KR20210096927A - Method, apparatus and computer-readable medium of analyzing related item of item for sale - Google Patents

Abstract

An objective of the present invention is to provide a technique for identifying a product with a good sales effect when sold with a specific product based on the product organization of other shopping mall sellers and sales for the product organization. In accordance with a first embodiment of the present invention, a method for organizing and analyzing products related to products for sale is realized by a computing device including one or more processors and one or more memories storing instructions executable by the processors and comprises: an analysis target selection step of selecting seller accounts selling products for sale from a database of at least one analysis target shopping mall when information on the products for sale is received from a user terminal; a related sale information extraction step of extracting sale information of first products other than the products for sale as information stored in seller accounts selected by the analysis target selection step from the database of the analysis target shopping mall; a product organizing step of using the sale information of the first products extracted in accordance with the performance of the related sale information extraction step to select a preset number of products determined to increase sales when organized as products related to the products for sale among the first products as second products; and a product recommendation step of recommending the second products as products related to the products for sale.

Description

Method, apparatus, and computer-readable recording medium for analyzing the composition of related products of products to be sold

The present invention relates to a technology for predicting sales of products that can be sold in association with a product and recommending product composition in configuring a product to be sold, which is a product that a seller wants to sell in a shopping mall, etc. It relates to a technology for collecting sales information of other related products on an online shopping mall, applying it to a fuzzy control algorithm, predicting sales of related products when selling a specific product, and recommending product composition based on this.

Compared to offline shopping malls, online shopping malls have advantages in that users have very good accessibility and can check and purchase various products at once.

In an online shopping mall, a seller who wants to sell a specific product registers a product to be sold online, and proceeds to sell according to the stock amount, and the user uses this to select a product online and complete payment, The business proceeds by receiving the product by means of a courier service or the like.

At this time, the seller configures his or her own virtual shopping mall, such as a general shopping mall or open market, selects and registers a product to be sold, and exposes and advertises the product to buyers (users) online. As described above, users inquire about exposed and advertised products and make a purchase decision.

Due to the characteristics of such an online shopping mall and ordering system, which product to select as a product to be sold is directly related to the sales of the seller. In this case, in order to generate additional sales when purchasing a specific product, sellers select other products related to the specific product to configure the product for sale.

For example, from the standpoint of a seller who sells a smartphone, a mobile phone case and the like can be configured together and sold in a shopping mall. However, in addition to these clear items, there is currently little information on which product composition will be prepared to increase sales based on the correlation between each product.

As a related technology, there is Korean Patent Application Laid-Open No. 10-2002-0003119, etc. In the prior art, the products are classified and packaged according to a certain standard, but the seller configures the desired package to provide the package to users. It is expected that the effect of increasing sales and the effect of diversifying the sales composition by selling the constituent products can be expected.

However, even with the technology, the seller must find out which product to sell in a package, etc. There is this.

Accordingly, the present invention recommends a product with a good sales effect when selling a specific product together as a related product based on the product composition and sales of sellers of other shopping malls as a related product, or a product to be sold together by a seller for a specific product The purpose of this is to provide a technology that enables you to predict the sales effect of the product when selecting a product, monitors the product composition and marketing information of other sellers, and predicts the optimal product sales strategy through this.

In order to achieve the above object, the method for analyzing the configuration of a related product of a product to be sold according to a first embodiment of the present invention is a computing device including one or more processors and one or more memories for storing instructions executable by the processor. In the implemented method for analyzing the composition of a product to be sold, when information on the product to be sold is received from a user terminal, an analysis target for selecting seller accounts that sell the product to be sold from a database of at least one analysis target shopping mall selection step; a related sales information extraction step of extracting, as information stored in the seller accounts selected by the analysis target selection step, sales information of a first product excluding the sales target product from a database of the analysis target shopping mall; Using the sales information of the first product extracted according to the execution of the related sales information extraction step, a preset number of products determined to have high sales when configured as products related to the target product for sale among the first products a product configuration step of selecting a second product; and a product recommendation step of recommending the second product as a product related to the product to be sold.

When the information on the product to be sold is received from the user terminal, identification information on at least one shopping mall is also received, and the analysis target selection step includes at least one shopping mall received from the user terminal among a plurality of analysis target shopping malls. It is preferable to select seller accounts that sell the target product from the database of the shopping mall corresponding to the identification information for .

In the extracting related sales information, data of the number of times of simultaneous selection of the target product and the first product extracted from purchase histories of purchaser accounts, inventory amount data of the first product, sales volume data of the first product, and the first product It is preferable to extract at least one of the inquiry count data for the product as sales information of the first product.

