KR102808603B1 - Method for providing a solution for automating the creation of analysis data for content planning and regular reporting for branding and marketing based on a generative artificial intelligence model - Google Patents

Abstract

According to one embodiment, a device can obtain product information, input the product information into a generative artificial intelligence model to generate a content plan, and when a request for product analysis is received, can output a product analysis report screen.

Description

{METHOD FOR PROVIDING A SOLUTION FOR AUTOMATING THE CREATION OF ANALYSIS DATA FOR CONTENT PLANNING AND REGULAR REPORTING FOR BRANDING AND MARKETING BASED ON A GENERATIVE ARTIFICIAL INTELLIGENCE MODEL}

The examples below relate to a technology that provides a solution for automating the creation of content planning proposals and periodic reporting analysis data for branding and marketing based on a generative artificial intelligence model.

In the modern marketing environment, it is becoming increasingly important to quickly understand consumer needs and market trends and propose customized content plans that match them. In particular, due to the development of various social media platforms and digital marketing channels, companies have come to require more data and complex analyses. In this environment, marketers are continuously investing a lot of time and resources to discover new ideas and establish effective marketing strategies. However, existing methods have limitations in efficiently processing massive amounts of data and quickly deriving optimal marketing plans.

Generative AI models are gaining attention as an innovative technology to solve these problems. Generative AI models can analyze large amounts of data, derive patterns, and automatically generate creative and effective content based on this by utilizing natural language processing and machine learning algorithms. This allows marketers to obtain high-quality marketing plans in less time and at less cost. In addition, since generative AI models continuously learn and evolve, they have the advantage of being able to quickly respond to market changes and consumer trends.

Accordingly, there is a need for the development of technology that provides a solution for automating the creation of content planning proposals and regular reporting analysis data for branding and marketing based on generative artificial intelligence models.

Republic of Korea Patent No. 10-2572579 (announced on August 30, 2023) Republic of Korea Patent No. 10-2613621 (announced on December 14, 2023) Republic of Korea Patent No. 10-2551997 (Announced on July 6, 2023) Republic of Korea Patent No. 10-2546328 (announced on June 22, 2023)

The examples aim to automatically generate efficient and customized marketing content plans using generative artificial intelligence models.

The examples aim to provide an automated solution that collects product information from various sales sites and analyzes the product's economic feasibility, usage complexity, and marketing channel efficiency based on the collected information to recommend the optimal marketing channel.

The examples provide visual reporting screens based on product sales data to help users intuitively understand sales performance and market trends.

The purpose of the present invention is not limited to the purposes mentioned above, and other purposes not mentioned can be clearly understood from the description below.

According to one embodiment, a method performed by a device may include: a step of obtaining product information; a step of inputting the product information into a generative artificial intelligence model to generate a content plan; and a step of outputting a product analysis report screen when a request for product analysis is received.

The step of generating the content plan comprises: a step of checking the average selling price and selling cycle of the product from a sales site selling the product; a step of determining the economic feasibility of the product based on the average selling price and selling cycle; a step of checking the amount of text and images included in the detailed information of the product from the sales site; a step of determining the complexity of use of the product based on the amount of text and images; a step of determining a product index based on the economic feasibility of the product and the complexity of use; a step of determining a description by marketing channel based on the amount of text and images of content included in a website corresponding to the marketing channel; a step of determining a delivery rate by marketing channel based on the content viewing time and reach of visitors visiting a website corresponding to the marketing channel; a step of determining an efficiency index by marketing channel based on the description and delivery rate by marketing channel; a step of classifying marketing channels into a first group, a second group, and a third group based on the efficiency index; a step of matching the product with the marketing channel by checking the efficiency index and the product index of the first to third groups; a step of checking a popular marketing channel on which marketing content with a number of views exceeding a preset standard among marketing content related to the product from the sales site is posted; a step of determining the spacing, size, amount, and sentences of text included in content included in the popular marketing channel. The method may include: analyzing a popular content style based on the length, number of images, size of images, number of videos, length of videos, and number of objects appearing; checking the content style of a marketing channel matched with the product; selecting the marketing channel matched with the product as a recommended marketing channel if the similarity between the content style of the marketing channel matched with the product and the popular content style satisfies a preset criterion; selecting the marketing channel with the highest efficiency index among the marketing channels belonging to the same group as the marketing channel matched with the product as a recommended marketing channel if the similarity between the content style of the marketing channel matched with the product and the popular content style does not satisfy the preset criterion; and generating a content plan for the product by inputting the product information and the content style of the recommended marketing channel into a generative artificial intelligence model.

The step of outputting the above product analysis report screen may include a step of outputting an analysis list display screen that displays an analysis list of products based on an analysis request for the product, a step of outputting a visualization report screen that displays the analysis results of the product in a graph, and a step of outputting a keyword analysis screen that displays the analysis results of keywords related to the product.

The above analysis list display screen displays basic product information including product sales form classification information including group purchase, open market, and home shopping, product category and product group, reflected data period, author, creation date, and viewing ID, and includes a product manufacturer list that visually displays the manufacturer of the product in the form of a tree map, and the tree map of the product manufacturer list displays the market share of each manufacturer in a relative area, and each manufacturer is distinguished by different colors and areas, and when one of the manufacturers is selected, detailed information of the selected manufacturer is displayed in a pop-up form, and includes a product brand list that visually displays the brand of the product in the form of a tree map, and the tree map of the product brand list displays the market share of each brand in a relative area, and each brand is distinguished by different colors and areas, and when one of the brands is selected, detailed information of the selected brand is displayed in a pop-up form, and includes a product list that visually displays products corresponding to the product group in the form of a tree map, and the tree map of the product list displays the sales volume of the product in a relative area, and each product is distinguished by different colors and areas, and when one of the products is selected, detailed information of the corresponding product can be displayed in a pop-up form.

The above visualization report screen displays the number of monthly product sales in the form of a bar graph, and when one of the bar graphs is selected, a list of products sold in the selected month is displayed in a table format at the bottom of the bar graph, and the list of products sold includes information on the sales period, product group, product name, brand, manufacturer, and number of sales of the product, and includes a display number selection button for selecting the number of information on products to be displayed, and includes a search bar for searching for products sold, and extracts popular sellers who satisfy preset criteria, and displays the products sold by popular sellers and the number of sales in the form of a bar graph in order of the number of sales for each product, and includes a tab interface for switching between a view by product and a view by seller, and when the view by product tab is selected, the height of each bar graph is displayed in a different color in proportion to the number of sales for each seller, and the number of sales of the seller who sold each sold product is displayed, and when the view by seller tab is selected, the height of each bar graph is displayed in a different color in proportion to the number of sales for each product, and the number of sales for each sold product is displayed, and the lowest sale price, highest sale price, Displays information on average selling price and minimum standard unit price, and includes a bar graph sorted in order of lowest price by minimum standard unit price for each product belonging to the same product group as the product, and when any of the bar graphs is selected, the product name and minimum standard unit price of the product corresponding to the bar graph can be displayed in a pop-up form.

The above keyword analysis screen visualizes keywords according to the keyword analysis results by classifying them into feed keywords, hashtags, and keywords. Each keyword is visualized in the form of a word cloud, and the larger the word, the higher its frequency. The keyword ranking is displayed in a table format, and the ranking of the top keywords for each classification can be listed.

The method performed by the above device further includes a step of determining an information collection period of a product for generating the content plan before the step of generating the content plan, wherein the step of determining the information collection period includes a step of extracting keywords related to the product from the product information and determining a main keyword among the extracted keywords, a step of checking the number of times the main keyword is mentioned in marketing content associated with the product from the sales site, a step of checking a first period from the current time point to the first time point with the highest number of mentions, a step of determining the first time point as the start time of the information collection period and determining the current time point as the end time of the information collection period if the first period is less than the preset reference period, a step of checking a second time point with the lowest number of mentions among the period from the first time point to the current time point if the first period is equal to or greater than the preset reference period, a step of checking a second period from the current time point to the second time point, a step of determining the first time point as the start time of the information collection period and determining the second time point as the end time of the information collection period if the second period satisfies the preset criterion, and a step of checking the second period from the current time point to the second time point, a step of determining the first time point as the start time of the information collection period and determining the second time point as the end time of the information collection period if the second period does not satisfy the preset criterion, The method may include a step of identifying a third time point having the highest number of mentions, excluding the first time point in the period, and a step of determining the third time point as the start time of the information collection period and determining the second time point as the end time of the information collection period.

The above analysis list display screen includes a download button capable of downloading the product basic information, the product manufacturer list, the product brand list, and the product list, respectively, and additionally includes a manufacturer site button for each manufacturer that includes a link connected to the manufacturer site of the manufacturer of the product, the product brand list additionally includes a brand site button for each brand that includes a link connected to the brand site of the product brand, and the product list additionally includes a sales site button for each product that includes a link connected to the sales site of the product, and among the product list, the product names of products including a raw material satisfying a preset standard in which a number of products including a specific raw material are included may be displayed in a first bold color, and the product names of products that do not correspond may be displayed in a second bold color different from the first bold color.

