KR102009713B1 - Online marketing education system - Google Patents

Abstract

The present invention relates to an online marketing education system comprising: a member information module (10) for collecting students who intend to complete marketing education and lecturers who carry out marketing education and receiving activity information of the collected students and lecturers; a data collecting module (20) for carrying out basic education for marketing according to an establishment plan for the students and receiving a request for education information for marketing according to a personal schedule for the lecturers; a capability evaluation module (30) for analyzing a learning activity based on the basic education of the students and analyzing an education level based on the education information of the lecturers; a member matching module (40) for matching a student and a lecturer whose learning activity and education levels are matched with each other and carrying out an education activity by exchanging information of the student and the lecturer; an education diagnosis module (50) for tracking an education activity of the student to predict a risk factor which may cause fail and correcting education information based on the predicted risk factor to inform the lecturer of the same; and a marketing strategy module (60) for collecting a marketing strategy for each type of business in which a student is to be involved and analyzing the collected marketing strategy to improve the same to an advanced marketing strategy satisfying conditions. Thus, according to the present invention, activity of a student is tracked and customized education information is provided, thereby increasing learning efficiency. Also, a subject which a student is to fail among a curriculum is predicted in advance and supplemented to expect a smooth completion of education, and a rational marketing strategy satisfying conditions of a student is automatically derived to minimize costs and time required for operation.

Description

Online marketing education system {Online marketing education system}

The present invention relates to an online marketing education system, and more specifically, to provide appropriate education information by analyzing the inclination and level of students and instructors, and immediately reflect the corrected education information to the instructor by tracking the activities of the participants in the curriculum. An online marketing education system that can be implemented to improve overall learning efficiency.

With the rapid development and popularization of Internet technology today, the Internet provides various information and provides various Internet services such as advertising, finance and e-commerce online. Therefore, the frequency and use time of Internet access are increasing for people to search for information through the Internet and to use Internet services.

As the access frequency and usage time of these people increase, the Internet has become the most exposed means to people. As a result, advertisements generally made through traditional media such as television, radio, newspapers and magazines are moving to the Internet.

Currently, website-based advertisements on the Internet are applied with various advertising techniques such as banner ads, over banner ads in which banner ads have evolved, and search query ads that target ads to be associated with search queries such as Google's 'AdSense'. .

In the case of the above-mentioned offline advertising through TV, radio, newspapers and magazines, the cost of advertising is high due to the production of advertisements, the limited time of advertisements and the space allotment cost, which causes small companies with poor financial structure. There is a problem that is difficult to use.

In addition, in the case of web advertising advertising through the Internet, there was a problem of reducing the effectiveness of the advertisement by showing advertisements irrelevant to the web page viewed by the Internet user. In the case of the existing web advertisements, there is a problem of decreasing the interest of Internet users by distractingly outputting a plurality of advertisements on a single web page, focusing only on the number of impressions.

Recently, a service that allows an individual to establish a relationship with another person in a virtual space on the Internet, that is, a social network service (SNS) that provides communication and shares various information, has been in the spotlight. Social network services include Instagram, Kakao Story, Twitter, Me Today, and Facebook.

The social network service merely provides basic services for establishing and maintaining relationships between users, and does not provide an advanced type of service in which various information or contents and social network services are linked.

As a part of actively reflecting these problems, Korean Patent Publication No. 10-2018-0106309 (name of the invention: marketing method using SNS) and Korean Patent Publication No. 10-2014-0126052 (name of invention: Marketing) A comprehensive portal system and a marketing providing method using the same) and Korean Patent Application Publication No. 10-2007-0115135 (name of the invention: a network marketing education system through a broadcasting system) are proposed.

The proposed documents are slightly different, but they provide more stable and active marketing by predicting the return of investment capital or providing prize money, while providing marketing from offline education to online education.

However, despite the different inclinations and levels of students and instructors, there is a drawback in not being able to perform proper education by providing uniform educational information. In addition, if there are subjects that students can fail in the curriculum, there is a dismissal that is passed only to supplementary education or to the participant.

Of course, it is possible to overcome the shortcomings by constructing a separate expert who analyzes the participants, the instructor, and the educational information, but these experts can also have subjective judgment and rely solely on the expert's judgment to increase the cost of inefficient operation. There is a problem. In addition, in addition to education, actual marketing depends on the experience of experts in strategy, and thus there is a problem that it is impossible to derive an effective strategy against cost and time.

