KR102564350B1 - A method for determining a selling price by predicting future exchange rates in consideration of product characteristic and a selling price determination system for performing the same - Google Patents

Abstract

Disclosed are a method for determining a selling price by predicting future exchange rates in consideration of product characteristics and a price determination system for performing the same. The method for determining a price, which is executed by a processor, may comprise: a step in which the processor receives purchase price information of a first point of a primary station with respect to a target product; a step in which the processor acquires a first exchange rate value between the primary station and a secondary station at the first point; a step in which the processor calculates a selling period prediction value with respect to the target product by using a similar product DB based on information with respect to the target product; a step in which the processor acquires a second exchange rate value of the second point based on the purchase period prediction value; a step in which the processor calculates an exchange rate prediction value at a third point based on the first exchange rate value and the second exchange rate value; a step in which the processor determines a selling price with respect to the target product based on the exchange rate prediction value; and a step in which the processor transmits the selling price. Therefore, prices identical to a foreign local price may be determined.

Description

Method for determining sales price by predicting future exchange rate considering product characteristics, and pricing system for performing the same SAME}

The present invention relates to a method for determining a sales price by predicting a future exchange rate and a pricing system for performing the same.

With the rapid development of Internet-related technologies, a number of online services are provided through the Internet. For example, customers may use their terminals (eg, smart phones) to access sales services provided by the online market. In general, an online market announces its business model through the Internet and uses a server to process data received from a customer terminal.

On the other hand, along with the development of such an online market, domestic sales of overseas products are increasing. The most important thing to consider when selling foreign products domestically is the fluctuation of the exchange rate. For example, in January 2021, the dollar / won exchange rate was 1080 won / dollar, while in October 2022, it rose to 1445 won / dollar, a difference of 33% in 1 year and 10 months. Due to these fluctuations in the exchange rate, it was not possible to predict the price at the time of sale in advance, and a situation occurred in which inventory was generated and losses were caused by purchase, and a pricing method that could consider this situation was required.
(Patent Document 1) KR 102413273 A "A method for operating a fresh food distribution platform that predicts an appropriate auction price based on artificial intelligence-based expiration date and supply" (2022.06.22)

An object of the present invention is to provide a price determination method capable of determining the same price as an overseas local price by predicting the exchange rate at the time of sale and determining the price based on the exchange rate at the time of sale, and a pricing system for performing the same.

A price determination method performed by a processor according to the technical idea of the present disclosure includes the steps of receiving, by the processor, purchase price information of a first country for a target product at a first point in time, by the processor, at the first point in time. obtaining a first exchange rate value between the first country and the second country in the processor, calculating a sales period prediction value for the target product by using a similar product DB based on the information on the target product; obtaining, by the processor, a second exchange rate value at a second point in time based on the sales period prediction value; by the processor, based on the first exchange rate value and the second exchange rate value, a third Calculating an exchange rate prediction value at a point in time, determining a sales price for the target product based on the exchange rate prediction value by the processor, and transmitting the sales price by the processor. there is.

According to an embodiment, the calculating of the sales period prediction value may include determining at least one first similar product having the same item code as the target product from the similar product DB; determining at least one second similar product having the same retailer as the target product; determining at least one third similar product having a price range similar to the purchase price among the at least one second similar product; The method may further include acquiring a sales period of the at least one third similar product in the similar product DB and determining an average of the sales periods as the sales period prediction value.

According to an embodiment, the obtaining of the second exchange rate value may include determining a time point obtained by subtracting the sales period prediction value from the first time point as the second time point, and and obtaining the second exchange rate value by retrieving the exchange rate of the second country.

According to an embodiment, the calculating of the predicted exchange rate at the third time point includes calculating a first exchange rate gradient at the first time point, and a second exchange rate between the first time point and the second time point. Calculating a slope value, calculating an exchange rate slope predicted value through a weighted average of the first exchange rate slope value and the second exchange rate slope value, predicted by multiplying the exchange rate slope predicted value and the sales period predicted value The method may include calculating an exchange rate increase value and determining the exchange rate prediction value by adding the expected exchange rate increase value to the first exchange rate value at the first time point.

According to one embodiment, the step of calculating the predicted exchange rate value at the third time point, the first exchange rate value , the second exchange rate value , the sales period forecast value , the predictive constant , the predicted exchange rate value can be determined so that the following expression is satisfied.

According to one embodiment, the prediction constant may be determined to be larger as the GDP of the first country is higher.

According to an embodiment, the prediction constant is determined as an integer greater than or equal to 0.2 and less than 0.3 when the first country is the United States, and an integer greater than or equal to 0.3 and less than 0.4 when the first country is China. determined, and when the first country is Vietnam, it may be determined as an integer greater than or equal to 0.6 and less than 0.8.

According to an embodiment, the prediction constant is determined as an integer greater than 0.7 when the sales period prediction value is less than 7 days, and is greater than 0.4 and greater than 0.7 when the sales period prediction value is greater than or equal to 7 days and less than 30 days. It may be determined as an integer smaller than or equal to, and determined as an integer smaller than or equal to 0.4 when the predicted sales period is 30 days or more.

According to one embodiment, the prediction constant is determined as an integer greater than 0.8 when the purchase price of the target product is less than 10 dollars, and greater than 0.2 when the purchase price of the target product is greater than 10 dollars and less than 100 dollars. It is determined as an integer less than or equal to 0.8, and may be determined as an integer less than or equal to 0.2 when the purchase price of the target product is greater than or equal to 100 dollars.

According to an embodiment, the determining of the sales price may include obtaining an inventory constant for the target product, calculating an inventory cost by multiplying the inventory constant by the sales period forecast value, and the exchange rate forecast value. Multiplying by the purchase price and determining a value obtained by adding the inventory cost as the selling price.

According to the technical idea of the present invention, by predicting the sales period and predicting the future exchange rate based on the predicted sales period, it is possible to predict in advance the appropriate sales price to be sold in the future, and accordingly, the expected profit and loss when selling You can check in advance, and as a result, you can maximize your profit on the transaction.

1 is a block diagram illustrating a pricing system according to an exemplary embodiment of the present disclosure.
Fig. 2 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure.
Fig. 3 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure.
Fig. 4 is a table showing a similar product DB according to an exemplary embodiment of the present disclosure.
Fig. 5 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure.
Fig. 6 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure.
7A and 7B are graphs showing exchange rate trends according to exemplary embodiments of the present disclosure.
Fig. 8 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure.
Fig. 9 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure.
Fig. 10 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure.
Fig. 11 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure.
12 is a flow chart illustrating a pricing method according to an exemplary embodiment of the present disclosure.
13 is a block diagram illustrating a computing system including a pricing system according to an exemplary embodiment of the present disclosure.
FIG. 14 is a flow chart showing the flow of a method ( S1 ) for determining the type and sale price of a product for sale in an online market according to an exemplary embodiment of the present disclosure.
15 is a flow chart showing the flow of recommending a product for sale in an online market using customer information according to an exemplary embodiment of the present disclosure ( S10a ).
16 is a flow chart showing the flow of recommending a product for sale in an online market using customer information according to an exemplary embodiment of the present disclosure ( S10b ).
17 is a flow chart showing the flow of recommending a product for sale in an online market using customer information according to an exemplary embodiment of the present disclosure ( S10c ).
FIG. 18 is a flow chart showing the flow of determining a selling price of a recommended selling product based on sales information of another online market and a customer's desired purchase price according to an exemplary embodiment of the present disclosure ( S20a ).
FIG. 19 is a flow chart showing the flow of determining a selling price of a recommended selling product based on sales information of another online market and a customer's desired purchase price (S20b) according to an exemplary embodiment of the present disclosure.

Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Advantages and features of the present disclosure, and methods of achieving them, will become clear with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the technical idea of the present disclosure is not limited to the following embodiments and can be implemented in various different forms, and only the following embodiments complete the technical idea of the present disclosure, and in the technical field to which the present disclosure belongs. It is provided to completely inform those skilled in the art of the scope of the present disclosure, and the technical spirit of the present disclosure is only defined by the scope of the claims.