A device according to one embodiment may be coupled with hardware and controlled by a computer program stored on a medium to execute any one of the methods described above.

Examples can save marketers time and resources by automatically generating marketing content plans using generative artificial intelligence models. This increases the efficiency of marketing plans and allows for the rapid establishment of optimal marketing strategies.

Examples can analyze the economic feasibility of a product, the complexity of use, and the effectiveness of marketing channels to automatically recommend the optimal marketing channel, allowing companies to establish more effective marketing strategies. This can maximize product sales performance and quickly respond to market changes and consumer trends.

Examples can visually display product sales data, allowing users to intuitively understand sales performance and market trends. This can support data-based decision-making and provide important insights when establishing marketing strategies.

Meanwhile, the effects according to the embodiments are not limited to those mentioned above, and other effects not mentioned can be clearly understood by a person having ordinary skill in the art from the description below.

Figure 1 is a drawing for explaining the configuration of a system according to one embodiment.
Figure 2 is a flowchart illustrating a process for providing a solution for automating the creation of content planning proposals and regular reporting analysis data for branding and marketing based on a generative artificial intelligence model according to one embodiment.
Figure 3 is a flowchart illustrating the process of creating a content plan according to an embodiment.
Figure 4 is a flowchart illustrating a process for outputting a product analysis report screen according to an embodiment.
Figure 5 is a drawing for explaining an analysis list display screen according to an embodiment.
Figure 6 is a drawing for explaining an analysis list display screen according to an embodiment.
Figure 7 is a drawing for explaining a visualization report screen according to an embodiment.
Figure 8 is a drawing for explaining a visualization report screen according to an embodiment.
Figure 9 is a drawing for explaining a visualization report screen according to an embodiment.
Figure 10 is a drawing for explaining a keyword analysis screen according to an embodiment.
Figure 11 is a flowchart illustrating a process for determining a product information collection period for creating a content plan according to an embodiment.
Figure 12 is an example diagram of the configuration of a device according to one embodiment.

Hereinafter, embodiments will be described in detail with reference to the attached drawings. However, since various modifications may be made to the embodiments, the scope of the patent application rights is not limited or restricted by these embodiments. It should be understood that all modifications, equivalents, or substitutes to the embodiments are included in the scope of the rights.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, the embodiments are not limited to specific disclosed forms, and the scope of the present disclosure includes modifications, equivalents, or alternatives included in the technical idea.

Although the terms first or second may be used to describe various components, such terms should be construed only for the purpose of distinguishing one component from another. For example, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component.

When it is said that a component is "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but there may also be other components in between.

The terms used in the examples are used for the purpose of description only and should not be construed as limiting. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this specification, the terms "comprises" or "has" and the like are intended to specify the presence of a feature, number, step, operation, component, part or combination thereof described in the specification, but should be understood to not exclude in advance the possibility of the presence or addition of one or more other features, numbers, 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 one of ordinary skill in the art to which the embodiments belong. Terms defined in commonly used dictionaries, such as those defined in common dictionaries, should be interpreted as having a meaning consistent with the meaning they have in the context of the relevant art, and shall not be interpreted in an idealized or overly formal sense, unless expressly defined in this application.

In addition, when describing with reference to the attached drawings, the same components will be given the same reference numerals regardless of the drawing numbers, and redundant descriptions thereof will be omitted. When describing an embodiment, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the embodiment, the detailed description thereof will be omitted.

The embodiments can be implemented in various forms of products such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent cars, kiosks, and wearable devices.

Figure 1 is a drawing for explaining the configuration of a system according to one embodiment.

A system according to one embodiment may include a user's terminal (10) and a device (30) capable of communicating with each other through a communication network.

First, the communication network can be configured regardless of the communication mode, such as wired or wireless, and can be implemented in various forms to perform communication between servers and between servers and terminals.

The user's terminal (10) may be a terminal used by a user who wishes to receive a content planning proposal for branding and marketing according to the present invention and an automated solution for generating regular report analysis data. The user's terminal (10) may be a desktop computer, a laptop, a tablet, a smartphone, etc. For example, as shown in Fig. 1, the user's terminal (10) may be a smartphone, and may be employed differently depending on the embodiment.

The user's terminal (10) may be configured to perform all or part of the computational functions, storage/reference functions, input/output functions, and control functions of a typical computer. The user's terminal (10) may be configured to communicate with the device (30) via wired or wireless means.

The user's terminal (10) may be connected to a web page operated by a person or group providing a service using the device (30), or an application developed and distributed by a person or group providing a service using the device (30) may be installed. The user's terminal (10) may be linked to the device (30) through a web page or application.

The user's terminal (10) can access the device (30) through a web page, application, etc. provided by the device (30).

Any singular expression in the claims is to be understood to include the plural.

The device (30) can provide an automated solution for creating content planning proposals for branding and marketing and generating periodic reporting analysis data.

The device (30) may be a server owned by a person or organization providing a service using the device (30), may be a cloud server, or may be a p2p (peer-to-peer) collection of distributed nodes. The device (30) may be configured to perform all or part of the computational functions, storage/reference functions, input/output functions, and control functions of a typical computer.

The device (30) can be configured to communicate with the user's terminal (10) via wired or wireless means, control the operation of the user's terminal (10), and control which information to display on the screen of the user's terminal (10).

Meanwhile, for convenience of explanation, only the user's terminal (10) is illustrated in Fig. 1, but the number of terminals may vary depending on the embodiment. There is no particular limitation on the number of terminals as long as the processing capacity of the device (30) allows.

The device (30) can provide a solution for automating content planning proposals for branding and marketing and generating analysis data for regular reporting. According to one embodiment, a database may be provided within the device (30), but is not limited thereto, and the database may be configured separately from the device (30). The device (30) can include a plurality of artificial neural networks for performing machine learning algorithms.

The device (30) can provide a product analysis report screen including an analysis list display screen, a visualization report screen, and a keyword analysis screen through a user interface. At this time, a detailed description of the process of providing a content planning proposal for branding and marketing based on a generative artificial intelligence model and an automated solution for generating regular report analysis data will be described later with reference to FIG. 2.

Figure 2 is a flowchart illustrating a process for providing a solution for automating the creation of content planning proposals and regular reporting analysis data for branding and marketing based on a generative artificial intelligence model according to one embodiment.

Referring to FIG. 2, first, in step S201, the device (30) can obtain product information.

Product information may include, but is not limited to, product name, type, target customer information, manufacturer, brand, features, ingredients, directions for use, marketing materials, user reviews, sales volume, marketing requirements, etc. The device (30) may store product information in a structured form in a database. At this time, product information may exist in various formats such as text, image, video, etc., and the device (30) may analyze this to extract and organize necessary data.

In one embodiment, the device (30) may collect product information through, but is not limited to, a product database, user input, online sources, etc. In one embodiment, the device (30) may automate product information collection.

For example, the device (30) can collect product information in real time from various sources such as online sales sites, social media, product review sites, and manufacturers' databases via API, but is not limited thereto. Through this, the device (30) can always maintain the latest product information and quickly generate content whenever a new product is released. The device (30) can automate data collection by utilizing a web crawler or API, and can refine the collected data to extract only the necessary information.

The device (30) can build a dataset including product information such as product key characteristics, consumer evaluations, and market trends.

At step S202, the device (30) can input product information into a generative artificial intelligence model to generate a content plan.

Generative AI (GAI) models are AI systems that have the ability to generate new data from given input data. Generative AI models are mainly based on deep learning technology and can generate various types of data such as text, images, music, and videos. Representative generative AI models include OpenAI's GPT series, Google's BERT, and GAN (Generative Adversarial Network). These models are used to learn from large datasets and create new content, and unlike existing rule-based systems, this is achieved through the ability to learn and generate data patterns on their own.

The main role of generative AI models is mainly in content generation and automated creative activities. For example, text generation models can generate natural sentences based on user input or write articles on specific topics. Image generation models are used to learn existing image styles to create new images or generate graphics that meet specific requirements. In addition, music generation models can automatically compose music of various genres, and video generation models can generate videos that meet specific scenarios. Through these roles, generative AI is being utilized in various industries such as advertising, marketing, entertainment, and education.

The functions of generative AI models can be broadly divided into data learning, pattern recognition, and new data generation. First, in the data learning stage, the model learns various patterns and characteristics through a large dataset. Then, in the pattern recognition stage, it performs understanding and analysis of new inputs based on the learned data. Finally, in the new data generation stage, it generates new data that meets given conditions using previously learned patterns. In this process, the generative AI model can demonstrate the ability to produce creative and original results while maintaining the characteristics of existing data.

Generative AI models can automatically generate marketing content tailored to product features and target markets based on input data.

The device (30) can utilize a large amount of product information for learning a generative artificial intelligence model. The model can learn product characteristics and consumer preferences through a machine learning algorithm, and derive effective marketing strategies based on this. For example, in the case of a diet supplement product, the device (30) can analyze weight loss effects, main ingredients, consumer reviews, etc. to generate optimal marketing messages and visual materials.

