KR20210052746A - Method, apparatus and computer program for estimating sales volume of content to be productized - Google Patents

Abstract

A method, apparatus, and computer program for estimating the sales volume of content to be productized are provided. The method for estimating the sales volume of content to be productized according to various embodiments of the present invention is performed by a computing device and comprises the steps of: selecting at least one content to be productized among a plurality of candidate content; obtaining data related to the at least one content from the outside; indexing the data according to a preset standard; and estimating expected sales volume for the at least one content by using the indexed data and an expected sales volume estimation model. It is possible to reduce the risk of productizing actual digital content such as inventory.

Description

Estimation method, device, and computer program of the expected sales volume of content subject to commercialization {METHOD, APPARATUS AND COMPUTER PROGRAM FOR ESTIMATING SALES VOLUME OF CONTENT TO BE PRODUCTIZED}

Various embodiments of the present invention relate to a method, an apparatus, and a computer program for estimating the expected sales amount of a content to be commercialized.

As the demand for portable devices with displays increases, portable devices such as tablet PCs and smart phones are being developed. Accordingly, the popularity of various services available through portable devices (eg, digital content providing services such as various images, webtoons and games through the Internet) is increasing. In fact, according to a survey conducted by Zenith, a North American research firm, users spend an average of 3 hours and 18 minutes a day more than the time they spend using the Internet through a portable device such as a smartphone compared to the time they spend content through a fixed display device such as a TV. It was investigated.

According to this trend, the demand for products (eg, goods) for digital content such as games and webtoons is increasing rapidly, and in line with this trend, service providers are making efforts to produce and sell goods along with various services.

On the other hand, although the demand for digital contents increases, there are many difficulties in commercializing digital contents in that IP trends are fluctuating and the consumer group consuming digital contents is limited, so that a small quantity of various kinds of production is essential.

Conventionally, service providers commissioned professional digital content producers, or service providers voluntarily commercialized digital content. However, in the case of a request to a professional digital content production company, profitability is often deteriorated by repeatedly modifying the product to meet the demanding requirements of IP license holders in a situation where the production company lacks the ability to maintain the quality of the product. Occurs. In addition, in order to solve this problem, when a service provider commercializes digital content on its own, a problem caused by lack of experience in the commercialization of digital content (e.g., a problem that arises due to failure to consider an increase in cost and production volume to overcome this problem It is easy to reduce the business due to increase and inventory burden).

Korean Patent Registration No. 10-1872223 (registered on June 22, 2018)

The problem to be solved by the present invention is to estimate the expected sales volume of the content to be commercialized using data related to the content to be commercialized, thereby reducing the risk of commercializing actual digital content such as inventory due to lack of experience. It is to provide a method, apparatus, and computer program for estimating the expected sales volume of content.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems that are not mentioned will be clearly understood by those skilled in the art from the following description.

According to an embodiment of the present invention for solving the above-described problem, the method for estimating the expected sales amount of content to be commercialized includes the steps of selecting at least one content to be commercialized from among a plurality of candidate content, and the at least one content to be commercialized from the outside. Obtaining data related to the product, indexing the data according to a preset criterion, and estimating a predicted sales volume for the at least one product target content using the indexed data and a predicted sales volume estimation model. have.

An apparatus for estimating the estimated sales amount of content to be commercialized according to another embodiment of the present invention for solving the above-described problem may include a memory for storing one or more instructions and a processor for executing the one or more instructions stored in the memory, , The processor executing the one or more instructions, selecting at least one product target content from among a plurality of candidate contents, obtaining data related to the at least one product target content from the outside, and the method according to a preset criterion. A method of estimating the expected sales volume of the content to be commercialized, including the step of indexing data and estimating the expected sales volume for the at least one product target content using the indexed data and a predicted sales volume estimation model.

The computer program for estimating the estimated sales amount of content to be commercialized according to another embodiment of the present invention for solving the above-described problem is combined with a computer that is hardware to select at least one content to be commercialized from among a plurality of candidate content, Obtaining data related to the at least one product target content from the outside, indexing the data according to a preset criterion, and predicting the at least one product target content using the indexed data and an estimated sales volume estimation model It may be stored in a computer-readable recording medium to perform a method of estimating the expected sales amount of the content to be commercialized, including the step of estimating the sales amount.

Other specific details of the present invention are included in the detailed description and drawings.

