KR102018888B1 - Artificial intelligence analysis system for establishing online marketing strategy - Google Patents

Abstract

According to an embodiment of the present invention, provided is an artificial intelligence analysis system, which comprises a server including a communication unit, a storage unit, and a control unit, a user device, and a partner company device. The control unit includes an artificial intelligence analysis unit deriving a result value for a purchase trend and an issue keyword. The artificial intelligence analysis system is to provide artificial intelligence-based feedback information on sales of goods made on the web including SNS.

Description

Artificial intelligence analysis system for establishing online marketing strategy

The present invention relates to an artificial intelligence analysis system for establishing an online marketing strategy.

Recently, marketing and sales activities on the web including SNS have been actively performed. As a result of this trend, interests in entrepreneurship using the Internet and marketing activities using SNS are also increasing.

The present invention has been devised to provide artificial intelligence based feedback information on sales of goods made on the web including SNS.

In addition, the present invention is designed to calculate the type information of customers for the sale of goods made on the web and to provide a personalized advertisement corresponding to each type information based on this.

Artificial intelligence analysis system according to an embodiment of the present invention includes a communication unit for collecting information required to perform data mining based on artificial intelligence; A storage unit which stores a result value calculated based on artificial intelligence and data mining performance data collected by the communication unit; In accordance with the request for a partnership from the user device, the company enters into a partnership with a specific selected partner company, provides the sales item information provided from the partner company to the user device, and confirms the purchase request event and payment payment event of the buyer from the user device. The controller determines that the purchase has occurred and requests the delivery of the purchased goods to the partner company device, and controls to allocate a certain percentage of the sales amount to the user device as a commission; including a server, a partnership conclusion request, and an SNS-based And a partner device device for uploading information on the goods to be sold to the server as the user inputs information on a purchase request of the goods generated by the user to the server, and the control unit includes the number of views and comments. Based on at least one of the criteria Collects public SNS information that is judged to have a gender level above a predetermined value, samples only information related to the field of sales of a registered partner company among the collected SNS information, and analyzes the data characteristics of the sampled data. And an artificial intelligence analysis unit for performing a data conversion operation of granting a code, and analyzing the correlation with the SNS information on which the data conversion operation is performed to derive a result value for a purchase trend and an issue keyword. It features.

According to the embodiment of the present invention, feedback information on sales using SNS may be calculated based on artificial intelligence. Accordingly, the information on marketing strategy establishment may be provided to users and sellers in a simpler manner. .

The embodiment of the present invention classifies the interests of the main customers by type based on the information obtained through the SNS account of the main customer of the user and calculates the interest type information for each customer so that promotion activities can be performed according to the characteristics of the customers. It works.

1 is a diagram showing the configuration of an artificial intelligence analysis system according to an embodiment of the present invention.
2 is a diagram illustrating a configuration of the server according to an embodiment of the present invention.
3 is a flow chart showing the overall operation sequence of the artificial intelligence analysis system according to an embodiment of the present invention.
4 is a flowchart illustrating a procedure for partnering in accordance with an embodiment of the present invention.
5 is a flowchart illustrating a procedure of selling goods and feeding back by an artificial intelligence analysis system according to an exemplary embodiment of the present invention.

As the present invention allows for various changes and numerous embodiments, particular embodiments will be illustrated in the drawings and described in detail in the written description.

However, this is not intended to limit the present invention to specific embodiments, it should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention. In describing the drawings, similar reference numerals are used for similar elements.

When a component is said to be 'connected' or 'connected' to another component, it may be directly connected to or connected to that other component, but it may be understood that another component may exist in between Should be. On the other hand, when a component is said to be 'directly connected' or 'directly connected' to another component, it should be understood that no other component exists in the middle.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting of the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, the terms 'comprise' or 'have' are intended to indicate that there is a feature, number, step, action, component, part, or combination thereof described in the specification, and one or more other features. It is to be understood that the present invention does not exclude the possibility of the presence or the addition of numbers, steps, operations, components, components, or a combination thereof.

1 is a diagram showing the configuration of an artificial intelligence analysis system according to an embodiment of the present invention.

As shown in FIG. 1, an artificial intelligence analysis system according to an exemplary embodiment of the present invention may include a server 100, a user device 200, and a partner company device 300.

The server 100 may serve to mediate a partnership between a user and a partner company. To this end, the server 100 may receive information on sales items that the user wants to sell and promote through SNS, and the server 100 may present a list of businesses corresponding to the sales items input by the user. For example, assuming that the user requested 'children's clothing' as an item to sell, the server 100 may present a list of partner companies in charge of selling children's clothing.

The server 100 may perform a partnership with a selected partner company according to a user's partner company selection. The server 100 may support a user to receive information on sellable items from a partner company after a partnership is concluded. The user may perform the sales and promotion activities by processing the information on the sale goods provided by the partner company with the user device 200 and uploading the information on the personal SNS.