In adding reference numerals to components of each drawing, it should be noted that the same components have the same numerals as much as possible even if they are displayed on different drawings. In addition, in describing the present disclosure, if it is determined that a detailed description of a related known configuration or function may obscure the gist of the present disclosure, the detailed description will be omitted.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those of ordinary skill in the art to which this disclosure belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined. Terminology used herein is for describing the embodiments and is not intended to limit the present disclosure. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase.

Also, terms such as first, second, A, B, (a), and (b) may be used in describing the components of the present disclosure. These terms are only used to distinguish the component from other components, and the nature, order, or order of the corresponding component is not limited by the term. When an element is described as being “connected,” “coupled to,” or “connected” to another element, that element is directly connected or connectable to the other element, but there is another element between the elements. It will be understood that elements may be “connected”, “coupled” or “connected”.

As used in this disclosure, "comprises" and/or "comprising" means that a stated component, step, operation, and/or element is one or more other components, steps, operations, and/or elements. Existence or additions are not excluded.

Components included in one embodiment and components including common functions may be described using the same names in other embodiments. Unless stated to the contrary, descriptions described in one embodiment may be applied to other embodiments, and detailed descriptions will be omitted to the extent of overlapping or to the extent that those skilled in the art can clearly understand. can

Hereinafter, the present invention will be described in detail with reference to preferred embodiments and accompanying drawings of the present invention.

1 is a block diagram illustrating a pricing system according to an exemplary embodiment of the present disclosure.

Referring to FIG. 1 , the pricing system 100 may calculate a sales price (SP) that can be sold in the future by predicting a future exchange rate based on target product information (Info_MP). To this end, the pricing system 100 may be configured as a server or a terminal, and when configured as a server, the pricing system 100 may collectively refer to a server or a computing device that operates the same, and may be configured as a terminal. The pricing system 100 includes a laptop, personal computer (PC), navigation, personal communication system (PCS), global system for mobile communications (GSM), personal digital cellular (PDC), personal handyphone system (PHS), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), Wibro (Wireless Broadband Internet) terminal, Smartpad ), a tablet PC, a cellular phone, and a smart phone.

In this specification, an operation performed by the pricing system 100 or a component included in the pricing system 100 is actually performed by using an application program built in the storage device of the pricing system 100 to determine the pricing system 100. It may be an operation performed by a processor of The storage device may include a non-volatile memory, a volatile memory, a flash-memory, a hard disk drive (HDD), or a solid state drive (SSD). Also, the processor may include at least one of a Central Processing Unit (CPU), a Graphic Processing Unit (GPU), a Neural Processing Unit (NPU), a RAM, a ROM, a system bus, and an application processor.

In addition, the contents performed by the price determination system 100 in this specification may be performed by at least one artificial intelligence module using a neural network, and the neural network may be expressed as a mathematical model using nodes and edges. . The neural network may be an architecture of a deep neural network (DNN) or n-layers neural networks. DNNs or n-layer neural networks may correspond to convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep belief networks, restricted boltzman machines, and the like. For example, the neural network may be implemented as a convolutional neural network (CNN), but is not limited thereto.

The price determination system 100 may include a sales period prediction unit 110, a prediction constant determination unit 120, an exchange rate prediction unit 130, an inventory cost determination unit 140, and a sales price determination unit 150. . The sales period predictor 110 may calculate a sales period prediction value EST, which is an expected period from purchase to sale of the target product, based on the target product information Info_MP. In an embodiment, the pricing system 100 obtains sales periods ST(i) for similar products using a similar product DB (DB_SP), and uses the obtained sales periods to obtain a sales period prediction value (EST). ) can be calculated. (n is the number of similar products)

The prediction constant determination unit 120 may determine the prediction constant (A) based on the target product information (Info_MP). In this specification, the prediction constant (A) is a constant used to calculate the exchange rate prediction value (Er_t2), and may be determined differently depending on the characteristics of the target product.

The exchange rate prediction unit 130 may obtain a first exchange rate value (Er_t1) of the first time point t1 corresponding to the current time point. In one example, the exchange rate prediction unit 130 may acquire the current exchange rate value as the first exchange rate value Er_t1 by accessing the exchange rate notification system based on the current time point. In an embodiment, the target product information (Info_MP)_ may include information on a purchase place and a seller, and the exchange rate prediction unit 130 uses country information of a purchase place and a seller included in the target product information (Info_MP) A first exchange rate value (Er_t1) may be obtained.

The exchange rate prediction unit 130 may calculate a second point in time t0 corresponding to the past by using the predicted sales period value EST and the first point in time t1. In one example, the exchange rate prediction unit 130 subtracts the sales period prediction value (EST) from the first time point (t1) (or returns to the past by the sales period prediction value (EST) from the first time point (t1)). It can be determined as 2 time points t0 (eg, Equation 2 below).

The exchange rate prediction unit 130 may obtain the second exchange rate value (Er_t0) of the second time point (t0) through the exchange rate request (Req_Er) for the second time point (t0). In one example, the exchange rate prediction unit 130 may obtain the second exchange rate value Er_t0 by accessing an exchange rate notification system.

The exchange rate prediction unit 130 may calculate an exchange rate prediction value (Er_t2) using the first exchange rate value (Er_t1), the second exchange rate value (Er_t0), the sales period prediction value (EST), and the prediction constant (A). In one embodiment, the exchange rate prediction unit 130 may calculate the exchange rate prediction value (Er_t2) using Equation 3 below. According to an embodiment of the present disclosure, the exchange rate prediction value Er_t2 may correspond to the exchange rate at a third time point t2, when a time equal to the sales period prediction value EST has elapsed from the first time point t1.

The inventory cost determination unit 140 may receive the target product information (Info_MP) and the estimated sales period value (EST), and determine the expected inventory cost (IP) to keep the target product as inventory until the target product is sold. there is. In one example, the inventory cost determining unit 140 acquires the inventory constant (IC) corresponding to the target product from the inventory constant DB (DB_IC) using the target product information (Info_MP), and the inventory constant as shown in Equation 4 below. Inventory cost (IP) can be determined by multiplying (IC)) by the selling period forecast (EST).

The selling price determination unit 150 receives the purchase price BP as the target product information Info_MP, multiplies the exchange rate prediction value Er_t2 by the purchase price BP, and adds the inventory cost IP to the selling price. (SP) can be determined (Equation 5).

According to an embodiment of the present disclosure, the pricing system 100 calculates a sales period forecast value (EST) for a target product based on a sales period of a similar product, and calculates an exchange rate point to be obtained using the EST. You can calculate the exchange rate at an exact point in time.

According to an embodiment of the present disclosure, the pricing system 100 applies characteristics of different target products to exchange rate prediction by determining a prediction constant (A) using information of the target product (Info_MP) to determine the characteristics of the product. Exchange rate forecasting can be performed.

According to an embodiment of the present disclosure, the pricing system 100 determines the past second exchange rate value (Er_t0) and the first exchange rate based on the sales period prediction value (EST) as well as the current exchange rate slope (dEr_t1/dt). In order to reflect the average slope ((Er_t1-Er_t0)/(t1-t0)) between the values (Er_t1) together, a weighted sum between the two elements can be performed to predict an accurate exchange rate considering both the instantaneous slope and the average slope. .

According to an embodiment of the present disclosure, the pricing system 100 can accurately predict inventory costs that may be required until sales by calculating inventory values using inventory constants (IC) reflecting product characteristics.

Fig. 2 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure.

Referring to FIG. 2 , the price determination system 100 may receive the purchase price BP of the first country at the first point in time t1 for the target product (S110). The price determination system 100 may obtain a first exchange rate value (Er_t1) between the first country, which is a purchasing country, and the second country, which is a selling country, at a first point in time t1 (S120).