According to various embodiments of the present invention, the estimated sales volume when the content to be commercialized is actually sold in consideration of various data related to the content to be commercialized (e.g., social data, the number of members of a fan cafe, data on the increase or decrease of the upload amount of posts, etc. By estimating, there is an advantage of reducing the risk of commercializing the content.

According to various embodiments of the present disclosure, by estimating a consumer group using data related to content to be commercialized, and correcting an expected sales volume according to the consumer group, there is an advantage in that it is possible to more accurately predict the sales volume.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a block diagram of a system for estimating an estimated sales amount of content to be commercialized according to an embodiment of the present invention.
2 is a hardware configuration diagram of an apparatus for estimating an expected sales amount of content to be commercialized according to another embodiment of the present invention.
3 is a flowchart of a method for estimating an expected sales amount of content to be commercialized according to another embodiment of the present invention.
4 is a diagram illustrating a process of selecting a candidate content by using a plurality of pieces of data by an apparatus for estimating an estimated sales amount of content to be commercialized, according to various embodiments.
FIG. 5 is a diagram illustrating a process of estimating a predicted sales amount by using a plurality of data by an apparatus for estimating a predicted sales amount of content to be commercialized, according to various embodiments.
FIG. 6 is a diagram illustrating a configuration in which an apparatus for estimating a predicted sales amount of content to be commercialized corrects an expected sales amount using a consumer group estimated through a consumer group estimation model in various embodiments.
FIG. 7 is a diagram illustrating a candidate content selection user interface (UI) provided by an apparatus for estimating an expected sales amount of content to be commercialized in various embodiments.

Advantages and features of the present invention, and a method of achieving them will become apparent with reference to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in a variety of different forms. It is provided to fully inform the skilled person of the scope of the present invention, and the present invention is only defined by the scope of the claims.

The terms used in the present specification are for describing exemplary embodiments and are not intended to limit the present invention. In this specification, the singular form also includes the plural form unless specifically stated in the phrase. As used herein, “comprises” and/or “comprising” do not exclude the presence or addition of one or more other elements other than the mentioned elements. Throughout the specification, the same reference numerals refer to the same elements, and "and/or" includes each and all combinations of one or more of the mentioned elements. Although "first", "second", and the like are used to describe various elements, it goes without saying that these elements are not limited by these terms. These terms are only used to distinguish one component from another component. Therefore, it goes without saying that the first component mentioned below may be the second component within the technical idea of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used with meanings that can be commonly understood by those of ordinary skill in the art to which the present invention belongs. In addition, terms defined in a commonly used dictionary are not interpreted ideally or excessively unless explicitly defined specifically.

The term "unit" or "module" used in the specification refers to a hardware component such as software, FPGA or ASIC, and the "unit" or "module" performs certain roles. However, "unit" or "module" is not meant to be limited to software or hardware. The “unit” or “module” may be configured to be in an addressable storage medium, or may be configured to reproduce one or more processors. Thus, as an example, "sub" or "module" refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays and variables. Components and functions provided within "sub" or "modules" may be combined into a smaller number of components and "sub" or "modules" or into additional components and "sub" or "modules". Can be further separated.

In the present specification, a computer (or computing device) refers to all kinds of hardware devices including at least one processor, and may be understood as encompassing a software configuration operating in the corresponding hardware device according to embodiments. . For example, the computer may be understood as including all of a smartphone, a tablet PC, a desktop, a laptop, and a user client and application running on each device, and is not limited thereto.

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

Each of the steps described herein is described as being performed by a computer, but the subject of each step is not limited thereto, and at least some of the steps may be performed by different devices according to embodiments.

1 is a block diagram of a system for estimating expected sales of content to be commercialized according to an embodiment of the present invention.

Referring to FIG. 1, the system for estimating the estimated sales amount of content subject to commercialization according to an embodiment of the present invention may include an apparatus 100 for estimating the estimated sales amount of content subject to productization, a user terminal 200 and an external server 300. have.

Here, the system for estimating the estimated sales amount of the content subject to productization illustrated in FIG. 1 is according to an exemplary embodiment, and the exemplary embodiment is not limited to the exemplary embodiment illustrated in FIG. 1, and may be added, changed, or deleted as necessary. . For example, the system for estimating the estimated sales amount of content to be commercialized according to another embodiment of the present invention may include a plurality of different external servers 300.