After that, the user uploads photos and articles for promotion and sales to his or her SNS based on the information on the items sold by the partner company through the server 100, so that the user can check the inquiries and purchase requests generated through the SNS. . According to various embodiments of the present disclosure, when the user confirms that a query and a purchase request received through the SNS of the user account are generated, the user may provide the generated purchase related information to the server 100. Accordingly, the server 100 may collect the purchase related information by monitoring while the user checks his or her SNS.

In addition, the server 100 collects purchase related information in a manner that the user monitors the user device 200, and the server 100 directly inputs purchase related information on an app or web supported by the server 100 through the user device 200. Actions can collect relevant information. The server 100 may analyze the user's purchase pattern and the preferred item related information based on artificial intelligence based on the collected purchase related information. The information calculated based on the artificial intelligence may be provided for feedback to the user device 200 and the partner company 300, so that the user and the partner company may refer to the feedback information when establishing a promotional strategy and a product purchase plan. Will be.

In addition, when the server 100 checks the purchase request event delivered by the user, the server 100 may transmit the same to the partner company to request that the partner company deliver the corresponding item. In addition, the server 100 may control to allocate to the user a commission calculated at a predetermined ratio (or a predetermined amount per sale) of the sales amount whenever the goods are sold.

According to various embodiments of the present disclosure, when the user becomes a 'recommended person', the server 100 may also allocate a predetermined commission to the user who becomes the recommender. According to an embodiment of the present invention, a user who has completed the start-up and marketer education provided by the server 100 may perform a full sale of paid education (start-up and marketer education provided by the server 100) to a specific person through an SNS account operated by the user. In this case, a user may be entitled to a commission. In this case, a user who has a right to receive the commission may be referred to as a 'recommended person'. Accordingly, the server 100 may distinguish a commission to be paid in response to the sale of goods to a user and a commission to be paid according to the 'recommended person' and perform commission payment.

2 is a diagram illustrating a configuration of the server 100 according to an embodiment of the present invention.

As illustrated in FIG. 2, the server 100 according to an embodiment of the present invention may include a communication unit 110, a storage unit 120, and a controller 130. The controller 130 may include a user education management unit 131, a partnership conclusion unit 132, a sales article information processing unit 133, a purchase information processing unit 134, an artificial intelligence analysis unit 135, and a commission management unit 136.

First, the communication unit 110 may use a network for data transmission and reception between a user device and a server, and the type of the network is not particularly limited. The network may be, for example, an IP (Internet Protocol) network providing a transmission / reception service of a large amount of data through an Internet protocol (IP), or an All IP network integrating different IP networks. The network may include a wired network, a wireless broadband network (WBRO), a mobile communication network including WCDMA, a mobile communication network including a high speed downlink packet access (HSDPA) network and a long term evolution (LTE) network, and an LTE advanced (LTE-A) network. ), Or one of a mobile communication network including 5G (Five Generation), a satellite communication network, and a Wi-Fi network, or a combination of at least one of them.

The communication unit 110 according to an embodiment of the present invention may receive information related to a sale item from a partner company. The communicator 110 may receive information on a purchase request generated through SNS from the user side and information monitored through other user SNS through wireless communication.

In addition, the communication unit 110 may receive various data required for data mining based on artificial intelligence. For example, data required for AI-based analysis may include a user's (member's) SNS posts, comments, conversations with buyers, and the like. The data required for the AI-based analysis may include SNS account or SNS post information having topicality (eg, may be determined based on the number of views, the number of comments, the number of followers, etc.) above a predetermined reference value. In addition, the data required for AI-based analysis may include information such as real-time search terms, news articles, broadcast clips, etc. of the portal site. In addition, the data required for AI-based analysis may include information such as sales items and reviews of Internet shopping malls.

The storage unit 120 may include an internal memory or an external memory. The internal memory may be, for example, volatile memory (for example, dynamic RAM (DRAM), static RAM (SRAM), or synchronous dynamic RAM (SDRAM), etc.), non-volatile memory (for example, OTPROM (one). time programmable ROM (PROM), programmable ROM (PROM), erasable and programmable ROM (EPROM), electrically erasable and programmable ROM (EEPROM), mask ROM, flash ROM, flash memory (such as NAND flash or NOR flash), hard drives, Or it may include at least one of a solid state drive (SSD).

The external memory may be a flash drive such as compact flash (CF), secure digital (SD), micro secure digital (Micro-SD), mini secure digital (mini-SD), extreme digital (XD), It may further include a multi-media card (MMC) or a memory stick. The external memory may be functionally and / or physically connected to the electronic device through various interfaces.

The storage unit 120 according to an embodiment of the present invention may store a result value calculated based on artificial intelligence. In addition, the storage unit 120 may store information collected from various portals and SNSs for data mining.

