KR20230112397A - Marketing process-based marketing strategy planning online system of training and practice functions - Google Patents

Abstract

In the present invention, education is provided so that users can have a basic understanding of the definition and concept of marketing through an online solution tool in the process of establishing a marketing strategy and action plan, and a method for enabling practice through the solution tool is disclosed. Through this, prospective entrepreneurs, startups, and small and medium-sized enterprises, etc. who want to establish a marketing strategy can easily and conveniently establish a company's marketing strategy by selecting elements presented for each process based on the marketing strategy process. Therefore, there is an effect of removing barriers and burdens for marketing activities, which is one of the most important business activities for companies, and enhancing marketing planning capabilities and marketing activity capabilities so that each company can establish and execute marketing strategies internally.

Description

Marketing process-based marketing strategy planning online system of training and practice functions}

The present invention relates to an online marketing education and practice system, and it relates to an online solution development method that allows prospective entrepreneurs, startups, small and medium-sized enterprises, etc. who want to establish a marketing strategy to easily and conveniently establish a marketing strategy for a company by selecting elements presented for each process based on the marketing strategy process.

As today's Internet technology rapidly develops and becomes popular, the Internet provides a variety of information and provides various Internet services such as advertisements, finance, and e-commerce through online. Therefore, the frequency and usage time of people accessing the Internet are increasing in order to search for information and use Internet services through the Internet. As the access frequency and use time of these people increase, the Internet has become the most exposed means to people. Therefore, advertising, which is generally done through traditional media such as television, radio, newspapers and magazines, is migrating to the Internet.

Currently, various advertising techniques are applied to website-based advertisements through the Internet, such as banner advertisements, over-banner advertisements in which banner advertisements have evolved, and search query advertisements targeting advertisements to be associated with search queries such as Google's 'AdSense'. In the case of offline advertising through TV, radio, newspapers and magazines, the cost of advertising is high due to the cost of advertising production, advertising time and space allocation within a limited period of time, which makes it difficult for small businesses with poor financial structures to use. In addition, in the case of web advertisements advertised through the Internet, there is a problem in that the effect of the advertisement is reduced by showing advertisements unrelated to the web page that the Internet user is viewing. In the case of existing web advertisements, there is a problem in that interest of Internet users is reduced by displaying a plurality of advertisements on a single web page in a distracting way, focusing only on the number of exposures. BACKGROUND OF THE INVENTION [0002] Recently, a service through which an individual can establish a relationship with others in a virtual space on the Internet, that is, a social networking service (SNS) capable of providing communication and sharing various information has been in the limelight.

Social network services are established in various forms such as Instagram, Kakao Story, Twitter, Me2day, and Facebook. These social network services only provide basic services for setting up and maintaining relationships between users, and do not provide advanced services in which various information or contents are linked with social network services. As part of actively reflecting these problems, Korean Patent Publication No. 10-2018-0106309 (title of invention: marketing method using SNS), Korean Patent Publication No. 10-2014-0126052 (title of invention: comprehensive marketing portal system and method for providing marketing using the same) and Korean Patent Publication No. 10-2007-0115135 (title of invention: network marketing education system through broadcasting system) are proposing Although the methods are slightly different, the proposed literature promotes more stable and active marketing by predicting the rate of return on investment or providing prize money while providing marketing from an offline education method to an online education method.

Republic of Korea Patent Publication No. 10-2018-0106309 (Title of Invention: Marketing method using SNS Republic of Korea Patent Publication No. 10-2014-0126052 (Title of Invention: Comprehensive Marketing Portal System and Method for Providing Marketing Using the Same) Republic of Korea Patent Publication No. 10-2007-0115135 (Title of Invention: Network Marketing Education System through Broadcasting System)

The present invention was created in order to more actively solve the above problems, and provides suitable educational information by analyzing the inclination and level of students and instructors, tracking the activities of students in the course of education and implementing the corrected educational information to the instructor immediately. Its purpose is to provide an online marketing education system implemented so that it can automatically derive a reasonable strategy that can satisfy the conditions required by students while minimizing the man-hours consumed in providing actual marketing strategies as well as improving overall learning efficiency.

