KR20240011096A - Electronic device for recommending advertising media based on scoring information and operating method of the same - Google Patents

Abstract

According to one embodiment of the present disclosure, the former for recommending advertising media includes: memory; A communication unit that transmits and receives data to and from an external server; and at least one processor to process the data; Including, wherein the at least one processor receives first advertisement target information including domain information of the advertisement target to be advertised from the user device, and generates a plurality of candidate advertisements for advertising the advertisement target based on the domain information. Generating a first advertising media list including media, generating a second advertising media list based on a scoring process for the plurality of candidate advertising media included in the generated first advertising media list, and generating the generated first advertising media list. 2 Transmit a list of advertising media to the user device, receive selection information about one or more candidate advertising media from the second advertising media list from the user device, and create a list of recommended advertising media based on the selection information. can be provided.

Description

Electronic device for recommending advertising media based on scoring information and method of operating the same {ELECTRONIC DEVICE FOR RECOMMENDING ADVERTISING MEDIA BASED ON SCORING INFORMATION AND OPERATING METHOD OF THE SAME}

The present disclosure relates to an electronic device for recommending advertising media and a method for controlling the same. More specifically, it relates to an electronic device for recommending optimal advertising media including advertising media scoring based on data and a method for controlling the same.

Recently, as the number of Internet users has rapidly increased, compared to the existing traditional advertising method of using newspapers, flyers, and outdoor electronic billboards, various events and promotions, including shopping mall product advertisements, are provided through various types of advertising media sites such as various portal sites and media sites. Online advertisements regarding the product are mainly used, and as a result, purchases through online shopping malls are becoming more active.

In other words, the distribution channel was shortened to 'business-consumer', there were no restrictions on transaction area and transaction time, and the sales base was moved to a virtual space, making normal commerce possible without a sales space. For such e-commerce, large-scale shopping malls are thriving through the Internet or PC communication networks. Meanwhile, conventional methods of informing the general public of a specific company's products or a specific company are very diverse, including methods such as magazines, newspapers, broadcasting, distributing advertisements to individuals, or attaching advertisements to posters.

However, this method is a method of indiscriminately and randomly advertising to an unspecified number of people, and not only does it consume a lot of advertising cost, but it also advertises a specific company's product or a specific company to unnecessary people, so the advertising effect is as expected. There are several problems, such as not being able to obtain it and also not being able to know at all how much was advertised. These problems still persist in online advertising.

Typically, the online advertisement refers to a banner advertisement displayed in a square or circle shape at a specific location within the web page of an advertising media site (e.g., an advertising media company), and a user who accesses the web page receives the banner advertisement. If you click , the advertisement will be performed in a format that links to a specific web page on the advertiser's site related to the advertising material.

The reality is that advertising effectiveness in online advertising is determined solely by the skills of the advertising marketer. In other words, advertising for a specific product can achieve high advertising efficiency when it is executed through an advertising media company where consumers of the product are actively engaged. Accordingly, it is very important to accumulate data on advertising types, advertising products, advertising types, industry/industry groups, and click through rates (CTRs) executed in specific advertising media.

However, data about these specific advertisements and their corresponding advertising media is manually entered individually or by manually parsing search results to classify the information. In other words, the conventional method of managing advertising data, etc. is that people manually input it separately and then visit and search multiple sites to match identifiers. This is inefficient, and not only does it cost a lot of money and time to process the work. , there is a problem that the possibility of mistakes due to manual input is always present, such as when one advertisement is advertised in multiple advertising media.

Therefore, there is a need for a method of acquiring advertising media information to be provided to advertisers, such as correlations regarding advertising efficiency between advertising media corresponding to advertising target products and/or industry groups.

One aspect of the disclosed invention seeks to provide an electronic device for recommending advertising media and a method for recommending the same.

Additionally, one aspect of the disclosed invention seeks to provide an efficient recommendation method including a scoring process to recommend advertising media suitable for users.

According to one embodiment of the present disclosure, the former for recommending advertising media includes: memory; A communication unit that transmits and receives data to and from an external server; and at least one processor to process the data; Including, wherein the at least one processor receives first advertisement target information including domain information of the advertisement target to be advertised from the user device, and generates a plurality of candidate advertisements for advertising the advertisement target based on the domain information. Generating a first advertising media list including media, generating a second advertising media list based on a scoring process for the plurality of candidate advertising media included in the generated first advertising media list, and generating the generated first advertising media list. 2 Transmit a list of advertising media to the user device, receive selection information about one or a plurality of candidate advertising media from the second advertising media list from the user device, and create a list of recommended advertising media based on the selection information. can be provided.

According to one embodiment, the at least one processor generates second advertising target information including an inflow path for the advertising target, target information, industry information, and competitive service information based on the domain information, and a plurality of Obtaining advertising media information including the inflow path for advertising media, target information, industry information, and competitive service information, and obtaining a match score calculated by calculating the degree of match between the second advertising target information and the advertising media information, The first advertising media list may be generated by determining an advertising medium whose consistency score is greater than or equal to a preset reference value as the candidate advertising medium.

According to one embodiment, when obtaining the match score, the at least one processor assigns a first weight to the match score based on the number of matching items between the items of the second advertisement target information and the advertising medium information. It can be set to grant.

According to one embodiment, the first advertisement target information includes an expected inflow path of the advertisement target, desired target information, industry information, and competitive service information, and the first advertisement target information and the second advertisement target information A second weight can be assigned to items that match between items.

According to one embodiment, the scoring process includes the transaction volume of advertising media, repurchase rate data, keyword exposure frequency, sales increase rate, CTR (Click Through Rate), advertisement success rate, and the match score as scoring items, and the scoring By assigning scores and different weights to each item, the scoring score can be calculated as the sum of the products of the scores and weights for each scoring item.

According to one embodiment, among the weights for each scoring item, the weight for the consistency score may have the largest value.

According to one embodiment, the second advertising media list may be provided to the user device by ranking the plurality of candidate advertising media based on the scoring score.

According to one embodiment, when the at least one processor obtains the selection information about one or more advertising media from the second advertising media list from the user device, the selected candidate advertising media has the highest advertising efficiency. The list of recommended advertising media is generated by adding past advertising information, and the generated list of recommended advertising media is transmitted to the user device, and the advertising efficiency is determined by the advertising re-execution rate of the advertising medium, the increase in sales from previous advertising, and It corresponds to the value determined by the user's selection among the total number of advertisement views in the previous advertisement, and can be calculated based on data from a preset period from the time when the advertisement efficiency is calculated.

According to one embodiment, after transmitting the list of recommended advertising media to the user device, the electronic device may match the second advertising target information and the selection information and store them in the memory.