The pricing system 100 may calculate a sales period prediction value (EST) for the target product using the similar product DB (DB_SP) (S130). The pricing system 100 may obtain the second exchange rate value Er_t0 at the second point in time t0 based on the predicted sales period value EST (S140). In one example, the second point in time t0 may be a past point in time different from the first point in time t1 by the predicted sales period value EST.

The pricing system 100 may calculate an exchange rate prediction value (Er_t2) at the third time point (t2) based on the first exchange rate value (Er_t1) and the second exchange rate value (Er_t0) (S150). In one embodiment, the pricing system 100 may calculate the exchange rate prediction value (Er_t2) by using a predetermined formula reflecting the prediction constant in which product characteristics are reflected (S150).

Pricing system 100 may determine the inventory cost based on the information of the target product (S160). In one example, the pricing system 100 may determine the inventory cost by determining an inventory constant based on the size and nature of the target product, and multiplying the inventory constant by an estimated sales period value (EST).

The pricing system 100 may determine the selling price (SP) based on the exchange rate prediction value (Er_t2) and the inventory cost (IP) (S170).

Fig. 3 is a flow chart showing a pricing method according to an exemplary embodiment of the present disclosure, and Fig. 4 is a table showing similar product DBs according to an exemplary embodiment of the present disclosure. In detail, FIG. 3 shows the calculation step (S130) of the sales period prediction value (EST) of FIG. 2 .

Referring to FIG. 3 , the price determination system 100 may determine at least one first similar product having the same item code of the target product from the similar product DB (DB_SP) (S131). In one example, the product code may be determined to correspond to the type of product, and if the product code is the same, the product type may be similar or identical. The price determination system 100 may determine at least one second similar product from among the first similar products having the same sales destination as the target product (S132). In another embodiment, the pricing system may determine at least one second similar product from the same producer among the first similar products.

The price determination system 100 may determine at least one third similar product having a price range similar to the purchase price among the second similar products (S133). In one embodiment, pricing system 100 may determine a third similar product as a second similar product that is within a predetermined offset of the purchase price.

The pricing system 100 may acquire a sales period of at least one third similar product (S134). The pricing system 100 may determine the average of sales periods as a sales period prediction value (EST) (S135).

Referring to FIG. 4 , assuming that the target product is PC2 as a product code, the seller is company B, and the price is 30 dollars, the pricing system 100 determines three to three items with the same product code as PC2 among the similar product DB (DB_SP). Product No. 6 may be determined as the first similar product. Among the first similar products, the price determination system 100 may determine products No. 4 to 6 having the same vendor as company B as the second similar products. The pricing determination system 100 may determine products No. 4 and No. 5, the price of which is similar to $30 (for example, within $10 as an offset value) among the second similar products, as the third similar product.

The pricing system 100 may determine 15 days, which is an average of 16 days of the sales period of product No. 4 and 14 days of the sales period of product No. 5, as the predicted sales period value (EST).

According to an embodiment of the present disclosure, similar products similar to the target product are filtered based on various criteria (eg, product code, vendor, price), and the average sales period for the similar products is calculated as a sales period prediction value (EST). ), it is possible to accurately predict the expected sales period from purchase to sale of the target product.

Fig. 5 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure. In detail, FIG. 5 shows the obtaining step S140 of the second exchange rate value Er_t0 of FIG. 2 .

Referring to FIG. 5 , the pricing system 100 may determine a time point obtained by subtracting the sales period prediction value EST from the first time point t1 as the second time point t0 (S141). The price determination system 100 may obtain an exchange rate between the first country (purchasing country) and the second country (seller country) at the second point in time t0 as a second exchange rate value (S142).

6 is a flowchart illustrating a price determination method according to an exemplary embodiment of the present disclosure, and FIGS. 7A and 7B are graphs illustrating exchange rate trends according to an exemplary embodiment of the present disclosure. In detail, FIG. 6 shows the calculation step (S150) of the predicted exchange rate value (Er_t2) of FIG. 2, the horizontal axis of FIGS. 7A and 7B represents the flow of time, and the vertical axis represents the exchange rate.

Referring to FIG. 6 , the pricing system 100 may calculate a first exchange rate gradient value S1 at a first time point t1 ( S151 ). In one example, the pricing system 100 may determine a differential value (instantaneous slope) of the exchange rate graph Er at a first time point t1 as a first exchange rate slope value S1 = dEr_t1/dt.

The pricing system 100 may calculate a second exchange rate gradient value S2 between the first time point t1 and the second time point t0 (S152). In one example, the pricing system 100 calculates the average slope between the first time point t1 and the second time point t0 of the exchange rate graph Er as a second exchange rate slope value S2 = (Er_t1-Er_t0)/( t1-t0)) can be determined.

The pricing system 100 may calculate an exchange rate gradient prediction value S through a weighted average of the first exchange rate gradient value S1 and the second exchange rate gradient value S2 (S153). In one embodiment, the pricing system 100 may determine a prediction constant (A) by reflecting product characteristics, and calculate an exchange rate gradient prediction value (S) according to Equation 6 based on the prediction constant (A). .

The pricing system 100 calculates an expected exchange rate increase value by multiplying the exchange rate gradient prediction value S and the sales period prediction value EST (S154), and adds the expected exchange rate increase value to the first exchange rate value Er_t1. By doing so, the predicted exchange rate value (Er_t2) can be determined (S155).

Referring to FIG. 7A , the pricing system 100 determines a second time point t0 that is as much as the sales period prediction value EST from the first time point t1 and determines the instantaneous slope of the first time point t1. 1 exchange rate slope value S1 may be calculated, and a second exchange rate slope value S2 may be calculated as an average slope between the second time point t0 and the first time point t1. In the case of FIG. 7A, a relatively small prediction constant (A) may be determined as an exchange rate for a product having a higher influence of the average slope (S2) on product characteristics (eg, country, price, sales period). . Accordingly, the predicted value S of the exchange rate slope may be determined to be similar to the second exchange rate slope S2 rather than the first exchange rate slope S1.

In the case of FIG. 7B, the prediction constant (A) may be determined to be relatively large as an exchange rate for a product in which the influence of the instantaneous slope (S1) is higher in terms of product characteristics (eg, country, price, sales period). . Accordingly, the predicted value S of the exchange rate slope may be determined to be similar to the first exchange rate slope S1 rather than the second exchange rate slope S2.

According to an embodiment of the present disclosure, the pricing system 100 considers both the average slope between the second time point t0 and the first time point t1 as well as the instantaneous slope S1 at the first time point t1. By determining the predicted value (S) of the exchange rate gradient, it is possible to reflect not only the exchange rate flow at the time of the first time point (t1) but also the exchange rate flow between the first time point (t1) and the second time point (t0), and as a result, an accurate exchange rate can predict

Also, according to an embodiment of the present disclosure, the pricing system 100 may determine the second time point t0 for calculating the average slope S2 based on the sales period prediction value EST, and accordingly, the sales price may increase. For the exchange rate value at the expected time, it can accurately reflect the exchange rate flow during the same period in the previous period.

In addition, according to an embodiment of the present disclosure, the reflection ratio of the average slope S2 and the instantaneous slope S1 can be adjusted by adjusting the prediction constant A according to the characteristics of the product, and as a result, the instantaneous It is possible to adaptively control the degree of reflection of the exchange rate flow and the average exchange rate flow.

Fig. 8 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure. In detail, FIG. 8 shows a method of determining the prediction constant (A) based on the sales period prediction value (EST) in the calculation step (S153) of the exchange rate gradient prediction value of FIG. 6 .

Referring to FIG. 8 , the price determination system 100 may determine a sales period prediction value (EST) (S153_1), and may determine a prediction constant (A) based on the sales period prediction value (EST) (S153_2). The pricing system 100 determines the prediction constant (A) as an integer greater than 0.7 when the sales period prediction value (EST) is less than 7 days, and when the sales period prediction value (EST) is 7 days or more and less than 30 days, the prediction constant ( A) is determined as an integer greater than 0.4 and less than or equal to 0.7, and when the predicted sales period value (EST) is 30 days or more, the prediction constant (A) may be determined as an integer less than 0.4.