The device (30) can output the generated content plan in various formats. For example, the content can be provided in the format of a social media post, a blog post, an advertising banner, etc., but is not limited thereto, and content optimized for each marketing channel can be generated.

In addition, the device (30) can track the performance of the generated content and continuously improve the content planning based on this. Through this, the device (30) can maximize marketing efficiency.

Initial content generated by the generative artificial intelligence model can be reviewed by the device (30) and modified as needed.

At this time, a detailed description of the process of creating a content plan using a generative artificial intelligence model will be described later with reference to Figure 3.

At step S203, when the device (30) receives a request for analysis of a product, it can output a product analysis report screen.

The product analysis report screen may include, but is not limited to, an analysis list display screen, a visualization report screen, and a keyword analysis screen.

The device (30) receives an analysis request from a user, and searches the database for necessary data according to the request to create an analysis list. The analysis list may include various analysis items such as product sales information, consumer reviews, and market trends, but is not limited thereto. The analysis list may be provided in a table format so that the user can easily access and understand it. The device (30) may also provide a function for sorting or filtering the analysis list, thereby supporting the user to quickly access the desired information.

The device (30) can output a visualization report screen that displays the product analysis results in a graph. To this end, the device (30) can use various analysis tools to visually express the product's sales performance, market trends, consumer responses, etc. For example, the device (30) can provide users with visualizations of sales volume trends, market share, consumer satisfaction, etc. in the form of bar graphs, pie charts, line charts, etc. Through this, users can easily understand complex data and analyze it effectively.

The device (30) can provide a customized visualization report screen. For example, the user can select a specific item of interest to preferentially display visualization data for that item, or adjust the shape and color of the graph according to the user's preference. In addition, the device (30) can provide an interactive function that allows the user to check detailed information by clicking on the visualized data, thereby supporting the user to perform a more in-depth analysis. Through this, the device (30) can provide the user with a better analysis tool.

The device (30) can update the visualization report screen in real time to reflect the latest data. To this end, the device (30) can establish a real-time data collection and analysis system and automatically update the visualization report screen whenever new data is input. In addition, the device (30) can provide the user with a notification function that allows the user to receive a visualization report that is automatically updated according to specific conditions. Through this, the device (30) can always provide the user with the latest information and support quick decision-making.

The device (30) can output a keyword analysis screen that displays the analysis results of keywords related to the product. To this end, the device (30) can collect keywords related to the product from various sources such as social media, review sites, and news articles, and analyze them to derive major keywords and trends.

For example, the device (30) may provide the user with the keyword analysis results in the form of a word cloud, a frequency graph, a trend chart, etc., but is not limited thereto. Through this, the user can easily identify the main keywords and consumer interests related to the product.

The device (30) can provide a keyword analysis screen customized to the user. For example, the user can select a specific keyword of interest and display the change trend of the keyword first, or adjust the visualization form and color of the keyword according to the user's preference.

In addition, the device (30) can provide an interactive function that allows the user to check detailed information by clicking on the frequency of keywords, related keywords, relationships between keywords, etc., thereby enabling the user to perform a more in-depth analysis. Through this, the device (30) can provide the user with a better keyword analysis tool.

The device (30) can update the keyword analysis screen in real time to reflect the latest data. To this end, the device (30) can establish a real-time data collection and analysis system and automatically update the keyword analysis screen whenever new data is input.

In addition, the device (30) can provide a notification function that allows the user to automatically receive updated keyword analysis results according to specific conditions. Through this, the device (30) can always provide the user with the latest information and support quick decision-making.

The device (30) can evaluate the performance of a content plan using various analysis tools and visually display the results.

The device (30) can design the product analysis report screen to be user-friendly. For example, it can be configured in a dashboard format so that various indicators can be viewed at a glance, and each indicator can be visualized as a graph or chart to provide an easy-to-understand presentation. In addition, the device (30) can provide filtering and search functions so that the user can easily find the information they want, and can support downloading the analysis results in a format such as PDF or Excel.

The device (30) can update the analysis results in real time, so as to always provide the latest information. To this end, the device (30) can establish a real-time data collection and analysis system, and automatically perform analysis whenever new data is input. In addition, the device (30) can provide a notification function to the user, so as to immediately notify when an important change or new analysis result occurs. Through this, the device (30) enables the user to perform continuous monitoring and real-time response.

At this time, a detailed description of the process of providing a product analysis report screen will be described later with reference to FIGS. 4 to 10.

Figure 3 is a flowchart illustrating the process of creating a content plan according to an embodiment.

Referring to FIG. 3, first, in step S301, the device (30) can determine a product index.

According to one embodiment, the device (30) can check the average selling price and selling cycle of the product from a sales site selling the product, and determine the economic feasibility of the product based on the average selling price and selling cycle.

For example, the device (30) may evaluate the economic feasibility of a specific product as low when the average selling price of the product is low and the sales cycle is short, and may evaluate the economic feasibility of the product as high when the average selling price is high and the sales cycle is long.

The device (30) can check the amount of text and images included in the detailed information of a product from a sales site and determine the complexity of use of the product based on the amount of text and images.

According to one embodiment, if a product description is composed of long and complex text and a large number of images, the device (30) may evaluate the product as having high complexity of use, and a product composed of a simple description and a small number of images may be evaluated as having low complexity of use.

The device (30) can determine a product index based on the economic feasibility and usage complexity of the product.

The device (30) can determine a product index by comprehensively considering the product's economic efficiency and complexity of use. The product index reflects the balance between economic efficiency and complexity of use, and can be used as an indicator for evaluating the product's overall market competitiveness.

According to one embodiment, the device (30) can determine a higher product index as the product's economic feasibility and usage complexity increase, and can determine a lower product index as the product's economic feasibility and usage complexity decrease.

At step S302, the device (30) can determine an efficiency index for each marketing channel.

Marketing channels can refer to various paths through which a product or service is communicated to consumers, promoted, and sold. For example, marketing channels can include, but are not limited to, blog posts, social media posts, video content, email marketing, display advertising, and offline marketing.

There are various forms of marketing channels, and each channel has its own characteristics and advantages. In order to establish an effective marketing strategy, it is important to understand the characteristics of each channel, select the appropriate channel for the product or service, and create the optimal combination.

According to one embodiment, the device (30) may determine a marketing channel-specific description based on the amount of text and images of content included in a website corresponding to the marketing channel.

According to one embodiment, the device (30) analyzes the amount of text and images of content provided on a website to determine a description for each marketing channel. The device (30) can measure the length of text and the number of images of each content to produce a description.

The descriptiveness indicates the amount of information and complexity of the content provided by each marketing channel, which reflects the time and effort it takes for a consumer to understand the content. For example, the device (30) may evaluate a marketing channel that includes content with long text and many images as having a high descriptiveness, and a marketing channel that includes content with short text and few images as having a low descriptiveness.

The device (30) can determine the delivery rate for each marketing channel based on the content viewing time and reach of visitors visiting the website corresponding to the marketing channel.

The delivery rate is an indicator for evaluating how effectively the content is delivered to the consumer, and can reflect how long the consumer watches the content and how many consumers the content reaches. For example, if the visitor watches the content for a long time and many consumers access the content, the device (30) can evaluate the delivery rate of the corresponding marketing channel as high, and if the visitor watches the content for a short time and few consumers access the content, the device (30) can evaluate the delivery rate of the corresponding marketing channel as low.

The device (30) can determine an efficiency index for each marketing channel based on the description and delivery diagram for each marketing channel.

According to one embodiment, the device (30) can determine a higher efficiency index as the explanatory power of the marketing channel is lower and the transmission power is higher, and can determine a lower efficiency index as the explanatory power is higher and the transmission power is lower.

The efficiency index of a marketing channel is an indicator that evaluates how effectively each marketing channel delivers information to consumers. The efficiency index is calculated by comprehensively considering the description and delivery of the content provided by the marketing channel, and plays an important role in predicting the possibility of success of marketing activities. The efficiency index can be used as an important reference for establishing marketing strategies and allocating resources.

Device (30) means that when the efficiency index of a marketing channel is high, the marketing channel is very effectively delivering information to consumers. A marketing channel with low explanatory power and high delivery indicates that consumers easily understand the content and that many people are accessing the content. Therefore, a marketing channel with a high efficiency index can be expected to have high performance compared to the investment in marketing activities, and can be evaluated as a channel that can maximize the marketing effect by intensively investing resources.

At step S303, the device (30) can classify marketing channels into a first group, a second group, and a third group based on an efficiency index.

For example, the device (30) may list marketing channels in order of their efficiency indices, and classify the marketing channels into a first group with the highest efficiency indices, a second group with an intermediate efficiency indices, and a third group with the lowest efficiency indices, but is not limited thereto.

At step S304, the device (30) can check the efficiency index and product index of the first to third groups to match the product with the marketing channel.

The device (30) can identify the marketing channel through which each product can be most effectively promoted, establish an optimal marketing strategy, and comprehensively consider the characteristics of the product and the efficiency of the marketing channel to maximize the marketing effect.