According to one embodiment, a method for recommending an advertising medium includes receiving first advertising target information including domain information of an advertising target to be advertised from a user device; generating a first advertising media list including a plurality of candidate advertising media for advertising the advertising target based on the domain information; generating a second advertising media list based on a scoring process for the plurality of candidate advertising media included in the generated first advertising media list; transmitting the generated second advertising media list to the user device; receiving, from the user device, selection information about one or a plurality of candidate advertising media from the second advertising media list; and providing a list of recommended advertising media based on the selection information. may include.

According to an embodiment of the present disclosure, the optimal advertising medium can be provided to the user based on data entered by the user.

According to an embodiment of the present disclosure, the convenience of using domestic advertising media can be improved through customized recommendations of advertising media by establishing a data-based marketing decision-making ecosystem.

According to an embodiment of the present disclosure, information imbalance between advertisers and advertising media companies in the digital advertising market can be resolved and help establish an optimal marketing strategy.

The effects of the present application are not limited to the effects described above, and effects not mentioned can be clearly understood by those skilled in the art from this specification and the attached drawings.

Figure 1 is a diagram schematically illustrating the overall configuration of an advertising media recommendation system using scoring according to an embodiment of the present disclosure.
FIG. 2 is a block diagram illustrating an electronic device for recommending advertising media according to an embodiment of the present disclosure.
Figure 3 is a flow chart showing step by step the overall advertising medium recommendation method performed by an electronic device, according to an embodiment of the present disclosure.
Figure 4 is a diagram illustrating a screen that can be displayed on a user terminal to obtain data of a second user in an embodiment of the present disclosure.
FIG. 5 is a flowchart illustrating steps in which an electronic device that acquires second user data generates a first advertising media list in a database according to an embodiment of the present disclosure.
Figure 6 is a flowchart showing the steps of generating a second advertising media list through a second process from a first advertising media list, according to an embodiment of the present disclosure.
7A to 7C are diagrams illustrating a UI/UX screen in which a second advertising media list generated by the electronic device 100 is provided to a second user.
Figure 8 is a flowchart of a third process for generating a fourth advertising medium list from a third advertising medium list according to an embodiment of the present disclosure.
Figure 9 is a schematic diagram showing how a fourth advertising medium list that can be provided to a second user can be provided in one embodiment of the present disclosure.

Specific structural and functional descriptions of the various embodiments are merely illustrative for the purpose of explanation of the various embodiments, and the various embodiments may be implemented in various forms and are limited to the embodiments described in the present specification or application. It should not be construed as such.

Since various embodiments may be subject to various changes and may have various forms, various embodiments will be illustrated in the drawings and described in detail in the present specification or application. However, the matters disclosed in the drawings are not intended to specify or limit the various embodiments, but should be understood to include all changes, equivalents, and substitutes included in the spirit and technical scope of the various embodiments.

The terms used in this specification are merely used to describe specific embodiments and are not intended to limit various embodiments. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as “include” or “have” are intended to indicate the existence of a described feature, number, step, operation, component, part, or combination thereof, but are not intended to indicate the presence of one or more other features or numbers. It should be understood that this does not preclude the existence or addition of steps, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the technical field to which this disclosure pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and unless clearly defined in this specification, should not be interpreted as having an ideal or excessively formal meaning. .

The term 'unit' used in this disclosure refers to software or hardware components such as FPGA (Field Programmable Gate Array) or ASIC (Application Specific Integrated Circuit). '~part' performs specific roles, but is not limited to software or hardware. The '~ part' may be configured to reside in an addressable storage medium and may be configured to reproduce on one or more processors. Therefore, according to some embodiments, '~ part' refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, and processes. Includes scissors, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and 'parts' may be combined into a smaller number of components and 'parts' or may be further separated into additional components and 'parts'. Additionally, components and 'parts' may be implemented to regenerate one or more CPUs within a device or a secure multimedia card. Additionally, according to various embodiments of the present disclosure, '˜unit' may include one or more processors.

Hereinafter, the operating principle of the present disclosure will be described in detail with reference to the attached drawings. In the following description of the present disclosure, if a detailed description of a related known function or configuration is determined to unnecessarily obscure the gist of the present disclosure, the detailed description will be omitted. In addition, the terms described below are terms defined in consideration of the functions in the present disclosure, and may vary depending on the intention or custom of the data provider or operator. Therefore, the definition should be made based on the contents throughout this specification.

This disclosure relates to an electronic device for advertising media recommendation and an advertising media recommendation process method.

According to an embodiment of the present disclosure, the electronic device may select a candidate group of advertising media based on domain information or user data.

According to an embodiment of the present disclosure, the electronic device may generate a list of advertising media for user selection through a scoring process.

According to an embodiment of the present disclosure, the electronic device may provide a list of recommended advertising media based on the advertising media selected by the user.

Figure 1 is a diagram illustrating the overall configuration of an advertising media recommendation system using scoring (hereinafter referred to as 'this system') according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the system 1000 may include an advertising media recommendation electronic device 100 (hereinafter referred to as 'electronic device'), a server 200, and a user terminal 300. .

According to an embodiment of the present disclosure, the system 1000 can recommend advertising media through the electronic device 100.

Meanwhile, in this disclosure, the term advertising medium refers to a platform that provides the Internet, applications, etc. that can display advertisements that customers who use the service of this device wish to receive. Additionally, in this disclosure, the term advertising media list refers to an advertising media list generated by the electronic device 100. On the other hand, the term advertising target refers to all information including product information, brand information, event information, web page address, images, etc. that customers using the service of this device wish to display on advertising media.

According to an embodiment of the present disclosure, the system 1000 may obtain data from the server 200. That is, the system 1000 can obtain and process data that is continuously and/or periodically updated in the server 200. In this case, the system 1000 can generate a candidate media list for advertising media based on the obtained data. Additionally, the electronic device 100 may perform a scoring process on the list generated by the server 200. As an example, the server 200 of the present disclosure may perform a domain profiling function such as a similar web.

The server 200 according to an embodiment of the present disclosure can process and store various data, and transmit and/or receive the processed data. More specifically, the server 200 may be connected to the electronic device 100 and the user terminal 300 through the network 1. Accordingly, the server 200 can transmit and/or receive data for linking data with each other through the network 1.

The server 200 may be, for example, a platform, a host server providing a specific service, and/or a cloud server, but is not limited thereto. For example, the server 200 may be configured integrally with a database and searches for data corresponding to the request according to a request from the electronic device 100, generates the requested specific information based on the retrieved information, and generates the generated information. Specific information may be transmitted back to the electronic device 100.