As the sales period gets longer, the average slope can have a greater impact than the instantaneous slope. As a result of hundreds of simulations, the most accurate exchange rate was calculated when the prediction constant (A) was 0.7 based on 7 days, and based on 30 days As a result, the most accurate exchange rate was calculated when the prediction constant (A) was 0.4.

According to an embodiment of the present disclosure, an accurate exchange rate considering product characteristics may be calculated by determining different prediction constants for each sales period.

Fig. 9 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure. In detail, FIG. 9 shows a method of determining the prediction constant based on the GDP of the purchasing country in the step of calculating the predicted value of the slope of the exchange rate in FIG. 6 (S153).

Referring to FIG. 9 , the pricing determination system 100 obtains the GDP of the first country, which is a purchasing country (S153_1a), and may determine a smaller prediction constant (A) as the GDP of the first country is higher (S153_2a).

As a country with a large GDP does not have a large change in the exchange rate, the average slope can have a greater effect than the instantaneous slope. As a result of hundreds of simulations, the prediction constant (A) is greater than or equal to 0.2 and less than 0.3 when the United States is a purchasing country. The most accurate exchange rate was calculated when it was an integer (preferably 0.25), and when China was a purchasing country, the most accurate exchange rate was calculated when the prediction constant (A) was an integer greater than or equal to 0.3 and smaller than 0.4 (preferably 0.32), and Vietnam In the case of this purchasing country, the most accurate exchange rate was calculated when the prediction constant (A) was an integer greater than or equal to 0.6 and less than 0.8 (preferably 0.72).

According to an embodiment of the present disclosure, an accurate exchange rate considering the characteristics of a purchasing country may be calculated by determining different prediction constants for each purchasing country in which a product is purchased.

Fig. 10 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure. In detail, FIG. 10 shows a method of determining a prediction constant based on the purchase price (BP) of a target product in the step of calculating an exchange rate gradient prediction value (S153) of FIG. 6 .

Referring to FIG. 10 , the price determination system 100 may obtain a purchase price (BP) of a target product (S153_1b) and determine a prediction constant (A) based on the purchase price (BP) (S153_2b). The pricing determination system 100 determines the prediction constant (A) as an integer greater than 0.8 when the purchase price (BP) is less than 10 dollars, and when the purchase price (BP) is greater than or equal to 10 dollars and less than 100 dollars, the prediction constant (A) is determined as an integer greater than 0.2 and less than or equal to 0.8, and when the purchase price (BP) is greater than or equal to 100 dollars, the prediction constant (A) may be determined as an integer less than 0.2.

The higher the purchase price (BP), the more difficult it is to sell, so the average slope can have a greater effect than the instantaneous slope. As a result of hundreds of simulations, the most accurate exchange rate is when the prediction constant (A) is 0.8 based on 10 dollars. , and the most accurate exchange rate was calculated when the prediction constant (A) was 0.2 based on 100 dollars.

According to an embodiment of the present disclosure, an accurate exchange rate for each product characteristic may be calculated by determining different prediction constants for each purchase price (BP).

Fig. 11 is a flowchart illustrating a pricing method according to an exemplary embodiment of the present disclosure. In detail, FIG. 11 shows the inventory cost (IP) determination step ( S160 ) of FIG. 2 .

Referring to FIG. 11 , the pricing system 100 may determine an inventory constant (IC) based on product characteristics (S161). The pricing system 100 may determine the inventory cost (IP) by multiplying the inventory constant by the sales period forecast value (EST) (S162).

According to an embodiment of the present disclosure, by determining an inventory constant based on product characteristics and reflecting it in a product price, not only exchange rates but also costs consumed as inventory during the sales period can be reflected in the sales price, resulting in accurate sales. A price can be determined.

12 is a flow chart illustrating a pricing method according to an exemplary embodiment of the present disclosure. In detail, FIG. 12 shows the stock constant (IC) determination step (S161) of FIG. 11 .

Referring to FIG. 12 , the pricing system 100 may receive target product information (Info_MP) (S161_1) and obtain a product size (wt) from the target product information (Info_MP) (S161_2). When the product size (wt) is smaller than the first size (w1), the pricing system 100 may determine the first value (k1) as the first sub inventory constant (IC1), and the product size (wt) is When greater than or equal to the first size w1 and smaller than the second size w2, the second value k2 may be determined as the first sub inventory constant IC1, and the product size wt is the second size w2 ), the third value k3 may be determined as the first sub inventory constant IC1. In one embodiment, the first value (k1) < the second value (k2) < the third value (k3) may have a large value in the order.

The pricing system 100 may obtain whether or not the frozen product is from the target product information (Info_MP) (S161_4). The pricing system 100 may determine the fourth value l1 as the second sub inventory constant IC2 when the product is a frozen product, and as the second sub inventory constant IC2 when the product is not a frozen product. A fifth value l2 may be determined. In one embodiment, the fourth value (11) < the fifth value (12) may have a large value in the order.

The pricing system 100 may determine the inventory constant (IC) by adding the first sub inventory constant (IC1) and the second sub inventory constant (IC2) (S161_6).

According to an embodiment of the present disclosure, by determining an inventory constant in consideration of product characteristics such as product size or frozen products, and calculating inventory costs based thereon, accurate inventory costs required for inventory maintenance can be calculated, and accurate inventory costs are calculated. Selling cost can be calculated.

13 is a block diagram illustrating a computing system including a pricing system according to an exemplary embodiment of the present disclosure.

Referring to FIG. 13 , a computing system 1000 may include a processor 1100, a memory device 1200, a storage device 1300, a power supply 1400, and an input/output device 1500. Meanwhile, although not shown in FIG. 13 , the computing system 1000 may further include ports capable of communicating with video cards, sound cards, memory cards, USB devices, etc., or with other electronic devices. .

As described above, the processor 1100, the memory device 1200, the storage device 1300, the power supply 1400, and the input/output device 1500 included in the computing system 1000 are an embodiment according to the technical idea of the present invention. It is possible to configure a management server that performs a price determination method according to the Specifically, the processor 1100 may perform the price determination method described above with reference to FIGS. 1 to 12 by controlling the memory device 1200, the storage device 1300, the power supply 1400, and the input/output device 1500. .

Processor 1100 may perform certain calculations or tasks. Depending on the embodiment, the processor 1100 may be a micro-processor or a central processing unit (CPU). The processor 1100 includes a memory device 1200, a storage device 1300, and an input/output device 1500 through a bus 1600 such as an address bus, a control bus, and a data bus. can communicate with According to an embodiment, the processor 1100 may also be connected to an expansion bus such as a Peripheral Component Interconnect (PCI) bus.

The memory device 1200 may store data necessary for the operation of the computing system 1000 . For example, the memory device 1200 may be implemented with DRAM, mobile DRAM, SRAM, PRAM, FRAM, RRAM, and/or MRAM. there is. The storage device 1300 may include a solid state drive, a hard disk drive, a CD-ROM, and the like. The memory device 1200 and the storage device 1300 may store programs related to the price determination method described above with reference to FIGS. 1 to 12 .

The input/output device 1500 may include input means such as a keyboard, keypad, and mouse, and output means such as a printer and a display. The power supply 1400 may supply an operating voltage necessary for the operation of the computing system 1000 .

FIG. 14 is a flow chart showing the flow of a method ( S1 ) for determining the type and sale price of a product for sale in an online market according to an exemplary embodiment of the present disclosure.

Referring to FIG. 14 , the method of determining the type and selling price of a product sold in an online market (S1) according to an exemplary embodiment of the present disclosure includes the steps of recommending a product for sale in the online market using customer information (S10). , Determining the price of the recommended sales product based on the sales information of other online markets and the customer's desired purchase price (S20), and controlling the sales price of the product based on the foreign exchange price (S30). can do. In one example, the price determination method of FIGS. 1 to 13 may be a detailed step of step S20.