In the product configuration step, the number of simultaneous selection number data, the inventory amount data, the sales volume data, and the inquiry number data is used as an input value, and the sales possibility predicted value of the first product is an output value, and the simultaneous selection number data , using a fuzzy control algorithm having an association function that determines that sales will be higher when the first product and the sales target product are configured as related products as the number of the inventory amount data, the sales volume data, and the number of inquiry data increases, It is preferable that a predetermined number of first products in the order of the highest sales possibility predicted values of one product is selected as the second product.

Meanwhile, the method for analyzing the configuration of a product to be sold related to a product to be sold according to the second embodiment of the present invention relates to a product for sale implemented by a computing device including one or more processors and one or more memories for storing instructions executable by the processor. A product composition analysis method, comprising: an analysis target selection step of selecting seller accounts selling the sales target product from a database of at least one analysis target shopping mall upon receiving information on a target product and a candidate product from a user terminal; a related sales information extraction step of extracting, as information stored in the seller accounts selected by the analysis target selection step, sales information of the candidate product from a database of the analysis target shopping mall; a related product sales prediction step of calculating a sales possibility predicted value when the candidate products are configured as products related to the target product by using the sales information of the candidate product extracted according to the execution of the related sales information extraction step; and a sales forecast value transmission step of transmitting the predicted sales potential value of the candidate product to the user terminal.

When the information on the product to be sold is received from the user terminal, identification information on at least one shopping mall is also received, and the analysis target selection step includes at least one shopping mall received from the user terminal among a plurality of analysis target shopping malls. It is preferable to select seller accounts that sell the target product from the database of the shopping mall corresponding to the identification information for .

In the extracting related sales information, data of the number of simultaneous selections of the target product and the candidate product extracted from purchase histories of purchaser accounts, inventory amount data of the candidate product, sales volume data of the candidate product, and the candidate product are inquired. It is preferable to extract at least one of the number of times data as sales information of the candidate product.

In the related product sales prediction step, the number of simultaneous selection data, the inventory amount data, the sales volume data, and the number of inquiry data is used as an input value, and the sales possibility predicted value of the candidate product is an output value, and the number of simultaneous selections Using a fuzzy control algorithm having an association function that determines that sales will be higher when the number of data, the inventory amount data, the sales volume data, and the number of inquiry data is higher, when the candidate product and the product to be sold are configured as related products, the candidate It is preferable to calculate the predicted value of sales potential of the product.

The apparatus for analyzing the configuration of a product for sale related to a product to be sold according to an embodiment of the present invention comprises one or more processors and one or more memories for storing instructions executable by the processor. An analysis apparatus comprising: an analysis target selection unit that selects seller accounts selling the sales target product from a database of at least one analysis target shopping mall when receiving information on a target product from a user terminal; a related sales information extracting unit for extracting, as information stored in the seller accounts selected by the analysis target selection unit, sales information of a first product excluding the target product for sale from a database of the analysis target shopping mall; Using the sales information of the first product extracted by the related sales information extracting unit, selecting a product that is determined to have high sales when it is composed of products related to the target product for sale among the first products as the second product product composition department; and a product recommendation unit that recommends the second product as a product related to the product to be sold.

Upon receiving the information on the product to be sold from the user terminal, information on a candidate product, which is a product to be analyzed for correlation with the product to be sold, may be received together, and the related sales information extracting unit may include: As information stored in the seller accounts selected by the selection unit, the sales information of the candidate product is extracted from the database of the analysis target shopping mall, and the device for analyzing the related product configuration of the target product is configured by the related sales information extracting unit. a related product sales prediction unit for calculating a sales probability prediction value when the candidate products are configured as products related to the target product for sale, using the extracted sales information of the candidate product; and a sales forecast value transmission unit for transmitting the predicted sales potential value of the candidate product to the user terminal.

According to the present invention, when a specific product is sold in another shopping mall, by analyzing the sales effect of a packaged product or separately sold product that is configured and sold together, a product effective for increasing sales when a specific product is sold together is recommended Alternatively, a forecast value of the sales effect of the product that the seller intends to sell together with the specific product is provided.

Through this, sellers can sell products that are predicted to be good to organize and sell together when selling certain products, thereby reducing concerns about product composition in shopping mall operation, and creating shopping malls according to the optimal product sales strategy. It is effective to operate.

1 and 2 are flowcharts of a method for analyzing the composition of a related product of a target product for sale according to each embodiment of the present invention.
3 is a block diagram of an apparatus for analyzing the composition of a product to be sold related to a product to be sold according to an embodiment of the present invention;
4 is an example for explaining a flow in which a product configuration analysis result related to a product to be sold is derived according to an embodiment of the present invention.
5 is a view for explaining an example in which sales information is specifically analyzed according to an implementation of an embodiment of the present invention;
6 and 7 are examples of screens output to the user terminal according to the implementation of each embodiment of the present invention.
8 is an example of an internal configuration of a computing device according to an embodiment of the present invention.