The server 200 may exist as a single server or may be comprised of multiple servers. According to one embodiment, the server 200 may include a plurality of different servers depending on the operator.

According to an embodiment of the present disclosure, the system can obtain user information from the user terminal 300. Information obtained from the user terminal 300 may be transmitted to the electronic device 100 or the server 200. Based on information obtained from the user terminal 300, the electronic device 100 and/or the server 200 may generate a list of advertising medium candidates. In the present disclosure, the user terminal providing user information may include any device capable of transmitting and receiving information to and from the system.

According to an embodiment of the present disclosure, the system can acquire user information from the user terminal 300 and create a list of advertising media based on it, and can use it as standard data in generating the list of advertising media. .

The user terminal 300 according to an embodiment of the present disclosure includes, for example, Personal Communication System (PCS), Global System for Mobile communication (GSM), Personal Digital Cellular (PDC), Personal Handyphone System (PHS), and PDA. (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (WCode Division Multiple Access), Wibro (Wireless Broadband Internet) terminal, smartphone, smart phone It may include, but is not limited to, all types of wired and wireless communication devices capable of input and output, such as a pad (SmartPad), tablet PC, laptop, wearable device, digital signage, etc.

Meanwhile, in this disclosure, the term user refers to a user who uses the electronic device 100 (hereinafter referred to as a first user in this disclosure), an institution/business providing the device and method of this disclosure, or an operator or direct user thereof. may refer to) or, a customer receiving a service according to an embodiment of the present disclosure of the electronic device 100 (hereinafter referred to as a second user in the present disclosure. For example, searching for a suitable advertising media company to execute advertisements) client), and should be interpreted correctly according to the intended context of this disclosure. Accordingly, the user terminal 300 in the present disclosure may be interpreted to mean the second user's terminal.

The electronic device 100, the server 200, and the user terminal 300 may each or all be connected to the network 1. Here, being able to be connected to the network 1 can be understood as being able to be connected electrically or communicationally.

An example of a network 1 includes a 3rd Generation Partnership Project (3GPP) network (3G network, 4G or Long Term Evolution (LTE) network, 5G or New Radio (NR) network, or 6G network, etc.), World Interoperability for WIMAX (WIMAX), etc. Microwave Access) network, Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), Bluetooth network, satellite broadcasting network, Includes, but is not limited to, analog broadcasting networks, DMB (Digital Multimedia Broadcasting) networks, etc.

The configuration of the entire system of the present disclosure is not limited to the above configuration and may include all other necessary configurations or omit some configurations. Additionally, it may include a configuration that can be interpreted the same as the above configuration.

FIG. 2 is a block diagram illustrating an advertising media recommendation electronic device 100 according to an embodiment of the present disclosure.

The advertising media recommendation electronic device 100 may include a communication unit 110 and a control unit 120.

The communication unit 110 may perform communication between the server 200 or the user terminal 300 and the advertising media recommendation electronic device 100, and the communication unit 110 may include a data transmission unit and a reception unit. The communication unit 110 may be implemented as a module of various standards that performs wired communication or wireless communication. For example, the communication unit 110 is a short-range communication unit 110 that performs in accordance with short-range communication standards such as BLE (Bluetooth Low Energy), Bluetooth, or Wi-Fi, or a long-distance communication unit that performs in accordance with long-distance communication standards such as 3G, 4G, or 5G. It may include (110).

According to an embodiment of the present disclosure, the communication unit 110 may obtain user information from the user terminal 300. Additionally, the communication unit 110 may transmit the acquired user information to the server 200. According to another example, the communication unit 110 may receive domain profiling information from the server 200. Additionally, the communication unit 110 transfers data between the electronic device 100 and the server 200 or the user terminal 300 in various ways, such as transmitting a list of recommended advertising media to the user terminal 300 and obtaining advertising media from a second user. can transmit and receive, and is not limited to the example disclosed above, and can perform all communication functions of the electronic device described below.

The control unit 120 may include a processor 121 and a memory 122.

The processor 121 may include a learning processor 121 for machine learning. Additionally, a model composed of an artificial neural network can be trained using learning data. The artificial neural network learned here can be referred to as a learning model. A learning model can be used to infer a result value for new input data other than learning data, and the inferred value can be used as the basis for a decision to perform an operation.

More specifically, the processor 121 of the electronic device 100 can build a machine learning model by analyzing and processing data based on actual transaction data. To ensure reliability, a machine learning model can be built by using data according to the terms of advertising media that has been traded and input data from second users as raw data. However, this is an example and is not limited thereto.

Meanwhile, the electronic device 100 may use an encoder and decoder of a neural network model. The electronic device 100 may input learning data into at least one encoder and obtain at least one vector value as an encoded result. The encoder and decoder are types of neural network models, each of which is Deep Neural Network (DNN), Recurrent Neural Network (RNN), Long Short-Term Memory models (LSTM), Bidirectional Recurrent Deep Neural Network (BRDNN), and Convolutional Neural Network (CNN). Networks), etc., but is not limited to this.

In another embodiment, the processor 121 of the electronic device 100 may train a learning model through a deep learning algorithm.

Deep learning algorithm is one of the machine learning algorithms and refers to a modeling technique developed from artificial neural networks modeled after human neural networks. Artificial neural networks can be composed of multi-layered hierarchical structures.

An artificial neural network (ANN) includes an input layer, an output layer, and at least one intermediate layer (or hidden layer) (e.g., a kernel) between the input layer and the output layer. It may be structured in a hierarchical structure. Deep learning algorithms can ultimately produce highly reliable results through learning to optimize the weights of the activation function between layers based on this multi-layer structure.

A deep learning algorithm applicable to the processor 121 of the electronic device 100 according to an embodiment of the present disclosure may include, for example, a convolutional neural network (CNN). However, it is not limited to this. It can be understood that different deep learning algorithms may be applied depending on the embodiment.

Unlike techniques in which a learning process is performed by extracting knowledge from existing data, Convolutional Neural Network (CNN) is characterized by having a structure that extracts features of data and identifies patterns of features. The convolutional neural network (CNN) can be performed through a convolution process and a pooling process. In other words, the convolutional neural network (CNN) may include an algorithm composed of a complex convolutional layer and a pooling layer. Here, a process of extracting features of data (eg, a convolution process (convolution process)) is performed in the convolution layer. The convolution process is a process of identifying features by examining adjacent components of each component in the data and deriving the identified features into one piece. As a compression process, the number of parameters can be effectively reduced. In the pooling layer, a process (for example, a pooling process) is performed to reduce the size of the layer that has undergone the convolution process. The pooling process can reduce the size of data, cancel out noise, and provide consistent features in fine details. For example, the convolutional neural network (CNN) can be used in various fields such as information extraction, sentence classification, and face recognition. Meanwhile, since the convolutional neural network (CNN) is a known technology, detailed description will be omitted below.

