KR20220010104A - B2b2c distribution system based on big data, distribution server, and method for data analysis and data processing - Google Patents

Abstract

The present invention relates to a big data-based B2B2C distribution system, a distribution server, and a data analysis and processing method. The distribution server according to the present embodiment is connected to each product producer terminal, manages product information provided by each product producer terminal, is connected to vendor terminals sharing content related to each product information online, receives customer information managed by each vendor terminal from each vendor terminal, and manages distribution information on a product purchased by a customer based on the customer information. The server includes: a registration unit registering each product information on a web page provided by the distribution server along with category keywords, model information, price information, and weight information related to each product information; a display unit displaying each product information online for each category keyword and model information; an analysis unit performing big data analysis of customer-specific preference and yield regarding a combination of products with higher-than-reference value preferences based on an online product purchase history; and a recommendation unit recommending a related product for each customer based on the customer-specific preference and the yield.

Description

Big data-based B2B2C distribution system, distribution server, and data analysis and processing method

Exemplary embodiments relate to big data analysis and data processing technology using artificial intelligence (AI) in a B2B2C (Business to Business to Consumer) distribution system.

In general, it takes a lot of money, time, and human effort to build a global sales network, and even if they have a good product (or content), they often suffer from sluggish sales due to lack of publicity. In addition, in the case of B2B (Business to Business) business, legal customs clearance problems arise when products are distributed abroad.

In addition, the existing distribution system merely distributes products to consumers, and does not reach the point of analyzing consumers' purchase histories, tastes, lifestyles, and emotions, or processing related data and reflecting them back on product display. .

Exemplary embodiments provide a B2B2C distribution system that can solve the problem of legal customs procedures during overseas distribution, and at the same time analyze consumers' purchase history, tastes, lifestyles, emotions, etc., or process related data to reflect them back on product display. This is to provide AI-based big data analysis and data processing technology that can

According to an exemplary embodiment, it is connected to each product producer terminal to manage product information provided by each product producer terminal, and is connected to a plurality of vendor terminals that share content related to each product information online to each vendor terminal A distribution server that receives customer information managed by each vendor terminal from a register for registering with a plurality of category keywords, model information, price information, and weight information related to each product information; a display unit for displaying the respective product information on-line according to the category keyword and the model information; an analysis unit that analyzes big data on a preference for each customer based on the purchase history of products generated online and a rate of return for a combination of products having the preference above a reference value; and a recommendation unit for recommending a related product for each customer based on the customer's preference and the rate of return.

The category keyword may include one or more of a product type, a season, and a color.

The analysis unit may analyze big data on at least one of a category keyword of a product for which purchase has occurred, a model name wearing a product for which purchase has occurred, and a price and weight of the product for which purchase has occurred for each customer.

The recommendation unit selects products for which the preference for each customer is greater than or equal to a reference value from among the products purchased by each customer, and a combination in which the yield is greater than or equal to a specific threshold among combinations of related products of the selected products and products having the preference greater than or equal to the reference value can be recommended for each of the above customers.

According to another exemplary embodiment, it is connected to each product producer terminal to manage product information provided by each product producer terminal, and is connected to a plurality of vendor terminals that share content related to each product information online to each vendor In the data analysis and processing method provided by a distribution server that receives customer information managed by each vendor terminal from a terminal, and manages distribution information of a product purchased by a customer based on the customer information, the data analysis and processing method provided by the distribution server registering the respective product information on a web page together with a plurality of category keywords, model information, price information, and weight information related to each product information; displaying the respective product information on-line for each of the category keywords and the model information; analyzing big data on a preference for each customer based on the purchase history of products generated online and a rate of return for a combination of products having the preference above a reference value; and recommending a related product for each customer based on the customer preference and the rate of return, a data analysis and processing method is provided.

The category keyword may include one or more of a product type, a season, and a color.

The analyzing may include big data analysis of at least one of a category keyword of a product for which purchase has occurred, a model name wearing a product for which purchase has occurred, and a price and weight of the product for which purchase has occurred for each customer.