In addition, the storage unit 120 may store educational content related to the sale of goods by the user through SNS.

The storage unit 120 may store the sales goods related information received from the partner company.

The controller 130 may also be referred to as a processor, a controller, a microcontroller, a microprocessor, a microcomputer, or the like. On the other hand, the control unit may be implemented by hardware (hardware) or firmware (firmware), software, or a combination thereof.

In the case of implementation by firmware or software, an embodiment of the present invention may be implemented in the form of a module, procedure, function, etc. that performs the functions or operations described above. The software code may be stored in a memory and driven by the controller. The memory may be located inside or outside the user terminal and the server, and may exchange data with the controller by various known means.

According to an embodiment of the present disclosure, the controller 130 may include a user education manager 131, a partnership fastener 132, a sales item information processor 133, a purchase information processor 134, an artificial intelligence analyzer 135, and a commission manager 136.

First, the user education manager 131 may perform a series of operations for matching a mentor who can be educated 1: 1 in response to a user's education request. The user education manager 131 may receive information on a sales interest field, a main SNS type, etc. from the user, and may match an appropriate education program and an educator (mentor) in response thereto. In addition, if the user wants to set up a specific member he or she found through SNS as an educator, the user can enter a desired educator ID or identification number on the server 100, and the user education management unit 131 requests such a request. You can combine the mentors and mentees.

Thereafter, the user education management unit 131 may manage the progress of the training after the mentor and the mentee are connected in a 1: 1 manner, and the contents of training may relate to a method of operating a mall through entrepreneurship and SNS. Information related to the training may be made by using an instant messenger or a telephone. The user training manager 131 checks the number of times the training is carried out by these means, the type of content delivered from the mentor to the mentee, and determines whether the training is completed. can do. In addition, according to various embodiments of the present disclosure, the user education manager 131 may educate users who want to be directly trained on the basis of educational contents uploaded by mentors who have completed the training provided by the server 100. It can be treated as having completed training on mentees that meet the evaluation criteria. The user education management unit 131 may grant a right to train another user when a user who has completed the training provided by the server 100 satisfies an immediate or additional schedule standard (eg, product sales history, etc.). .

The training may be paid, and a part of the training fee obtained from the user who wants to complete the training may be paid as a commission for the implementation of the training to the mentor who performed the training and the recommender who invited the mentee. According to various embodiments of the present disclosure, the mentor and the recommender may be the same person, may not be the same person, and if the same person is not the same person, commissions for the mentor and the recommender may be set to different amounts.

On the other hand, if the user training management unit 131 entitles the user who has completed the training to perform the training to other users, the user may perform an activity for recruiting other users who will perform the marketer activity to receive the commission. According to various embodiments of the present disclosure, when a specific user for completing the training inputs the training request to the server 100, the recommender information may be registered together, and at the same time, the recommender may be designated as a mentor. In this manner, as soon as the mentee-mentor relationship is established, the user education management unit 131 may monitor the mentor's education activity on the mentee, or the mentor may report its education activity to the server 100 side. The user education management unit 131 may check the reported training activity, evaluate vulnerabilities and strengths of the mentee, and recommend additional training materials for supplementing the vulnerabilities.

If the user education management unit 131 confirms that the user has completed the training course, the user education management unit 131 may grant a qualification to execute a partnership with any company.

The partnership fastening unit 132 may provide a list of goods sellers corresponding to information related to the sales item field of the user, and may transmit a partnership conclusion request to the device of the corresponding company as the user selects a specific company. The partnership fastener 132 confirms that both the user and the company agree to the partnership, and in response, may perform an operation for establishing the partnership between them. For example, the partnership fastening unit 132 may provide sales-related information to the user device immediately after uploading sales-related information such as photographs, size information, and delivery information about sales items provided by a partner company or upon user's request. Can be.

In addition, the partnership execution unit 132 may determine the main visitor type of the user account based on the comments of other accounts registered on the SNS of the user account, the posted content, etc. according to various embodiments of the present disclosure. It may calculate matching products and types of matching partners, and provide recommended partner company information.

The sales item information processing unit 133 may perform an operation of confirming that information about sales items is uploaded to the server 100 from a plurality of partner companies. In this case, the information on the sales items provided by the partner companies may be in a form in which only essential information (eg, size, price, color, etc.) is described, not processed in order to satisfy actual purchasers' purchase needs. Accordingly, the user may perform the promotion and sales promotion activities by receiving the information on the goods to be sold by the information transferring operation performed by the goods to be sold information processing unit 133 and then processing them and uploading them on their SNS. .