According to an embodiment of the present disclosure, the processor 121 includes a first process for generating a first advertising media list based on a category, a second process for performing scoring on the first advertising media list, and a second process for performing scoring on the first advertising media list, and selecting the selected advertising media by scoring. A third process may be included to select the most efficient campaign for the second advertising medium list.

In this disclosure, the term category may be used for either an advertising medium or an advertising target. In advertising media, it refers to classification items for classification by characteristics or attributes, and may typically include target age group, target gender, industry, MAU (number of users who used the service per month), etc. In terms of advertising targets, a category refers to a classification item for classifying by characteristics or attributes of the advertising target. An advertising category may refer to the characteristics or properties of the current advertisement usage status, and a second user may wish to provide an advertisement. It may also refer to the characteristics or attributes of the targeted advertising audience. Accordingly, the term category in this disclosure should be interpreted correctly according to the intended context.

Meanwhile, in the present disclosure, category information may include attribute values of the category. For example, when the category of advertising media is classified by age and gender, the category information of the advertising media may include people in their 30s and men. Additionally, when the category of the advertisement target is classified by industry, the category information of the advertisement target may include travel and lodging industry, clothing sales industry, etc.

In the present disclosure, the scoring process means generating a new list by scoring each advertising medium included in the created advertising medium list based on a certain standard. Additionally, in this disclosure, the term campaign is a unit for an advertisement target and refers to each advertisement performed in an advertising medium. Even if the same advertisement is carried out in the same advertising medium, if the advertisement is carried out under a new contract after the end of the predetermined advertising period, it can be considered a new campaign.

Meanwhile, the first advertising media list may refer to an advertising media list created based on target information, competitive services, etc. based on data input by a second user. Additionally, a scoring process for the first advertising media list Based on this, a second advertising media list may be created and provided to the second user. Additionally, when the second user selects at least one desired advertising medium from the second advertising media list, the list of desired advertising media selected by the user may be defined as the third advertising media list. In this regard, each advertising media list and the process for generating each list will be described in more detail below.

In one embodiment of the present disclosure, the processor 121 may use data stored in the memory 122 or obtain data from the server 200 to perform each process. At this time, data obtained from memory or the server may include data generated based on the user terminal 300. Data acquired by the processor 121 may be processed by a process according to the present disclosure and transmitted to the server 200 or the user terminal 300 through the communication unit 110.

The processor 121 includes a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a state machine, and a custom semiconductor (Application). There may be Specific Integrated Circuit (ASIC), Radio-Frequency Integrated Circuit (RFIC), etc., but it is not limited thereto.

According to an embodiment of the present disclosure, the memory 122 may store user data received through the communication unit 121 in order for the advertising media recommendation electronic device 100 to recommend advertising media. As another example, the memory 122 may store a list of a plurality of advertising media obtained by the processor 121.

Data obtained from the server 200 may be stored in the memory 122.

The memory 122 may store processes performed by the processor 121 and related data.

The memory 122 is a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (for example, SD or XD memory, etc.), and RAM. (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory) magnetic memory, magnetic disk , and may include at least one type of storage medium among optical disks. In addition, memory can store information temporarily, permanently, or semi-permanently, and can be provided as a built-in or removable type.

Additionally, the memory 122 may be implemented with at least one independent sub-database. For example, each sub-s included in the database can be connected wirelessly through Bluetooth, Wi-Fi, etc., and exchange data with each other through the network. Of course, the database is not limited to the above description, such as being implemented as an integrated database in which at least one independent sub-database is integrated into one. According to one embodiment, the memory 122 may be implemented as an independently implemented database or may be implemented in a format that exchanges data through communication with an independently configured database.

However, the electronic device 100 may further include all necessary components in addition to the above configuration, and may include all configurations that perform the same functions as the above configuration and are determined to be substantially the same configuration.

Figure 3 is a flow chart showing step by step the overall advertising medium recommendation method performed by an electronic device, according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the electronic device 100 may obtain data from a second user and generate a first advertising media list through a first process. Additionally, the electronic device may generate a second advertising media list through a second process from the generated first advertising media list. Additionally, the electronic device may generate a third advertising medium list based on the second user's selection from the generated second advertising medium list. In addition, the electronic device can determine the most efficient campaign for each advertising medium included in the generated third advertising media list, and generate a fourth advertising media list in the form of a media mix including this data and present it again. 2 Can be provided to users. Below, we will look at each step of the advertising medium recommendation method according to an embodiment of the present disclosure in detail.

In step s301, the electronic device 100 may obtain user data for media recommendation from the second user.

According to an embodiment of the present disclosure, the electronic device 100 may obtain information related to an advertisement target for which an advertisement is desired from the second user. Advertising target information is information related to the advertisement desired by the second user, such as the domain address of the advertisement target (e.g., url (uniform resource locator)), advertisement period, industry, target information, campaign purpose, campaign period, and expected marketing budget. Can contain data. The UI/UX through which the electronic device 100 acquires the above data will be described in detail with reference to FIG. 4.

In step s302, the electronic device 100 may generate a first advertising media list based on information obtained from the user terminal 300 through the first process.

In one embodiment of the present disclosure, the first process may be a candidate generation process, and the first advertising medium list may correspond to a candidate group list including candidate advertising media for advertising by posting the advertising target.

According to an embodiment of the present disclosure, the electronic device 100 may transmit data obtained from the user to the server 200 to create a first advertising media list. The server 200 may analyze the second user's target information, competitive service, user inflow path of the domain address, industry information, etc. based on domain information included in the data received from the electronic device 100. In the present disclosure, domain profiling may be interpreted to mean domain analysis or investigation. The electronic device 100 may perform domain profiling through the server 200. At this time, the server 200 may transmit the result data obtained through domain profiling back to the electronic device 100. At this time, the information obtained as a result of domain profiling can be expressed as advertising target information.

Alternatively, the electronic device 100 may profile the domain of the advertisement for which the second user wishes to advertise or the domain of the advertising medium for which the second user wishes to advertise. In this case, the electronic device 100 may select another advertising medium that matches the category information of the advertising medium obtained based on profiling.

As another example, if there is no domain information about the advertising medium or advertising target, the electronic device may select a candidate group using category information input by the second user. At this time, the electronic device 100 may obtain category information for each advertising medium and match it with the advertising target category obtained from the second user to select a group of candidates. Category information for each advertising medium may correspond to information obtained from an advertising media company, and can be obtained directly from the electronic device 100 through domain profiling. The information obtained at this time can be expressed as advertisement target information, and in order to distinguish it from the advertisement target information obtained as a result of domain profiling, the advertisement target information obtained based on the user's input data is classified into first advertisement target information and domain profiling. The obtained information can be classified into second advertising target information. Separately from this, category information obtained from a plurality of advertising media may be expressed as advertising media information.

