WO2024128334A1

WO2024128334A1 - Buyer recommendation device and buyer recommendation method

Info

Publication number: WO2024128334A1
Application number: PCT/KR2022/020200
Authority: WO
Inventors: 김덕규
Original assignee: 메타트레이드 주식회사
Priority date: 2022-12-13
Filing date: 2022-12-13
Publication date: 2024-06-20

Abstract

The present invention relates to a buyer recommendation device and a buyer recommendation method. According to one embodiment, provided is a buyer recommendation method comprising the steps of: (step S1) a screen configuration module providing a product input box that enables a user to input the name of a product desired to be exported or imported; (step S2) a needs provision module providing a needs selection box that enables needs of the user to be input therein; and (step S3) a buyer recommendation module recommending a buyer by applying, to a buyer recommendation algorithm, the input product and the needs of the user.

Description

Buyer recommendation device and buyer recommendation method

The present invention relates to a buyer recommendation device and a buyer recommendation method.

Due to globalization, exports and imports are actively occurring between countries, and numerous trade and bill of lading data are being accumulated according to exports and imports between each country.

However, despite the existence of a lot of trade and bill of lading data, there is a lack of research on how to utilize it to benefit companies.　

For example, exporters who want to sell their products overseas must find buyers who meet their conditions.　

Previously, exporters used their own know-how to find buyers. However, companies with no export experience or lack of export know-how often had difficulty selling their products because they could not easily find buyers to sell them.　

Embodiments of the present invention seek to provide a buyer recommendation device and a buyer recommendation method that enable a user terminal to find a buyer that meets desired conditions in order to solve the above-mentioned problems.　

According to one embodiment, a screen configuration module provides a product input field in which the user can input the name of the product desired to be exported or imported (step S1); A needs selection box for entering the user's needs is provided by the needs provision module (step S2); A buyer recommendation method may be provided, including a step (step S3) in which the product and the user's needs input by the buyer recommendation module are applied to a buyer recommendation algorithm to recommend a buyer.

In addition, step S2 includes setting a plurality of need categories to be selected by the user by a category setting unit (step S21); A step of setting survey questions and a plurality of answers subordinate to each of the needs categories by a survey question and answer setting unit (step S22); And a step of assigning weights for each product to each of the need categories by a weight setting unit (step S23) may be provided.

In addition, step S3 is a step in which answers to each need category selected by the user are received by the score calculation unit, and scores of a plurality of buyers stored in the buyer database are calculated according to the selected answers (step S31). ; And a buyer recommendation method comprising a step (step S32) of recommending a buyer by reflecting the score for the needs category of each buyer and the weight for each product assigned to the needs category may be provided. there is.

According to one embodiment, a screen configuration module that provides a product input field where the user can input the name of the product desired to be exported or imported;　 a needs providing module capable of providing a needs selection box into which the user's needs can be input; and a buyer recommendation module that can recommend a buyer by applying the input product and the user's needs to a buyer recommendation algorithm.

In addition, the needs providing module includes a category setting unit that can set a plurality of need categories that the user must select; a survey question and answer setting unit that can set survey questions and a plurality of answers dependent on the needs category; And, a buyer recommendation method including a weight setting unit that can assign weights to each product for the above needs categories may be provided.

In addition, the buyer recommendation module includes a score calculation unit that receives answers to each need category selected by the user and calculates scores of a plurality of buyers stored in a buyer database according to the selected answers; and a buyer recommendation unit that can recommend a buyer by reflecting the score for the needs category of each buyer and the weight for each product assigned to the needs category.

The buyer recommendation device and buyer recommendation method according to an embodiment of the present invention have the advantage of enabling users to find buyers that meet their desired conditions.　

1 is a diagram conceptually showing a trade data analysis system according to an embodiment of the present invention.

FIG. 2 is a diagram conceptually showing the sub-configuration of the buyer recommendation device of FIG. 1.

FIG. 3 is a diagram conceptually showing the lower configuration of the processor of FIG. 2.　

FIG. 4 is a diagram schematically showing a flowchart of a buyer recommendation method implemented by the trade data analysis system of FIG. 1.　

Figure 5 is a diagram illustrating a product input field configured by the screen configuration module of the buyer recommendation device into which the product desired to be exported can be entered.

Figure 6 is a diagram illustrating the needs category set by the category setting unit of the buyer recommendation device, the survey questions set by the survey question and answer setting unit, and the answers dependent thereon.　