Hereinafter, the steps of the method (S1) of determining the type and selling price of a product for sale in an online market according to the present disclosure will be described in more detail.

15 is a flow chart showing the flow of recommending a product for sale in an online market using customer information according to an exemplary embodiment of the present disclosure ( S10a ).

Referring to FIG. 15 , in the step of recommending a product for sale in an online market using customer information according to an exemplary embodiment of the present disclosure (S10a), a customer including at least one of age, gender, and occupation of the customer from a customer terminal Acquiring information (S110), classifying a customer into one of a plurality of preset customer lists based on the obtained customer information (S120), website and social network accessed by the customer from the customer terminal Acquiring a plurality of search keywords of the service (SNS) from the customer terminal (S130), arranging search records among the plurality of search keywords in order of increasing number of search records to obtain target keywords (S140), synonyms and similar words of the target keyword Obtaining a target keyword group by extracting (S150), extracting a sales product with a name overlapping with synonyms and similar words in the target keyword group among a plurality of recommended sales products for each preset target customer list (S160), and A step of determining the extracted sales product as a first recommended sales product (S170) may be included.

In step S110, the sales product recommendation server (FIG. 19, 2000) may obtain customer information such as the customer's age, gender, and occupation from the customer terminal (FIG. 19, 1000).

In an exemplary embodiment, the sales product recommendation server 2000 may obtain customer information from the customer terminal 1000 through a scraping technique. The scraping technology may be defined as a technology of automatically extracting customer's personal information using customer's authentication information. However, it is not limited thereto, and the product for sale recommendation server 2000 may call customer information from the customer terminal 1000 using an API.

For example, in step S110, the product for sale recommendation server 2000 requests the customer terminal 1000 to authenticate the customer himself. 1000) may include collecting data related to customer information using text and pictures provided.

In addition, in step S110, the sales product recommendation server 2000 stores data related to the personal information collected from the customer terminal 1000 in a memory (FIG. 19, 2200), and can extract and use the data as needed. . Accordingly, in the method of recommending a product for sale in an online market, the product for sale recommendation server 2000 may continuously use customer information obtained through one-time scraping.

In step S120 , the sales product recommendation server 2000 may classify a customer into any one of a plurality of preset customer lists based on the customer information obtained from the customer terminal 1000 .

In an exemplary embodiment, in step S120 , the product for sale recommendation server 2000 may use a plurality of customer lists previously stored in the memory 2200 . The plurality of customer lists may be customer lists classified by age, gender, and occupation.

In an example embodiment, the first plurality of customer lists are age (eg, teenagers, 20's, 30's, etc.), gender (eg, male, female), and occupation (eg, office worker, professional, doctor, businessman, etc.) may be a list of customers classified by any one.

For example, the first plurality of customer lists may be customer lists set by age, such as those in their teens, 20s, and 30s. Also, the first plurality of customer lists may be male and female customer lists. Also, the first plurality of customer lists may be customer lists set to include office workers, professionals, doctors, and businessmen.

In an example embodiment, the second plurality of customer lists may be sorted by a combination of two of age, gender, and occupation. That is, the second plurality of customer lists may be customer lists classified according to age and gender of customers, gender and occupation of customers, or age and occupation of customers.

For example, the second plurality of customer lists may be a list of customers classified as male teenagers, female teenagers, male in their 20s, female in their 20s, and the like. Also, the second plurality of customer lists may be customer lists classified into female students, male students, female entrepreneurs, male professionals, and the like. Also, the second plurality of customer lists may be customer lists classified into students in their twenties, office workers in their thirties, and businessmen in their fifties.

In an example embodiment, the third plurality of customer lists may be a customer list classified by a combination of all age, gender and occupation.

In step S120 , the product for sale recommendation server 2000 may classify the customer into one of the first to third customer lists based on the customer information obtained in step S110 .

For example, in step S110, when the product for sale recommendation server 2000 acquires customer information of one of age, gender, and occupation, the product for sale recommendation server 2000 selects one of the first plurality of customer lists. Customers can be classified as one. In addition, when the sales product recommendation server 2000 acquires two pieces of customer information among age, gender, and occupation, the sales product recommendation server 2000 may classify the customer as one of a second plurality of customer lists. there is. In addition, when the sales product recommendation server 2000 acquires three pieces of customer information among age, gender, and occupation, the sales product recommendation server 2000 may classify the customer as one of a third plurality of customer lists. there is.

In an exemplary embodiment, the sales product recommendation server 2000 may automatically classify a customer into one of a plurality of customer lists by utilizing artificial intelligence (AI) based on customer information acquired from the customer terminal 1000. can For example, the sales product recommendation server 2000 obtains at least one customer information of age, gender, and occupation from the customer terminal 1000 by using artificial intelligence, and based on the obtained customer information, a plurality of predetermined information are set. A customer can be classified as one of the customer lists of

In step S130 , the product for sale recommendation server 2000 may acquire a plurality of search keywords of a website and a social network service (SNS) accessed by the customer from the customer terminal 1000 .

In an exemplary embodiment, in step S130, the product for sale recommendation server 2000 scrapes and collects keywords searched for on websites, portal sites, social network services, etc. accessed by the customer terminal 1000, and stores them in the memory 2200. can be saved However, it is not limited thereto, and in step S130, the sales product recommendation server 2000 uses an API (Application Programming Interface) to call a keyword searched for on a website, portal site, social network service, etc. accessed by the customer terminal 1000. and can be stored in the memory 2000.

For example, when a customer searches for shoes, bags, neck pain, dizziness, anemia, etc. on portal sites and social networks, in step S130, the sales product recommendation server 2000 uses scraping technology or API to the customer terminal ( Keywords such as shoes, bag, neck pain, dizziness, and anemia may be called from 1000 and stored in the memory 2000.

In operation S140 , the product for sale recommendation server 2000 may acquire a target keyword by arranging a plurality of search keywords based on at least one of search frequency and search time.

In an exemplary embodiment, in step S140, the product for sale recommendation server 2000 may sort the keywords acquired in step S130 in order of search frequency. For example, the number of searches for 'shoes, bags, sore throat, dizziness, anemia', which is a keyword searched by a customer on a portal site and a social network from a customer terminal 1000, is '5 times, 3 times, 12 times, and 1 time', respectively. In the case of 'time, twice', the sales product recommendation server 2000 may sort the keywords in the order of 'sore throat, shoes, bag, anemia, dizziness'.

At this time, the product for sale recommendation server 2000 may determine a keyword with the greatest number of searches as a target keyword. For example, the sales product recommendation server 2000 may determine a keyword of 'throat pain', which is searched the most, as a target keyword.

However, it is not limited thereto, and the product for sale recommendation server 2000 may determine a keyword whose number of searches exceeds the number of references as a target keyword. For example, when the number of references is 4 times, the sales product recommendation server 2000 may determine the keyword 'shoes, sore throat' as the target keyword.

In an exemplary embodiment, the product for sale recommendation server 2000 sequentially sorts search keywords obtained by utilizing artificial intelligence based on at least one of search frequency and search time, and selects a target keyword based on the sorted order. can be determined automatically.

Also, in step S140, the product for sale recommendation server 2000 may sort the keywords obtained in step S130 in order of search time. For example, the search time for 'shoes, bag, sore throat, dizziness, anemia', which is a keyword searched by a customer on a portal site and a social network from a customer terminal 1000, is '1 minute, 5 minutes, 2 minutes, and 10 minutes'. , 7 minutes', the sale product recommendation server 2000 may sort the keywords in the order of 'dizzy, anemia, bag, sore throat, shoes'.

At this time, the product for sale recommendation server 2000 may determine a keyword having the longest search time as a target keyword. For example, the product for sale recommendation server 2000 may determine the keyword 'dizzy', which takes the most search time, as the target keyword.

In step S150, the product for sale recommendation server 2000 may obtain a target keyword group by extracting synonyms, synonyms, and related words of the target keyword obtained in step S140.