According to an embodiment of the present disclosure, a candidate group may be generated based on the degree of consistency between category information in the first process. The degree of matching of category information can be calculated in numbers or a list can be created in a format that classifies whether or not it matches.

According to an embodiment of the present disclosure, when determined by whether or not there is a match, the category information of the advertising medium may be divided into groups such as match, mismatch, and similarity with the category information obtained by the domain. At this time, advertising media with matching category information can be created as a first advertising media list. At this time, separate values can be given to similar groups and the degree of consistency can be calculated using a numerical value.

In addition, the degree of consistency between category information can be quantified and the first advertising media list can be selected through this.

In step s303, the electronic device 100 may select recommended media based on the first advertising media list and generate a second advertising media list through a second process. The second process in this disclosure may include a scoring process.

In the present disclosure, the scoring process refers to scoring each advertising medium included in the first advertising media list to select recommended advertising media to be provided to the second user from the first advertising media list primarily selected based on the category. Courses provided may be included.

According to an embodiment of the present disclosure, the electronic device 100 selects advertising media included in the selected first advertising media list based on target suitability, advertising success rate, recent transaction volume, repurchase rate data, advertising media company's average weekly traffic, and average Scoring can be done based on CTR. In the present disclosure, this scoring process is referred to as a scoring process, and the second process may include a scoring process.

According to one embodiment, the second advertising media list provided to the user terminal 300 may be provided by sorting advertising media according to the degree of recommendation for each advertising media included in the list. At this time, the scoring and ordering method will be described later in FIG. 6.

In step s304, the second user may select an advertising medium for which he or she wishes to advertise from the second advertising medium list provided to the user terminal 300, and the electronic device 100 may generate a third advertising medium list based on this. .

According to an embodiment of the present disclosure, the advertising medium selected by the second user may be newly configured into a separate list. That is, a third advertising media list is created with advertising media selected from the second advertising media list by a random selection by the second user.

In step s305, information about the most efficient campaign among advertising media included in the third advertising media list generated based on the second user's selection can be obtained.

The advertising efficiency of a campaign can be calculated in a variety of ways, and examples include a method of calculating it based on indicators such as CPC (Cost per Click) and CPM (Cost per Mille).

As another example of the present disclosure, if the campaign-specific information already acquired by the device is not sufficient, the advertising efficiency for each campaign can be obtained from the server 200 and calculated through this, and the third advertising media list is sent to the server 200. After transmission, the server 200 determines advertising efficiency, data on this can be obtained.

In one embodiment of the present disclosure, the electronic device 100 may calculate a campaign according to campaign efficiency for each advertising medium. At this time, campaigns can be elected within a certain time range. As an example, efficiency can be calculated for campaigns lasting less than 3 months. In addition, the above period may change depending on the number of campaign data, changes in advertising market trends by industry, etc.

According to an embodiment of the present disclosure, the electronic device 100 may provide information for each campaign after selecting the campaign with the highest efficiency. This campaign-specific information may include information provided to the second user, such as options related to specific media, type of advertisement, and target information.

The electronic device 100 may provide the second user with a fourth advertising media list created by adding campaign information obtained from the third advertising media list. At this time, the format of the fourth advertising media list may be provided in a predetermined manner or through user settings.

In one embodiment of the present disclosure, the electronic device 100 may transmit a fourth advertising media list to the second user's terminal. As a result, the second user can be recommended an appropriate advertising medium through campaign information for each advertising medium that best suits his or her needs from the advertising medium recommendation device.

FIG. 4 is a diagram illustrating an interface displayed on the user terminal 300 to obtain data of a second user in an embodiment of the present disclosure.

Information about the second user that the electronic device 100 can obtain may include information such as advertisement (campaign) period, advertisement (campaign) purpose, marketing budget, industry, target information, and address of the web page to be advertised. . At this time, industry information may include categories such as fashion beauty, F&B (Food and Beverage), IT, finance, manufacturing/distribution, and lifestyle. Target information may include the age, gender information, interests, etc. of the desired customer who is the target of the advertisement.

The acquired user data can be used to select candidates for creating a first advertising media list, and can also be used as a scoring standard when creating a second advertising media list. In addition, it can be used when necessary for the operation of the electronic device 100, such as adding additional recommended advertisements or adding additional media lists based on recommendations from advertising media companies. Hereinafter, in FIG. 5, the first process performed by the electronic device 100 will be described in detail.

FIG. 5 is a flowchart showing steps in which the electronic device 100, which has acquired second user data, generates a first advertising media list in a database according to an embodiment of the present disclosure.

As seen in s302, the electronic device 100 may calculate the degree of match based on the category of the advertising medium and the category of the second user's advertisement. At this time, the category of advertising media may be information obtained from an advertising media company or may be information obtained through domain profiling by an electronic device.

Methods for obtaining category information of the second user's advertisement include obtaining first advertisement target information by entering the domain on which the second user wishes to advertise, and obtaining second advertisement target information by entering the domain of the advertising medium on which the second user wishes to advertise. There may be a method of obtaining first advertisement target information desired by a user, a method of obtaining second advertisement target information by directly inputting a desired category and category information by a second user, etc. The electronic device 100 may select other advertising media having advertising media information that matches the category information of the advertising media based on the obtained profiling.

When there is no advertising medium or advertising domain information, the electronic device 100 may select a candidate group using category information input by the second user. However, even if there is domain information of an advertising medium or advertisement, if category information is obtained from a second user, this category information can be used to calculate a more accurate candidate group.

In the present disclosure, the first advertising medium list according to category can be created in various ways. At this time, the process of generating the first advertising medium list based on the first advertising target information obtained through the domain profiling described above will be described in detail step by step.

The electronic device 100 may receive data from a second user. (s501) At this time, the data is user information related to the advertisement and may include at least one of the domain of the second user's advertisement, the domain of the advertisement medium desired by the second user, and information on the second user's desired advertisement category.

The electronic device 100 may analyze domain information among data obtained from the second user. (s502) In this case, the domain's target information, competitor information, inflow path of domain users, industry information, advertising period, campaign purpose, campaign period, expected marketing cost, gender of target customer, age of target customer, directly by domain. , By acquiring the target customer's interests, competitive service information, etc., the category and category information for the service being advertised can be obtained, and based on this, a candidate group can be created according to the category consistency.