Figure 7 is a diagram illustrating a buyer's score calculation performed by the score calculation unit.　　

Figure 8 is a diagram conceptually explaining the buyer recommendation process performed by the buyer recommendation unit.　

Figure 1 is a diagram conceptually showing a trade data analysis system 1 according to an embodiment of the present invention.

Referring to FIG. 1, the trade data analysis system 1 according to an embodiment of the present invention may include a user terminal 10, a buyer recommendation device 20, and an external server 30.　

The buyer recommendation device 20 can recommend a buyer by reflecting the needs of the user and the product the user wants to import or export, which is input through the user terminal 10.　

The buyer recommendation device 20 can provide different needs categories depending on the product, and can recommend buyers by reflecting different weights depending on the product, which will be explained in detail later.

Additionally, the buyer recommendation device 20 can recommend a buyer desired by the user using various trade data. Here, trade data may include trade and bill of lading data.

The buyer recommendation device 20 is provided to communicate with the external server 30 and can receive various trade data (for example, trade and bill of lading) from the external server 30. Here, a plurality of external servers 30 may be provided.　For example, the external server 30 may include servers operated by the International Trade Association, the Korea Customs Service, etc. However, the type of external server 30 is not limited.

In this embodiment, it is explained as an example that the user who uses the buyer recommendation device 20 is a business that exports its own products. However, the spirit of the present invention is not limited to this, and users who use the buyer recommendation device 20 may include companies that import products.　　

The user terminal 10, the buyer recommendation device 20, and the external server 30 can communicate through the communication network 40 to transmit and receive data. Mutual communication between the user terminal 10, the buyer recommendation device 20, and the external server 30 through the communication network 40 can be provided by Internet wired or wireless communication, Bluetooth, NFC, Wifi, etc.　

FIG. 2 is a diagram conceptually showing a lower configuration of the buyer recommendation device 20 of FIG. 1, and FIG. 3 is a diagram conceptually illustrating a lower configuration of the processor 300 of FIG. 2.　

Referring to FIGS. 2 and 3 , the buyer recommendation device 20 may include a memory 200, a processor 300, and a communication module 400.

The processor 300 can be understood as a component that processes computer program instructions by performing basic arithmetic, logic, and input/output operations. Commands may be provided to the processor 300 from the memory 200 or the communication module 400.

The processor 300 performs various functions such as input/output of data necessary to recommend buyers by reflecting the products the user wants to import or export and the user's needs, data processing, data management, and communication using the communication network 40. can do. Specific components of the processor 300 for executing this will be described later.

The memory 200 is a computer-readable recording medium and may include non-perishable large-capacity recording devices such as RAM, ROM, and disk drives.

The processor 300 can load the program code stored in the memory 200 and recommend a buyer by reflecting the needs of the user and the product the user wants to import or export.

These program codes can be loaded from a recordable medium on a separate computer or stored in the memory 200 from another device through the communication module 400.

The memory 200 may include a needs database 211 and a buyer database 212 built by the processor 300.　

The needs database 211 may store a plurality of need categories (e.g., income size, income proportion, number of buyers, income frequency, criminal history, etc.), survey questions, and answers that the user must select.　

The buyer database 212 may store information on buyers (companies) matching each needs category (for example, income size, income proportion, number of buyers, income frequency, criminal history, etc.).　

The processor 300 may include a screen configuration module 310, a needs provision module 320, a buyer recommendation module 330, and a word bundle formation module 340, which are physical components.　

Here, the screen configuration module 310 may include a screen providing unit 311, and the needs providing module 320 may include a category setting unit 321, a survey question and answer setting unit 322, and a weight setting unit 323. ), the buyer recommendation module 330 may include a score calculation unit 331 and a buyer recommendation unit 332, and the word bundle forming module 340 may include a word bundle forming unit 341. You can. A detailed explanation of this will be provided later.　

Figure 4 is a diagram schematically showing a flowchart of the buyer recommendation method executed by the trade data analysis system 1 of Figure 1, and Figure 5 shows the export configured by the screen configuration module 310 of the buyer recommendation device 20. It is a diagram illustrating a product input box where a desired product can be entered, and Figure 6 shows the needs category set by the category setting unit 321 of the buyer recommendation device 20, the questionnaire question, and the answer setting unit 322. 7 is a diagram illustrating the set questionnaire questions and answers dependent thereon, FIG. 7 is a diagram illustrating the buyer's score calculation performed by the score calculation unit 331, and FIG. 8 is a diagram showing the buyer's score calculation performed by the score calculation unit 331, and FIG. This is a diagram conceptually explaining the buyer recommendation process carried out by.　