In an exemplary embodiment, the product for sale recommendation server 2000 may obtain a target keyword group by accessing the Internet vocabulary and extracting synonyms, synonyms, and related words of the target keyword acquired in step S140.

In an exemplary embodiment, the sales product recommendation server 2000 may extract synonyms, synonyms, and related words of a target keyword by accessing a synonym dictionary, synonym dictionary, and related word dictionary provided by a portal site by using an API.

The product for sale recommendation server 2000 is not limited thereto, and may directly input target keywords into the synonym dictionary, synonym dictionary, and related word dictionary, and extract searched synonyms, similar words, and related words. A set of synonyms, similar words, and related words of the extracted target keyword may be defined as a target keyword group.

For example, if the product for sale recommendation server 2000 acquires 'throat pain' as a target keyword in step S140, the product for sale recommendation server 2000 uses an API in step S150 to obtain a synonym dictionary provided by the portal site, Synonyms, synonyms, and related words of 'throat pain, sore throat, runny nose, cough, corona, cold, sore throat medicine, corona medicine, quince tea, vitamins, etc.' can be obtained by accessing a synonym dictionary or a related word dictionary. Also, the product for sale recommendation server 2000 may set a combination of synonyms, similar words, and related words as a target keyword group.

In an exemplary embodiment, the product for sale recommendation server 2000 may obtain a target keyword group by automatically extracting synonyms, synonyms, and related words of the target keyword using artificial intelligence.

In step S160 , the product for sale recommendation server 2000 may extract a product for sale having a name overlapping with synonyms, similar words, and related words in the target keyword group from among a plurality of pre-set products for sale for each target customer list.

The sales product recommendation server 2000 may previously hold data related to a plurality of sales products in the memory 2200 for each target customer list. As described above, the sales product recommendation server 2000 may classify a plurality of customer lists based on customer information such as age, gender, and occupation, and may provide data of sales products expected to be sold a lot for each customer list. may have in advance.

In an exemplary embodiment, the product for sale recommendation server 2000 divides the customer list into first to third plural customer lists based on age, gender, and occupation of the customer, and the first to third plural customer lists. Data of sales products that are expected to be sold in particular may have data in advance.

For example, the product for sale recommendation server 2000 may classify the customer as a 'male in his 30s' based on age and gender information of the customer. In addition, the sales product recommendation server 2000 may have lists of sales products that are expected to sell a lot among 'men in their 30s' in advance.

In operation S160 , the product for sale recommendation server 2000 may extract a product for sale having a name overlapping with synonyms, similar words, and related words in the target keyword group from among a plurality of pre-set products for sale for each customer list.

In an exemplary embodiment, in step S150, the sale product recommendation server 2000 sets a target keyword group consisting of 'throat pain, sore throat, runny nose, cough, corona, cold, sore throat medicine, corona medicine, quince tea, vitamins, etc.' If acquired, in step S160, the sales product recommendation server 2000 selects 'throat pain, sore throat, runny nose, cough, corona, cold, sore throat medicine, corona medicine, quince tea, vitamins' among a plurality of pre-set sales products for each customer list. It is possible to extract sales products with overlapping names with 'etc.'.

For example, if the sales product recommendation server 2000 has a list of sales products such as 'vitamins, iron content, dumbbells, quince tea, citron tea, throat pain medicine, etc.' The product recommendation server 2000 may extract 'vitamin, quince tea, sore throat', which is a name overlapping with a word in the target keyword group.

In an exemplary embodiment, the product for sale recommendation server 2000 may automatically extract a product for sale whose name overlaps with synonyms, similar words, and related words in a target keyword group from among a plurality of products for sale by using artificial intelligence.

In step S170 , the sales product recommendation server 2000 may determine the sales product extracted in step S160 as the first recommended sales product. For example, the sales product recommendation server 2000 determines 'vitamins, quince tea, sore throat medicine' extracted in step S160 as a sale product in the online market, and provides a service for sale of the sale product to the customer terminal 1000. can provide

Method S10a of the present disclosure obtains customer information from the customer terminal 1000, classifies the customer into a customer list based on the acquired customer information, obtains a search keyword from the customer terminal 1000, and acquires the customer terminal Based on the search frequency and search time of (1000), a target keyword among search keywords is acquired, a target keyword group including synonyms, similar words, and related words of the target keyword is acquired, and a plurality of pre-set sales products for each customer list Among them, sales products with names overlapping with words in the target keyword group can be extracted, and the extracted sales products can be determined as recommended sales products. You can sell the kinds of products you need on the online market. Accordingly, market competitiveness of the online market using the method of the present disclosure can be improved.

16 is a flow chart showing the flow of recommending a product for sale in an online market using customer information according to an exemplary embodiment of the present disclosure ( S10b ).

Step S10b may be performed after step S10a described with reference to FIG. 15 . Specifically, steps S210 to S230 may be performed after step S170.

Referring to FIG. 16 , in the step (10b) of recommending a product for sale in an online market using customer information of the present disclosure, a plurality of sales items exceeding a first standard sales volume among a plurality of sale items sold in a plurality of other online markets. Acquiring a list of 1 reference sale items (S210), if the sum of the sales volume of the first reference sale items in a plurality of other online markets exceeds the second reference sales amount, the first reference sale product is sold as a second reference sale It may include determining as a product (S220), and determining a product overlapping with the name of the second reference selling item among the first recommended selling products as a second recommended selling product (S230).

In step S210, the sales product recommendation server 2000 may acquire a list of a plurality of first reference sales items exceeding the first standard sales amount from another online market server (3000 in FIG. 19). The first reference sales amount may be a value arbitrarily determined by a manager of the sales product recommendation server 2000 .

However, it is not limited thereto, and the sales product recommendation server 2000 calls information related to sales products of other online markets and the sales volume of the sales products using API, and removes products that exceed the first standard sales volume among the sales products. 1 Can be determined as a reference sale item.

For example, if the first reference sales volume is determined to be 1000 units, in step S210, the sales product recommendation server 2000 acquires a list of first reference sales products whose sales volume exceeds 1000 units among products sold in other online markets. can do.

In step S220 , the sale product recommendation server 2000 may determine a second reference sale product based on the sum of the sales volume of the first reference sale product in a plurality of other online markets. In an exemplary embodiment, the sales product recommendation server 2000 may sell the first reference sales product as a second reference sales when the total of the sales volume of the first reference sales product in a plurality of other online markets exceeds the second reference sales volume. product can be determined. The second reference sales volume may be a value arbitrarily determined by a manager of the sales product recommendation server 2000 .

In an exemplary embodiment, in step S220, the sale product recommendation server 2000 calculates the sales volume of the first reference sale product in each other online market, and sums the sales volume of the first reference sale product in other online markets. It may include the step of determining whether the combined sales volume exceeds the second reference sales volume, and determining the first reference selling product as the second reference selling product if it exceeds.

For example, when the second reference sales volume is determined to be 100,000 units, in step S220, the sales product recommendation server 2000 calculates the total sales volume of a plurality of other online markets for the first reference sales products, and the total sales volume is If the number exceeds 100,000, the first reference selling product may be determined as the second reference selling product.

In step S230, the sales product recommendation server 2000 may extract products that overlap with the names of the second reference sales products from among the first recommended sales products determined in step S170, and determine the extracted products as the second recommended sales products. .

Since the method of recommending a product for sale in an online market of the present disclosure may further include S10b in addition to the above-described S10a, the method for recommending a product for sale in an online market of the present disclosure includes customer personal information, customer search information, and Sales information from other online markets can also be considered. Accordingly, the online market using the method of the present disclosure can recommend products with excellent sales power to customers, thereby improving customer attraction. can

17 is a flow chart showing the flow of recommending a product for sale in an online market using customer information according to an exemplary embodiment of the present disclosure ( S10c ).

Step S10c may be performed after step S10b described with reference to FIG. 16 . Specifically, steps S310 and S320 may be performed after step S220.