In one embodiment of the present disclosure, in order to calculate the degree of consistency between categories, overlapping items between the category of the advertising medium and the target category of the second user may be determined. The degree of agreement can be determined for overlapping categories. As an example, the category consistency can be determined by classifying the range into the same range, similar range, and dissimilar range for each category and assigning a score to each category. For example, if the target age is in the 30s, a set value is given to each group, such as 1 point if the advertising media's age group is in the 30s, 0.7 points if the age group is in the 20s or 40s, and 0 points otherwise. In this case, the score distribution may be a value set differently depending on other category information such as industry.

As an example, a consistency score can be calculated using the variance value. The variance of the category information of the advertising medium can be obtained based on the category information obtained from the second user's domain. Based on this variance value, a score can be derived in the form of a match score by calculating a conversion value that sets 1 when it matches the category information.

If the match score of the category is greater than or equal to a preset reference value, the electronic device 100 may include it in the first advertising medium list. In one example, when the match score for each category is set to a value between 0 and 1, a value of (total number of categories)/2 or more is set as the standard value, and advertising media with a total match score equal to or greater than the standard value are selected as the first advertising media. It can be created as a list.

The aforementioned first process may be performed in the server 200, the processor 121 within the device, or may be performed by both the server 200 and the processor 121. The category created at this time may be an item preset by the first user, but may be determined by an item obtained through analysis obtained through domain analysis or an item selected by the second user upon input.

If there is no category information input by the second user other than the domain information, the first advertising medium list can be created based on the above match score calculation.

If there is category information other than domain information, the electronic device 100 may have a step of comparing the first advertisement target information and the second advertisement target information. (s503)

If the domain analysis result and the category entered by the second user partially match, the electronic device 100 may assign weight to the matching category. At this time, the weight may be given in proportion to the number of categories with matching category information, and the weight may be given according to the type of matching category.

According to another embodiment of the present disclosure, when there is no domain input value, the electronic device 100 may generate a candidate group based on the first advertisement target information to generate the first advertisement medium list from the second user data. . (s504) In this case, the first process may include a process of selecting an item with a high degree of match between the second user's information and the category based on the match based on data, as described in FIG. 3, and the degree of match as in s502. Scores can also be calculated. According to an embodiment of the present disclosure, the first advertising media list generated in s505 may not be displayed on the user terminal 300. The first advertising media list obtained according to user data or analysis data may not be displayed on the user terminal 300. The first advertising media list may be stored in memory 122 and used again in a second process. Additionally, the first advertising media list may include overlapping advertising media in the list by category, and is not limited by the format of the generated list.

Figure 5 shows a method of generating a first advertising media list by considering both domain and category input values, and some steps may be omitted, the order may be changed, or steps that may be generally included may be added.

Figure 6 is a flowchart showing the steps of generating a second advertising medium list through a second process from a first advertising medium list, according to an embodiment of the present disclosure.

The electronic device 100 obtains the first advertising media list to create a second advertising media list (s601)

According to an embodiment of the present disclosure, the electronic device 100 may perform a scoring process to recommend media based on device data.

According to an embodiment of the present disclosure, scoring items may include elements such as recent transaction volume for each advertising medium, repurchase rate data, current operating medium, campaign budget, advertising purpose, traffic, keyword exposure frequency, sales increase rate, advertising keywords, etc. .

For example, when a second user inputs an advertising keyword as user data, the advertising keyword may be used as a scoring item based on the keyword in the same range for each campaign or the exposure frequency including key words within the keyword.

As another example, you can consider the category congruence score. The category match may be calculated based on the match between the category information by domain analysis and the second user's category information, and since this is explained with reference to FIGS. 3 and 5, it is omitted.

One embodiment of the scoring process of the present disclosure is as follows.

Scoring scores for each advertising medium are , , It can be expressed as, and the categories are A, B, , If X, the score for each category of advertising medium n is , , It can be expressed as At this time, the score for each category of advertising medium n can be calculated as (corresponding value for category A of advertising medium n)/(maximum value for each advertising medium in category A). In this case, the score for each category can have a value between 0 and 1.

At this time, the scoring points for each advertising medium are as follows.

In the above formula , , are categories A, B, , corresponds to the weight of X.

In one embodiment of the present disclosure, weights may be obtained through a machine learning model or deep learning model. Weights for each category can be obtained by using scoring items as the input layer and advertising success indicators as the output layer. At this time, the advertising success index is related to the success of advertising media, such as the number of successful advertisements, the ratio of the number of times the advertisement was successful among the number of times included in the fourth advertising media list, and the ratio of the number of times selected by the user compared to the number of times included in the second advertising media list. Indicators may be included. In particular, the user's preference may be reflected in the advertisement success rate among the fourth advertising media list, the user's selection rate among the second advertising media list, etc.

For example, in order to reflect the characteristics of each industry, the weights can be created differently for each industry, and a model that reflects trends in media use by industry can be used.

For example, when a second user directly inputs category information, additional weight may be added to scoring items that match the category. In addition, a higher category match score may mean that the advertisement desired by the second user is appropriately targeted to the advertising medium, so the weight for the category match score can be set to be the largest among the weights for each scoring item.

As an example, engines that recommend advertising media can be developed in various ways depending on the actual data collected by the device.

In s603, when scoring of advertising media for the second advertising media list is completed, the electronic device 100 performs a process of determining ranking to create a list.

In an embodiment of the present disclosure, the ranking of the second advertising medium list may be determined by a category match score, a selection rate based on the second user's selection from the advertising medium list, a scoring score, an advertisement success rate, etc. For example, if one advertising medium overlaps with multiple categories, a weight in the ranking may be assigned to this, and this may be reflected in the category match score. The selection rate clearly means the number of times it is included in the third advertising medium list out of the number of times it is included in the second advertising medium list, and may include preference information of previous users. In this case, the top exposure ranking can be determined by the sum of each item.

The judgment method for each element of the above ranking process may change.

For example, to prevent the selection of advertising media from being biased towards a specific media and to ensure the overall utility of the advertising media company, advertising media included in the list of third-party advertising media but with a low number of successful advertisements may be exposed with priority. Additionally, in cases where the advertising media success rate is low or the second user has no previous use history, the advertising success rate may be weighted to increase the success rate, and media with a previously high advertising success rate may be exposed preferentially. The driving method for the ranking process is not limited to the above example, and the first user can change it by considering the type of the second user, the transaction situation at the time of setting, etc.

According to an embodiment of the present disclosure, when setting a sorting item, priority weight may be given to the item in the ranking process. At this time, sorting items that the user can select include MAU (Monthly Active Users), DAU (Daily Active Users), number of recent advertisement executions, and review ratings. The second user can sort the list based on the sort items in ascending or descending order. However, sorting items are not limited to the above example.