The device (30) can check the product index calculated based on the product's economic feasibility and usage complexity. The product index is an indicator of the product's market competitiveness, and the higher the product index, the higher the product index is evaluated as the economic feasibility is high and the usage complexity is low. The device (30) can identify the product's characteristics and market position based on this product index. For example, a product with high economic feasibility and low usage complexity is very attractive to consumers and is likely to be effectively promoted through various marketing channels.

The device (30) can check the efficiency index of each marketing channel of the first to third groups. The efficiency index is an index that comprehensively evaluates the explanatory power and the delivery power of each marketing channel. The efficiency index is evaluated higher when the explanatory power is lower and the delivery power is higher. The device (30) can determine the effectiveness of each marketing channel based on this efficiency index and select a marketing channel to match with a specific product. For example, a channel with a high efficiency index is evaluated as a channel from which high performance can be expected in product promotion.

The device (30) can match the product with the most suitable marketing channel by comprehensively considering the product index and the marketing channel efficiency index. Through this, the device (30) can identify the marketing channel through which each product can be most effectively promoted and establish an optimal marketing strategy. For example, a product with high economic feasibility and low complexity of use can be matched with a marketing channel with high efficiency index to deliver a strong message to consumers and expect a high conversion rate. On the other hand, a product with low economic feasibility and high complexity of use needs to be matched with a more effective channel through appropriate improvement rather than a channel with low efficiency index.

The device (30) can efficiently distribute marketing resources by evaluating the efficiency of each marketing channel and matching it with a product. By focusing investment on channels with a high efficiency index, maximum effects can be obtained, and channels with a low efficiency index can be improved or resources can be reallocated to other channels. This can optimize the performance of a marketing campaign and reduce unnecessary costs. In addition, the automated matching process minimizes the investment of human resources and enables quick and accurate decisions.

By optimally matching products and marketing channels through the device (30), a company can strengthen its competitiveness in the market. Through data-based marketing strategies, it can more effectively approach target consumers, increase brand awareness, and promote product sales. This ultimately contributes to expanding market share and improving the growth and profitability of the company.

The device (30) performs optimal matching by considering the characteristics of each product and marketing channel, thereby maximizing the effectiveness of marketing activities.

In step S305, the device (30) can check popular marketing channels where marketing content with a number of views exceeding a preset standard among marketing content related to a product from a sales site is posted. At this time, the preset standard may be set differently depending on the embodiment.

The device (30) can extract marketing content related to a product included in a sales site, and check the number of views of the extracted marketing content to check the type of marketing channel on which the marketing content with a number of views exceeding a preset standard is posted. For example, marketing content related to a product may include, but is not limited to, blog posts, social media posts, video reviews, influencer-sponsored posts, display advertisements, etc., as marketing content created to promote the product.

Popular marketing channels are evaluated as channels from which high performance can be expected in product promotion, and the device (30) can optimize marketing strategies based on these.

At step S306, the device (30) can analyze popular content styles.

According to one embodiment, the device (30) can analyze popular content styles based on the spacing, size, amount, sentence length, number of images, size of images, number of videos, length of videos, and number of objects featured in the content included in popular marketing channels.

The device (30) analyzes in detail various elements of content posted on popular marketing channels to identify the characteristics of content that is popular with consumers.

The device (30) can analyze text of content included in popular marketing channels. To this end, the device (30) can measure the spacing, size, amount, sentence length, etc. of the text to determine the text style preferred by consumers.

The device (30) can analyze images and videos included in the content. By measuring the number and size of images, the number and length of videos, etc., it is possible to determine how visual elements are composed.

The device (30) can analyze the number of objects appearing in videos and images. By identifying the number and types of objects, it can determine which elements are attracting the attention of consumers. For example, the device (30) can analyze objects such as products, people, and backgrounds appearing in videos to evaluate whether content containing specific objects is receiving more views and participation.

At step S307, the device (30) can check the content style of the marketing channel matched with the product.

The device (30) can check a marketing content channel matched with a product in step S304 and check the content style of the marketing channel matched with the product.

For example, if the marketing channel matched with the product is a blog, the device (30) can comprehensively evaluate the content style of the channel by analyzing the spacing, size, amount, sentence length, number of images, size of images, number of videos, length of videos, and number of objects appearing in the text of the blog post.

At step S308, the device (30) can select a recommended marketing channel based on the similarity between the content style of the marketing channel matched with the product and the popular content style.

The device (30) can evaluate the similarity between the content style of the marketing channel matched with the product and the popular content style. To this end, the device (30) compares the popular content style analyzed in step S306 with the content style of the marketing channel matched with the product identified in step S307. In the similarity evaluation, the degree of correspondence between the two styles can be analyzed by comprehensively considering the structure of the text, the arrangement of images and videos, the types of objects appearing, etc.

According to one embodiment, the device (30) may set a marketing channel corresponding to a popular content style in which the similarity between the content style of the marketing channel matched with the product and the popular content style exceeds a preset standard as a recommended marketing channel.

The device (30) can select a recommended marketing channel based on the similarity evaluation result. A channel with a high similarity reflects the characteristics of popular content well, and is evaluated as a channel that can reach consumers more effectively. Accordingly, the device (30) can maximize the effectiveness of marketing activities by preferentially recommending a marketing channel with a high similarity.

The device (30) can establish a marketing strategy that can more effectively reach consumers by selecting a marketing channel with high similarity. By promoting a product through a channel that reflects a popular content style, consumer participation and conversion rate can be increased.

By continuously monitoring popular content styles and quickly selecting marketing channels that fit new trends, you can quickly respond to changing market environments through real-time data analysis.

At step S309, the device (30) can input product information and content styles of recommended marketing channels into a generative artificial intelligence model to generate a content plan for the product.

The device (30) can automatically generate marketing content most suitable for the product by utilizing a generative artificial intelligence model.

The device (30) can input product information into the generative artificial intelligence model. The product information can include data related to the product, such as product characteristics, functions, advantages, target markets, and consumer reviews, but is not limited thereto. Through this, the generative artificial intelligence model can understand the unique characteristics and market positioning of the product and generate content tailored thereto.

For example, the device (30) can input information such as ingredients, efficacy, and usage of a specific diet supplement into the model to generate marketing content reflecting the characteristics of the product.

The device (30) can also input the content style of the recommended marketing channel into the generative artificial intelligence model. The content style may include, but is not limited to, data such as text structure, image placement, video length, and components analyzed in step S308.

Through this, the generative artificial intelligence model can learn the content style that works effectively on the channel and generate optimized marketing content based on this. For example, the device (30) can input the style of a popular video on a YouTube channel into the model and generate a product promotional video that reflects the style.

The device (30) enables a generative artificial intelligence model to generate an optimal content plan based on product information and the content style of the recommended marketing channel. The generative artificial intelligence model comprehensively analyzes the input data to generate marketing content that can be most effectively delivered to consumers.

For example, a generative AI model can automatically generate various forms of marketing content, such as product description videos, social media posts, blog posts, etc. This allows the device (30) to quickly produce efficient and consistent marketing content.

The device (30) can collect and preprocess various data sets to train a generative artificial intelligence model. In the initial stage, the device (30) collects data such as text, images, and videos from various sources such as websites, social media, and product reviews to build a data set. The collected data can be converted into a form suitable for learning through a preprocessing process such as removing unnecessary information, tokenizing text, and resizing images. Through this, the device (30) prepares the generative artificial intelligence model to learn various content styles and formats.

The device (30) can input preprocessed data into a generative artificial intelligence model to perform learning. The generative artificial intelligence model is mainly based on the Transformer architecture and can learn various tasks such as language modeling, image generation, and video analysis through a large-scale dataset.

Generative AI models learn patterns and relationships in input data and are able to generate new content that meets given conditions. During the learning process, the model's performance can be periodically evaluated and the model can be tuned using a validation dataset to prevent overfitting.

The device (30) can continuously update and improve the learned generative artificial intelligence model. When new data is collected, it is added to the model to perform re-learning, and content can be created that reflects the latest trends and consumer preferences. Through this, the device (30) can always create content that reflects the latest information and technology, and maximize marketing effects. In addition, errors or improvements that occur during the learning process of the generative artificial intelligence model can be continuously monitored and feedback can be reflected to continuously improve the performance of the model.

The device (30) can automatically generate marketing content based on product information and content styles of recommended marketing channels by utilizing a generative artificial intelligence model. This represents a significant technological advancement in that it can save time and cost compared to existing manual content production methods and can generate consistent and high-quality content. The introduction of a generative artificial intelligence model maximizes the efficiency of marketing content production and increases the flexibility of marketing strategies.

The device (30) can significantly shorten the content production time by automatically generating marketing content through a generative artificial intelligence model. This enables rapid execution of marketing campaigns and reduces the marketing workload. In addition, the generative artificial intelligence model can rapidly generate a large amount of content, so that a diversified marketing strategy can be effectively executed.

The device (30) can generate marketing content of consistent quality through a generative artificial intelligence model. This plays an important role in maintaining consistency of brand image and message, and can convey reliability and professionalism to consumers. The generative artificial intelligence model can continuously generate high-quality content based on input data and learned patterns.