Republic of Korea Patent Publication No. 10-2018-0106309 (name of the invention: marketing method using SNS) Republic of Korea Patent Application Publication No. 10-2014-0126052 (Name of the invention: marketing comprehensive portal system and marketing providing method using the same) Republic of Korea Patent Publication No. 10-2007-0115135 (Name of the invention: Network marketing education system through the broadcast system)

The present invention was created in order to more actively solve the above problems, to provide the appropriate educational information by analyzing the inclination and level of students and instructors, and to track the activities of the participants in the curriculum corrected educational information It is designed to be automatically reflected to customers to improve overall learning efficiency and to automatically derive reasonable strategies to satisfy the requirements of the students while minimizing the labor required to provide the actual marketing strategy. Its purpose is to provide a marketing education system.

In order to achieve the above problems, the configuration of the online marketing education system proposed by the present invention is as follows.

The online marketing education system includes a member information module for recruiting lecturers who perform marketing education and instructors performing marketing education, and receiving activity information of the recruited students and lecturers; A data collection module which performs basic training on marketing according to a plan for setting up students, and receives education information on marketing according to a personal schedule for a lecturer; A capacity evaluation module for analyzing learning activities based on the basic education of the students and analyzing the education level based on the education information of the instructor; A member matching module for matching learners and instructors whose learning activities and education levels coincide with each other, and performing education activities by exchanging information of the learners and instructors; An education diagnosis module for tracking a student's educational activity to predict a failable risk factor, and correcting the education information based on the predicted risk factor to notify the instructor; The marketing strategy module that collects the marketing strategy for each participant to be active, analyzes the collected marketing strategy and evolves it into an evolved marketing strategy that satisfies the conditions.

At this time, the educational diagnosis module according to the present invention tracks the problem based on the learner's learning time and performance, diagnoses the cause, predicts the risk factors that can be failed in the subject, and based on the predicted risk factors, part of the educational information of other instructors. It is characterized by notifying the instructor in real time the correction information that can be corrected or added by extracting.

In addition, the above marketing strategy module according to the present invention analyzes the marketing unit and the collected marketing strategies to collect random marketing strategies, which are divided into keywords, banners, and virals related to the type of business to which the learner is going to work while training. Set up an analysis unit that extracts construction and operation costs, a load unit that sorts in descending order based on the extracted cost, a processing unit that crosses the sorted marketing strategies to generate an evolved hybrid marketing strategy, and sets the generated hybrid marketing strategies. Characterized in that the filter unit for selecting a new marketing strategy that satisfies one filter condition.

In addition, the above-mentioned processing unit according to the present invention hybrid culture that has evolved by alternatively or sequentially performing any one of the sequential cross between the adjacent ones of the marketing strategies belonging to the upper or sequential cross between the highest marketing strategy and the lowest marketing strategy. Generate strategies.

In addition, the filter unit according to the present invention is characterized in that it is selected as a new marketing strategy that the same strategy that satisfies the filter conditions equal to or lower than the cost set by the participants among the hybrid marketing strategies is derived three or more times.

In addition, the filter unit according to the present invention is characterized by repeating the hybrid by replacing the hybrid marketing strategies with the previous marketing strategies until a new marketing strategy is selected.

According to the present invention having the above-described configuration, the following effects are provided.

First, it analyzes surveys, quizzes, and time spent on basic education, and analyzes the contents and inclination of students and lecturers by analyzing the contents of lecturers' documents, videos, and online addresses. It provides effective educational information by analyzing and tracking the activities of the participants in the curriculum so that the corrected educational information can be immediately reflected to the instructor, thereby improving the learning efficiency.

Second, as a result of analyzing big data that collects marketing strategies by industry and breeding into an evolved marketing strategy that satisfies the conditions, it is a reasonable strategy that can satisfy the conditions required by the participants while minimizing the labor required to provide the actual marketing strategy. It provides the effect that can be derived automatically.

1 is a schematic diagram showing an overall system according to the present invention.
2 is a schematic diagram showing main parts of a system according to the present invention;

Hereinafter, with reference to the accompanying drawings will be described in a batch about the configuration of the present invention and its effects, effects.