Hereinafter, various embodiments and/or aspects are disclosed with reference to the drawings. In the following description, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of one or more aspects. However, it will also be recognized by one of ordinary skill in the art that such aspect(s) may be practiced without these specific details. The following description and accompanying drawings set forth in detail certain illustrative aspects of one or more aspects. These aspects are illustrative, however, and some of the various methods in principles of various aspects may be employed, and the descriptions set forth are intended to include all such aspects and their equivalents.

As used herein, “embodiment”, “example”, “aspect”, “exemplary”, etc. may not be construed as an advantage or an advantage in any aspect or design described herein over other aspects or designs. .

Also, the terms "comprises" and/or "comprising" mean that the feature and/or element is present, but excludes the presence or addition of one or more other features, elements and/or groups thereof. should be understood as not

Also, terms including an ordinal number such as 1st, 2nd, etc. may be used to describe various components, but the components are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component. and/or includes a combination of a plurality of related listed items or any of a plurality of related listed items.

In addition, in the embodiments of the present invention, unless otherwise defined, all terms used herein, including technical or scientific terms, are those commonly understood by those of ordinary skill in the art to which the present invention belongs. have the same meaning. Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in an embodiment of the present invention, an ideal or excessively formal meaning is not interpreted as

1 to 2 are flowcharts of a method for analyzing the composition of a related product of a product to be sold according to each embodiment of the present invention.

In the method for analyzing the configuration of a related product of a product to be sold according to an embodiment of the present invention to be described below, each step and sub by a computing device including one or more processors and one or more memories for storing instructions executable by the processor It is characterized in that the specific function of the step is performed, and the computing device in the present invention can access remote data through a network such as, for example, a smart phone, a tablet PC, a personal PC terminal, and output the user terminal through data processing. It means any terminal capable of outputting the result value through the device.

In particular, in each embodiment of the present invention, it may be understood that the above-described computing device may be configured as an independent computer terminal or a software configuration in a user terminal. In addition, each component for performing a function according to each embodiment of the present invention may be installed or disposed in a computing device after being divided in terms of hardware or software.

In the computing device, data processed to perform a function according to each embodiment of the present invention and an algorithm applied to the processing of each data may be stored and managed in one storage space or in a separate storage space, or each data and algorithm It can be stored and managed in this separate database.

Referring to FIG. 1 based on the above description, in order to perform the function of the present invention, when a computing device receives information about a product to be sold from a user terminal, the product to be sold from the database of at least one analysis target shopping mall Performs an analysis target selection step (S10) of selecting seller accounts that sell .

In performing step S10, the user terminal is a terminal to which a merchant account who wants to increase profits by registering and selling products in an online virtual shopping space as described above is connected, for example, the seller's personal PC terminal, smart It means a mobile terminal such as a phone or tablet.

Receiving the information on the product to be sold from the user terminal means that the seller account stores attribute information on a specific product to be analyzed in order to configure a related product according to the performance of the function of the present invention among products that can be sold by registering in the shopping mall. When inputting through the terminal and performing an input requesting analysis of a product in order to receive a result value through performing the function of the present invention, the specific product described above is set as a target product for sale according to the analysis request input, It means that a product to be sold is specified by receiving attribute information.

In this case, the information on the product to be sold may refer to any information that can identify the product to be sold, such as a serial number, product name, product type, price, quantity, and weight of the product.

At least one analysis target shopping mall is opened even if it is linked or not linked with a service provider according to the performance of a function according to each embodiment of the present invention to be described or described later, and attributes as described above for products sold in the shopping mall It refers to a shopping mall capable of collecting information and information on the inventory amount, sales volume, number of views, and the like of the corresponding product.

For such a shopping mall, the computing device collects the seller accounts for selling the product to be sold received from the user terminal through step S10, the seller account of each shopping mall, and information on products identified as being sold by each seller in connection therewith. use to select. That is, since the database of each shopping mall will store attribute information on products sold by the respective sellers in connection with or included in each account information, along with the account information of the sellers, the information about the products to be sold received from the user terminal is stored in the database. It is to select at least a seller account that sells the product to be sold by using the information about the product.

Meanwhile, in performing step S10, the users may select a specific shopping mall and determine it as an analysis target in order to analyze the sales by reflecting the user's preference according to the user's selection. In order to reflect this embodiment, according to an embodiment of the present invention, the computing device may receive identification information on at least one shopping mall when receiving information on a product to be sold from the user terminal.

In this case, when step S10 is performed, the computing device retrieves the target product from the database of the shopping mall (one or more) corresponding to the identification information on at least one shopping mall received from the user terminal as described above among the plurality of analysis target shopping malls described above. You can choose a merchant account that sells .

When the seller accounts selling the target product to be analyzed are selected, the computing device extracts sales information of the first product excluding the target product as information stored in the selected seller accounts from the database of the analysis target shopping mall. Step S20 is performed.