At this time, the second advertising media list provided to the second user may include data calculated as a scoring score and may include category match score, advertisement success rate, etc.

When the second advertising media list is determined through this step, the display method on the user terminal 300 is described in detail in FIG. 7.

7A to 7C are diagrams illustrating an interface through which a second advertising media list generated by the electronic device 100 is provided to a second user.

Figure 7a may correspond to a screen providing a second advertising media list displayed on a smartphone.

In the drawing, a list of advertising media by category according to current age and industry is displayed, but it is not necessarily generated by the above category, and is an example of the present disclosure as a value that can be determined by data obtained from a second user in the electronic device 100. Categories based on target information not included in may also be provided.

Category information according to age and industry shown in the drawing may also include categories such as gender, age, industry, MAU, and DAU. The first user can set the number or range of advertising media for each category or category provided, and the second user can set the number they wish to receive within the corresponding range. As another example, the first user is initially provided with a value set, and if the second user needs an additional media list, the first user is provided with an additional number set as the number of media that can be provided. It can be provided by . The above-mentioned display method is merely an example and may be changed according to the settings of the first user.

FIG. 7B is a diagram illustrating a screen that is activated to create a third advertising media list when a second user selects one or more advertising media from the second advertising media list. For example, when the second user selects an advertising medium from the second advertising medium list, a third advertising medium list may be created based on the selected advertising medium. When the second user selects a medium, a separate selected list may be displayed on the UI/UX screen, and a third advertising medium list creation tab may be activated in response to the selection signal.

FIG. 7C is a diagram illustrating a screen on which a second advertising media list is displayed on the user terminal 300 according to another embodiment of the present disclosure.

In one embodiment of the present disclosure, the second user may select some or all of the set categories. When the second user selects a category, in response to the selection signal, the electronic device 100 may repeat the second process for each advertising medium including a category matching the category selected by the second user, and then The second advertising medium list may be updated each time. At this time, since the ranking process is explained in FIG. 6, description is omitted in this figure.

According to an embodiment of the present disclosure, when the second user selects a sort item, the electronic device 100 may repeatedly update the list in response. When you set a sort item, that item is given priority weight in the ranking process. At this time, sorting items that the user can select include MAU (Monthly Active Users), DAU (Daily Active Users), number of recent advertisement executions, and review ratings. The second user can sort the list based on the sort items in ascending or descending order.

At this time, category information and sorting items may overlap. Category information may be a value determined by range, such as target age and industry, or may be filtered by a format that sets the range, such as MAU or DAU.

FIGS. 7A to 7C show some of various examples of UI/UX screens that may appear when the electronic device 100 displays the second advertising media list on the user terminal 300, and are not limited thereto.

FIG. 8 is a flowchart illustrating a third process for generating a fourth advertising medium list from a third advertising medium list according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, in step s801, the electronic device 100 may obtain a list of third advertising media. The third advertising media list may refer to advertising media obtained by selection by the second user from the second advertising media list. While acquiring the third media list, the processor 122 may receive data provided with the fourth advertising media list in the following steps, such as pre-stored member information and a previously selected advertising media list.

The electronic device 100 may acquire a benchmark dataset in step s802. In this disclosure, the benchmark data set is a data set created based on the cumulative data of advertising media, and the benchmark database includes advertisers' past sales increase, number of views during advertisement execution, advertisement re-execution rate, and CTR (Click-Through Rate). ), CVR (Conversion Rate), etc.

In step s803, the electronic device 100 may execute a third process that calculates efficiency for each advertising medium or campaign.

In one embodiment of the present disclosure, the electronic device 100 may calculate the efficiency of each campaign for each advertising medium. As an example, the electronic device 100 may calculate the CTR value as an efficiency index. At this time, the CTR value may mean the number of advertisement clicks compared to the number of advertisement impressions. As another example, the electronic device 100 may calculate the CVR value as an efficiency index. The CVR value can refer to the behavioral conversion rate compared to the number of clicks on an advertisement.

In another embodiment, the electronic device 100 may select the most efficient campaign based on the advertising medium's CTR, CVR, advertisement rerun rate, past sales increase of the campaign, number of advertisement views of the campaign, etc.

In one embodiment of the present disclosure, the criteria for efficiency in calculating the most efficient campaign may be determined by the second user. Additionally, the period of efficiency calculation can be set by the first user or the second user. For example, it can be set to calculate the efficiency index based on information from the last three months.

As an example, when a second user selects an efficiency item that he or she wishes to consider as a top priority, the efficiency items are sorted in descending order to obtain the most efficient campaign for each item. However, depending on the user's needs, the sorting method can include both ascending and descending sorting.

In one embodiment of the present disclosure, efficiency may be calculated for each campaign. In this case, the efficiency of the advertising medium can be expressed as the highest efficiency value among the campaigns performed. Alternatively, it may be presented as the average value of the campaign efficiency value of the advertising medium or as the range of the efficiency value of the campaign.

In step s804, the electronic device 100 may create a fourth advertising media list by adding the most efficient campaign obtained from real data to the third advertising media list. At this time, the fourth advertising medium list can be created as a media mix.

At this time, campaign information that can be provided together in the fourth advertising media list may include specific media options for the campaign, advertisement type, targeting information, material, and CTA (Call to Action). Additionally, when providing a list of fourth advertising media, the average CPM and CPC unit price of industries that selected the same advertising media option may also be provided.

In addition, targeting information and advertising key messages required for campaign settings are provided, allowing a differentiated, detailed operation plan service to be provided.

The above list of fourth advertising media may be provided in the form shown in Figure 9.

Figure 9 is a schematic diagram showing how a fourth advertising medium list that can be provided to a second user can be provided in one embodiment of the present disclosure.

The provided information according to the settings of the first user may be combined with the list of advertising media selected by the second user and provided in the form of a media mix. Depending on the settings of the first user, the provided information may be provided differentially according to methods such as membership, payment of additional fees, etc. However, Figure 9 is only an example of the provided list method and is not limited thereto.

In step s805, when a CTA is generated by a second user in the fourth advertising medium list, the electronic device 100 may learn the corresponding advertising medium by matching the second user data and the domain analysis result as valid data. Through this, actual transaction data can be effectively stored to improve proposal accuracy.

Additionally, advertising media match information, traffic growth rate through advertising, CTR, etc. can be presented to other secondary users as success stories.

As described above, although the embodiments have been described with limited examples and drawings, various modifications and variations can be made 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 components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the claims described below.

In the specific embodiments of the present disclosure described above, elements included in the disclosure are expressed in singular or plural numbers depending on the specific embodiment presented. However, singular or plural expressions are selected to suit the presented situation for convenience of explanation, and the present disclosure is not limited to singular or plural components, and even components expressed in plural may be composed of singular or singular. Even expressed components may be composed of plural elements.