Referring to FIG. 17 , the step of recommending a product for sale in an online market using customer information of the present disclosure (S10c) includes the delivery possibility of the second recommended product for sale within the deadline, the delivery distance, the search frequency in the customer terminal, and other online products. Calculating a sales suitability score of the second recommended sale product based on the search frequency in the market (S310), and determining a third recommended sale product based on the sale suitability score of the second recommended sale product. there is.

In step S310 , the product for sale recommendation server 2000 may have in advance in the memory 2200 data on delivery possibility for each product for sale, delivery distance, search frequency in the customer terminal, and search frequency in other online markets.

In an exemplary embodiment, the sales product recommendation server 2000 may calculate a sales suitability score of the second recommended sales product by Equation 1 below.

In Equation 7, Ga may be a sales suitability score Ga of the second recommended selling product. In addition, Ca is a delivery weight (Ca) determined differently based on the location of the customer terminal, Pa is a delivery possibility (Pa) of the second recommended selling product within the period, and Da is the current location of the second recommended selling product and is a delivery distance (Da) between customer terminals, Ta is a search weight (Ta) that is set differently based on the type of product, Ea is a search frequency (Ea) of a second recommended selling product in a customer terminal, and Fa is The search frequency (Fa) of the second recommended selling product in another online market may be a search frequency (Fa), and Xa may be an age/gender weight (Xa) differently set based on the age and gender of the customer.

Specifically, the sales suitability score Ga for the second recommended sale product may be calculated in consideration of the aspect of prompt delivery of the second recommended sale product. The sales suitability score Ga may consider the delivery distance between the current location of the second recommended sale product and the customer terminal, and the possibility of delivery within a deadline.

For example, the sales suitability score (Ga) for the second recommended sale product with a high possibility of delivery within the deadline may have a relatively large value, and the sales suitability score (Ga) for the second recommended sale product with a long delivery distance may have a relatively low value.

In addition, the sales suitability score Ga for the second recommended selling product may be calculated in consideration of the search frequency of the second recommended selling product searched in the customer terminal and other online markets. When the search frequency (Ea) of the customer terminal and the search frequency (Fa) of the second recommended selling product are high, the sales suitability score (Ga) may have a relatively large value.

In addition, the sales suitability score Ga for the second recommended selling product may further consider an age/gender weight Xa that is differently set based on the customer's age and gender. The age/gender weight Xa may be a value determined in advance based on a correlation between the customer list into which customers are classified in step S120 and the second recommended selling product.

In addition, the sales suitability score Ga for the second recommended selling product is differently based on the location of the customer terminal in order to adjust the sum of the scores for delivery, search frequency, and age/gender of the second recommended selling product. The determined delivery weight (Ca), the search weight (Ta) set differently based on the type of product, and the age/gender weight (Xa) set differently based on the age and gender of the customer may be considered.

The selling product recommendation server 2000 of the present disclosure may calculate the selling suitability score Ga in consideration of delivery information of the second recommended selling product, search information of the second recommended selling product, and personal information of the customer. Accordingly, the sales suitability score Ga may be determined by reflecting customer attractiveness, prompt delivery, and market competitiveness.

In step S320 , the sales product recommendation server 2000 may determine a third recommended sales product based on the sales suitability score Ga of the second recommended sales product.

In an exemplary embodiment, the sale product recommendation server 2000 may determine a sale product having the highest sales suitability score Ga among a plurality of second recommended sale products as the third recommended sale product.

However, the product recommendation server 2000 is not limited thereto, and the sales product recommendation server 2000 may determine a sales product whose sales suitability score Ga exceeds the standard sales suitability score among the plurality of second recommended sales products as the third recommended sales product.

Since the method of recommending a product for sale in an online market of the present disclosure may further include S10c in addition to the above-described S10a and S10b, the method for recommending a product for sale in an online market of the present disclosure includes product delivery information, search information, and Products for sale in the online market may be recommended based on age/gender information. Accordingly, the online market using the method of the present disclosure may recommend a product desired by the customer to the customer terminal, and may additionally consider whether delivery of the product can proceed smoothly. Accordingly, customer attraction, delivery speed, and market competitiveness of the online market using the method of the present disclosure can be improved.

FIG. 18 is a flow chart showing the flow of determining a selling price of a recommended selling product based on sales information of another online market and a customer's desired purchase price according to an exemplary embodiment of the present disclosure ( S20a ).

S20a may be performed after step S10c. Specifically, S20a may be performed after step S320 of S10c.

Determining the selling price of the recommended selling product based on the sales information of other online markets and the customer's desired purchase price of the present disclosure (S20a) includes the sales volume and selling price of the third recommended selling product in a plurality of other online markets. Obtaining (S410), determining a weighted average selling price of a third recommended selling product based on sales volume and selling price of a plurality of other online markets (S420), third recommended selling product from a customer terminal Obtaining a desired purchase price of (S430), determining a price adjustment value of the third recommended selling product based on the raw material content, raw material price fluctuation rate, inventory amount, shipping cost, and foreign currency fluctuation rate of the third recommended selling product (S430) S440), and determining the sum of the customer's desired purchase price and the price adjustment value as the product suggested selling price of the third recommended selling product (S450).

In step S410 , the sales product recommendation server 2000 may acquire the sales volume and sales price of the third recommended sales product from the other online market server 3000 . In an exemplary embodiment, the sales product recommendation server 2000 may automatically obtain the sales volume and sales price of the third recommended sales product from the other online market server 3000 by utilizing artificial intelligence.

However, the product recommendation server 2000 is not limited thereto, and the sales product recommendation server 2000 may call and store information related to the sales volume and sales price of the third recommended sales product in other online markets by using an API and store it in the memory 2200 .

In step S420, the sales product recommendation server 2000 may determine a weighted average selling price of the third recommended sales product based on the sales volume and sales price of the third recommended sales product in a plurality of other online markets. The weighted average selling price may be an average selling price of the third recommended selling product determined by considering sales volume in other online markets.

Specifically, the weighted average selling price of the third recommended selling product may be calculated by Equation 8 below.

In Equation 8, Aa is the weighted average selling price of the third recommended selling product in n other online markets, Mai is the sales weight of the third recommended selling product in other online markets, and Pai is the third selling product in other online markets. 3 is the selling price of the recommended selling product, and Zai may mean the sales volume of the third recommended selling product in another online market.

Based on Equation 8, a weighted average selling price of the third recommended selling product considering sales volume of a plurality of other online markets selling the third recommended selling product may be determined.

In step S430 , the sale product recommendation server 2000 may acquire a desired purchase price of the third recommended sale product from the customer terminal 1000 . The desired purchase price of the third recommended sale product may be a value input by the customer terminal 1000 and transmitted to the sale product recommendation server 2000, or may be a value determined by the customer.

In step S440 , the sale product recommendation server 2000 may determine a price adjustment value of the third recommended sale product based on the raw material content, raw material price change rate, stock amount, shipping cost, and foreign currency change rate of the third recommended sale product. In an exemplary embodiment, the sale product recommendation server 2000 may automatically obtain price adjustment values for a plurality of third recommended sale products.

Specifically, the price adjustment value of the third recommended selling product may be calculated by Equation 9 below.

In Equation 9, Ba is the price adjustment value of the third recommended selling product, La is the raw material price change weight differently set based on the type of raw material, Qa is the raw material price change rate, and Ha is the third recommended selling product. Is the raw material content contained in , Ia is a shipping weight that is set differently based on the type of the third recommended selling product, Ka is the shipping cost of the third recommended selling product, Ja is the inventory quantity of the third recommended selling product, Oa is a foreign currency price change weight differently set based on the type of foreign currency, and Ra means a foreign currency change rate.

Specifically, the price adjustment value Ba of the third recommended selling product may be calculated in consideration of the content of the raw material contained in the third recommended selling product and the price change of the raw material. For example, when the content of the raw material is high and the price of the raw material is currently increasing, the price adjustment value Ba may increase.