The recommending may include selecting products having a preference value equal to or greater than a reference value for each customer from among the products purchased by each customer, and selecting related products of the selected products and a combination of products having a preference value greater than or equal to a specific threshold value. The above combinations may be recommended for each of the above customers.

According to the present embodiments, as the service provider secures customers for product sales through several vendors and at the same time normally performs overseas distribution through customer information provided from vendors, a legal customs clearance problem does not occur.

In addition, according to the present embodiments, by analyzing a consumer's purchase history, taste, lifestyle, emotion, etc. or processing related data and reflecting it back on the product display, a customized product suitable for the consumer is recommended and, at the same time, according to product sales yield can be maximized.

1 is a detailed configuration diagram of a distribution system according to an exemplary embodiment;
Fig. 2 is a detailed configuration diagram of a distribution server according to an exemplary embodiment;
3 is an example of product registration according to an exemplary embodiment;
4 is an example of product registration according to an exemplary embodiment;
5 is a detailed flowchart of a data analysis and processing method according to an exemplary embodiment;
6 is a block diagram illustrating and describing a computing environment including a computing device suitable for use in example embodiments;

Hereinafter, specific embodiments of the present invention will be described with reference to the drawings. The following detailed description is provided to provide a comprehensive understanding of the methods, devices, and/or systems described herein. However, this is merely an example, and the present invention is not limited thereto.

In describing the embodiments of the present invention, if it is determined that the detailed description of the known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted. And, the terms to be described later are terms defined in consideration of functions in the present invention, which may vary according to intentions or customs of users and operators. Therefore, the definition should be made based on the content throughout this specification. The terminology used in the detailed description is for the purpose of describing embodiments of the present invention only, and should in no way be limiting. Unless explicitly used otherwise, expressions in the singular include the meaning of the plural. In this description, expressions such as “comprising” or “comprising” are intended to indicate certain features, numbers, steps, acts, elements, some or a combination thereof, one or more other than those described. It should not be construed to exclude the presence or possibility of other features, numbers, steps, acts, elements, or any part or combination thereof.

1 is a detailed configuration diagram of a distribution system 100 according to an exemplary embodiment. 1 , a distribution system 100 according to an exemplary embodiment includes a distribution server 110 , a product producer terminal 120 , a vendor terminal 130 , a customer terminal 140 , and an international logistics server 150 . ) is included.

The distribution server 110 is a device used to provide a B2B2C (Business to Business to Consumer) distribution service. The distribution server 110 provides a web page for providing the distribution service, and product producers, vendors, and general customers can provide/check product information or purchase products using the web page. The distribution server 110 provides a distribution service in which a B2B (Business to Business) business and a B2C (Business to Consumer) business are combined.

To this end, the distribution server 110 is first connected to each product producer terminal 120 to manage product information provided from each product producer terminal 120 . The product producer terminal 120 represents a terminal owned by each brand company that produces and manufactures a product, that is, a product producer. Here, the brand company is used in a broad sense including, for example, a designer brand company, an institutional brand company, and a non-institutional brand company. The product producer may provide the produced product and product information to the service provider on the distribution server 110 side.

In addition, the distribution server 110 may be connected to a plurality of vendor terminals 130 , and may provide product information to the vendor terminals 130 . The vendor terminal 130 represents a terminal possessed by vendors that promote products disclosed on the web page of the distribution server 110 to general customers. Here, vendor is used in a broad sense including all of on-line and offline wholesalers, platform vendors such as Tmall and Jingdong, and social network service (SNS) shopping malls such as Xiaohongshu. Vendors may share marketing and promotional content for the products online through the vendor terminal 130 . As an example, vendors may share marketing and promotional content for the products on SNS through the vendor terminal 130 . The marketing and promotional content may be produced in the distribution server 110 and provided to the vendor terminal 130, but is not limited thereto, and the vendor terminal 130 may directly produce the marketing and promotional content and share it online. have.