In one embodiment, the apparatus 100 for estimating the expected sales amount of content to be commercialized may obtain data related to a plurality of candidate contents provided by a specific service from the external server 300 and may obtain data related to a plurality of candidate contents provided by a specific service from the user terminal 200. Preference information for a plurality of candidate contents may be obtained, and at least one of the plurality of candidate contents may be selected by using data and preference information related to a plurality of candidate contents.

Here, the specific service may be a game providing service providing various types of game contents, a webtoon providing service providing various types of webtoon contents, and a video providing service providing various genres of video contents, and the plurality of candidate contents are webtoon. One or more webtoon contents that can be commercialized among a plurality of webtoon contents provided by the provision service and one or more game contents that can be commercialized from among a plurality of game contents provided by the game provision service may be used. However, it is not limited thereto.

In addition, here, the data is social data related to a plurality of candidate content including at least one product target content, data on the increase or decrease of the number of members of a website (for example, a fan cafe site) related to the plurality of candidate content, and posts on the website. It may include at least one of upload amount increase/decrease data and. However, it is not limited thereto.

In various embodiments, the apparatus 100 for estimating the expected sales amount of the content to be commercialized may include a separate communication module (not shown), and the communication module is connected to the user terminal 200 via a network 400 by wired or wirelessly. Preference information may be provided from the terminal 200, or data related to a plurality of candidate contents may be provided by wired or wireless connection with the external server 300.

In one embodiment, the apparatus 100 for estimating the expected sales amount of the content subject to productization may index data related to at least one content subject to productization among data obtained from the external server 300 according to a preset criterion, and the indexed data and The estimated sales volume for at least one product target content may be estimated using the estimated sales volume estimation model.

In one embodiment, the user terminal 200 may include a display on at least a portion of the user terminal 200, and various users provided from the service provider and the apparatus 100 for estimating the estimated sales amount of content to be commercialized through the display. An interface (User interface, UI) (eg, a content providing UI, a candidate content selection UI, and an estimated sales amount output UI) may be output. For example, the user terminal 200 may include at least one of a smartphone, a tablet PC, a notebook, and a desktop, but is not limited thereto.

In various embodiments, the user terminal 200 is wired or wirelessly connected to the expected sales amount estimating device 100 of the content to be commercialized, and provides preference information for a plurality of candidate content to the expected sales amount estimating device 100 of the content to be commercialized. I can. For example, a plurality of users may input preference information for a specific candidate content by touching and inputting a specific candidate content in a candidate content selection UI (eg, FIG. 7) output on their smartphone, and The included communication module may provide preference information for specific candidate content through the network 400 to the apparatus 100 for estimating the expected sales amount of the content to be commercialized.

In an embodiment, the external server 300 may store various services and data related to a plurality of contents provided from each of the various services. In various embodiments, the external server 300 may include an SNS server that provides a social network service (SNS), and the external server 300 is uploaded by users using the SNS service through their accounts. You can store social data. In various embodiments, the external server 300 may include a web site (eg, a fan cafe) management server related to the content to be commercialized, and the external server 300 may include data on the number of members of the web site, data for increasing or decreasing the number of members, and You can save the increase or decrease of the posting data. In various embodiments, the social data may include social data in the form of text and social data in the form of images.

In various embodiments, the external server 300 may receive a request for transmission of data related to a plurality of candidate contents from the apparatus 100 for estimating the expected sales amount of the content to be commercialized through the network 400, and According to a request for transmission of related data, only data related to a specific service is extracted from a large amount of previously stored data and provided to the apparatus 100 for estimating the estimated sales amount of the content to be commercialized.

Here, the apparatus 100 for estimating the expected sales amount of the content subject to productization is described as receiving information related to a plurality of candidate content from the external server 300, selecting the subject content for productization, and estimating the expected sales amount, but is not limited thereto. , The apparatus 100 for estimating the estimated sales amount of the content subject to productization includes a control signal (eg, a control signal instructing the extraction of data related to a plurality of candidate content) to the external server 300, a control signal instructing the selection of the content subject to productization, and It can perform only the function of transmitting a control signal instructing the estimation of the expected sales amount of the content to be commercialized), and the external server 300 receives a control signal from the expected sales amount estimating device 100 of the content to be commercialized and transmits a control signal to the control signal. You can perform the following functions.