The device (30) can generate customized marketing content based on product information and the content style of the recommended marketing channel. This can increase consumer participation and increase conversion rates by providing content that reflects the interests and preferences of target consumers. Customized marketing content contributes to strengthening interaction with consumers and increasing brand loyalty.

Figure 4 is a flowchart for explaining a process for outputting a product analysis report screen according to an embodiment. The product analysis report screen may include an analysis list display screen, a visualization report screen, and a keyword analysis screen.

Referring to FIG. 4, first, in step S401, the device (30) can output an analysis list display screen.

The analysis list display screen may include, but is not limited to, product basic information, a product manufacturer list, a product brand list, and a product list.

At this time, the process of outputting the analysis list display screen will be described later with reference to FIGS. 5 and 6.

At step S402, the device (30) can output a visualization report screen.

At this time, a detailed description of the process of outputting the visualization report screen will be described later with reference to FIGS. 7 to 9.

At step S403, the device (30) can output a keyword analysis screen.

At this time, a detailed description of the process of outputting the keyword analysis screen will be described later with reference to Fig. 10.

Figure 5 is a drawing for explaining an analysis list display screen according to an embodiment.

The device (30) can display an analysis list display screen on the screen of the user's terminal (10).

Referring to (a) of FIG. 5, the device (30) can display product basic information including information on product sales form classification, product category and product group, reflection data period, author, creation date, and viewing ID.

The sales form of the product may include, but is not limited to, group purchase, open market, and home shopping. Classification information can clearly inform consumers through which channel the product is sold. For example, it is easy to determine whether a particular product is mainly sold through home shopping or more sold through open markets.

The device (30) can also display the category and product group of the product. For example, it can visually display which category the product belongs to, such as food, health food, nutritional supplement, etc., and more specifically, which product group it belongs to. This information helps consumers easily navigate the products and quickly find the product that suits their needs.

The device (30) can also display information about the product's reflection data period, author, creation date, and viewing ID. The reflection data period means the period during which the product information was collected, and the viewing ID enables tracking of records viewed by a specific user.

Referring to (b) of FIG. 5, the device (30) can display a list of product manufacturers that visually display the product manufacturers in the form of a tree map.

The device (30) displays the market share of each manufacturer as a relative area in a tree map of a list of product manufacturers, and each manufacturer can be displayed with a different color and area. The tree map format displays the market share of each manufacturer as a relative area, making it easy to visually compare.

Each manufacturer is distinguished by a different color and area, which helps users quickly identify manufacturers and see their position in the market at a glance. For example, manufacturers with a large market share are displayed with a larger area and a more prominent color, making it easy to identify major manufacturers.

The device (30) can display detailed information of the selected manufacturer in a pop-up form when one of the manufacturers is selected by the user. For example, the detailed information can include, but is not limited to, the manufacturer's product lineup, major achievements, customer reviews, etc. By providing detailed information of the manufacturer, it is possible to provide in-depth information on the manufacturer selected by the user.

Figure 6 is a drawing for explaining an analysis list display screen according to an embodiment.

Referring to (a) of FIG. 6, the device (30) can display a product brand list that visually displays the product brands in the form of a tree map.

The device (30) can display the market share of each brand in a tree map of a product brand list as a relative area, and each brand can be displayed by distinguishing it with a different color and area. The device (30) displays the market share of each brand as a relative area, so that a user can easily identify the position of the brand in the market. Each brand can be distinguished with a different color and area, so that they can be visually clearly distinguished. For example, a brand with a large market share can be displayed with a larger area and a prominent color, so that a major brand can be easily identified.

The device (30) allows users to compare various brands at a glance through a product brand list in the form of a tree map. Users can easily determine which brand has greater influence by visually comparing the market share of the brands. In addition, by providing detailed information on each brand in the form of a pop-up, users can compare the characteristics and pros and cons of each brand and select a brand that suits them. For example, users can compare the product line and product features provided by a specific brand with those of other brands to select the most suitable brand.

The device (30) can display detailed information of the selected brand in a pop-up form when one of the brands is selected by the user.

For example, brand details may include, but are not limited to, a list of the brand's products, key features, customer ratings, etc.

Referring to (b) of FIG. 6, the device (30) can display a product list that visually displays products corresponding to a product group in the form of a tree map.

The device (30) displays the tree map of the product list with the sales volume of the products as a relative area, and each product can be distinguished by a different color and area. The device (30) displays the sales volume of each product as a relative area, so that the user can visually compare the popularity of the products. Each product is distinguished by a different color and area, so that they can be visually clearly distinguished. For example, a product with a large sales volume is displayed with a larger area and a prominent color, so that the main product can be easily identified.

When one of the products is selected, the device (30) can display detailed information about the product in a pop-up form.

Detailed information can include a variety of information, such as the product's key features, how to use it, and customer reviews.

For example, you can check what features a specific product has and what evaluations it has received from customers through detailed information. The device (30) allows you to compare various products at a glance through a product list in the form of a tree map. Users can easily determine which product is more popular by visually comparing the sales volume of each product. In addition, by providing detailed information on each product in a pop-up form, you can compare the characteristics and pros and cons of each product.

The device (30) can effectively convey product brands and product lists to users through visual displays in the form of treemaps. This represents an important technological advancement that helps users easily understand and compare information through visual data analysis. Visualization in the form of treemaps can concisely express complex data, thereby helping users understand and support decision-making.

The device (30) visually displays product brands and product lists in the form of a tree map, thereby clearly showing the market position of each brand and product, and the detailed information pop-up function provides in-depth information to the user. Through this, the device (30) can increase the convenience and reliability of product selection and maximize user satisfaction.

Meanwhile, the analysis list display screen may include download buttons that can download product basic information, product manufacturer list, product brand list, and product list, respectively.

Users can easily download basic product information, including product category, product group, reflected data period, author, creation date, and viewing ID, by clicking the download button. This allows users to save the analyzed data to local storage and refer to or share the data whenever necessary.

The Analysis List display screen may additionally include a Manufacturer Site button for each manufacturer that contains a link to the manufacturer site of the product's manufacturer.

The device (30) may additionally include a manufacturer site button that includes a link to the manufacturer site for each manufacturer, along with a button for downloading a list of product manufacturers on the analysis list display screen. By clicking the manufacturer site button, the user can go directly to the official website of the manufacturer. Through this, the user can obtain additional information about the manufacturer and promote direct communication with the manufacturer. In addition, the entire manufacturer list can be downloaded through the manufacturer list download button, so that the manufacturer information can be managed efficiently.

The Product Brands list may additionally include a Brand Site button for each brand that contains a link to the brand site of that brand of the product.

The device (30) may additionally include a brand site button that includes a link to a brand site for each brand, along with a button for downloading a product brand list on the analysis list display screen. By clicking the brand site button, the user can go directly to the official website of the brand. Through this, the user can obtain additional information about the brand and promote direct communication with the brand. In addition, the entire brand list can be downloaded through the brand list download button, so that brand information can be managed efficiently.

Product listings may additionally include a Sales Site button for each product that contains a link to the product's sales site.

The device (30) may additionally include a sales site button that includes a link to a sales site for each product, along with a button for downloading a product list on the analysis list display screen. A user may click the sales site button to directly move to the sales page for the corresponding product. Through this, the user may check detailed information about the product and purchase it directly. In addition, the entire product list may be downloaded through the product list download button, so that product information may be managed efficiently.

The device (30) provides a download button and a link button on the analysis list display screen, thereby helping the user to easily access and efficiently manage the necessary information. In particular, by adding a manufacturer site button, a brand site button, and a sales site button to the product manufacturer list, product brand list, and product list, respectively, user convenience is greatly improved. This function allows the user to utilize the data more flexibly, thereby increasing the efficiency of data management and analysis.

The device (30) can display the product names of products that include a raw material that satisfies a preset criterion for the number of products that include a specific raw material among the product list in a first thickness, and display the product names of products that do not include the raw material in a second thickness that is different from the first thickness. At this time, the preset criterion can be set differently depending on the embodiment. At this time, the first thickness and the second thickness are different from each other, can be set differently depending on the embodiment, and can be applied differently depending on the importance of the product or user needs.

For example, if you want to highlight a product that contains a particular vitamin or nutrient, this feature can be used to draw the user's visual attention by making the product name bolder.

The device (30) can display product names containing specific raw materials in a first bold font, and product names not containing specific raw materials in a second bold font. At this time, the first bold font and the second bold font are set differently from each other, thereby allowing the user to easily distinguish the product list and quickly identify important information. For example, the first bold font can be set to bold, and the second bold font can be set to regular. This visual difference helps the user quickly identify products containing specific raw materials.

For example, a user can set different thickness settings depending on the importance of a particular raw material, which increases the flexibility of the user interface. By allowing the user to directly specify the thickness value through the user interface setting menu, a display method that suits the individual's preference can be provided. Through this, the device (30) can improve the user experience and increase the accessibility of product information.