As a result of the vendors promoting the products through the vendor terminal 130 , general customers connected to the vendors can purchase the products. In this case, the vendor terminal 130 may provide customers who purchased the product, that is, customer information they manage, to the distribution server 110 . Accordingly, the distribution server 110 may create distribution information of the product purchased by the customer based on the customer information, and transmit the customer information and the distribution information to the international distribution server 150 . The international logistics server 150 is a server of a company in charge of overseas distribution, and may transmit a delivery completion message to the customer terminal 140 when delivery of the product is completed. Meanwhile, for convenience of explanation, the distribution server 110 and the international distribution server 150 are functionally illustrated separately, but the present invention is not limited thereto, and the distribution server 110 and the international distribution server 150 may be formed integrally. That is, in this case, the service provider on the side of the distribution server 110 is responsible for the entire process of marketing, sales, and distribution of the product.

In addition, the service provision form between the vendor terminal 130 and the customer terminal 140 is B2C (Business to Consumer), and the service provision form between the distribution server 110 and the vendor terminal 130 is B2B (Business to Business). . The service provider provides the B2B service to vendors who provide the B2C service, and consequently provides the B2B2C service. In this case, the service provider receives customer information from vendors and distributes overseas, so that the service provider can normally go through legal customs procedures. Therefore, according to the present embodiments, the service provider secures customers for product sales through several vendors and at the same time normally performs overseas distribution through customer information provided from vendors, so that there is no legal customs clearance problem do.

In addition, the distribution server 110 provides artificial intelligence-based big data analysis and data processing technology that can analyze consumers' purchase histories, tastes, lifestyles, emotions, etc. or process related data and reflect them back on product display. can This will be described in detail later with reference to FIGS. 2 to 5 .

Fig. 2 is a detailed configuration diagram of the distribution server 110 according to an exemplary embodiment. As shown in FIG. 2 , the distribution server 110 according to the exemplary embodiment includes a registration unit 202 , a display unit 204 , an analysis unit 206 , and a recommendation unit 208 .

The registration unit 202 registers each product information in a web page provided by the distribution server 110 together with a plurality of category keywords, model information, price information, and weight information related to each product information. The manager may register each product information together with a plurality of category keywords, model information, price information, and weight information related to each product information on a web page for product sales. In this case, the category keyword may include, for example, one or more of a product type, a season, and a color.

3 and 4 are examples of product registration according to an exemplary embodiment.

First, referring to FIG. 3 , an administrator may register classification information of a product to be registered. As an example, an administrator may register a basic classification (eg, "casual"), a secondary classification ("jacket"), a tertiary classification, and a search keyword ("summer") of a product to be registered.

Next, referring to FIG. 4 , the administrator may register detailed information of a product to be registered. As an example, the administrator may register a product code, product name, basic description, output order on a web page, manufacturer/origin/brand of the product, name/ID of a model wearing the product, store company, and the like.

In addition, the administrator can register product description, size description, product material/color/size, etc. as product information.

In this case, the manager may register each product information together with a plurality of category keywords, model information, price information, and weight information related to each product information. As described above, the category keyword may be, for example, a product type, a season, and a color. In addition, the price information may be a cost, purchase price, selling price, consumer price, etc. of the corresponding product. In addition, the weight information may be the weight of the product, and the weight information is required for overseas distribution as described above.

In addition, the manager may register several products for each model, and may register a combination of several products mixed and matched to the model. Accordingly, general customers or vendors may select or purchase products by looking at a model image in which a combination of each product is projected.

The display unit 204 displays the respective product information online for each category keyword and the model information. The display unit 204 may display the respective product information for each category keyword and the model information on a web page provided by the distribution server 110 . In this case, the display unit 204 may display a combination of several products mixed and matched to each model, and general customers or vendors may select or purchase products by looking at the model image in which the combination of each product is projected.