Thereafter, on the SNS of the user, a purchase request for a specific product may occur after the promotion and sales promotion activity is performed. Accordingly, the user may check a purchase request (eg, a purchase request message received through an SNS messenger) made on his SNS, and input a purchase request to the server. In this case, the purchase information processor 134 may confirm that information on a purchase request input by the user is input, and may perform a subsequent operation corresponding thereto. For example, the purchase information processing unit 134 may check information (eg, size, quantity, etc.) about a product requested to be purchased, a vendor of the product requested to be purchased, and the like based on the purchase request information input by the user. In addition, the purchase information processing unit 134 may support payment for the item requested to be purchased, for example, may support virtual account information for each company, a payment link, and the like. Accordingly, the user can provide payment information so that the sales amount corresponding to the item to be purchased can be paid to the customers who make a purchase request through their SNS.

According to various embodiments of the present disclosure, the purchase information processing unit 134 may check contact information such as a message or a comment received through the user's SNS by a user's request, collect the contents of the received contact information, and naturalize the collected content. It can be classified into items such as purchase request and product inquiry. In order to perform the monitoring and information collection function, the purchase information processing unit 134 may activate the monitoring function according to the selection of the monitoring function on the app or the web supported by the server 100 according to a user request. When the user performs an operation of checking the SNS comments and messages while the monitoring function is activated, the purchase information processing unit 134 automatically checks the contents of the comments and messages to extract only inquiry information (message or comment) related to the purchase request. can do. Further, the purchase information processing unit 134 may analyze the contents of the inquiry information related to the purchase request and extract detailed information such as item information, quantity, delivery time, etc., which are requested for purchase based on the result. According to various embodiments of the present disclosure, the purchase information processing unit 134 may reconfirm the extracted detail information to the user, and if the user processes the approval, the purchase information processing unit 134 may be immediately delivered to the partner company device 300 to request delivery.

Typically, an alarm signal may be provided in response to an event such as a chat message and a comment registration received in association with the user's SNS. According to various embodiments of the present disclosure, when the alarm event for the inquiry information is confirmed, the purchase information processing unit 134 may support to automatically provide an answer to the inquiry information. For example, the purchase information processing unit 134 interoperates with the user's SNS account at the user's request and automatically selects the corresponding notification contents as soon as a notification indicating that a new comment registration and message receiving event occurs has occurred, and the corresponding contact information (comments and Message) can be displayed on the screen. Accordingly, the purchase information processing unit 134 may check the contents displayed on the screen with respect to the inquiry information delivered to the user SNS account in real time, and collect and analyze the contents of the inquiry information. Thereafter, the purchase information processing unit 134 may input and provide an answer content corresponding to the analyzed inquiry content.

In addition, the purchase information processing unit 134 may distinguish between the case of automatically inputting the answer content corresponding to the inquiry content and the case where the purchase information processing unit 134 does not. For example, the user may be configured not to directly reply to a comment including the word 'price'. Accordingly, the purchase information processing unit 134 responds to a comment including the word 'price' in response to the 'price as a message'. You can enter a preset phrase, such as 'Please.' Meanwhile, the purchase information processing unit 134 may confirm that the customer inputs a comment such as 'what is the size of the L size' for the specific product image uploaded to the user's SNS (eg, Instagram). Among the registered size information (eg, S, M, L, XL, etc.), only size information corresponding to L may be automatically input as a comment.

According to various embodiments, the purchase information processor 134 analyzes the monitored comment and message information, and when it is determined that the purchase request is a purchase request, the purchase information processor 134 automatically transmits a message to the purchased user account, but includes payment related information (eg, payment link information, Bank account transfer information). In addition, the purchase information processor 134 may check whether payment is completed. The purchase information processing unit 134 may automatically transmit the completion message to the inquiry user account when the payment request and the payment are completed by the user message or comment. When the payment is completed, the purchase information processing unit 134 may deliver the purchase related information to the partner company. Accordingly, when the partner company receives the purchase related information by the purchase information processing unit 134, the partner company may proceed with a subsequent procedure such as product purchase and product delivery.

The order and sales process through SNS may be generally performed by the purchase information processor 134 as described above. However, the server 100 according to an embodiment of the present invention may perform the operation of calculating the feedback information using the artificial intelligence through the artificial intelligence analysis unit 135.

The artificial intelligence analysis unit 135 provides a user who sells and promotes the goods to be sold through his or her SNS, and provides information of the goods to be sold to the user, and purchases and manages the goods and delivers the items requested for purchase. It is possible to provide feedback information calculated based on artificial intelligence to all partner companies. In addition, the artificial intelligence analysis unit 135 may present decisions regarding the establishment of a sales strategy, selection of a main selling item, etc. to the user and partner companies in response to the feedback information analyzed based on the artificial intelligence. For example, the AI analyzing unit 135 may check the textual conversation data between the user 200 and the purchaser provided from the user device, and may perform AI-based analysis on the conversation data on the SNS.