The device (30) provides a product name bold display function according to whether a specific raw material is included in the product list, thereby helping the user to quickly identify important product information. In particular, the visual difference between the first bold and the second bold plays an important role in clearly distinguishing product information and emphasizing important information. This function increases the efficiency of the user interface and represents an important technological advancement that improves information accessibility.

The device (30) provides a button for downloading various information on the analysis list display screen, so that the user can easily save the data and access it whenever necessary. This allows the user to efficiently manage the data and greatly improve the efficiency of the analysis work. In addition, by saving various data to a local storage through the download button, the preservation and sharing of the data becomes easy.

The device (30) provides a manufacturer site button, a brand site button, and a sales site button, so that the user can quickly access the information he needs. Through this, the user can directly connect to the manufacturer, brand, and sales site, and obtain additional information or communicate. This function maximizes the convenience of the user and helps the user to efficiently utilize the information he needs.

The device (30) supports users to make strategic decisions based on data by providing downloads and links to various information. Users can comprehensively analyze information on manufacturers, brands, and products to establish effective marketing strategies and respond quickly to market changes. Through this, the device (30) helps users develop and execute competitive business strategies.

Figure 7 is a drawing for explaining a visualization report screen according to an embodiment.

Referring to (a) of Fig. 7, the device (30) can display the number of monthly product sales in the form of a bar graph.

Visual representations allow users to see at a glance the number of products sold in a given month. For example, it is easy to visually recognize that sales are noticeably high in certain months such as January, March, and November. This is useful for understanding the sales trend of a product and analyzing the reasons for the increase or decrease in sales at a given point in time.

The device (30) can display the height of the bar graph in proportion to the number of sales in each month. The height of each bar represents the total number of products sold in the corresponding month, thereby allowing the user to visually check the change in sales volume.

The device (30) provides an important tool that helps users easily understand product sales trends through visual data. By clearly and visually displaying the number of monthly product sales and the list of products sold, users can intuitively understand and analyze the data. This visual tool concisely expresses complex data, greatly improving the efficiency of users' data analysis.

The device (30) visually displays the number of monthly product sales, allowing users to quickly understand sales trends. This allows users to analyze sales patterns at a specific point in time and establish effective marketing strategies. In addition, by providing detailed information through a list of products sold, the accuracy of data analysis can be increased.

Referring to (b) of Fig. 7, when one of the bar graphs is selected by the user, the device (30) can display a list of products sold in the selected month in a table format at the bottom of the bar graph.

A list of products for sale may include, but is not limited to, information about the time of sale, product family, product name, brand, manufacturer, and number of sales.

The list of products for sale may include, but is not limited to, a display quantity selection button that allows selection of the number of product information to be displayed, and a search bar that allows selection of products for sale.

Users can adjust the number of list items they can see at one time, allowing them to see more information at a glance or look at it in more detail, as needed. For example, if the user sets the page to display 10 items, information for 10 products will be displayed, and additional items can be viewed by flipping through the pages.

Users can quickly find specific products or brands by entering search terms, which helps them easily find the information they want in a vast amount of data. For example, if a user searches for a product of a specific brand, the list can be filtered to display only products of that brand.

The device (30) can provide a function that allows the user to sort or filter information included in the list of products for sale according to criteria selected by the user. For example, the user can sort by product name or filter to view only products from a specific brand or manufacturer. This allows the user to find the information he or she needs more quickly and improve the efficiency of data analysis.

The device (30) provides a list of products sold in the selected month in a table format, allowing users to easily access the information they need. The display quantity selection and search functions allow users to quickly find the information they need even within a vast amount of data, which greatly improves the user experience. These functions facilitate users' data exploration and quickly provide necessary information, thereby increasing work efficiency.

The device (30) provides monthly sales data and detailed product information to enable users to make strategic decisions based on data. Users can analyze sales data and trends to establish effective marketing strategies and respond quickly to market changes. Through this, the device (30) helps users develop and execute competitive business strategies.

Figure 8 is a drawing for explaining a visualization report screen according to an embodiment.

Referring to (a) of Fig. 8, the device (30) can display the sales products and sales frequency of popular sellers in the form of a bar graph in order of the number of sales for each product. At this time, the device (30) can extract popular sellers that satisfy preset criteria. At this time, the preset criteria can be set differently depending on the embodiment. For example, the criteria can include the number of sales, sales amount, number of reviews, etc., but are not limited thereto.

For example, if the criterion is the number of sales, the device (30) can check the number of sales of the seller from the sales site and extract the seller whose number of sales satisfies the preset standard number. At this time, the preset standard number can be set differently depending on the embodiment.

The device (30) displays the products sold by popular sellers and the number of sales in the form of a bar graph in order of the number of sales for each product, and may include a tab interface that can switch between a view by product and a view by seller.

When the product-specific view tab is selected, the device (30) can display the height of each bar graph in proportion to the number of sales by seller, each bar graph in a different color preset for each seller, and the bar graph can display the number of sales by the seller who sold each product.

The database of the device (30) may have the seller's seller code and color matched in advance and stored.

When the View by Product tab is selected, the height of each bar graph is proportional to the number of sales of each product, and the bar graphs are displayed in different colors for each seller for easy visual distinction.

The device (30) can visualize the sales data of each product in detail when the product-specific view tab is selected. The height of each bar graph indicates the total number of sales of the corresponding product, and the color of the bar can visually distinguish the seller who sold the product.

Referring to (b) of Fig. 8, when the seller-specific view tab is selected, the device (30) can display the number of sales by seller in the form of a bar graph in order of the number of sales by seller.

The device (30) can display the height of each bar graph in different colors in proportion to the number of sales for each product, and display the number of sales for each product.

The device (30) arranges bar graphs in order of the number of sales by seller, so that the sales performance of popular sellers can be seen at a glance. At this time, the height of each bar graph indicates the total number of products sold by the seller, and the color of the bar graph can be used to distinguish each product.

The device (30) can visually clearly display the number of sales of each product by distinguishing the products sold by each seller with different colors. For example, the seller code B026S is displayed as a bar graph of various colors, and each color represents a product sold by the seller. Each bar graph displays the number of sales of the product, so that the user can easily determine which product and how many times a specific seller has sold it.

The device (30) can compare sales data by seller to visually analyze which seller has the highest performance. The visual comparison can help users easily understand sales performance by seller and establish effective sales strategies.

The device (30) provides an important tool that helps users to understand sales performance at a glance by visually clearly displaying sales data by seller. In particular, by applying different colors to the bar graph to distinguish the number of sales by product, the readability of the data and the analysis efficiency are greatly improved. This means an important technological advancement that allows users to easily understand and analyze the data by concisely visualizing complex sales data.

The device (30) visually displays sales data by seller clearly, allowing users to quickly understand sales trends. Through this, users can analyze the performance of a specific seller and establish effective sales strategies based on this. In addition, since the performance of each seller can be visually compared, methods for increasing sales efficiency can be sought.

The device (30) visualizes seller-specific data in the form of a bar graph, allowing users to easily access the information they need. The ability to switch between product-specific and seller-specific views maximizes user convenience and supports data analysis from various perspectives.

The device (30) provides an important tool to help users understand sales performance at a glance by visually displaying sales data of popular sellers. In particular, the tab interface that allows switching between product-by-product and seller-by-seller views provides users with a variety of visual analysis methods, thereby enhancing the understanding of data.

The device (30) visually displays sales data clearly, allowing users to quickly understand sales trends. Through this, users can analyze the popularity of specific products and performance among sellers, and based on this, establish effective marketing strategies. In addition, since performance by seller can be visually compared, methods for increasing sales efficiency can be sought.

The device (30) visualizes data of popular sellers in the form of a bar graph, allowing users to easily access the information they need. The ability to switch between product-by-product and seller-by-seller views maximizes user convenience and supports data analysis from various perspectives. This feature helps users intuitively understand sales data and make decisions quickly.

The device (30) provides sales data of popular sellers, thereby enabling users to make strategic decisions based on data. Users can analyze sales data and trends to establish effective marketing strategies and respond quickly to market changes.

Figure 9 is a drawing for explaining a visualization report screen according to an embodiment.

Referring to (a) of FIG. 9, the device (30) can display information on the lowest selling price, highest selling price, average selling price, and minimum standard unit price of a product in a table format, but is not limited thereto.

This allows users to see the price distribution and economic feasibility of a product at a glance. For example, the lowest and highest selling prices can be used to determine the price fluctuation range of a product, and the average selling price can be used to determine the general price range. The minimum standard unit price can be used as an important indicator to evaluate the economic feasibility of a product.

According to one embodiment, the device (30) clearly visually distinguishes each price information so that the user can easily understand it. For example, the lowest sale price may be displayed in blue, the highest sale price in red, the average sale price in green, and the minimum standard unit price in gray, but is not limited thereto. Such color distinction is useful for helping visual recognition and emphasizing important information.

Referring to (b) of FIG. 9, the device (30) can display a bar graph that displays the minimum standard unit price for each product belonging to the same product group as the product, sorted in order of lowest price.

This allows users to compare the cost effectiveness of each product within the same product group. For example, a graph showing various nutritional supplement products sorted by their minimum unit price can help users easily see which product is the most cost-effective.