As described above, as information automatically or manually registered by the seller account or the system in each seller account, attribute information on products sold by each seller account may be included or managed in conjunction with each other. Among these information, the computing device performs the function of step S20 to extract sales information of the first product as products other than the target product inputted by the user terminal from the database, to be precise, the database of the shopping mall to be analyzed. carry out As mentioned in the description of step S10, when the input of the user terminal for a specific shopping mall is received together, selection is made based on the shopping mall in the selection of step S10 for the seller account, which is similarly applied to the execution of step S20 Thus, the sales information of the first products in the shopping mall corresponding to the selection of the user terminal among the sales information of the seller account may be extracted.

At this time, the extracted sales information of the first product is related to sales and sales of the first product, is managed in the database of each shopping mall, etc., and is information that the computing device can collect (crawl) from the database of each shopping mall, which will be described later. It means information that can be collected to predict the sales of each product.

For example, in the sales information, data on the number of simultaneous selections of the target product and the first product extracted from purchase histories of purchaser accounts, inventory amount data of the first product, sales volume data of the first product, and inquiry on the first product Count data may be included.

In the database of the shopping mall, not only seller accounts but also buyer accounts are managed. In this case, the purchase history information may be included or managed in conjunction with the purchaser account, and the purchase history information may include identification information of a product purchased by the purchaser account, information on a purchase amount, purchase quantity, and the like. In addition, at this time, through the log information of the product purchased or inquired by the purchaser account according to the user interface, information on the history of conversion or new entry to the inquiry or purchase interface of any one of the first products from the inquiry or purchase interface of the product to be sold Information can be extracted Such information can be managed as the simultaneous selection information of the above-described sales target product and the first product, and the number of times the corresponding simultaneous selection information is generated can be extracted as the above-described simultaneous selection number data. Other inventory amount, sales volume, etc. are the corresponding number of times, and the inquiry number data may be understood to mean the number of times that information on a specific product is checked or a purchase interface is output. All data may be extracted as total counts or cumulative counts by period (eg, within the last year).

As described above, when the sales information is extracted for each first product, the computing device determines that sales will be high when the first products are composed of products related to the target product, using the extracted sales information of the first product. A product configuration step (S30) of selecting a preset number of products to be used as the second product is performed.

When analyzing the first product, which is expected to have high sales in the seller account that sells the product to be sold, for multiple seller accounts, this is a prediction that the sale of the product will be smoothly performed when packaged or sold together with the product to be sold. refers to the technical characteristics of the invention.

Step S30 may be performed, for example, by applying a fuzzy control algorithm. At this time, each of the sales information as in the above example can form each fuzzy set, set the relationship between the variables belonging to the fuzzy set and the output value, and apply it, system evaluation and adjustment steps to the combination of each fuzzy set. The weights and the like are updated by artificial intelligence to reinforce the accuracy of the algorithm.

Specifically, in performing the function of step S30 to which the above-described fuzzy control algorithm is applied in the present invention, for example, the number of simultaneous selection count data, inventory quantity data, sales volume data, and inquiry count data described above as input values, Using the predicted value of sales possibility of 1 product as an output value, the higher the number of simultaneous selection data, inventory data, sales volume data, and inquiry count data, the higher the sales will be when the first product and the target product are composed of related products. A fuzzy control algorithm with an associative function is used. At this time, as an output value, the predicted value of sales potential may have a step-by-step result value according to the fuzzy theory rather than a numerical value. For example, the predicted value may consist of "sell very well", "sell well", "sell poorly", "sell very poorly", and the like. An example of this is shown in FIG. 5 .

Referring to FIG. 5 , as each sales information, an example in which three fuzzy sets of sales volume, inventory amount, and inquiry count data are combined is illustrated. At this time, in the case of sales volume and number of inquiry data, partial prediction values according to each input value are intuitively configured, and in the case of inventory, partial prediction values are matched step by step using matching data for the number of sellers. These partial prediction values are combined with each weight (a, b, x) applied, and then a result value (w) is derived by defugerization.

For such a fuzzy set, a sales probability predicted value as an output value is derived by reverse fuzzyization, and a conclusion may be derived by, for example, a center of gravity method.

In addition to such a fuzzy control algorithm, any algorithm capable of deriving a sales possibility predicted value as step-by-step information through weight application of the correlation between the above-described various sales information and predicted values may be applied in step S30.

If the sales potential predicted value is derived for each first product according to this example, the first product of a preset number (eg, two) belonging to the highest sales potential predicted value, for example, "sold very well" in the above example is finally selected as a second product to be recommended as a product configuration.

Thereafter, the computing device recommends the information of the second product selected by the function execution of step S30 to the user terminal as a related product for the above-described sales target product, and when the user terminal sells the target product, a certain product is sold together. A product recommendation step (S40) of performing a recommendation function on whether to do it is performed. An example of this is shown in FIG. 4 .