The analysis unit 206 analyzes big data on a preference for each customer and a rate of return for a combination of products having the preference above a reference value based on the purchase history of the product generated online. The analysis unit 206 may analyze the purchase history of the product in big data based on artificial intelligence (AI). Specifically, the analysis unit 206 analyzes big data at least one of a category keyword of a product for which purchase has occurred, a model name wearing a product for which purchase has occurred, and the price and weight of the product for which purchase has occurred for each customer, Accordingly, it is possible to grasp the preferences, tastes, lifestyles, emotions, etc. of each customer. In addition, the analysis unit 206 may calculate the yield for each combination of products having the preference value equal to or greater than a reference value based on the cost, purchase price, sale price, consumer price, etc. of the purchased products.

The recommendation unit 208 recommends a related product for each customer based on the customer preference and the rate of return. Specifically, the recommendation unit 208 selects products having a preference value equal to or greater than a reference value for each customer from among the products purchased by each customer, and the rate of return among a combination of related products of the selected products and products having a preference value greater than or equal to the reference value. A combination greater than or equal to this specific threshold may be recommended for each customer. Here, the related products indicate, for example, products having the same category keyword as products for which purchase has occurred or corresponding to a similar category.

In addition, the analyzed information on products that combine customers' preferences, tastes, and yields can be stored in an internal database (not shown), and the manager can refer to this information to secure products and adjust inventory or new products can decide whether to enter the

That is, according to the present embodiments, by analyzing a consumer's purchase history, taste, lifestyle, emotion, etc. or processing the related data and reflecting it back on the product display, customized products suitable for the consumer are recommended and, at the same time, according to product sales yield can be maximized.

Fig. 5 is a detailed flowchart of a data analysis and processing method according to an exemplary embodiment. In the illustrated flowchart, the method is described by dividing the method into a plurality of steps, but at least some of the steps are performed in a different order, are performed in combination with other steps, are omitted, are performed in separate steps, or are not shown. One or more steps may be added and performed.

In step S102, the registration unit 202 registers each product information on the web page provided by the distribution server 110 together with a plurality of category keywords, model information, price information, and weight information related to the respective product information.

In step S104 , the display unit 204 displays the respective product information online for each category keyword and the model information.

In step S106 , the analysis unit 206 analyzes big data on a preference for each customer and a rate of return for a combination of products having the preference equal to or greater than a reference value based on the purchase history of the product generated online.

In step S108 , the recommendation unit 208 recommends a related product for each customer based on the customer preference and the rate of return.

6 is a block diagram illustrating and describing a computing environment including a computing device suitable for use in example embodiments.

The illustrated computing environment 10 includes a computing device 12 . In one embodiment, computing device 12 may be one or more components included in distribution server 110 , or distribution system 100 .

Computing device 12 includes at least one processor 14 , computer readable storage medium 16 , and communication bus 18 . The processor 14 may cause the computing device 12 to operate in accordance with the exemplary embodiments discussed above. For example, the processor 14 may execute one or more programs stored in the computer-readable storage medium 16 . The one or more programs may include one or more computer-executable instructions that, when executed by the processor 14, configure the computing device 12 to perform operations in accordance with the exemplary embodiment. can be

Computer-readable storage medium 16 is configured to store computer-executable instructions or program code, program data, and/or other suitable form of information. The program 20 stored in the computer readable storage medium 16 includes a set of instructions executable by the processor 14 . In one embodiment, computer-readable storage medium 16 includes memory (volatile memory, such as random access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash It may be memory devices, other forms of storage medium accessed by computing device 12 and capable of storing desired information, or a suitable combination thereof.

Communication bus 18 interconnects various other components of computing device 12 , including processor 14 and computer readable storage medium 16 .