The configuration of the online marketing education system proposed by the present invention in order to achieve the above solution is as follows. The above online marketing training system recruits students who want to complete marketing training and instructors who conduct marketing training, and a member information module that receives activity information of the recruited students and instructors; A data collection module that conducts basic training on marketing according to the established plan for students and receives training information on marketing from instructors according to individual schedules; A competency evaluation module that analyzes learning activities based on the basic education of students and analyzes the educational level based on the instructor's educational information; A member matching module that matches students and instructors whose learning activities and educational levels match each other, and conducts educational activities by mutually exchanging information between students and instructors; An educational diagnosis module that tracks the student's educational activities, predicts possible risk factors for failure, corrects educational information based on the predicted risk factors, and notifies the instructor; It is characterized by comprising; a marketing strategy module that collects marketing strategies for each industry in which the student intends to be active, analyzes the collected marketing strategies, and crosses them 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 student's learning time and performance, diagnoses the cause, predicts risk factors that can fail in the course, and based on the predicted risk factors, extracts some of the educational information of other instructors and notifies the instructor of correction information that can be corrected or added in real time.

In addition, the marketing strategy module according to the present invention includes a storage unit that randomly collects historical marketing strategies classified into keywords, banners, and virals related to the industry in which students intend to be active while completing education, an analysis unit that analyzes the collected marketing strategies and extracts construction and operation costs, a loading unit that sorts in descending order based on the extracted cost, a processing unit that crosses the sorted marketing strategies to create an evolved hybrid marketing strategy, and a new marketing strategy that satisfies the filter conditions set for the generated hybrid marketing strategies. It is characterized in that it consists of a filter unit to select. In addition, the above processing unit according to the present invention alternatively or sequentially executes either sequential crossing of adjacent marketing strategies or sequential crossing of the highest marketing strategy and the lowest marketing strategy to generate evolved hybrid marketing strategies. In addition, the above filter unit according to the present invention is characterized by selecting, as a new marketing strategy, among hybrid marketing strategies, the same strategy satisfying the filter condition equal to or lower than the cost set by the student is repeatedly derived three or more times. In addition, the filter unit according to the present invention is characterized in that the hybrid marketing strategies are replaced with successive marketing strategies and mating is repeated until a new marketing strategy is selected.

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

First, it analyzes the time spent on surveys, quizzes, and basic education, and analyzes the instructor's own contents such as documents, videos, and online addresses to identify the tendencies and levels of the students and instructors, analyzes the tendencies and levels of the students and instructors to provide appropriate educational information, tracks the students' activities in the curriculum, and implements the corrected educational information so that it can be immediately reflected to the instructor, providing the effect of improving learning efficiency.

Second, by analyzing big data collected from marketing strategies by industry and breeding them with evolved marketing strategies that satisfy the conditions, it provides an effect of automatically deriving a reasonable strategy that can satisfy the conditions required by students while minimizing the man-hours consumed in providing actual marketing strategies.

1 is a schematic diagram showing the system according to the present invention as a whole.
Figure 2 is a schematic diagram showing the main part of the system according to the present invention.
Figure 3 is a detailed schematic diagram showing the main part of the system according to the present invention.

Hereinafter, with reference to the accompanying drawings, the configuration of the present invention and the resulting actions and effects will be collectively described. Advantages and features of the present invention, and methods of achieving them, will become clear with reference to the detailed description of the following embodiments taken in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in a variety of different forms, and only this embodiment is provided to complete the disclosure of the present invention and to fully inform those skilled in the art of the scope of the invention to which the present invention belongs, and the present invention is only defined by the scope of the claims. And like reference numerals designate like elements throughout the specification.