First, with respect to the items (I1, I2, I3) sold in a plurality of seller accounts including the specific seller account (S1), when the user selects I3 as the item to be sold, the computing device performs the functions of steps S10 to S30. As a result of deriving the predicted sales potential for the first product (I1, I2) through the comparison, the I2 product is configured as a product better to be sold when the I3 product is sold rather than the I1 product, and the corresponding information is provided to the user.

In this case, in order to provide convenience of product registration in the seller account, in performing the function of step S40, the computing device may immediately register the second product as a sale product on a user interface that outputs information about the second product. By providing an interface that allows the user to check the second product, it is possible to provide a service that can be registered as a product for sale immediately.

According to the present invention, by analyzing sales information for each first product using an artificial intelligence algorithm, when a seller wants to sell a specific product, the seller recommends a product that will help sales if sold together, and the seller sells the product. It helps to organize the products you want, and it is more effective in terms of marketing than direct sales products. In addition, since the information is configured based on the sales of other seller accounts, it is possible to configure an optimal marketing strategy by referring to the marketing strategies of other sellers.

Meanwhile, referring to FIG. 2 , there is shown a flowchart of a method for analyzing the composition of a product to be sold related to a product to be sold according to a second embodiment of the present invention. In the following description, descriptions of the same parts as those of FIG. 1 will be omitted.

Referring to FIG. 2 , when the computing device receives information on a product to be sold and a candidate product from the user terminal, the computing device selects the seller accounts selling the product to be sold from the database of at least one analysis target shopping mall. Step S11 is performed.

In the first embodiment of the present invention, the system directly selects and recommends a second product to be sold together with respect to the target product, whereas in the second embodiment, the seller wants to sell a specific product together with the target product. This is an example of predicting the sales of the corresponding product when the Accordingly, in step S11, in addition to step S10, information on a candidate product, which is a product for which the seller desires to predict sales, is input together.

At this time, as mentioned in the description of step S10, the computing device receives identification information for at least one shopping mall together when receiving information on a product to be sold, and in performing step S11, one of a plurality of shopping malls to be analyzed , may select seller accounts for selling the product to be sold from the database of the shopping mall corresponding to the identification information on the at least one shopping mall received from the user terminal.

When step S11 is performed, the computing device performs a related sales information extraction step S21 of extracting sales information of a candidate product from a database of an analysis target shopping mall as information stored in the selected seller accounts. Unlike step S20, in step S21, sales information of all the first products is not extracted, but only sales information of candidate products. As mentioned in the description of step S20, the sales information includes data on the number of times of simultaneous selection of a target product and a candidate product extracted from purchase histories of purchaser accounts, inventory amount data of the candidate product, sales volume data of the candidate product, and the candidate It will be understood as a concept including at least one of the inquiry count data for the product.

When step S21 is completed, the computing device performs a related product sales prediction step (S50) of calculating a sales probability predicted value when the candidate products are configured as products related to the target product by using the extracted sales information of the candidate product. carry out In step S30, the predicted value of sales potential is calculated and a predetermined number of first products in ascending order are selected as the second products. The predicted value of the sales potential of the company is calculated through the step S50.

Specifically, step S50 is similar to step S30, with the number of simultaneous selection data, inventory data, sales volume data, and inquiry count data as input values, and the sales possibility prediction value of candidate products as output values, simultaneous selection count data, inventory amount The higher the number of data, sales data, and number of inquiry data, the higher the sales probability of the candidate product is calculated using a fuzzy control algorithm having an association function that determines that sales will be high when the candidate product and the target product are composed of related products. can

When the predicted sales potential of the candidate product is calculated, the computing device transmits the sales potential predicted value of the candidate product to the user terminal, so that the user can check the sales potential when the candidate product is sold together with the target product. A prediction value transmission step ( S60 ) is performed.

Unlike the first embodiment of the present invention, according to the second embodiment, users can roughly predict the sales of the candidate product when they want to sell the target product and a specific candidate product together, so that the product configuration strategy is intuitive. It has the effect of being able to evaluate and devise an optimal product composition strategy.

The method for analyzing the related product composition of the product to be sold according to the first embodiment and the second embodiment may be implemented independently or in combination with each other, and an interface configured according to a service, program, or application provided according to the implementation of the present invention. Accordingly, it can be implemented so that each embodiment can be used together.

3 is a block diagram of an apparatus for analyzing the configuration of a product to be sold related to a product to be sold according to an embodiment of the present invention. In the following description, descriptions overlapping with those of FIGS. 1, 2, 4 and 5 will be omitted.