Computing device 12 may also include one or more input/output interfaces 22 and one or more network communication interfaces 26 that provide interfaces for one or more input/output devices 24 . The input/output interface 22 and the network communication interface 26 are coupled to the communication bus 18 . Input/output device 24 may be coupled to other components of computing device 12 via input/output interface 22 . Exemplary input/output device 24 may include a pointing device (such as a mouse or trackpad), a keyboard, a touch input device (such as a touchpad or touchscreen), a voice or sound input device, various types of sensor devices, and/or imaging devices. input devices and/or output devices such as display devices, printers, speakers and/or network cards. The exemplary input/output device 24 may be included in the computing device 12 as a component constituting the computing device 12 , and may be connected to the computing device 12 as a separate device distinct from the computing device 12 . may be

Although the present invention has been described in detail through representative embodiments above, those of ordinary skill in the art to which the present invention pertains can make various modifications to the above-described embodiments without departing from the scope of the present invention. will understand Therefore, the scope of the present invention should not be limited to the described embodiments, but should be defined by the claims described below as well as the claims and equivalents.

10: Computing Environment
12: computing device
14: processor
16: computer-readable storage medium
18: communication bus
20: Program
22: input/output interface
24: input/output device
26: network communication interface
100: distribution system
110: distribution server
120: product producer terminal
130: vendor terminal
140: customer terminal
150 : International Logistics Server
202 : Register
204: display unit
206: analysis unit
208: recommendation

Claims (8)

It is connected to each product producer terminal to manage product information provided by each product producer terminal, and is connected to a plurality of vendor terminals that share the contents related to each product information online, and each vendor terminal is managed from each vendor terminal In the distribution server for receiving customer information and managing the distribution information of the product purchased by the customer based on the customer information,
a registration unit for registering each product information in a web page provided by the distribution server together with a plurality of category keywords, model information, price information, and weight information related to each product information;
a display unit for displaying the respective product information on-line according to the category keyword and the model information;
an analysis unit that analyzes big data on a preference for each customer based on the purchase history of products generated online and a rate of return for a combination of products having the preference above a reference value; and
A distribution server comprising a recommendation unit for recommending a related product for each customer based on the customer preference and the rate of return.
The method according to claim 1,
The category keyword, the distribution server, including one or more of product type, season, and color.
The method according to claim 1,
The analysis unit, for each customer, the category keyword of the product for which the purchase occurred, the model name wearing the product for which the purchase occurred, and a distribution server for big data analysis of one or more of the price and weight of the product for which the purchase occurred.
The method according to claim 1,
The recommendation unit selects products having a preference value equal to or greater than a reference value for each customer from among the products purchased by each customer, and a combination in which the yield is greater than or equal to a specific threshold among combinations of related products of the selected products and products having the preference greater than or equal to the reference value A distribution server that recommends for each customer above.
It is connected to each product producer terminal to manage product information provided by each product producer terminal, and is connected to a plurality of vendor terminals that share the contents related to each product information online, and each vendor terminal is managed from each vendor terminal In the data analysis and processing method provided by the distribution server for receiving customer information and managing the distribution information of products purchased by the customer based on the customer information,
registering each product information on a web page provided by the distribution server together with a plurality of category keywords, model information, price information, and weight information related to each product information;
displaying the respective product information on-line for each category keyword and the model information;
analyzing big data on a preference for each customer based on the purchase history of products generated online and a rate of return for a combination of products having the preference above a reference value; and
Data analysis and processing method comprising the step of recommending a related product for each customer based on the customer preference and the rate of return.
6. The method of claim 5,
The category keyword, including one or more of product type, season, and color, data analysis and processing method.
6. The method of claim 5,
The analyzing step includes big data analysis of at least one of a category keyword of a product for which purchase has occurred, a model name wearing a product for which purchase has occurred, and price and weight of the product for which purchase has occurred for each customer, data analysis and processing method.
6. The method of claim 5,
The recommending may include selecting products having a preference value equal to or higher than a reference value for each customer from among the products purchased by each customer, and the yield is set at a specific threshold value among a combination of related products of the selected products and products having the preference value equal to or greater than the reference value A data analysis and processing method that recommends the above combinations for each customer.

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