When a user selects one of the bar graphs, the device (30) can display the product name and the minimum unit price of the product corresponding to the bar graph in a pop-up form. This helps the user check the detailed information of a specific product and makes price comparison easier. For example, when the user clicks on a specific bar, the name of the product and the unit price can appear in a pop-up form, providing information necessary for product selection.

The device (30) can visually and clearly compare prices between products within the same product group through a bar graph. This allows users to easily evaluate the price competitiveness of products and select the most economical product. For example, a cost-effective decision can be made by selecting the product with the lowest price per unit among several nutritional supplement products.

The device (30) provides an important tool that helps users easily understand the economic feasibility of a product by visually clearly displaying the price information of the product. In particular, it allows users to understand complex price information concisely by clearly displaying the lowest selling price, highest selling price, average selling price, and minimum standard unit price.

The device (30) visually and clearly displays the price information of a product, allowing the user to quickly understand the range of price fluctuations and economic feasibility. This allows the user to easily compare prices between products and make cost-effective purchasing decisions. In addition, the bar graph allows the user to easily evaluate the price competitiveness of each product within the same product group.

The device (30) visually displays product price information clearly so that users can easily access the information they need. Detailed information provided in a pop-up form helps users check detailed price information for a specific product and enables convenient price comparison. This function can improve the user's shopping experience and increase satisfaction.

The device (30) provides price information on products, thereby enabling users to make strategic decisions based on data. Users can analyze price data to select cost-effective products and effectively manage their budgets.

Figure 10 is a drawing for explaining a keyword analysis screen according to an embodiment.

The device (30) can visualize keywords based on keyword analysis results by classifying them into feed keywords, hashtags, and keywords.

This allows users to see key keywords related to the product at a glance and obtain important insights necessary for establishing a marketing strategy. The device (30) visualizes each keyword in the form of a word cloud, and displays keywords with high frequency in a larger size to provide a visual emphasis effect.

At this time, the device (30) visualizes each keyword in the form of a word cloud, with larger words having higher frequency, and displays the ranking of keywords in a table form, so that the ranking of top keywords for each classification can be listed.

The device (30) can visualize feed keywords in the form of a word cloud. For example, keywords such as 'vitamin', 'product', and 'omega' are displayed in a large size in the feed keyword word cloud, visually indicating that the keywords have a high frequency. Through this, the user can quickly identify the main feed keywords and plan marketing content centered on the keywords. In addition, the keyword ranking can be displayed in the form of a table, so that the top feed keywords can be clearly listed.

At this time, the device (30) can collect text data from social media platforms, blogs, news articles, etc. related to the product in order to collect feed keywords. The device (30) can automatically crawl text data including product names, related keywords, user reviews, etc. from these sources, analyze them, and extract high-frequency keywords as feed keywords. For example, keywords such as 'vitamins', 'products', and 'omega' are frequently mentioned in various social media posts and user reviews, and the device (30) can collect feed keywords based on such data.

The device (30) can visualize hashtags and keywords in the form of word clouds. In the hashtag word cloud, keywords such as 'hairmess', 'biolectra', and 'omega 3' are displayed in a large size, indicating that they are hashtags with a high frequency in marketing content.

At this time, the device (30) can collect data from social media platforms where hashtags are frequently used, such as Instagram, Twitter, and Facebook, in order to collect hashtags. The device (30) can analyze hashtags used in posts related to a specific product or brand to extract hashtags with high frequency. For example, hashtags such as 'Hairmes', 'Biolectra', and 'Omega 3' are hashtags frequently used in social media posts related to the corresponding products, and the device (30) can collect hashtags based on such data. The device (30) can also analyze the frequency of use and trends of hashtags to identify currently popular hashtags.

In the word cloud of the small business, keywords such as ‘calcium’, ‘mineral’, and ‘magnesium’ are displayed in large size, indicating the factors that consumers consider important when purchasing a product. Through this, users can effectively analyze hashtags and small business and establish target marketing strategies based on this.

The device (30) can collect and analyze text data from consumer reviews, survey results, forum posts, etc. to collect marketing keywords. Marketing keywords reflect features or benefits that consumers consider important when selecting a product. The device (30) can analyze such data to extract keywords that consumers frequently mention, such as 'calcium', 'mineral', and 'magnesium'. For example, if consumers frequently mention specific ingredients or effects of a product in reviews of a health supplement, the device (30) can collect marketing keywords based on this. The device (30) can also analyze such keywords to identify key selling points of a product and optimize marketing strategies.

The device (30) provides an important tool that helps users easily identify key keywords related to a product by visually clearly displaying the keyword analysis results. In particular, the visualization in the form of a word cloud emphasizes keywords with high frequency, thereby improving the readability and comprehensibility of the data. In addition, the keyword analysis results classified into feed keywords, hashtags, and selling points allow users to analyze keywords from various perspectives and establish comprehensive marketing strategies.

The device (30) visually displays the keyword analysis results clearly so that the user can quickly identify the main keywords. This allows the user to quickly obtain the insights necessary for planning marketing content and greatly improve the efficiency of data analysis. In addition, the keyword analysis results classified into feed keywords, hashtags, and selling points provide useful information for establishing various marketing strategies.

The device (30) enables users to intuitively understand important keywords through visualization in the form of a word cloud. Large keywords represent keywords with high frequency, which emphasizes important keywords that users should pay attention to. By providing the ranking of keywords in a table form, users can clearly identify top keywords and establish effective marketing strategies based on this.

Referring to FIG. 10, the device (30) can clearly and visually display the keyword analysis results. The keyword analysis results classified into feed keywords, hashtags, and keywords are visualized in the form of a word cloud, thereby helping the user easily identify the main keywords. Through this, the user can obtain the insights necessary for planning marketing content and establish an effective marketing strategy. The device (30) provides the keyword analysis results, thereby supporting the user to make strategic decisions based on data.

Figure 11 is a flowchart illustrating a process for determining a product information collection period for creating a content plan according to an embodiment.

The device (30) can determine a period of product information collection for creating a content plan prior to the step of creating a content plan.

Referring to FIG. 11, first, in step S1101, the device (30) can extract keywords related to the product from product information and determine main keywords among the extracted keywords.

The device (30) can extract keywords related to a product from product information and determine major keywords among these keywords. For example, keywords can be extracted from various sources such as product descriptions, user reviews, and social media posts, and important keywords can be identified through frequency analysis.

At step S1102, the device (30) can check the number of times a main keyword is mentioned in marketing content associated with a product from a sales site.

The device (30) can identify the first period from the current point in time to the first point in time with the highest number of mentions.

In step S1103, if the first period is less than the preset reference period, the device (30) can determine the first point in time as the start point of the information collection period and determine the current point in time as the end point of the information collection period. At this time, the preset reference period can be set differently depending on the embodiment. For example, the reference period can be set to 3 months, 6 months, or 1 year, and this can be adjusted according to market research or marketing strategy. Through this, the device (30) can quickly and flexibly set the information collection period.

The device (30) aims to secure the latest data by limiting the temporal scope of data collection, prevents excessive inclusion of past data, and focuses on reflecting recent market trends and consumer behavior.

This is because past data may not match the current market situation, and market trends change rapidly, so data that is too old may not reflect current consumer preferences or competitive environments. Therefore, by setting a preset reference period and using the latest data within that period, more accurate and valid analysis results can be derived.

Analysis based on the latest data enables the establishment of strategies tailored to the current market situation, contributing to the planning of highly effective marketing campaigns. In addition, it enhances the competitiveness of companies by enabling rapid responses to rapidly changing market trends. It also plays an important role in securing the up-to-dateness of data and maintaining the validity of analysis. This emphasizes the importance of the latest data in the field of data science and analysis, and innovatively improves the way data is utilized in establishing marketing and business strategies. In addition, this approach increases the efficiency of data processing and analysis, enabling faster decision-making.

In step S1104, the device (30) can determine the first point in time as the start point of the information collection period and determine the second point in time as the end point of the information collection period if the first period is longer than the preset reference period and the second period satisfies the preset criterion.

The device (30) can check the second time point with the lowest number of mentions among the period from the first time point to the present time point when the first time period is longer than the preset reference period, and check the second period from the present time point to the second time point.

The device (30) can set a more accurate information collection period by considering the volatility of the data in order to collect data reflecting the latest trends, since the information collection period may be too old if the first period is longer than the preset reference period. For example, if the second time point is a time point when the product is mentioned less frequently, this can be set as the end point of the information collection period to secure the validity of the data.

If the first period is longer than the preset reference period and the second period satisfies the preset criteria, determining the first point in time as the start point of the information collection period and the second point in time as the end point of the information collection period means considering both the volatility and stability of the data. This is not simply using the latest data, but rather analyzing the trend of the data to set the most effective period.

If the first period is longer than the reference period, this may mean that the keyword has been dealt with for a long time, but including data that is too old may not match the current trend. Therefore, by setting the second period with a low mention frequency as the end point and determining the information collection period, the volatility of the data can be minimized and the consistency of the analysis can be maintained. This contributes to increasing the validity of the marketing strategy.