Referring to FIG. 3 , the apparatus 10 (hereinafter referred to as "device") for analyzing the composition of a related product of a product to be sold according to an embodiment of the present invention includes an analysis target selection unit 11 and a related sales information extraction unit 12 ), a product configuration unit 13 , a product recommendation unit 14 , a related product sales prediction unit 15 , and a sales forecast value transmission unit 16 . At this time, as in the configuration of the first and second embodiments described above, the analysis target selection unit 11 , the related sales information extraction unit 12 , the product configuration unit 13 and the product recommendation unit 14 , the first embodiment To correspond to, the analysis target selection unit 11, the related sales information extraction unit 12, the related product sales prediction unit 15, and the sales forecast value transmission unit 16 may be independently configured to correspond to the second embodiment. However, it may preferably be configured as shown in FIG. 3 .

When the analysis target selection unit 11 receives information on the target product from the user terminal 20, the seller account ( It is a configuration that performs a function of selecting P1 to P4). The analysis target selection unit 11 will be understood as a configuration that performs all functions included in the descriptions of S10 and S11 in the description of FIGS. 1 and 2 described above.

The related sales information extraction unit 12, as information stored in the seller accounts P1 to P4 selected by the analysis target selection unit, analyzes sales information of the first product excluding the target product to be analyzed in the shopping malls S1 and S2 to be analyzed. It is a configuration that performs the function of extracting from the database (30) of. The related sales information extracting unit 12 will be understood as a configuration that performs all functions included in the descriptions of S20 and S21 in the description of FIGS. 1 and 2 described above. That is, when applied to the description of FIG. 2 , it will be understood that the first product is replaced with a candidate product.

The product configuration unit 13, using the sales information of the first product extracted by the related sales information extraction unit 12, determines that sales will be high when the product is composed of products related to the target product among the first products. A function of selecting a product as a second product is performed. That is, it will be understood as a concept of performing all functions mentioned in the description of step S30 among the description of FIG. 1 .

The product recommendation unit 14 is configured to perform a function of recommending the second product to the user terminal 20 as a product related to the product to be sold. That is, it will be understood as a concept of performing all functions mentioned in the description of step S40 among the description of FIG. 1 .

Meanwhile, the device 10 may include a related product sales prediction unit 15 and a sales prediction value transmission unit 16 as a configuration of the second embodiment as described above. In this case, the related product sales prediction unit 15 calculates a sales possibility prediction value when the candidate products are composed of products related to the target product for sale, using the sales information of the candidate product extracted by the related sales information extraction unit 12 . perform the function That is, it will be understood as a configuration that performs all functions mentioned in the description of step S50 in the description of FIG. 2 .

The sales forecast value transmission unit 16 performs a function of transmitting the predicted sales potential value of the candidate product calculated as described above to the user terminal 20 . That is, in the description of FIG. 2, it will be understood as a configuration that performs all functions mentioned in the description of step S60.

6 and 7 are examples of screens output to the user terminal according to the implementation of each embodiment of the present invention.

First, FIG. 6 shows an example of a screen 100 that may be output to the user terminal as a result of performing the above-described first embodiment. When the user configures I1 as a product to be sold, for example, if I2 and I3 are sold together, it can be confirmed that the product is recommended as the product predicted to be most helpful to sales.

Meanwhile, FIG. 7 shows an example of the screen 110 that can be output to the user terminal as a result of performing the second embodiment described above. When a user registers I1 as a product to be sold and I2 as a candidate product, the probability of sales of the candidate product (I2) is predicted as a specific value (sold well), and the user confirms this and selects the candidate product (I2) as I1 You can provide information to guide you in deciding whether to sell with

8 illustrates an example of an internal configuration of a computing device according to an embodiment of the present invention, and in the following description, descriptions of unnecessary embodiments that overlap with those of FIGS. 1 to 7 will be omitted. do it with

8, the computing device 10000 includes at least one processor 11100, a memory 11200, a peripheral interface 11300, an input/output subsystem ( It may include at least an I/O subsystem) 11400 , a power circuit 11500 , and a communication circuit 11600 . In this case, the computing device 10000 may correspond to a user terminal connected to the tactile interface device (A) or the aforementioned computing device (B).

The memory 11200 may include, for example, a high-speed random access memory, a magnetic disk, an SRAM, a DRAM, a ROM, a flash memory, or a non-volatile memory. there is. The memory 11200 may include a software module, an instruction set, or other various data required for the operation of the computing device 10000 .

In this case, access to the memory 11200 from other components such as the processor 11100 or the peripheral interface 11300 may be controlled by the processor 11100 .

Peripheral interface 11300 may couple input and/or output peripherals of computing device 10000 to processor 11100 and memory 11200 . The processor 11100 may execute a software module or an instruction set stored in the memory 11200 to perform various functions for the computing device 10000 and process data.