2 is a hardware configuration diagram of an apparatus for estimating an expected sales amount of content to be commercialized according to another embodiment of the present invention.

Referring to FIG. 2, the apparatus 100 for estimating the expected sales amount of content subject to commercialization according to an embodiment of the present invention (hereinafter, “computing apparatus 100”) may include a processor 110 and a memory 120. have. In various embodiments, the computing device 100 may further include a network interface (or communication interface) (not shown), a storage (not shown), and a bus (not shown).

In one embodiment, the processor 110 may control the overall operation of each component of the computing device 100. The processor 110 may be configured to include a central processing unit (CPU), a micro processor unit (MPU), a micro controller unit (MCU), or any type of processor well known in the art.

In various embodiments, the processor 110 may perform an operation on at least one application or program for executing the method according to the embodiments of the present invention. In various embodiments, the processor 110 includes one or more cores (not shown) and a graphic processing unit (not shown) and/or a connection path (eg, a bus) for transmitting and receiving signals with other components. can do.

In various embodiments, the processor 110 temporarily and/or permanently stores a signal (or data) processed inside the processor 110. -Only Memory, not shown) may be further included. In addition, the processor 110 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processor, RAM, and ROM.

In one embodiment, the processor 110 may execute one or more instructions stored in the memory 120 to perform a method to be described with reference to FIGS. 2 to 6 (eg, a method of estimating the expected sales amount of content to be commercialized). . For example, the processor 110 executes one or more instructions stored in the memory 120 to select at least one product target content from among a plurality of candidate content, and at least one product target content from the outside. Prediction of content to be commercialized, including an operation of obtaining related data, an operation of indexing data according to a preset criterion, and an operation of estimating the estimated sales volume for at least one product target content using the indexed data and an estimated sales volume estimation model. You can perform the method of estimating the sales volume.

In one embodiment, the memory 120 may store various types of data, commands, and/or information. The memory 120 may store programs (one or more instructions) for processing and controlling the processor 110. Programs stored in the memory 120 may be divided into a plurality of modules according to functions.

In various embodiments, steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or a combination thereof. Software modules include Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), Flash Memory, hard disk, removable disk, CD-ROM, or It may reside on any type of computer-readable recording medium well known in the art to which the present invention pertains. Hereinafter, a method of estimating the expected sales amount of content to be commercialized performed by the computing device 100 will be described with reference to FIGS. 3 to 7.

3 is a flowchart of a method for estimating an expected sales amount of content to be commercialized according to another embodiment of the present invention.

Referring to FIG. 3, in step S110, the computing device 100 may select a plurality of candidate contents for selecting a content to be commercialized from among a plurality of contents provided from a specific service.

In an embodiment, the computing device 100 may be communicatively connected with an external server 300 that stores data related to a specific service, and may obtain data related to a specific service from the external server 300. The computing device 100 may extract a keyword indicating a specific service from data related to the specific service, and select a candidate content from among a plurality of contents by using the extracted keyword. Hereinafter, it will be described with reference to FIG. 4.

4 is a diagram illustrating a process of selecting a candidate content by using a plurality of pieces of data by an apparatus for estimating an expected sales amount of content to be commercialized, according to various embodiments.

Referring to FIG. 4, the computing device 100 may obtain social data 10 related to a specific service from the external server 300. Here, the social data 10 may include at least one of open social media data, closed social media data, online data, and mobile and web homepage data, but is not limited thereto.

In various embodiments, the computing device 100 may obtain a plurality of social data 10 for each of a plurality of contents provided by a specific service, and group the plurality of social data 10 by content.

Thereafter, the computing device 100 may extract the keyword 20 for each social data 10 related to a specific service by using a previously stored keyword extraction model. For example, the computing device 100 recognizes a character string included in social data related to a specific service using an optical character reader (OCR) technology, and corresponds to a pre-stored keyword list within the recognized character string. The keyword 20 can be extracted.

In various embodiments, the computing device 100 may generate a keyword 20 for each content by performing keyword analysis on each of the grouped social data 10.

Thereafter, the computing device 100 may score each content using the keyword 20 for each content. For example, the computing device 100 includes the number of times the keyword 20 is extracted from a plurality of contents, the number of times the keyword 20 is extracted, and the emotional information of the keyword 20 (eg, the extracted keyword 20). Based on information indicating whether it is a keyword indicating a positive meaning or a keyword indicating a negative meaning), a score may be calculated for each of a plurality of contents.