The present invention is related to an online marketing education system, and as shown in FIG. 1, a member information module 10, a data collection module 20, a competency evaluation module 30, a member matching module 40, an education diagnosis module 50, and a marketing strategy module 60. It is an online marketing education system with main components. The main point of the marketing education system of the present invention is to improve learning efficiency by tracking students' activities and providing customized education information, and by predicting and supplementing subjects that will be failed during the curriculum in advance, smooth education completion can be expected, while automatically deriving a reasonable marketing strategy that satisfies the students' conditions, thereby minimizing the cost and time consumed in operation.

The member information module 10 recruits students who intend to complete marketing education and instructors who conduct marketing education, and receives activity information of the recruited students and instructors. In other words, it induces access from students and instructors through online. When connecting for the first time, an ID and password are created and personal information is entered. Of course, it is induced to be able to connect by dividing it into students' use and instructor's use.

At this time, after registration is completed, students and instructors are induced to input activity information. That is, students receive ID and consent to be linked with search sites, personal blogs, online cafes, and social networks in which they are active, and instructors receive information about public institutions and private academies in the past or present as well as lecture addresses disclosed online. Such activity information of students and instructors is used as data to be analyzed in the member matching module 40 and the education diagnosis 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 students. The data collection module 20 conducts basic marketing education for students according to an established plan, and receives a request for marketing education information from instructors according to a personal schedule. That is, when a student applies for marketing education in the member information module 10, a basic education plan is established according to the schedule and time desired by the student. Next, basic marketing education content items consisting of online documents and videos are provided for the student's ID. Here, students are asked to participate in surveys and quizzes that can analyze their tendencies according to the set rules.

In addition, when a labor contract is concluded in the member information module 10, the instructor is requested for self-content such as marketing documents, videos, and online addresses according to the instructor's schedule. In other words, it enables instructors to upload content previously used for marketing education or newly created content. Here, the instructor may be a person who performs a business such as a professor or a teacher, or a person who intends to perform a business, and may be a person who performs a business of producing content or a person who intends to perform a business. The competency evaluation module 30 analyzes learning activities based on the basic education of students and analyzes the education level based on the instructor's education information. In other words, the students' daily activity information such as search sites, personal blogs, online cafes, social networks, surveys, quizzes, and basic marketing education are analyzed and calculated as quantitative values.

In addition, the educational level is calculated as a quantitative value by analyzing the history of the instructor's activity information and the educational information that has been awarded and uploaded. Excluding surveys, quizzes, and time, activity information is comprehensively collected, and among the collected information, marketing-related words are sorted to analyze the same number of uses. After the first evaluation is performed through the difficulty of the words set in advance, the evaluation information is visualized and the level of the students and instructors is finally evaluated through the second evaluation by a plurality of experts.

The member matching module 40 matches learners and instructors whose learning activities and educational levels match each other, and conducts educational activities by mutually exchanging information between learners and instructors. That is, in the competency evaluation module 30, learners and instructors whose learning activity levels and educational levels are identical or similar are matched with each other. In addition, the trainee receives the training information entered by the matched instructor and completes the training, and the instructor supplements the training information customized to the student based on the student's learning activity and conducts training.

The education diagnosis module 50 tracks the student's educational activities, predicts possible risk factors for failure, corrects educational information based on the predicted risk factors, and notifies the instructor. In other words, based on the student's learning time and performance, the problem is tracked, the cause is diagnosed, and risk factors that can fail in the course are predicted in advance. In addition, based on the predicted risk factors, part of the educational information of other instructors is extracted and correction information that can be corrected or added is notified to the instructor in real time. Here, the instructor can quickly and easily supplement the above-mentioned educational information in a customized manner for the student based on the notified correction information.

The marketing strategy module 60 is an algorithm that collects marketing strategies for each industry in which the student intends to be active, analyzes the collected marketing strategies, and crosses them into evolved marketing strategies that satisfy 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. The storage unit 61 randomly collects past marketing strategies divided into keywords, banners, and virals related to the industry in which the student intends to be active while completing the education. In other words, it collects all marketing strategies currently being implemented through data center servers and search engines.