The input/output subsystem 11400 may couple various input/output peripherals to the peripheral interface 11300 . For example, the input/output subsystem 11400 may include a controller for coupling a peripheral device such as a monitor, keyboard, mouse, printer, or a touch screen or sensor as needed to the peripheral interface 11300 . According to another aspect, input/output peripherals may be coupled to peripheral interface 11300 without going through input/output subsystem 11400 .

The power circuit 11500 may supply power to all or some of the components of the terminal. For example, the power circuit 11500 may include a power management system, one or more power sources such as batteries or alternating current (AC), a charging system, a power failure detection circuit, a power converter or inverter, a power status indicator, or a power source. It may include any other components for creation, management, and distribution.

The communication circuit 11600 may enable communication with another computing device using at least one external port.

Alternatively, as described above, if necessary, the communication circuit 11600 may include an RF circuit to transmit and receive an RF signal, also known as an electromagnetic signal, to enable communication with other computing devices.

The embodiment of FIG. 8 is only an example of the computing device 10000, and the computing device 11000 may omit some components shown in FIG. 8, or further include additional components not shown in FIG. 8, or 2 It may have a configuration or arrangement that combines two or more components. For example, a computing device for a communication terminal in a mobile environment may further include a touch screen or a sensor in addition to the components shown in FIG. 8 , and various communication methods (WiFi, 3G, LTE) are provided in the communication circuit 1160 . , Bluetooth, NFC, Zigbee, etc.) may include a circuit for RF communication. Components that may be included in the computing device 10000 may be implemented in hardware, software, or a combination of both hardware and software including an integrated circuit specialized for one or more signal processing or applications.

Methods according to an embodiment of the present invention may be implemented in the form of program instructions that can be executed through various computing devices and recorded in a computer-readable medium. In particular, the program according to the present embodiment may be configured as a PC-based program or an application dedicated to a mobile terminal. The application to which the present invention is applied may be installed in the user terminal through a file provided by the file distribution system. As an example, the file distribution system may include a file transmission unit (not shown) that transmits the file according to a request of the user terminal.

The device described above may be implemented as a hardware component, a software component, and/or a combination of the hardware component and the software component. For example, the devices and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), It may be implemented using one or more general purpose or special purpose computers, such as a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that can include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

Software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or device, to be interpreted by or to provide instructions or data to the processing device. may be permanently or temporarily embody in The software may be distributed over networked computing devices, and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and used by those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and carry out program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with reference to the limited embodiments and drawings, various modifications and variations are possible by those skilled in the art from the above description. For example, the described techniques are performed in a different order than the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result. Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (12)