The artificial intelligence analysis unit 135 mainly asks customers who purchased a specific product based on artificial intelligence, the age of consumption of the specific product, the reason for purchasing the specific product (gift, group order, etc.), and the product of interest of the specific customer. Information such as type, questions of inquiries that have increased above the threshold (eg radon), major complaint types, major returns or exchange request types can be calculated. Accordingly, the artificial intelligence analysis unit 135 may present a sales strategy based on the calculated feedback information. When the inquiry keyword increased by a predetermined percentage or more is determined to be 'fluorescent material,' the 'fluorescent material' is included. Analyzing the main questions, it can be judged that the question of the detection of the fluorescent substance has increased. In addition, the artificial intelligence analysis unit 135 may calculate a strategy for uploading a test result for detecting a fluorescent substance of a specific sales item, and may present this as feedback to a partner company. In addition, the artificial intelligence analysis unit 135 may calculate a sales strategy for emphasizing that no fluorescent substance is detected for a specific sales item, and may provide feedback of the contents to the user.

In more detail, the artificial intelligence analysis unit 135 may operate based on data mining when analyzing data and calculating feedback information. The artificial intelligence analysis unit 135, as well as inquiries and complaints received through the SNS account of the members already registered on the server 100, inquiries, complaints, etc. for each product type of the public SNS, shopping mall web page, etc. Collect information In addition, the artificial intelligence analysis unit 135 may collect the content of the posts and comments, the posts and comments of the celebrity SNS account based on the number of views, the response (for example, the number of likes, the number of comments, etc.) on the SNS. The AI analysis unit 135 may perform data mining and AI-based analysis operations for calculating result values related to major interests, purchase trends, etc. of real-time SNS users.

First, the artificial intelligence analysis unit 135 may collect SNS information that is determined that the topicality is more than a predetermined value (for example, based on the number of views and comments) among a plurality of published SNS information. Thereafter, the AI analyzing unit 135 may perform an operation of sampling information to be used for data mining among the collected SNS information. Sampling may be, for example, an operation of extracting only SNS information (eg, children's wear photos, children's photos, etc.) determined to be related to a sales field of users or a sales goods field (eg, children's clothing) of a pre-registered partner company. .

Thereafter, the artificial intelligence analysis unit 135 may perform a data conversion operation on the extracted data. At this time, the artificial intelligence analysis unit 135 may perform an operation such as deleting duplicate SNS information, and may code according to the characteristics of the collected information. For example, the artificial intelligence analysis unit 135 may assign a code according to a color of a children's clothing, a tag keyword, or the like to the sampled children's clothing images.

The artificial intelligence analysis unit 135 may perform a modeling operation for performing correlation analysis and cluster analysis on SNS information that has undergone data collection, sampling, and data conversion according to topicality. Similarly, the artificial intelligence analysis unit 135 may collect posts (eg, broadcasts, news articles, etc.) determined to be topical from general web pages other than SNS, and perform sampling, data conversion, and modeling processes through them. Can be.

As described above, the AI analysis unit 135 may perform an AI analysis operation, and thus may derive a result value for a purchase trend, an issue keyword, and the like. The AI analyzing unit 135, for example, when a 'fluorescent material' is derived as a keyword related to children's clothing having increased topicality for a predetermined period, the AI analyzing unit 135 increases the buyer's interest in the 'fluorescent material'. If it is determined that the information is received, the information about the posting can be posted on the app and the web supported by the server 100. Accordingly, the AI analysis unit 135 may help the user and the partner company to check the issue information related to their sale products and establish a sales strategy.

According to various embodiments of the present disclosure, the artificial intelligence analysis unit 135 may calculate main questions and additional information required by consumers about a specific product based on information collected about user inquiries and complaints. For example, if it is determined that the material inquiry about a specific product has occurred more than a predetermined number of times based on the real-time input comment information monitored by the purchase information processing unit 134, the material information of the corresponding product is missing or insufficient. You can judge that it is not. In addition, the AI analyzer 135 may request a partner company to additionally provide material related information about the corresponding product. In this case, the request for providing additional information may be made through a push notification.

In addition, according to various embodiments of the present disclosure, the artificial intelligence analysis unit 135 may perform various procedures for providing a user-customized advertisement.

For example, the artificial intelligence analysis unit 135 may extract a list of SNS accounts of major question customers, a list of accounts registered as friends (eg, a following list, a follow list, etc.), and a list of SNS accounts of major customers. Based on this, posts uploaded to SNS accounts of major customers can be collected. In this case, the collected information may be provided from a user device. Alternatively, the collected information (eg, comments) may be obtained by accessing an SNS account of a user who is registered in the server. Specifically, the artificial intelligence analysis unit 135 according to an embodiment of the present invention may collect the contact information of a customer who has sent a question about a product and a purchase request customer as the contact information of a user (member), and associates with the corresponding contact based thereon. The SNS account of the customer.