The analysis unit 62 analyzes the collected marketing strategies and extracts construction and operating costs. In other words, based on the collected information, the cost required for construction and operation is immediately summed up and extracted. The loading unit 63 sorts in descending order based on the extracted cost. That is, if the cost of keyword is 1 million won, the cost of banner is 2 million won, and the cost of viral is extracted as 1.5 million won, sort by viral, banner, and keyword. Processing unit 64 hybridizes the aligned marketing strategies to create an evolved hybrid marketing strategy. That is, evolved hybrid marketing strategies are generated by performing either sequential crossing of adjacent marketing strategies or sequential crossing of the top marketing strategy and the bottom marketing strategy. The filter unit 65 selects a new marketing strategy that satisfies a filter condition set for the generated hybrid marketing strategies. That is, among the hybrid marketing strategies, the same strategy that satisfies the filter condition equal to or lower than the cost set by the student is repeatedly derived three or more times and is selected as a new marketing strategy. Until a new marketing strategy is selected, the hybrid marketing strategies are replaced with previous marketing strategies and crossbreeding is repeated.

Hereinafter, a process until a new strategy is selected based on an embodiment will be reviewed. First, the storage unit 61 collects all marketing (advertisement) strategies on the web through a search engine, distributes and stores them in categories such as keywords, banners, and viral in the data center server. At this time, the virals are distributed and stored in the categories of blog, knowledge iN, and search advertisement/site. In addition, the analysis unit 62 extracts an estimated cost by reflecting the construction and operating costs input in advance by itself for the collected marketing strategies. For example, it can be set at 1 million won for keywords, 2 million won for banners, 500 thousand won for blogs, 100 thousand won for Knowledge iN, and 1.5 million won for search ads/sites.

Subsequently, the loading unit 63 arranges the banners in descending order according to the extracted cost, such as 1st, search advertisement/site 2nd, keyword 3rd, blog 4th, and registered patent 10-2009713-7-knowledge iN 5th. Then, the processing unit 64 crosses the first-ranked banner with the fifth-ranked Knowledge iN to generate a first hybrid marketing strategy in which the banner and the knowledge iN are mixed, and a second hybrid marketing strategy in which the second-ranked search advertisement/site and the fourth-ranked blog are mixed.

Subsequently, the filter unit 65 calculates the costs of the hybrid marketing strategies and filters only the hybrid marketing strategies that satisfy the filter conditions. That is, the first hybrid marketing strategy is calculated as 2.5 million won, and the second hybrid marketing strategy is calculated as 2 million won. If the student has set the filter condition of 2 million won, the second hybrid marketing strategy is selected. Here, if the student sets the filter condition of 1 million won, the first and second hybrid marketing strategies are distributed and stored like the previously stored marketing strategy information in the storage unit 61, and again through the analysis unit 62, the loading unit 63, the processing unit 64, and the filter unit 65, and continuously repeated infinitely until the filter condition is satisfied.

Therefore, it is possible to derive a new marketing strategy that is differentiated from the existing ones, thereby securing competitiveness, as well as maximizing overall consumer satisfaction by providing strategies along with low-cost marketing training by reducing man-hours. At this time, in order to help the understanding of the invention, marketing strategy information has been described for each item of keywords, banners, blogs, knowledge iN, and search advertisements/sites, but it should be noted that there are hundreds to thousands of sub-strategies actually included in each item, so it is more complicated and diverse than the aforementioned description.

On the other hand, as an additional explanation, keyword marketing is basically a method of exposing one's homepage at the top, and costs are incurred by Google's Adwords, Naver's power/sponsored links, etc. Banner marketing is a video, flag, and advertising images are registered on media sites and large portals (Naver, Daum, Sun Brisbane, Brisbane Story, etc.), and costs are incurred according to the popularity of the site. And viral is online marketing that spreads like a computer virus, and companies or products are voluntarily promoted through various media such as e-mail, blogs, and cafes.

Here, viral marketing can be divided into blog, knowledge-iN, and search advertisement/site areas. Blogs use services from Naver and Daum, which are currently the most active portals, and anyone can easily form a community connected online/offline, so it can be used for maximum efficiency marketing with high reliability and loyalty. Jisik iN has a higher reliability than other channels, and it is a method using Naver intellectual service that is actively searched and utilized according to the person's demand, and can be used for low-cost, highly reliable, and continuous marketing.