In the method for analyzing the configuration of a related product of a product to be sold, which is implemented as a computing device including one or more processors and one or more memories for storing instructions executable by the processor,
an analysis target selection step of selecting seller accounts selling the target product from a database of at least one analysis target shopping mall upon receiving information on the target product from the user terminal;
a related sales information extraction step of extracting, as information stored in the seller accounts selected by the analysis target selection step, sales information of a first product excluding the sales target product from a database of the analysis target shopping mall;
Using the sales information of the first product extracted according to the execution of the related sales information extraction step, a preset number of products determined to have high sales when configured as products related to the target product for sale among the first products a product configuration step of selecting a second product; and
and a product recommendation step of recommending the second product as a related product for the target product.
According to claim 1,
When receiving the information on the product to be sold from the user terminal, the identification information for at least one shopping mall is received together,
The analysis target selection step is,
A related product of a product to be sold, characterized in that the seller accounts for selling the product to be sold are selected from a database of the shopping mall corresponding to the identification information on at least one shopping mall received from the user terminal among a plurality of shopping malls to be analyzed. Composition analysis method.
According to claim 1,
The related sales information extraction step includes:
At least among the data on the number of simultaneous selections of the product to be sold and the first product extracted from the purchase history of purchaser accounts, the inventory amount data of the first product, the sales volume data of the first product, and the number of inquiry data for the first product A related product composition analysis method of a target product for sale, characterized in that one is extracted as sales information of the first product.
4. The method of claim 3,
The product composition step is,
Using the number of simultaneous selection data, the inventory amount data, the sales volume data, and the inquiry number data as input values, the sales possibility predicted value of the first product as an output value, the simultaneous selection number data, the inventory amount data, the Using a fuzzy control algorithm having an association function that determines that sales will be high when the first product and the sales target product are configured as related products as the number of sales data and the number of inquiry data is higher, the predicted value of the sales potential of the first product A related product configuration analysis method of a target product for sale, characterized in that a predetermined number of first products in the highest order is selected as the second product.
In the method for analyzing the configuration of a related product of a product to be sold, which is implemented as a computing device including one or more processors and one or more memories for storing instructions executable by the processor,
an analysis target selection step of selecting seller accounts selling the sales target product from a database of at least one analysis target shopping mall upon receiving information on the target product and the candidate product from the user terminal;
a related sales information extraction step of extracting, as information stored in the seller accounts selected by the analysis target selection step, sales information of the candidate product from a database of the analysis target shopping mall;
a related product sales prediction step of calculating a sales possibility predicted value when the candidate products are configured as products related to the target product by using the sales information of the candidate product extracted according to the execution of the related sales information extraction step; and
A related product configuration analysis method of a target product for sale, comprising a; a sales forecast value transmission step of transmitting the sales probability predicted value of the candidate product to the user terminal.
6. The method of claim 5,
When receiving the information on the product to be sold from the user terminal, the identification information for at least one shopping mall is received together,
The analysis target selection step is,
A related product of a product to be sold, characterized in that the seller accounts for selling the product to be sold are selected from a database of the shopping mall corresponding to the identification information on at least one shopping mall received from the user terminal among a plurality of shopping malls to be analyzed. Composition analysis method.
6. The method of claim 5,
The related sales information extraction step includes:
At least one of data on the number of simultaneous selections of the target product and the candidate product extracted from purchase histories of purchaser accounts, inventory amount data of the candidate product, sales volume data of the candidate product, and inquiry count data for the candidate product is selected as the candidate A method for analyzing the composition of a related product of a product to be sold, characterized in that it is extracted as sales information of the product.
8. The method of claim 7,
The related product sales prediction step is,
The number of simultaneous selection data, the inventory amount data, the sales volume data, and the inquiry number data is taken as an input value, and the sales possibility prediction value of the candidate product is an output value, the simultaneous selection number data, the inventory amount data, and the sales volume Using a fuzzy control algorithm having an associative function that determines that sales will be high when the candidate product and the sales target product are configured as related products as the number of data and the number of inquiry data is higher, calculating the sales probability prediction value of the candidate product A method for analyzing the composition of a related product of a product to be sold, characterized in that.
In the apparatus for analyzing the composition of a product to be sold, which is implemented as a computing device including one or more processors and one or more memories for storing instructions executable by the processor,
an analysis target selection unit for selecting seller accounts selling the sales target product from a database of at least one analysis target shopping mall upon receiving information on the target product from the user terminal;
a related sales information extracting unit for extracting, as information stored in the seller accounts selected by the analysis target selection unit, sales information of a first product excluding the target product for sale from a database of the analysis target shopping mall;
Using the sales information of the first product extracted by the related sales information extracting unit, selecting a product that is determined to have high sales when it is composed of products related to the target product for sale among the first products as the second product product composition department; and
and a product recommendation unit that recommends the second product as a related product with respect to the product to be sold.
10. The method of claim 9,
When receiving the information on the product to be sold from the user terminal, it is possible to receive information on the candidate product, which is a product to be analyzed for correlation with the product to be sold, together,
The related sales information extraction unit,
extracting sales information of the candidate product from the database of the analysis target shopping mall as information stored in the seller accounts selected by the analysis target selection unit;
The device for analyzing the related product composition of the product to be sold,
a related product sales prediction unit for calculating a sales possibility prediction value when the candidate products are configured as products related to the target product, using the sales information of the candidate product extracted by the related sales information extraction unit; and
A related product configuration analysis apparatus of the target product for sale, further comprising a; a sales forecast value transmitting unit for transmitting the predicted sales potential value of the candidate product to the user terminal.
A computer-readable recording medium comprising:
The computer-readable recording medium stores instructions for causing a computing device to perform the following steps, the steps of:
an analysis target selection step of selecting seller accounts selling the target product from a database of at least one analysis target shopping mall upon receiving information on the target product from the user terminal;
a related sales information extraction step of extracting, as information stored in the seller accounts selected by the analysis target selection step, sales information of a first product excluding the sales target product from a database of the analysis target shopping mall;
Using the sales information of the first product extracted according to the execution of the related sales information extraction step, a product that is determined to have high sales when it is composed of products related to the target product among the first products is set as the second product. product configuration step to select; and
and a product recommendation step of recommending the second product as a product related to the product to be sold.
A computer-readable recording medium comprising:
The computer-readable recording medium stores instructions for causing a computing device to perform the following steps, the steps of:
an analysis target selection step of selecting seller accounts selling the sales target product from a database of at least one analysis target shopping mall upon receiving information on the target product and the candidate product from the user terminal;
a related sales information extraction step of extracting, as information stored in the seller accounts selected by the analysis target selection step, sales information of the candidate product from a database of the analysis target shopping mall;
a related product sales prediction step of calculating a sales possibility predicted value when the candidate products are configured as products related to the target product by using the sales information of the candidate product extracted according to the execution of the related sales information extraction step; and
A computer-readable recording medium comprising a; a sales forecasted value transmitting step of transmitting the predicted sales potential of the candidate product to the user terminal.

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