In addition, the computing device 100 may select a candidate content 30 from among a plurality of content according to the scoring result. For example, the computing device 100 selects content (eg, content #1, #2, and #N) in which a result value calculated as a scoring result among a plurality of content is equal to or greater than a preset reference value (eg, 60 points) as a candidate content. You can select (30). However, the present invention is not limited thereto and may be performed by the computing device 100, and any method of selecting the candidate content 30 from among a plurality of contents provided by a specific service may be applied.

In various embodiments, the computing device 100 may repeat the operation of scoring the plurality of contents N times when there is no content in which a result value calculated as a scoring result is equal to or greater than a preset reference value among the plurality of contents. . In addition, even though the operation of scoring a plurality of contents is repeated N times, the computing device 100 selects the candidate contents 30 among a plurality of contents provided by a specific service when there is no contents having a result value equal to or greater than a preset reference value. A guide message informing that there is no content to be selected may be output.

In one embodiment, various means for scoring a plurality of contents may be utilized. For example, it is possible to collect and generate information on the number of comments on the content itself, users' preference for the content, users' expectations for the product of the content, and the type of goods that users want.

For example, even if a lot of mentions are made about the content itself, and the likelihood is high, users may not necessarily want to produce goods. Therefore, it is required to analyze the expectations of users for the goods of the content, which may require an additional step different from the method of analyzing the popularity, likelihood, and awareness of the content itself.

For example, when a content is mentioned, other keywords mentioned with the content may be collected in association. From the collected keywords, positive and negative words related to the content may be classified, and keywords related to goods corresponding to the previously stored goods database may be classified. For example, the goods database may mean a database that stores information on the types of goods that can be produced or are currently being sold. For example, the more often users mention a keyword corresponding to a specific content and a keyword such as a cup or cell phone case, the more likely it is that users expect to produce goods such as a cup or cell phone case made with the content. I can. The computing device 100 may not only check the expectations for the production of goods by users from the collected information, but also check information indicating that users want a specific type of goods in some cases.

In an embodiment, the computing device 100 may generate and upload an image of goods produced based on one or more candidate contents through a social network service or a community. For example, the computing device 100 may generate product images such as dolls, figures, cups, cell phone cases, etc. including characters or logos of candidate content, and for image generation, synthesis, 3D modeling, further GAN technology, etc. This may be utilized, but is not limited thereto.

The computing device 100 may collect user feedback on the uploaded image. For example, the computing device 100 collects information by searching for user's empathy feedback, comments, sharing, and social information (eg, blog posts, etc.) cited for the uploaded image. The amount of data and positive or negative keywords included in the data can be analyzed and used to determine the content to be produced among candidate contents. For example, when candidates for content to be produced are determined through the above-described scoring method, the scoring result can be corrected or additional points can be given by distributing a virtual goods image including the corresponding content and then searching and collecting feedback on the virtual goods image. .

Referring back to FIG. 3, in step S120, the computing device 100 may select at least one content to be commercialized from among a plurality of candidate content selected in step S110.

In an embodiment, the computing device 100 may obtain preference information for at least one candidate content from a plurality of users, and select a content to be commercialized from among at least one candidate content using the preference information. Here, the preference information may include content selection data generated by selecting a preferred candidate content, and search history data generated by searching for a content.

In various embodiments, the computing device 100 may provide a candidate content selection UI to the user terminal 200 corresponding to a plurality of users, and provide a candidate content list including candidate content through the candidate content selection UI. In addition, content to be commercialized may be selected using information on the candidate content selected through the candidate content selection UI. For example, the computing device 100 may provide URL (Uniform Resource Locator) information to each of the user terminals 200 corresponding to a plurality of users. Likewise, a UI in the form of a web site 70 including the candidate content list 80 may be provided.

In one embodiment, the candidate content list 80 matches the candidate content 81 and each of the candidate content 81, and may include a selection button 82 that can be selectively input by a user. The computing device 100 may receive an input (eg, click input) for the selection button 70 from the plurality of user terminals 200, and store the input for the selection button 82 as preference information. In various embodiments, the computing device 100 may receive inputs for a plurality of selection buttons 82 from a single user.