Advantages and features of the present invention, and methods for achieving them will be apparent with reference to the embodiments described below in detail with reference to the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be embodied in various different forms, and only the present embodiments are provided to make the disclosure of the present invention complete and common knowledge in the technical field to which the present invention belongs. It is provided to fully inform the person having the scope of the invention, which is defined only by the scope of the claims. And like reference numerals refer to like elements throughout the specification.

The present invention relates to an online marketing education system, as shown in Figure 1 member information module 10, data collection module 20, competency evaluation module 30, member matching module 40, education diagnostic module 50, marketing It is an online marketing education system having a strategy module 60 as a main component.

The marketing education system of the present invention tracks the activities of the participants and provides customized educational information to improve learning efficiency and to predict and supplement the subjects to be failed in the curriculum in advance so that the learners can expect smooth training. The main point is to minimize the cost and time spent in operation by automatically deriving a reasonable marketing strategy that satisfies the conditions.

The member information module 10 recruits lecturers who want to complete the marketing education and instructors who perform the marketing education, and receives activity information of the recruited students and the lecturer. In other words, access is derived from students and instructors online. Here, the first time you create a username and password and receive personal information. Of course, it is induced to access the classifier and the lecturer.

At this time, after the subscription is completed, the learners and lecturers are induced to input the activity information. In other words, the learner enters the ID and consent in conjunction with the search site, personal blog, online cafe, and social network where the student works, and the lecturer initiates the online lecture along with the information of the public or private institutes of the past or present. Enter the address.

Activity information of these students and instructors is utilized as data to be analyzed in the member matching module 40 and the education diagnostic module 50 to be described later. In addition, the activity information provided by the instructor is used as educational information to be provided to the learners.

The data collection module 20 performs a basic education on marketing according to the establishment plan for the learners, and the instructor receives a request for educational information on marketing according to a personal schedule. That is, when the learner applies for marketing training in the member information module 10, the basic education plan is established according to the schedule and time zone desired by the learner. Subsequently, marketing basic education contents items consisting of online documents and videos are provided for the IDs of the participants. Here, participants are asked to participate in surveys and quizzes to analyze their dispositions according to established rules.

In addition, when the instructor concludes a labor contract in the member information module 10, the instructor receives a request for self-content such as marketing documents, videos, and online addresses. In other words, the instructor can upload the content or newly produced content that was previously used for marketing training. Here, the instructor may be a person who performs a job or a job such as a professor or a teacher, a person who performs a job that produces content, or a person who wants to perform a job.

The competency evaluation module 30 analyzes the learning activity based on the basic education of the learner, and analyzes the education level based on the education information of the instructor. In other words, quantitative figures are calculated by analyzing the time spent on students' usual activity information such as search sites, personal blogs, online cafes, social networks, surveys, quizzes, and basic marketing. The education level is quantitatively calculated by analyzing the instructor's activity history, awards and uploaded educational information.

Here, except for surveys, quizzes, and time, comprehensively collect activity information and analyze the same number of times by sorting marketing-related words among the collected information. The first evaluation is performed based on the difficulty level of the preset word, and the evaluation information is visualized to finally evaluate the level of the students and the instructor through the second evaluation by a plurality of experts.

The member matching module 40 matches learners and instructors with matching learning activities and education levels, and performs education activities by exchanging information between the learners and instructors. That is, the competency evaluation module 30 matches the learners and instructors with the same or similar learning level and education level. The learner receives the education information input by the matched instructor and completes the education, and the instructor supplements the education with customized education information based on the learner's learning activities.

The educational diagnosis module 50 tracks the educational activities of the learners, predicts possible risk factors for failing, and corrects the educational information based on the predicted risk factors and notifies the instructor. In other words, the problem is traced and the cause is diagnosed based on the learner's learning time and performance, and predictable risk factors in the subject are predicted in advance.

Based on the predicted risk factors, the instructor informs the instructor of correction information that can extract or correct some of the instructor's education information in real time. Here, the instructor can quickly and easily supplement the above-described education information with the learner's customized on the basis of the announced correction information.

The marketing strategy module 60 is an algorithm that collects marketing strategies for each type of students to be active, analyzes the collected marketing strategies, and crosses them into evolved marketing strategies that satisfy the conditions. The marketing strategy module 60 includes a storage unit 61, an analysis unit 62, a load unit 63, a processing unit 64, and a filter unit 65 as shown in FIG. 2.

