a specific product input through the user terminal. delete According to clause 11,
The public indicator is at least one of suitability, popularity, and reliability.
The suitability is an indicator that determines whether the product meets the consumer's search intention,
The popularity is an indicator that determines whether the product is frequently sought after by consumers and sold a lot,
The reliability is an indicator for determining whether product information is a trustworthy product. A method of providing exposure information on shopping mall products. delete According to claim 11,
The step of 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;
Obtaining detailed product information including detailed information about the product and information corresponding to the public indicator of the product from product information; and
Predicting the exposure algorithm using the change in each product detail information and each product ranking,
A method of providing exposure information on shopping mall products where the public indicator is at least one of suitability, popularity, and reliability. According to clause 17,
The detailed information about the product is product-related information exposed on the shopping mall server and includes at least one of product description content, product representative photo, and product title,
The exposure algorithm is obtained for each shopping mall server, and is formed to obtain a reference value for each public index necessary for changing the ranking of the product in each shopping mall server. According to clause 18,
The step of predicting the exposure algorithm is,
A method of providing exposure information on shopping mall products configured to confirm changes in information corresponding to the public index of the product or detailed information about the product, and learn changes in product rankings according to the confirmed changes. According to clause 19,
The step of delivering the marketing proposal information to the user terminal is,
Obtaining a selection keyword selected by the user from the user terminal;
Analyzing the selected keywords, calculating a necessary index for a specific product requested by the user using the exposure algorithm, and generating a selected keyword analysis result;
calculating a required value for the specific product to reach the reference value of the necessary index, and outputting a calculation result including a result of analyzing the selected keywords; and
Formed to include; generating marketing information that is a marketing method for the specific product including the selected keyword analysis result and the calculation result and delivering it to the user terminal;
The above necessary indicators are:
A method of providing exposure information for shopping mall products where 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 exposure algorithm.