According to this process, the artificial intelligence analysis unit 135 may obtain post related information uploaded to the SNS from the SNS account of the main customers. The post-related information may include, for example, content uploaded to the SNS account by customers, such as photos and videos, and posts, tags (hashtags), and comment content on the post. have. The AI analysis unit 135 may collect post related information of major customers, and classify customer types for each taste (or interest) based on the post information.

First, the artificial intelligence analysis unit 135 may perform a customer type classification operation according to taste through the collected post related information. In order to classify the customer type according to taste, the artificial intelligence analysis unit 135 may collect product model names of products that the customer showed interest. In general, when marketing using SNS, a picture or video of a product is uploaded as a post on the SNS. Using this point, the artificial intelligence analysis unit 135 may determine a brand name corresponding to a post in which the customer has left a comment as a product that the customer is interested in and perform a subsequent operation. When the customer leaves a comment on the post of the user's SNS, the AI analyzer 135 may store the post image by associating the post image with the SNS account information of the customer who registered the comment. Alternatively, the artificial intelligence analysis unit 135 may match the product model information corresponding to the customer SNS account and the post image, such as "th_esw (customer SNS account) -M045 (product model name)", and store the information as interest product information for each customer. In addition, data about a product purchased by a specific customer through a plurality of users (members) or interested in may be accumulated in the storage 120 of the server by the artificial intelligence analysis unit 135. Accordingly, the artificial intelligence analysis unit 135 may determine a company (partner company) of a product mainly interested by a specific customer, an item type mainly interested in the product, and the like.

According to various embodiments of the present disclosure, the artificial intelligence analysis unit 135 matches post SNS account information of a customer who has registered a comment with a post image with a comment, and stores the product as interest product information for each customer. Product type classification can be performed and saved. That is, the product type information of the customer's SNS account and the post image may be matched and stored as product information of interest for each customer. For example, the artificial intelligence analyzer 135 may classify the use-specific classifications such as inner, outer, top, and bottom, and classify by design such as material, pattern, and color when assuming that the sale item is clothing through image sensing. Accordingly, the artificial intelligence analysis unit 135 may calculate that a particular customer prefers a black image in the case of bottoms.

Alternatively, the artificial intelligence analysis unit 135 performs a matching operation with the image information stored in the server, through the operation of matching the detailed information of the sale product (e.g., a partner company that sells the corresponding item, the size information of the corresponding item, etc.). ) Can be obtained. Accordingly, the artificial intelligence analysis unit 135 may obtain the size information of the product that the customer mainly purchases or is interested in, and thus infers the customer's body size and the customer's family body size (eg, the child's body size). You can do it.

For example, if it is determined that the size of the children's clothing that is mainly concerned for a predetermined period of time is a children's clothing for 2 to 3 years old, the artificial intelligence analysis unit 135 may generate family data in response to the SNS account information of the customer. The family data may be set to as many items as the number of expected families. The family data may include expected gender, expected relationship, expected age, and estimated body size information. For the specific customer, family data may be generated as "first family data: woman, child, 2-3 years old, height 98cm". Similarly, family data includes "second family data: male, spouse, early 40s, height 178-180 cm, weight 65 kg", "third family data: female, mother, 70s, no information, no information" and Can be generated together. The artificial intelligence analysis unit 135 may request to newly collect and update related information for each preset period of time for family data about family members predicted to be minors. In addition, the artificial intelligence analysis unit 135 may invalidate when the height and weight information of the family member predicted as a minor is not calculated by post related information acquired within a predetermined period.

Thereafter, the artificial intelligence analysis unit 135 may calculate a list of recommended products for each time in response to the relationship information with the family stored in the family data of the specific customer. For example, the artificial intelligence analysis unit 135 determines whether there is a family member associated with a specific time such as Mother's Day, Halloween Day, Christmas, New Year's Day, Children's Day, Adult's Day, Vacation, Entrance Ceremony, Graduation Ceremony, etc. It is possible to calculate a result that a customer having a member increases the number of advertisements for corresponding products over time. For example, the artificial intelligence analysis unit 135 promotes a parent's day related product to a user who has an SNS account (SNS account having family data corresponding to a parent's relationship) requiring advertisement for a parent's day related product to a friend list or more. You can request an activity.

Alternatively, the artificial intelligence analysis unit 135 may request the user device to promote the inventory item when the inventory corresponding to the family size of the specific account or the body size of the customer is held. Accordingly, the user (member) may perform personalized advertisements by sending event notification information on the corresponding items to specific accounts.

In addition, the artificial intelligence analysis unit 135 may infer the interests of the corresponding customers based on the information of the posts, tags (hash tags, etc.) uploaded to their SNS account, and classify the types accordingly. The artificial intelligence analysis unit 135 may configure the interests of the customers as child care, exercise, games, travel, restaurants, animals, etc., and may set the ranking of the interests of specific customers. The artificial intelligence analysis unit 135 may set the interest ranking of the customer in proportion to the number of cumulative tag keywords collected within a preset period.