This allows for balanced reflection of past and present data when establishing marketing strategies, which contributes to increasing actual marketing effectiveness. In addition, an approach that considers data volatility increases the validity of analysis results, providing more reliable insights.

At step S1105, if the second period does not satisfy the preset criteria, the device (30) may determine the third point in time as the start point of the information collection period and determine the second point in time as the end point of the information collection period.

If the second period does not satisfy the preset criteria, the device (30) can identify a third time point with the highest number of mentions in the first period, excluding the first time point, determine the third time point as the start time of the information collection period, and determine the second time point as the end time of the information collection period.

Through this, the device (30) can set an accurate information collection period by minimizing variability while maintaining data consistency. For example, if the third point in time is a point in time when the frequency of mention of the product increases, this can be set as the starting point to collect more reliable data.

In the case where the second period does not meet the pre-established criteria in step S1105, determining the third time point as the start time of the information collection period and the second time point as the end time of the information collection period means flexibly adjusting the information collection period to ensure optimal data collection. This is a method of readjusting the data collection period when necessary while maintaining the validity and reliability of the data.

If the second period does not meet the criteria, this may mean that the data for that period is not sufficient for analysis. Therefore, by setting the third time point, which showed the highest frequency of mentions in the first time point, as the starting time point, more reliable data collection is possible. This is aimed at minimizing the variability of the data and increasing the validity of the analysis.

The device (30) provides a function to efficiently determine the information collection period by analyzing the change in the mention frequency of keywords related to the product. Through this, the device (30) secures the accuracy and reliability of data, and various marketing strategy devices (30) can flexibly set the information collection period by applying various criteria.

This allows users to quickly respond to market changes and establish effective marketing strategies by setting the optimal information collection period. Flexible information collection period setting allows for securing optimal data according to various marketing situations. It can provide accurate information required for establishment.

The device (30) can support strategic decision-making by analyzing the frequency of keyword mentions to determine the information collection period. Through this, the user can establish a marketing strategy that reflects the characteristics of the product and market trends, and develop a competitive business strategy. The device (30) effectively analyzes data to provide meaningful insights to the user.

The device (30) provides a function to efficiently determine an information collection period by analyzing the change in the mention frequency of keywords related to a product. Through this, the user can secure the accuracy and reliability of the data and establish an effective marketing strategy. The device (30) flexibly sets an information collection period by applying various criteria and supports strategic decision-making, thereby providing meaningful insights to the user. Through this, the user experience can be improved and data-based marketing efficiency can be maximized.

Determining the information collection period at each stage is an important process for ensuring the accuracy and reliability of the data. Through this, the device (30) provides reliable data necessary for establishing a marketing strategy and can flexibly respond to various business situations. The decision process at each stage increases the validity of data analysis, provides reliable insights, and maximizes data-based marketing efficiency.

Fig. 12 is an example diagram of the configuration of a device (30) according to one embodiment.

A device (30) according to an embodiment includes a processor (31) and a memory (32). The device (30) according to an embodiment may be the server or terminal described above. The processor (31) may include at least one of the devices described above through FIGS. 1 to 11, or may perform at least one method described above through FIGS. 1 to 11. The memory (32) may store information related to the method described above, or may store a program in which the method described above is implemented. The memory (32) may be a volatile memory or a nonvolatile memory.

The processor (31) can execute a program and control the device (30). The code of the program executed by the processor (31) can be stored in the memory (32). The device (30) can be connected to an external device (e.g., a personal computer or a network) through an input/output device (not shown in the drawing) and exchange data.

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

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

The software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing device to perform a desired operation or may independently or collectively command the processing device. The software and/or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal waves, for interpretation by the processing device or for providing instructions or data to the processing device. The software may also be distributed over network-connected computer systems, and stored or executed in a distributed manner. The software and data may be stored on one or more computer-readable recording media.

Although the embodiments have been described with limited drawings as described above, those skilled in the art can apply various technical modifications and variations based on the above. For example, even if the described techniques are performed in a different order than the described method, and/or the components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or are replaced or substituted by other components or equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents to the claims are also included in the scope of the claims described below.

Claims (3)

In a method performed by a device,
Steps for obtaining product information;
A step of generating a content plan by inputting the above product information into a generative artificial intelligence model; and
When receiving a request for product analysis, the step of outputting a product analysis report screen is included.
The steps for creating the above content plan are:
Step 1: Check the average selling price and sales cycle of the product from the sales site that sells the product.
A step for determining the economic feasibility of a product based on the above average selling price and sales cycle;
Step of checking the amount of text and images included in the product details from the above sales site;
A step of determining the complexity of use of the product based on the amount of text and images above;
A step of determining a product index based on the economic feasibility and complexity of use of the above product;
Step 1: Determine the description for each marketing channel based on the amount of text and images contained in the content on the website corresponding to the marketing channel.
Step to determine the delivery rate by marketing channel based on the content viewing time and reach of visitors visiting the website corresponding to the marketing channel.
Step of determining the efficiency index for each marketing channel based on the description and delivery diagram for each marketing channel above;
A step of classifying marketing channels into Group 1, Group 2, and Group 3 based on the above efficiency index.
A step of matching products and marketing channels by checking the efficiency index of the first to third groups and the product index,
A step for checking popular marketing channels where marketing content related to the product from the above sales site is posted with a number of views exceeding a preset standard;
A step of analyzing popular content styles based on the spacing, size, amount, sentence length, number of images, size of images, number of videos, length of videos, and number of objects featured in the content included in the popular marketing channels above.
Step 1: Check the content style of the marketing channel that matches the above product;
A step of selecting the marketing channel matched with the product as a recommended marketing channel when the similarity between the content style of the marketing channel matched with the product and the popular content style satisfies a preset criterion.
If the similarity between the content style of the marketing channel matched with the above product and the popular content style does not meet the preset criteria, a step of selecting the marketing channel with the highest efficiency index among the marketing channels belonging to the same group as the marketing channel matched with the above product as the recommended marketing channel, and
It includes a step of generating a content plan for the product by inputting the above product information and the content style of the above recommended marketing channel into a generative artificial intelligence model.
The steps for printing the above product analysis report screen are:
A step of outputting an analysis list display screen that displays an analysis list of products based on an analysis request for the above product;
A step for outputting a visual report screen that displays the analysis results of the above product in a graph, and
Including a step of outputting a keyword analysis screen displaying the analysis results of keywords related to the above product,
The above analysis list display screen is
Displays basic product information including product sales format classification information including group purchase, open market, and home shopping, product category and product group, reflected data period, author, creation date, and viewing ID.
Includes a list of product manufacturers, visually displaying the manufacturers of the products in a treemap format;
The treemap of the above product manufacturer list shows the market share of each manufacturer as a relative area, and each manufacturer is distinguished by a different color and area.
When one of the manufacturers is selected, detailed information about the selected manufacturer is displayed in a pop-up form.
Includes a list of product brands that visually display the product brands in a treemap format;
The tree map of the above product brand list shows the market share of each brand as a relative area, and each brand is distinguished by a different color and area.
When one of the brands is selected, detailed information about the selected brand is displayed in a pop-up form.
Includes a product list that visually displays products that fall into a product family in a treemap format;
The tree map of the above product list displays the sales volume of products as a relative area, and each product is distinguished by a different color and area.
When one of the products is selected, detailed information about that product is displayed in a pop-up form.
The above visualization report screen is,
Display the monthly product sales figures in the form of a bar graph,
When any of the above bar graphs is selected, the list of products sold in the selected month is displayed in table format at the bottom of the bar graph.
The above list of products for sale includes information on the product's sales period, product group, product name, brand, manufacturer, and number of sales.
Includes a display quantity selection button that allows you to select the number of product information to be displayed;
Includes a search bar to search for products for sale;
Extract popular sellers that meet preset criteria,
Displays the products sold by popular sellers and the number of sales in the form of a bar graph in order of the number of sales for each product, but includes a tabbed interface that allows switching between a view by product and a view by seller.
When the View by Product tab is selected, the height of each bar graph is displayed in a different color in proportion to the number of sales by seller, and shows the number of sales by the seller who sold each product.
When the View by Seller tab is selected, the number of sales by seller is displayed in the form of a bar graph in order of the number of sales, and the height of each bar graph is displayed in a different color in proportion to the number of sales by product, and the number of sales of each product is displayed.
Displays the lowest selling price, highest selling price, average selling price and minimum standard unit price information for the product;
Includes a bar graph showing the price per minimum standard unit for each product in the same product family, sorted in order of lowest price;
When one of the bar graphs is selected, the product name and minimum unit price of the product corresponding to that bar graph are displayed in a pop-up form.
The above keyword analysis screen is,
The keywords based on the keyword analysis results are visualized by classifying them into feed keywords, hashtags, and keywords.
Each keyword is visualized in the form of a word cloud, and the larger the word, the higher its frequency.
The ranking of keywords is displayed in a table format, listing the ranking of top keywords for each category.
Method for providing a solution for automating the creation of content planning proposals and periodic reporting analysis data for branding and marketing based on generative artificial intelligence models. delete delete