Referring to Figures 1 to 8, the buyer recommendation method includes the steps of providing a product input field in which the user can enter the name of the product desired to be exported or imported by the screen configuration module 310 (step S1); A step of providing a needs selection box for entering the user's needs by the needs providing module 320 (step S2); And it may include a step (step S3) in which the product input by the buyer recommendation module 330 and the user's needs are applied to a buyer recommendation algorithm to recommend a buyer.　

First, a detailed description of the step (S1 step) in which the screen configuration module 310 provides a product input field where the user can enter the name of the product desired to be exported or imported is as follows.

The screen providing unit 311 of the screen configuration module 310 may provide a product input field where the user can enter the name of the product desired to be exported or imported.　

FIG. 5 is a diagram illustrating an exemplary product input field configured by the screen providing unit 311 of the screen configuration module 310 in which the name of the product desired to be exported can be entered.

As shown in FIG. 5, the product input field displays a plurality of products desired to be exported (e.g., cosmetics, beverages, health functional foods, home appliances, clothing, shoes, and tableware) and can then be provided for selection by the user.　However, the idea of the present invention is not limited to this, and the user can enter an arbitrary product after leaving the product input field blank.　

Next, the step (step S2) in which a needs selection box for entering the user's needs is provided by the needs providing module 320 will be described in more detail.　

Specifically, step S2 includes setting a plurality of need categories to be selected by the user by the category setting unit 321 (step S21); A step of setting survey questions and a plurality of answers subordinate to each of the above needs categories by the survey question and answer setting unit 322 (step S22); And it may include a step (step S23) in which weights for each product are assigned to each need category by the weight setting unit 323.　

Figure 6 is a diagram illustrating the needs category set by the category setting unit 321, the survey questions set by the answer setting unit 322, and the answers dependent thereon.　

To describe step S21 in more detail, the category setting unit 321 can set a plurality of needs categories that the user must select.　

For example, the category setting unit 321 is a needs category that includes the import scale of the buyer who wants to export, the buyer's import proportion of the product the user wants to export, the number of the buyer's business partners, the buyer's import frequency, the buyer's criminal history, etc. can be provided. 　

These need categories are stored in the needs database 211 and can be extracted and displayed by the category setting unit 321. Needs categories stored in the needs database 211 can be changed by user settings.　　

Step S21 may include providing different needs categories according to the product selected by the user in step S1 by the category setting unit 321.　

For example, if the user selects beverages as the product they wish to export, the needs category may be provided as import size, import proportion, and number of business partners, and if the user selects home appliances as the product they wish to export, the needs category may be provided as the import frequency. , can be provided as a criminal record. However, this is an example, and the combination of each need category is not limited to this.　

The needs category set by the category setting unit 321, the survey questions set by the answer setting unit 322, and the answers dependent thereon may be transmitted to the screen configuration module 310. The needs category, survey questions, and answers delivered to the screen configuration module 310 can be displayed to the user through the user terminal 10, as shown in FIG. 6.　

To explain step S22 in more detail, a questionnaire and a plurality of answers subordinate to each of the above needs categories may be set by the questionnaire and answer setting unit 322.　

For example, if the needs category is set to income size, the survey question dependent on income size can be set to 'Select the income size of the buyer who wants to sell', and the answer is less than 10 billion won, 10 billion to 100 billion won. It can be set to less than 100 billion to less than 1 trillion, or more than 1 trillion.　

To describe step S23 in more detail, a weight for each product may be assigned to each need category by the weight setting unit 323.

Here, the sum of product weights for each selected needs category can be provided as 1. For example, when the product desired to be exported is selected as a beverage, the product-specific weight of the import volume is 0.3, the product-specific weight of the import proportion is 0.3, the product-specific weight of the number of business partners is 0.2, and the product-specific weight of the import frequency is 0.1. , the product-specific weight for criminal history can be set to 0.1.　

Step S23 may include setting different weights for each product for the needs category depending on the product selected by the weight setting unit 323.　

For example, if a beverage is selected as the product, the weight for the import scale may be set to 0.3, and if the product is a home appliance, the weight for the import scale may be set to 0.5.　