The storage unit 61 randomly collects marketing strategies, which are divided into keywords, banners, and virals, which are related to the industry in which the learner is going to be active while completing the training. That is, data center servers and search engines collect all current marketing strategies.

The analysis unit 62 extracts the construction and operating costs by analyzing the collected marketing strategies. In other words, based on the collected information, the cost of building and operating immediately is added and extracted.

The rod 63 sorts in descending order based on the extracted cost. In other words, if the cost of the keyword is 1 million won, the cost of the banner is 2 million won, and the cost of the viral is 1.5 million won, it is sorted in the order of viral, banner, and keyword.

The processor 64 crosses the aligned marketing strategies to generate an evolved hybrid marketing strategy. That is, the hybrid marketing strategies that have evolved are generated by alternatively or sequentially performing one of sequential crosses between adjacent ones of the marketing strategies belonging to the higher or sequential crosses between the highest and lowest marketing strategies.

The filter unit 65 selects a new marketing strategy that satisfies the filter condition that sets the generated hybrid marketing strategies. That is, a new marketing strategy is selected in which the same strategy that satisfies the filter condition equal to or lower than the cost set by the participants among the hybrid marketing strategies is repeatedly derived three or more times. Here, hybridization is repeated by replacing hybrid marketing strategies with all-new marketing strategies until a new marketing strategy is selected.

Hereinafter, a process of selecting a new strategy based on the embodiment will be described.

First, the storage unit 61 collects all marketing (advertisement) strategies on the web through a search engine and stores them in a data center server by distributing them into categories such as keywords, banners, and viral. In this case, the viral is distributed and stored in a blog, knowledge iN, search advertisement / site category.

In addition, the analysis unit 62 extracts the expected cost by reflecting the construction and operation costs input in advance for the collected marketing strategies. For example, the keyword may be set to 1 million won, banner 2 million won, blog 500,000 won, knowledge iN 100,000 won, search ad / site, 1.5 million won.

Subsequently, the load unit 63 sorts the banners in descending order according to the extracted cost such as first rank, search advertisement / site second rank, keyword third rank, blog fourth rank, and knowledge iN fifth rank.

In addition, the processor 64 generates a first hybrid marketing strategy where the banner and the knowledge iN are mixed by crossing the banner with the first rank and the knowledge iN with the fifth rank, and the search advertisement / site with the second rank and the blog with the fourth rank are mixed. Create a second hybrid marketing strategy.

Subsequently, the filter unit 65 calculates the costs of the hybrid marketing strategies to filter only the hybrid marketing strategies that satisfy the filter conditions. That is, the first hybrid marketing strategy is calculated at 2.5 million won, and the second hybrid marketing strategy is calculated at 2 million won. If the student sets the filter condition of 2 million won, the second hybrid marketing strategy is selected.

Here, if the participant has set the filter condition of 1 million won, the first and second hybrid marketing strategies are distributed and stored like the pre-stored marketing strategy information of the storage unit 61, and the analysis unit 62, the load unit 63, and the processing unit again. (64), the filter section 65 is continuously repeated until the filter condition is satisfied.

Therefore, it is possible to derive a new marketing strategy that is different from the existing ones, thereby securing competitiveness, and maximizing overall customer satisfaction by providing strategies with low-cost marketing education by reducing labor costs.

In this case, the marketing strategy information is written for each keyword, banner, blog, knowledge iN, search advertisement / site for the purpose of understanding the invention, but sub-strategies that contain each item actually range from hundreds to thousands depending on the type of business. Therefore, it should be noted that there is more complexity and variety than the above description.

On the other hand, as an additional explanation, keyword marketing is basically a method of exposing one's homepage to a higher level, and costs are incurred by Google's Adwords, Naver's power / sponsored link, and banner marketing. After registering on the press site and large portals (Naver, Daum, Sun Brisbane, Brisbane Story, etc.), costs will be incurred depending on the popularity of the site.

Viral is an online marketing that spreads like a computer virus, and voluntarily promotes companies or products through various media such as email, blogs, and cafes. Viral marketing can be divided into blog, knowledge iN, search advertisement / site area.