Meanwhile, the embodiments of the present disclosure disclosed in the specification and drawings are merely provided as specific examples to easily explain the technical content of the present disclosure and aid understanding of the present disclosure, and are not intended to limit the scope of the present disclosure. In other words, it is obvious to those skilled in the art that other modifications based on the technical idea of the present disclosure can be implemented. Additionally, each embodiment can be operated in combination with each other as needed. For example, portions of one embodiment of the present disclosure and another embodiment may be combined with each other and implemented in the form of embodiments not specified in the specification.

Meanwhile, in the drawings explaining the method of the present disclosure, the order of description does not necessarily correspond to the order of execution, and the order of precedence may be changed or executed in parallel.

Alternatively, the drawings explaining the method of the present disclosure may omit some components and include only some components within the scope that does not impair the essence of the present disclosure.

In addition, the method of the present disclosure may be implemented by combining some or all of the content included in each embodiment within the scope without impairing the essence of the disclosure.

Claims (18)

In an electronic device for recommending advertising media,
Memory;
A communication unit that transmits and receives data; and
At least one processor to process the data; Including,
The at least one processor,
Receiving first advertisement target information including domain information of the advertisement target to be advertised from the user device,
Generating a first advertising media list including a plurality of candidate advertising media for advertising the advertising target based on the domain information,
Generate a second advertising media list based on a scoring process for the plurality of candidate advertising media included in the generated first advertising media list,
Transmitting the generated second advertising media list to the user device,
receive, from the user device, selection information about one or more candidate advertising media from the second advertising media list;
Set to provide a list of recommended advertising media based on the selection information,
Electronic devices. According to paragraph 1,
The at least one processor
Based on the domain information, generate second advertising target information including an inflow path for the advertising target, target information, industry information, and competitive service information,
Obtain advertising media information including inflow paths, target information, industry information, and competitive service information for multiple advertising media,
Obtaining a match score calculated by calculating the degree of match between the second advertisement target information and the advertisement medium information,
Set to generate the first advertising media list by determining an advertising medium whose match score is greater than or equal to a preset reference value as the candidate advertising medium,
Electronic devices. According to paragraph 2,
The at least one processor,
When obtaining the match score, set to give a first weight to the match score based on the number of matching items between the items of the second advertisement target information and the advertisement medium information,
Electronic devices. According to paragraph 3,
The first advertising target information includes an expected inflow path of the advertising target, desired target information, industry information, and competitive service information,
Giving a second weight to items that match the items of the first advertising target information and the second advertising target information,
Electronic devices. According to clause 4,
The scoring process is,
The transaction volume of advertising media, repurchase rate data, keyword exposure frequency, sales increase rate, CTR (Click Through Rate), advertisement success rate, and the above match score are included in the scoring items,
Scores and different weights are assigned to each scoring item,
The scoring score is calculated as the sum of the product of the scores for each scoring item and the weight,
Electronic devices. According to clause 5,
The scoring process is,
Characterized in that among the weights for each scoring item, the weight for the consistency score is the largest,
Electronic devices. According to clause 6,
The second advertising media list is provided to the user device by ranking the plurality of candidate advertising media based on the scoring score.
Electronic devices. In clause 7,
The at least one processor,
When the selection information is obtained, the list of recommended advertising media is created by adding past advertising information with the highest advertising efficiency for each selected candidate advertising medium,
is set to transmit the generated list of recommended advertising media to the user device,
The advertising efficiency is,
Corresponds to the value determined by the user's selection among the advertisement re-execution rate of the advertising medium, the increase in sales from the previous advertisement, and the total number of views of the advertisement in the previous advertisement,
Characterized in that the advertising efficiency is calculated based on data from a preset period from the time of calculation,
Electronic devices. According to clause 8,
After transmitting the list of recommended advertising media to the user device,
Characterized in that the second advertisement target information and the selection information are matched and stored in the memory.
Electronic devices. In a method for recommending advertising media,
Receiving first advertisement target information including domain information of an advertisement target to be advertised from a user device;
generating a first advertising media list including a plurality of candidate advertising media for advertising the advertising target based on the domain information;
generating a second advertising media list based on a scoring process for the plurality of candidate advertising media included in the generated first advertising media list;
transmitting the generated second advertising media list to the user device;
receiving, from the user device, selection information about one or a plurality of candidate advertising media from the second advertising media list; and
providing a list of recommended advertising media based on the selection information; Including,
method. According to clause 10,
The above method is,
Generating second advertising target information including an inflow path for the advertising target, target information, industry information, and competitive service information based on the domain information;
Obtaining advertising media information including inflow paths, target information, industry information, and competitive service information for a plurality of advertising media;
Obtaining a match score calculated by calculating the degree of match between the second advertisement target information and the advertisement medium information; and
determining an advertising medium whose consistency score is equal to or higher than a preset reference value as the candidate advertising medium and generating the first advertising medium list; Containing more,
method. According to clause 11,
The match score is characterized in that a first weight is given based on the number of matching items between the items of the second advertising target information and the advertising medium information,
method. According to clause 12,
The first advertising target information includes an expected inflow path of the advertising target, desired target information, industry information, and competitive service information,
The consistency score is characterized in that a second weight is given to items that match between the items of the first advertising target information and the second advertising target information,
method. According to clause 13,
The scoring process is,
The transaction volume of advertising media, repurchase rate data, keyword exposure frequency, sales increase rate, CTR (Click Through Rate), advertisement success rate, and the above match score are included in the scoring items,
Characterized in that a score and a different weight are assigned to each scoring item, and the scoring score is calculated as the sum of the product of the score for each scoring item and the weight.
method. According to clause 14,
The scoring process is,
Characterized in that among the weights for each scoring item, the weight for the consistency score is the largest,
method. According to clause 15,
The second advertising media list is provided to the user device by ranking the plurality of candidate advertising media based on the scoring score.
method. According to clause 16,
The above method is,
When the selection information is obtained, generating the list of recommended advertising media by adding past advertising information with the highest advertising efficiency for each selected candidate advertising media; and
Further comprising: transmitting the generated list of recommended advertising media to the user device,
The advertising efficiency is,
Corresponds to the value determined by the user's selection among the advertisement re-execution rate of the advertising medium, the increase in sales from the previous advertisement, and the total number of views of the advertisement in the previous advertisement,
Characterized in that the advertising efficiency is calculated based on data from a preset period from the time of calculation,
method. According to clause 17,
Matching and storing the second advertisement target information and the selection information; Containing more,
method.

Images