Search advertisement/site area is a method of marketing through Internet search sites, and it is a good means for meeting potential customers who are interested in products or services because Internet users can easily access the information they want, and efficiency can be maximized according to the exposure area. As such, the present invention analyzes the time spent on questionnaires, quizzes, and basic education, analyzes the instructor's own content such as documents, videos, and online addresses to identify the tendencies and levels of the students and instructors, analyzes the tendencies and levels of the students and instructors, provides appropriate educational information, tracks the student's activities in the curriculum, and implements the corrected educational information to immediately reflect to the instructor, thereby improving learning efficiency.

In addition, by analyzing big data collected from marketing strategies for each industry and breeding them with evolved marketing strategies that satisfy conditions, reasonable strategies that can satisfy the conditions required by students while minimizing the man-hours consumed in providing actual marketing strategies can be automatically derived.

10: member information module 20: data collection module
30: competency evaluation module 40: member matching module
50: education diagnosis module 60: marketing strategy module
61: storage unit 62: analysis unit
63: loading unit 64: processing unit

Claims (3)

A member information module (10) that recruits prospective entrepreneurs, start-ups, small and medium-sized enterprises, etc. who want to establish marketing strategies, and receives activity information of recruited students and instructors; A data collection module (20) that conducts basic training on marketing for learners according to an established plan and requests training information on marketing for instructors according to a personal schedule; A competency evaluation module 30 that analyzes learning activities based on the basic education of students and analyzes the educational level based on the instructor's educational information; A member matching module 40 that matches students and instructors whose learning activities and educational levels match each other, and conducts educational activities by mutually exchanging information between students and instructors; An education diagnosis module 50 that tracks the student's educational activities, predicts possible risk factors for failure, corrects educational information based on the predicted risk factors, and notifies the instructor; In the online marketing education system comprising a marketing strategy module (60) that collects marketing strategies for each industry in which the student intends to be active, analyzes the collected marketing strategy, and breeds it with an evolved marketing strategy that satisfies the conditions, the competency evaluation module (30) analyzes the student's daily activity information, such as search sites, personal blogs, online cafes, social networks, and surveys, quizzes, and calculates the time spent on basic marketing education to quantify it, and analyzes the instructor's activity history, awards, and uploaded education information to obtain educational level is calculated as a quantitative value, but the first evaluation is made through the same number of uses and word difficulty by aligning marketing-related words among the collected information. The method of claim 1, wherein the evaluation information is visualized in the process and the level of students and instructors is finally evaluated through secondary evaluation by a plurality of experts, the education diagnosis module (50) tracks problems based on the learner's learning time and performance, diagnoses the cause, predicts possible risk factors in the course, extracts some of the education information of other instructors based on the predicted risk factors, and notifies the instructor of correction information that can be corrected or added in real time, and the marketing strategy module (60) A storage unit (61) that randomly collects past marketing strategies related to the industry to be active during training, classified into keywords, banners, and virals, an analysis unit (62) that analyzes the collected marketing strategies and extracts construction and operation costs, a loading unit (63) that sorts in descending order based on the extracted costs, and a processing unit (64) that creates an evolved hybrid marketing strategy by crossing the sorted marketing strategies, and a new marketing strategy that satisfies the filter conditions set for the generated hybrid marketing strategies. It is composed of a filter unit 65 that selects, and the processing unit 64 selects either sequential crossing of marketing strategies that are adjacent to each other or sequential crossing of the highest marketing strategy and the lowest marketing strategy. Generates evolved hybrid marketing strategies by performing either one or the other sequentially. The online marketing education system according to claim 2, wherein the filter unit 65 selects, as a new marketing strategy, a new marketing strategy that satisfies a filter condition equal to or lower than a cost set by the student among hybrid marketing strategies, and repeats hybridization by substituting the hybrid marketing strategies with successive marketing strategies until a new marketing strategy is selected.