In addition, the price adjustment value Ba of the third recommended selling product may be selected in consideration of the stock quantity and delivery cost of the third recommended selling product. For example, if the inventory of the third recommended selling product is large and the delivery cost is low, the price adjustment value Ba may increase, and if the inventory quantity is small and the delivery cost is high, the price adjustment value Ba may decrease.

In addition, the price adjustment value Ba of the third recommended selling product may be calculated in consideration of the foreign currency price fluctuation. For example, when the third recommended selling product is a product that needs to be imported and the price of a foreign currency is currently rising, the price adjustment value Ba may increase.

The price adjustment value (Ba) of the third recommended selling product is a raw material price fluctuation weight (La), which is set differently based on the type of raw material, in order to adjust the value considering all of the raw material price variable, foreign currency price variable, and delivery variable. 3 A delivery weight (Ia) differently set based on the type of recommended selling product and a foreign currency price change weight (Oa) differently set based on the type of foreign currency may be considered.

The price adjustment value (Ba) of the third recommended selling product of the present disclosure may be calculated considering all of the raw material price variable, the foreign currency price variable, and the delivery variable. Accordingly, the product for sale recommendation server 2000 of the present disclosure reflects the price adjustment value Ba in the customer's desired purchase price to set the selling price of the product that can improve all of the attractiveness of the customer, prompt delivery, and market competitiveness. It can be proposed to the customer terminal 1000 .

In step S450 , the sale product recommendation server 2000 may determine the sum of the customer's desired purchase price Ya and the price adjustment value Ba as the proposed product sale price of the third recommended sale product.

Specifically, the proposed product selling price of the third recommended selling product may be determined by Equation 10 below.

In Equation 10, CPa is the proposed product selling price (CPa) of the third recommended selling product, Ya is the customer's desired purchase price (Ya) of the third recommended selling product, and Ba is the price adjustment of the third recommended selling product. value (Ba).

Method S20a of the present disclosure obtains a customer's desired purchase price (Ya) for a third recommended selling product, calculates a price adjustment value (Ba) considering raw materials, delivery, and foreign currency, and calculates the customer's desired purchase price (Ya) and A product proposed sales price (CPa) in which the price adjustment value (Ba) is reflected may be determined.

That is, the sale product recommendation server 2000 may reflect the price desired by the customer and global economic conditions such as exchange rates and raw material prices in the sale price of the third recommended sale product. Accordingly, the sales product recommendation server 2000 can secure customer attraction and market competitiveness.

FIG. 19 is a flow chart showing the flow of determining a selling price of a recommended selling product based on sales information of another online market and a customer's desired purchase price (S20b) according to an exemplary embodiment of the present disclosure.

S20b may be performed after the aforementioned S20a. Specifically, S20b may be performed after step S450 of S20a.

S20b of the present disclosure includes determining whether the proposed product selling price (Cpa) of the third recommended selling product is smaller than the weighted average selling price (Aa) of the third recommended selling product obtained in step S420 (S510), the proposed product selling price If (CPa) is less than the weighted average selling price (Aa), determining the price of a third recommended selling product as the product suggested selling price (CPa) (S520), the product suggested selling price (CPa) is the weighted average selling price (Aa) ), stopping the sale of the third recommended selling product (S530) may be included.

In an exemplary embodiment, the sale product recommendation server 2000 utilizes artificial intelligence to determine the product proposed selling price (Cpa) of the plurality of third recommended sales products by using artificial intelligence (S420). It can be automatically determined whether it is smaller than Aa).

In general, when the selling product recommendation server 2000 presents a price higher than the weighted average selling price (Aa) of other online markets for the third recommended selling product to the customer terminal 1000, the price competitiveness is low, and customer attraction will be low. can

Accordingly, the product recommendation server 2000 according to the present disclosure adds the price adjustment value Ba calculated by the product recommendation server 2000 to the customer's desired purchase price Ya suggested by the customer, and the proposed product selling price. (CPa) can be determined. Also, the sale product recommendation server 2000 may determine the sales price of the third recommended sale product as the proposed product sale price Cpa only when the proposed product sale price CPa is smaller than the weighted average sale price Aa. Accordingly, price competitiveness of the third recommended selling product may be improved, and customer attraction may be improved.

As above, exemplary embodiments have been disclosed in the drawings and specifications. Embodiments have been described using specific terms in this specification, but they are only used for the purpose of explaining the technical spirit of the present disclosure, and are not used to limit the scope of the present disclosure described in the meaning or claims. Therefore, those of ordinary skill in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical scope of protection of the present disclosure should be determined by the technical spirit of the appended claims.

Claims (10)

In a pricing method performed by a processor,
receiving, by the processor, purchase price information of a target product in a first country at a first point in time;
obtaining, by the processor, a first exchange rate value between the first station and the second station at the first time point;
calculating, by the processor, a sales period prediction value for the target product using a similar product DB based on the information on the target product;
obtaining, by the processor, a second exchange rate value at a second point in time based on the predicted sales period value;
calculating, by the processor, an exchange rate prediction value at a third time point based on the first exchange rate value and the second exchange rate value;
Determining, by the processor, a selling price for the target product based on the predicted exchange rate value; And
Transmitting, by the processor, the selling price;
Calculating the predicted exchange rate at the third point in time,
calculating a first exchange rate gradient value at the first time point;
calculating a second exchange rate gradient value between the first time point and the second time point;
calculating an exchange rate gradient prediction value through a weighted average of the first exchange rate gradient value and the second exchange rate gradient value;
Calculating an expected exchange rate increase value by multiplying the exchange rate gradient prediction value and the sales period prediction value; And
and determining the predicted exchange rate value to satisfy the following equation by adding the expected exchange rate increase value to the first exchange rate value at the first time point.

(Er_t1: first exchange rate value, Er_t0: second exchange rate value, EST: sales period forecast value, A: forecast constant (0.1<A<0.9), Er_t2: exchange rate forecast value, t1: time at the first point in time, t0: time of the second point in time)
According to claim 1,
Calculating the sales period forecast value,
determining at least one first similar product having the same product code as the target product in the similar product DB;
determining at least one second similar product from among the at least one first similar product having the same sales location as the target product;
determining at least one third similar product having a price range similar to the purchase price among the at least one second similar product;
obtaining a sales period of the at least one third similar product in the similar product DB; and
and determining the average of the sales periods as the predicted sales period value.
According to claim 1,
Obtaining the second exchange rate value,
Determining a point in time obtained by subtracting the predicted sales period value from the first point in time as the second point in time; and
and obtaining the second exchange rate value by retrieving exchange rates between the first country and the second country at the second time point.
delete delete According to claim 1,
The prediction constant is determined to be larger as the GDP of the first country is higher.

According to claim 1,
The predictive constant is,
If the first station is the United States, it is determined as an integer greater than or equal to 0.2 and less than 0.3;
When the first country is China, it is determined as an integer greater than or equal to 0.3 and less than 0.4;
When the first country is Vietnam, the price determination method, characterized in that determined by an integer greater than or equal to 0.6 and less than 0.8.
According to claim 1,
The predictive constant is,
If the purchase price of the target product is less than 10 dollars, it is determined as an integer greater than 0.8,
When the purchase price of the target product is greater than 10 dollars and less than 100 dollars, it is determined as an integer greater than 0.2 and less than or equal to 0.8;
When the purchase price of the target product is 100 dollars or more, the price determination method characterized in that it is determined as an integer less than or equal to 0.2.
According to claim 1,
The predictive constant is,
When the sales period prediction value is less than 7 days, it is determined as an integer greater than 0.7;
When the sales period prediction value is 7 days or more and less than 30 days, it is determined as an integer greater than 0.4 and less than or equal to 0.7;
When the sales period prediction value is 30 days or more, it is determined as an integer less than or equal to 0.4.
According to claim 1,
The step of determining the selling price,
obtaining a stock constant for the target product;
calculating an inventory cost by multiplying the inventory constant by the sales period forecast; and
and multiplying the exchange rate prediction value by the purchase price and determining a value obtained by adding the inventory cost as the selling price.