Blog is a service that is currently used in the most active portals such as Naver, Daum, etc. Anyone can easily form an online / offline linked community, so it can be used as the most efficient marketing with high reliability and loyalty.

Knowledge iN has higher reliability than other channels, and it is a method using Naver intellectual service that actively searches and utilizes according to the demand of the user, and can be used for reliable and continuous marketing at low cost.

Search advertisement / site area is a marketing method through internet search site, and it is a good way to meet potential customers interested in products or services as internet users can easily access the information they want, maximizing efficiency according to the exposure area. can do.

As described above, the present invention analyzes the time spent on questionnaires, quizzes, and basic education, and analyzes the contents of the lecturer, such as documents, videos, and online addresses, to grasp the inclination and level of the lecturer and lecturer, and the inclination of the lecturer and lecturer It can improve learning efficiency by analyzing the level of education and providing appropriate educational information, and tracking the activity of students in the curriculum so that the corrected educational information can be immediately reflected to the instructor.

In addition, by analyzing big data that collects marketing strategies by industry and breeding into an evolved marketing strategy that satisfies the conditions, a reasonable strategy that satisfies the conditions required by the participants is minimized while minimizing the labor required to provide the actual marketing strategy. It can be derived automatically.

10: member information module 20: data collection module
30: competency evaluation module 40: member matching module
50: Educational Diagnosis Module 60: Marketing Strategy Module
61: storage 62: analysis
63: load portion 64: processing portion
65: filter unit

Claims (6)

A member information module 10 for recruiting lecturers who perform marketing education and lecturers who perform marketing education, and receiving activity information of the recruited students and lecturers; A data collection module 20 which performs basic education on marketing according to the establishment plan for the learners and receives education information on marketing according to a personal schedule for the instructor; A competency evaluation module 30 for analyzing learning activities based on the basic education of the learners and analyzing the education level based on the education information of the lecturer; Member matching module 40 for matching the learners and instructors with matching learning activities and education level, and perform the educational activities by exchanging the information of the learners and lecturers; An education diagnosis module 50 for predicting a failing risk factor by tracking a student's education activity and correcting the education information based on the predicted risk factor and notifying the instructor; In the online marketing education system comprising: a marketing strategy module (60) for collecting the marketing strategy for each participant to be active, and analyzes the collected marketing strategy and evolves it into an evolved marketing strategy that satisfies the conditions.
The competency evaluation module 30 analyzes the time spent on students' usual activity information such as search sites, personal blogs, online cafes, social networks, surveys, quizzes and marketing basic education, and calculates them in quantitative figures, and activity information of instructors. Calculate the educational level as a quantitative figure by analyzing the history and the awards and uploaded educational information, and then align the marketing related words among the collected information and make the first evaluation based on the same number of times and the difficulty of the words, and then evaluate the evaluation information. Visualize and finalize the level of students and instructors through secondary evaluation by multiple experts.
The above educational diagnosis module 50 tracks problems based on the learner's learning time and performance, diagnoses the causes, predicts the risk factors that can fail in the subject, and extracts some of the instructor's educational information based on the predicted risk factors. Inform the instructor of the correction information that can be corrected or added in real time,
The marketing strategy module 60 is a storage unit 61 for randomly collecting historical marketing strategies, which are divided into keywords, banners, and viralities related to the type of business to which the learner is going to work while training, and analyzing the collected marketing strategies. Analysis unit 62 for extracting the construction and operation costs, the load unit 63 for sorting in descending order based on the extracted cost, and the processing unit 64 for generating an evolved hybrid marketing strategy by crossing the sorted marketing strategies And a filter unit 65 for selecting a new marketing strategy that satisfies the filter condition in which the generated hybrid marketing strategies are set.
The processor 64 generates an evolved hybrid marketing strategy by alternately or sequentially performing one of sequential crosses between adjacent ones of the marketing strategies belonging to the upper or sequential crosses between the highest and lowest marketing strategies. and,
The filter unit 65 selects as the new marketing strategy that the same strategy that satisfies the filter condition equal to or lower than the cost set by the participants among the hybrid marketing strategies is repeatedly derived three or more times, until the new marketing strategy is selected. An online marketing education system characterized by repeating crosses by replacing hybrid marketing strategies with previous marketing strategies. delete delete delete delete delete

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