In this way, the weight of each product for the needs category is set differently depending on the product, so it is possible to easily recommend buyers who fit the characteristics of the product and the needs of the user.　

Next, the step (step S3) in which the product and user's needs input by the buyer recommendation module 330 are applied to the buyer recommendation algorithm and the buyer is recommended will be described in more detail as follows.

Specifically, step S3 is a step in which answers for each need category selected by the user are received by the score calculation unit 331, and scores of a plurality of buyers stored in the buyer database 212 are calculated according to the selected answers. (Step S31); And it may include a step (step S32) in which buyers are recommended by the buyer recommendation unit 332 by reflecting the scores for each buyer's needs category and the weight for each product assigned to the needs category.　

Here, the buyer recommendation algorithm may include steps S31 and S32.

To describe step S31 in more detail, scores of a plurality of buyers stored in the buyer database 212 may be calculated according to the answers to each need category.　

Figure 7 is a diagram conceptually illustrating the calculation of the buyer's score according to the answer in step S31.　

For example, referring to FIG. 7, when the user selects less than 10 billion won as the answer for the income size as the needs category, Buyer A (Company A) stored in the buyer database 212 is given 100 points by the score calculation unit 331. , B buyers can be calculated as 75 points, C buyers as 50 points, D buyers as 75 points, and E buyers as 25 points. Here, in the buyer database 212, buyer A's income is KRW 9 billion, buyer B's income is KRW 50 billion, buyer C's income is KRW 200 billion, buyer D's income is KRW 50 billion, and buyer E's income is KRW 50 billion. There may be more than 1 trillion stored.

Likewise, if the user selects 30% as the answer for the import proportion of beverages as a needs category, buyer A (Company A) stored in the buyer database 212 by the score calculation unit 331 receives 25 points, buyer B receives 100 points, C buyers can be calculated as 75 points, D buyers as 50 points, and E buyers as 0 points. Here, in the buyer database 212, buyer A's beverage import proportion is less than 10%, buyer B's beverage import proportion is 30%, buyer C's beverage import proportion is 50%, and buyer D's beverage import proportion is 50%. 80%, E Buyer's beverage import share can be stored at 0.1.

Next, the step (step S32) in which buyers are recommended by the buyer recommendation unit 332 by reflecting the scores for each buyer's needs category and the weight for each product assigned to the needs category will be described in more detail.　

Specifically, the buyer with the highest score calculated by multiplying the score of each need category of each buyer and the product weight assigned to the need category can be recommended. 　

FIG. 8 is a diagram conceptually explaining the buyer recommendation process performed by the buyer recommendation unit 332.　

Referring to FIG. 8, if explained by way of example, the A buyer score can be calculated as 60 points through the following process. The buyer recommendation unit 332 can calculate the following process.　

A buyer score: 100 (import size score) )　+ 25 (import frequency score)

Through the same process, the B buyer score can be calculated as 80 points, the C buyer score as 50 points, the D buyer score as 40 points, and the E buyer score as 30 points.　

Here, Buyer A can be recommended because Buyer A has the highest score.　

Additionally, the buyer recommendation unit 332 may recommend buyers from 1st place to a preset ranking. 　

Such rankings can be displayed by the user terminal 10.　

In addition, the name of the product desired to be exported or imported is entered in the product input field of step S1 described above. The word bundle forming unit 341 of this embodiment can provide a similar word bundle similar to the name of the product input in step S1.

By providing a set of similar words, errors that may occur due to different product names even though they are the same or similar products can be reduced.　

　For example, there are cases where the same product is given different names, such as window film and tinzing paper. Search accuracy can be improved by providing similar word clusters.　

Specifically, the above-described step S1 may further include the following steps.　

Step S1 includes translating the name of the product entered into the product input field into one or more languages by the word bundle forming unit 341 (step S11); A step of searching a specific site for product names translated into one or more languages by the word bundle forming unit 341 (step S12); and a step of forming and displaying the name of a product similar to the name of a product searched on a specific site as a similar word bundle by the word bundle forming unit 341 (step S13).

For example, if the product name is entered as window film in the product input field, it can be translated into English as window film selected by the user (example in step S11).

Afterwards, the names 'window film' and 'window film' can be searched on a specific site (for example, the International Trade Association site) (example of step S12).