As such, the AI analysis unit 135 may set an interest ranking for a specific customer and classify the type of the customer based on the interest ranking. The AI analysis unit 135 classifies the customers corresponding to the account accessing the SNS of the user (member), the account that left a comment, the account that sent the product inquiry message, the account included in the friend list by type, and the customer by type. The distribution status of can be calculated. For example, the artificial intelligence analysis unit 135 calculates the information that 50% of customers who access the SNS of a specific user (member) and ask for a product are parenting type, 30% are restaurant type, and 20% are animal type. It can be provided to a user (member).

Furthermore, the artificial intelligence analysis unit 135 may further classify types of interests of customers, derive a sales strategy using the same, and present them to users (members).

The commission management unit 136 may pay the user 200 the commission of a certain percentage of the sale proceeds for the completion of the purchase. In addition, the commission management unit 136 may perform an operation of calculating a commission to be allocated to a mentor and a recommender who trained the specific user when the user education management unit 131 completes payment and training for paid training of the specific user.

3 is a flow chart showing the overall operation sequence of the artificial intelligence analysis system according to an embodiment of the present invention.

As shown in FIG. 3, the AI analysis system may proceed to step 310 of signing a partnership of a user who has completed training with a partner company after step 305 of performing SNS marketing education of the user. Thereafter, the artificial intelligence analysis system may proceed to step 315 in which the user acquires product information from a partner company. Thereafter, the user goes through step 320 of promoting and selling the goods on his SNS based on the acquired goods information. When the item is sold by the user, the artificial intelligence analysis system performs step 325 of paying the user a certain commission on the sales revenue of the partner company.

4 is a flowchart illustrating a procedure for partnering in accordance with an embodiment of the present invention.

The artificial intelligence analysis system according to the embodiment of the present invention may include a server 100, a user device 200, and a partner company device 300.

First, the user 200 may access the server 100 and perform operation 405 for requesting paid training. Accordingly, the server 100 may perform operation 410 for providing training material. In this case, the operation 410 includes an operation of providing training materials, an operation of confirming whether 1: 1 training with a mentor has been performed, an operation of confirming whether a report of training of a specific item has been performed by the set mentor, or the like. It may include.

Thereafter, the user 200 may perform an operation 415 for completing an education process, which may include, for example, an operation of confirming completion of an operation such as viewing and playing the provided education content. Accordingly, the user device 200 may perform operation 420 of transmitting the training completion to the server 100. When the server 100 confirms that the user has completed the training, the server 100 may perform operation 425 requesting the partner company 300 for partner information. In response, the partner company 300 may perform operation 430 of providing the company information to the server 100. Thereafter, the server 100 may perform operation 435 of providing the received partner company information to the user 200.

The operations 425 to 435 may be replaced by providing the registered partner company information in the server 100 to the user 200.

Thereafter, the user may perform operation 440 to select a feature company that wants to enter into a partnership among the partner company information provided, and when the selection information is transmitted from the user 200 to the server 100, the server 100 performs operation 450 to enter into a partnership. And performing the operation 455 of transmitting the partnership conclusion information to the partner company 300.

According to various embodiments of the present disclosure, the server 100 may analyze a user's SNS activity type and recommend appropriate sales items and partner companies in response to the analyzed type information. For example, the server 100 may calculate seller-related information, such as a user's main visitor type and interest, based on a user's age group, main tag items of SNS posts, SNS post content classification, and comment content. The server 100 may recommend suitable sales goods and partner companies based on the calculated seller related information.

5 is a flowchart illustrating a procedure of selling goods and feeding back by an artificial intelligence analysis system according to an exemplary embodiment of the present invention.

The server 100 of the artificial intelligence analysis system according to an embodiment of the present invention may perform operation 505 of entering into a partnership between a user and a company by a user request. In this case, the company may be a subject who actually purchases a product to be sold to a consumer from a wholesaler, a factory, and the like, and receives a payment paid by the consumer. In addition, through the partnership with the company, the user promotes goods that are not actually purchased directly, and recruits buyers and leads actual purchases through customer response activities such as product guidance and inquiries to buyers. If these items are sold according to these activities, you can be paid commission.

After operation 505, the server 100 may perform operation 510 of confirming the item information request from the user. Accordingly, the server 100 may perform operation 515 of providing item information provided by a partner company to the user. The goods information provided by the partner company may be stored and managed in the storage unit of the server 100, and the server 100 may provide the goods information of the partner company only to users who have entered into a partnership.

Thereafter, the server 100 may perform operation 520 for determining whether a purchase of an item has occurred through the user SNS. When a buyer who has contacted via SNS has made a purchase intention, the user needs to input information about the occurrence of the buyer to the server 100 to deliver the goods. For this reason, the server 100 may determine whether a purchase request is generated through the SNS by the user. In addition, the server 100 may determine whether the purchase of the goods occurs as the payment of the sales price can be confirmed on the account and the payment system managed by the server 100.