Thereafter, the computing device 100 calculates the number of times the selection button 82 is selected for each of the plurality of candidate contents (eg, the number of times the selection button 82 is input) by using the input to the selection button 82 received from the plurality of users. In addition, a predetermined number of candidate contents may be sequentially selected from candidate contents having a high number of times selected from a plurality of users as target contents for commercialization. For example, when the preset number is one, the computing device 100 may select, among a plurality of candidate contents, the candidate contents that have received the most selection from users as the contents to be commercialized.

In various embodiments, the computing device 100 may obtain search history data for each of a plurality of candidate contents from a service providing server that provides a specific service. For example, users input through a content providing UI from a service providing server (eg, a webtoon providing server) that provides a content providing UI (UI for providing various content, eg, a webtoon providing UI) to the user terminal 200 One content search history data (eg, a history of searching a specific webtoon) can be obtained.

In addition, the computing device 100 may calculate the number of searches and the frequency of searches for each candidate content by using the search history data obtained from the service providing server, and store the calculated number of searches and the frequency of searches as preference information. For example, the computing device 100 may rank each candidate content and set a preference rating for each candidate content according to the number of searches and the frequency of the search.

Thereafter, the computing device 100 may select a content to be commercialized from a preset number of candidate contents from among the plurality of candidate contents according to the search history data of each of the plurality of candidate contents (or a preference rating set for each candidate contents). . For example, when the preset number is one, the candidate content that is most searched by users among a plurality of candidate content may be selected as the content to be commercialized, or the candidate content having the highest set preference rating may be selected as the content to be commercialized. . However, it is not limited thereto.

In various embodiments, the computing device 100 may obtain content purchase history information of a plurality of users from the external server 300, and use the content purchase history information of the plurality of users to obtain preference information for a plurality of candidate contents. Can be collected. For example, the computing device 100 uses the content purchase history information of a first user who has not selected at least one candidate content through the candidate content selection UI, The candidate content identical to the content or candidate content having a high similarity to the first content may be recognized as being selected by the first user through the candidate content selection UI and reflected in statistics.

In step S130, the computing device 100 may obtain data related to the content to be commercialized from the external device 300. For example, the computing device 100 may transmit a request for data related to the content to be commercialized to the external server 300, and in response to the transmitted data request, the social device related to the content to be commercialized from the external server 300 Data, data on the increase or decrease in the number of members of a website (eg, a fan cafe) related to the content to be commercialized, and data on the increase or decrease in the upload amount of postings on the website may be provided. However, the data provided from the external server 300 is not limited thereto, and any data available for estimating the expected sales volume of the content to be commercialized may be applied (e.g., data on the number of members of the website, the daily average of the website). Post count data, data indicating the number and frequency of searches through the search portal site, etc.).

In step S140, the computing device 100 may index data related to the content to be commercialized provided from the external server 300 through step S130 according to a preset criterion, and in step S150, the computing device 100 is indexed. The estimated sales volume for the content subject to commercialization can be estimated using the data and the estimated sales volume estimation model. Hereinafter, it will be described with reference to FIG. 5.

FIG. 5 is a diagram illustrating a process of estimating a predicted sales amount by using a plurality of pieces of data by an apparatus for estimating a predicted sales amount of content to be commercialized in various embodiments.

Referring to FIG. 5, in various embodiments, the computing device 100 may obtain data 40 related to content to be commercialized from the external server 300. Here, the data 40 related to the content to be commercialized is social data on the content to be commercialized (e.g., data in the form of text uploaded on SNS and data indicating the content to be commercialized among data in the form of images), and the number of fan cafe members increases or decreases. It may include data and data of increase or decrease in the upload amount of fan cafe posts.

Thereafter, the computing device 100 may generate the indexed data 50 by indexing the data 40 related to the content to be commercialized.

In one embodiment, the computing device 100 extracts a keyword from the data 40 related to the content to be commercialized, and uses the extracted keyword and a list of pre-stored interest and preference values 51 for productization target content. By quantifying the degree of interest and preference, the data 40 related to the content to be commercialized can be indexed. However, it is not limited thereto.

Thereafter, the computing device 100 can estimate the expected sales volume of the product target content using the output result value by inputting the interest level and the likelihood value (indicated data) for the product target content into the estimated sales volume estimation model 61. have.