Afterwards, the names of products similar to the names of products searched on a specific site can be formed into a similar word bundle (e.g., glass film, window tint, solar gard) and displayed. 　

Users can enter the product name in the product input field by referring to the similar word cluster.

However, the method of forming a similar word cluster is not limited to this.　

For example, step S1 may include a step (step S11') of forming and displaying the name of a product similar to the name of the product entered in the product input field as a similar word bundle by the word bundle forming unit 341, ,

Here, the similar word bundle may be stored in the needs database 211, and the word bundle forming unit 341 may display the name of a product similar to the name of the input product as a similar word bundle. 　

Below, the configuration of the buyer recommendation device 20 described above will be described in more detail.　

The buyer recommendation device 20 includes a screen configuration module 310, a needs provision module 320, and a buyer recommendation module 330. It may include a word bundle forming module 340.　

The screen configuration module 310 may include a screen providing unit 311, and the needs providing module 320 may include a category setting unit 321, a survey question and answer setting unit 322, and a weight setting unit 323. It may include, the buyer recommendation module 330 may include a score calculation unit 331 and a buyer recommendation unit 332, and the word bundle forming module 340 may include a word bundle forming unit 341. .　

The screen providing unit 311 may provide a product input field where the user can enter the product he or she wishes to export or import.　

The category setting unit 321 can set a plurality of needs categories that the user must select.　

The survey questions and answers setting unit 322 can set survey questions and a plurality of answers dependent on the needs category.　

The weight setting unit 323 can assign weights for each product to the needs category.

The score calculation unit 331 receives answers for each need category selected by the user, and can calculate the scores of a plurality of buyers stored in the buyer database 212 according to the selected answers.　

The buyer recommendation unit 332 can recommend buyers by reflecting the score for each buyer's needs category and the weight for each product assigned to the needs category.

The word bundle forming unit 341 can translate the name of the product entered in the product input field into one or more languages.

Additionally, the word bundle forming unit 341 can search for product names translated into one or more languages on a specific site.

In addition, the word bundle forming unit 341 can form a similar word bundle with the names of products similar to the names of products searched for on a specific site.

In addition, the word bundle forming unit 341 can form a similar word bundle and display the name of a product similar to the name of the product entered in the product input field.　

The buyer recommendation device and buyer recommendation method of this embodiment can be used industrially.

Claims

A step of providing a product input field where the user can input the name of the product desired to be exported or imported by the screen configuration module (step S1);

A needs selection box for entering the user's needs is provided by the needs provision module (step S2); and　

Including a step (S3 step) in which the product and the user's needs entered by the buyer recommendation module are applied to the buyer recommendation algorithm to recommend the buyer.

How to recommend buyers.

　
According to claim 1,

In step S2,　

A step of setting a plurality of need categories to be selected by the user by a category setting unit (step S21);

A step of setting survey questions and a plurality of answers subordinate to each of the needs categories by a survey question and answer setting unit (step S22); and

Including a step (step S23) in which a weight for each product is assigned to each of the above needs categories by a weight setting unit.

How to recommend buyers.

　
According to clause 2,

In step S3,　

A step of receiving answers to each need category selected by the user by a score calculation unit, and calculating scores of a plurality of buyers stored in a buyer database according to the selected answers (step S31); and

　Including a step (step S32) in which buyers are recommended by the buyer recommendation unit by reflecting the score for the needs category of each buyer and the weight for each product assigned to the needs category.

How to recommend buyers.

　
A screen configuration module that provides a product input field where the user can enter the name of the product he or she wishes to export or import;　

a needs provision module capable of providing a needs selection box into which the user's needs can be input; and　

Comprising a buyer recommendation module that can recommend buyers by applying the inputted product and the user's needs to a buyer recommendation algorithm.　

Buyer recommendation device.

　
According to claim 1,

The needs provision module is,

a category setting unit that can set a plurality of needs categories to be selected by the user;

a survey question and answer setting unit that can set survey questions and a plurality of answers dependent on the needs category; and　

Including a weight setting unit that can assign product-specific weights to the needs categories

How to recommend buyers.

　
According to clause 4,

The buyer recommendation module is,

a score calculation unit that receives answers to each need category selected by the user and calculates scores of a plurality of buyers stored in a buyer database according to the selected answers; and　

A buyer recommendation unit that can recommend buyers by reflecting the scores for the needs categories of each buyer and the weight of each product assigned to the needs categories.

Buyer recommendation device.