If it is determined that the purchase of goods does not occur, the server 100 may end the process of FIG. 5. On the other hand, when the server 100 confirms that the purchase of the goods has occurred as the purchase request of the goods and the payment of the payment of the goods is confirmed, the operation 525 of acquiring the purchase related information and requesting delivery of the purchased goods to the partner company is performed. can do.

In addition, according to the purchase occurrence, the server 100 may perform operation 530 of analyzing characteristics of the buyer and the purchased item based on the acquired purchase related information. The operation 530 may be performed by the artificial intelligence analysis unit 135, and may be performed based on a purchaser's pre-purchase inquiry and other goods information purchased by the buyer.

Thereafter, the server 100 may perform operation 535 for calculating analysis information and providing feedback to a company and a user.

Although the present invention has been described in detail with reference to the above examples, those skilled in the art can make modifications, changes, and variations to the examples without departing from the scope of the invention. In short, in order to achieve the intended effect of the present invention, it is not necessary to separately include all the functional blocks shown in the drawings or to follow all the orders shown in the drawings in the order shown; Note that it may fall within the scope.

100: server
110: communication unit
120: storage unit
130: control unit
131: User Education Management Department
132: Partnership Connection
133: sales article information processing unit
134: Purchase Information Processing Unit
135: artificial intelligence analysis unit
136: Commission Management
200: user device
300: partner vendor devices

Claims (7)

In the artificial intelligence analysis system including a user device, a server and a partner company device,
The server is
In accordance with the request for the conclusion of the partnership from the user device, the partnership is concluded with the selected specific partner company, and the sale goods information provided from the partner company is provided to the user device side, and the purchase event and purchase payment of the buyer's goods from the user device are paid. And a controller for determining that a purchase has occurred according to an event, requesting delivery of a purchased item to the partner company device, and controlling a predetermined ratio of the sale amount to be paid to the user device as a commission.
The user device
As a user enters information about a partnership conclusion request and a purchaser's purchase request of goods based on SNS, the server transmits the information to the server.
The third party device
Upload information about the goods to be sold to the server,
The control unit
Based on at least one of the number of views and comments, collect public SNS information that is determined to be more than a preset value, and information related to the field of sales of registered partner companies among the collected SNS information. Performs a data conversion operation that samples only the sample, assigns a code according to the data characteristics of the sampled data, and analyzes the correlation of the SNS information that has performed the data conversion operation. AI analysis unit for deriving a value; includes,
The artificial intelligence analysis unit
Extracts feedback information including major complaint types, major return request types, major exchange request types, and inquiries keywords that have increased above the threshold from customers who have purchased a particular product from text data that records the conversation with the buyer on the user's SNS. Calculate and apply a sales strategy corresponding to the extracted feedback information,
Acquire post-related information including at least one of content, posts, tags, and comments previously uploaded to the SNS account of the main purchaser, perform customer type classification, and based on the classified customer type information, Calculate type information, provide advertisements corresponding to the type information of the main customers,
Generate the data of the customer and the family data of the customer based on the purchase history of the customer and the history of commenting on the SNS post of the user;
The family data is set to as many items as the number of families, each item including estimated sex, estimated relationship information with the customer, estimated age and estimated body size information,
When the stock corresponding to the data of the customer or the family data is received, event notification information on the inventory item is sent to the user device,
Artificial intelligence analysis, characterized in that for calculating a list of recommended products for a particular time corresponding to the relationship information stored in the family data, and setting the number of advertisements of the recommended product when the specific time arrives system. The method of claim 1,
The control unit
Including a partnership conclusion unit for performing a partnership with a specific goods seller in response to a user request;
The partnership fastening part
The visitor type is determined based on at least one of the comments of other people registered on the SNS of the user account and the contents registered in the user account, and the suitable selling products and the suitable partner types matching with the visitor type are calculated. AI analysis system, characterized in that to provide information The method of claim 1,
The artificial intelligence analysis unit
Based on at least one of the number of views and comments, collect public SNS information that is determined to be more than a preset value, and information related to the field of sales of registered partner companies among the collected SNS information. Performs a data conversion operation that samples only the sample, assigns a code according to the data characteristics of the sampled data, and analyzes the correlation of the SNS information that has performed the data conversion operation. AI analysis system, characterized in that for deriving a value. delete delete Claim 6 has been abandoned upon payment of a setup registration fee. The method of claim 1,
The artificial intelligence analysis unit
When a customer enters a comment on a user's SNS post, the product analyzes the post image in which the comment is entered and classifies the product type of the image. By matching the product type information of the customer's SNS account and the post image, the product of interest for each customer AI analysis system, characterized in that stored as information. delete

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