In one embodiment, the estimated sales volume estimation model is based on the estimated sales volume and actual sales volume data by pre-stored content type, the sales volume fluctuation data according to the increase or decrease in the upload amount of posts on the website (eg, fan cafe site), and the increase or decrease in the number of members At least one of the sales volume fluctuation data may be a pre-trained model as training data.

In various embodiments, the computing device 100 is an initial estimated sales volume estimation model for estimating the expected sales volume of the content to be commercialized by using the sales volume information of various types of content or goods obtained from an external server and social data related to the content or goods. May be generated, and an initial estimated sales amount estimation model may be trained by using information on the expected sales amount of the content subject to productization and the actual sales amount of the productized content as training data.

For example, the computing device 100 may receive various types of contents or goods from a first external server (eg, a server that stores data generated and generated in a commerce site that sells various types of contents or goods). You can get related data and data related to sales volume. As an example, the computing device 100 may obtain information on the sales volume of specific content or goods from phrases such as "Limited quantity of xx sold out" and "sales volume exceeded xx" written on the commerce site. In addition, the computing device 100 may obtain social data for specific content or goods from a second external server (eg, a server that stores social data uploaded by a plurality of users).

Thereafter, the computing device 100 uses data related to various types of content or goods obtained from the first external server, data related to sales, and social data obtained from the second external server as learning data, and the sales amount and proportion according to the social data, It is possible to create an initial estimated sales volume estimation model capable of predicting a correlation between the sales volume and the words mentioned by a plurality of users on social media.

Thereafter, the computing device 100 may continuously learn the initial estimated sales volume estimation model by using information on the estimated sales volume of the content to be commercialized and the actual sales volume of the productized content estimated through step S150 as training data. . Through this, the predicted sales amount estimation algorithm of the predicted sales amount estimation model may be more accurately updated, thereby increasing the reliability of the result value extracted from the estimated sales amount estimation model.

In various embodiments, the computing device 100 selects at least one product type that can be produced using the product target content according to the type of product target content, and individually estimates the estimated sales volume for the at least one product type. can do. For example, if the target content for productization is webtoon content, select the type of product that can be produced as webtoon content (e.g., stationery, stickers, postcards, bags, mugs, tumblers, figures, etc.), and individually for each selected product. You can estimate the expected sales volume.

In various embodiments, the computing device 100 may estimate a consumer group of the content to be commercialized by using data related to the content to be commercialized, and correct the estimated estimated sales volume according to the estimated consumer group. Hereinafter, it will be described with reference to FIG. 6.

FIG. 6 is a diagram illustrating a configuration in which an apparatus for estimating a predicted sales amount of content to be commercialized corrects an expected sales amount using a consumer group estimated through a consumer group estimation model, in various embodiments.

Referring to FIG. 6, in various embodiments, the computing device 100 may estimate a consumer group of content to be commercialized using the consumer group estimation model 62. For example, the computing device 100 may use the result value output by inputting data related to the content to be commercialized into the consumer group estimation model 62 as an input value, and consume it when the content to be commercialized is actually produced and sold. It is possible to estimate the age and gender of expected consumers.

Here, the consumer group estimation model 62 may be a model that has previously learned data related to a plurality of candidate contents, information about a plurality of users, and preference information of users for a plurality of candidate contents as learning data. A result value indicating a consumer group may be output by using information (eg, the type of content to be commercialized, the age, gender, etc. of users who have selected the content to be commercialized) as input values.

Thereafter, the computing device 100 may correct the expected sales volume by using the consumer group estimated using the consumer group estimation model 62. For example, if the age group of consumers who are expected to consume the content subject to productization is in their teens, the amount of money that can be spent on purchasing the product is relatively small compared to other age groups (eg, 20s to 40s). Sales of expensive products (e.g. high quality figures, etc.) may be low. In addition, when the gender of a consumer who is expected to consume the content to be commercialized is male, the sales volume of products used for decoration purposes (eg, small items or cosmetics) is smaller than that of female consumers. Therefore, the computing device 100 corrects the estimated sales volume estimated through the estimated sales volume estimation model 61 using the information on the consumer group estimated from the consumer group estimation model 62, so that a more accurate estimated sales volume can be estimated. have. However, the present invention is not limited thereto, and any factor capable of calculating a more accurate estimated sales amount may be applied by correcting the estimated sales amount estimated from the expected sales amount estimation model 61.

In an embodiment, the computing device 100 may collect information on users who have uploaded specific content in order to estimate a predicted consumer group. The computing device 100 may extract features of users who have uploaded a lot of information about the corresponding content through statistical analysis of the collected information. For example, the characteristics of the user may include, but are not limited to, the user's age group, gender, residential area, and occupation.

In addition, the computing device 100 may estimate the likelihood of each user with respect to a specific content, which is uploaded with information on the content and the user's emotion information (e.g., positive or negative expression for the corresponding content). Inclusion, etc.), but is not limited thereto.

In addition, the computing device 100 is a community or platform in which users who have uploaded a lot of information about a specific content, or a site or service frequently visited by users who have uploaded a lot of information about a specific content, and information about the content to be viewed. Can be collected.

Accordingly, when the computing device 100 produces and sells goods for a specific content, it is possible to collect personal information about customers who will be the consumer class, and information about the platforms that are mainly active or contents that are frequently viewed, and based on this Thus, it is possible to estimate the sales volume of goods based on the corresponding content, or to determine a marketing method and channel for selling goods.

Components of the present invention may be implemented as a program (or application) and stored in a medium to be executed by being combined with a computer that is hardware. Components of the present invention may be implemented as software programming or software elements, and similarly, embodiments include various algorithms implemented with a combination of data structures, processes, routines or other programming elements, including C, C++. , Java, assembler, or the like may be implemented in a programming or scripting language. Functional aspects can be implemented with an algorithm running on one or more processors.

In the above, embodiments of the present invention have been described with reference to the accompanying drawings, but those skilled in the art to which the present invention pertains can be implemented in other specific forms without changing the technical spirit or essential features. You will be able to understand. Therefore, the embodiments described above are illustrative in all respects, and should be understood as non-limiting.

100: Estimated device (or computing device) of the expected sales amount of content to be commercialized
200: user terminal
300: external server

Claims (10)

In the method performed by the computing device,
Selecting at least one content to be commercialized from among a plurality of candidate content;
Obtaining data related to the at least one product target content from the outside;
Indexing the data according to a preset criterion; And
And estimating an expected sales amount for the at least one product target content using the indexed data and a predicted sales amount estimation model. The method of claim 1,
The above data is:
Productization target content including at least one of social data related to the at least one product target content, increase or decrease data of the number of members of the website related to the at least one product target content, and data of increase or decrease the upload amount of posts of the website How to estimate estimated sales. The method of claim 1,
The estimated sales volume estimation model,
Pre-learned model as training data using at least one of predicted sales volume and actual sales volume data for each type of previously stored content, sales volume fluctuation data according to an increase or decrease in the uploaded amount of posts on the website, and sales volume fluctuation data according to an increase or decrease in the number of members A method of estimating the expected sales volume of content targeted for commercialization. The method of claim 1,
The step of indexing the data,
Extracting a keyword related to the content to be commercialized from the data; And
And digitizing the degree of interest and the degree of likelihood of the content to be commercialized using the extracted keyword and a pre-stored interest and preference value for each keyword. The method of claim 1,
The step of estimating the expected sales volume,
Productization comprising the step of selecting at least one product type that can be produced using the productization target content according to the type of productization target content, and individually estimating the expected sales volume for the at least one product type How to estimate the estimated sales of the target content. The method of claim 1,
The step of estimating the expected sales volume,
And estimating a consumer group of the content to be commercialized using data related to the content to be commercialized, and correcting the estimated estimated sales amount according to the estimated consumer group. The method of claim 1,
The step of selecting the at least one product target content,
Providing a candidate content list including the plurality of candidate content through a candidate content selection user interface (UI), and selecting one or more candidate content from among the at least one candidate content through the candidate content selection UI ; And
And sequentially selecting a preset number of candidate contents from among the plurality of candidate contents having a high number of selections as the contents to be commercialized. The method of claim 7,
The step of selecting the at least one product target content,
Providing a candidate content creation UI for creating, modifying, and uploading the new candidate content;
Uploading the new candidate content as candidate content; And
Determining a production possibility of the uploaded new candidate content, and adding a new candidate content determined to be capable of being produced to the candidate content list. A memory for storing one or more instructions; And
A processor that executes the one or more instructions stored in the memory,
The processor executes the one or more instructions,
An apparatus for performing the method of claim 1. A computer program combined with a computer as hardware and stored in a recording medium readable by a computer to perform the method of claim 1.

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