Method and system for providing information related to franchise start-up

Abstract

According to an embodiment of the present invention, a method for providing franchise startup-related information is provided, comprising: a step in which a franchise startup information provision service server receives franchise business information from a plurality of franchise headquarters servers and classifies and stores it by category; a step in which information on prospective entrepreneurs, information on franchise startups, and information on startup results are collected, and a step in which a franchise success matching algorithm is constructed based on the collected data; a step in which condition data for a desired startup is received from a prospective entrepreneur terminal; a step in which the received condition data is input into the franchise success matching algorithm to calculate a franchise startup optimization matching rate; and a step in which the calculated franchise startup optimization matching rate information is transmitted to the prospective entrepreneur terminal.

Description

Technology Field

[0001] The present invention relates to a method and system for providing information related to franchise startup. Background Technology

[0002] The franchise industry was perceived as a part of the distribution industry, but it is evolving into an industry capable of strengthening the competitiveness of not only the distribution industry but also the food service and service sectors.

[0003] A franchising agreement is a contract between independent businesses in which a franchisee agrees to sell goods or services meeting certain quality standards as part of the headquarters' marketing objectives and to pay a franchise fee in return.

[0004] Franchise businesses based on such franchise agreements sell to customers and consumers while maintaining a consistent quality of the same goods or services sold by franchisees, so that small-scale economic effects of specific products and services are generated for the franchising headquarters and many franchisees.

[0005] In the past, the age group hoping to start a business was mainly those in their 40s or older, but recently, with the increase in unmanned franchises, the age group interested in starting a business is also growing among those in their 20s and 30s due to the advantages of being able to run a second job, having low work fatigue despite high initial costs, and the trend of young people seeking a breakthrough for unemployment.

[0006] However, for prospective entrepreneurs, visiting company booths at franchise exhibitions held primarily offline is still the most concrete way to obtain startup information. Yet, holding a nationwide exhibition faces practical difficulties regarding disease prevention, costs, venue, and time. Furthermore, as interest in starting a business is growing across a wide range of age groups and the types of franchises are becoming more diverse, there is a need for a medium that can efficiently act as an intermediary between franchise headquarters and prospective entrepreneurs. The problem to be solved

[0007] The purpose of the present invention is to solve the problems of the aforementioned prior art.

[0008] The objective of the present invention is to enable the selection of an optimized franchise brand based on the characteristics of the planned franchise location and the prospective entrepreneur.

[0009] The objectives of the present invention are not limited to those mentioned above, and other unmentioned objectives will be clearly understood from the description below. means of solving the problem

[0010] According to one embodiment of the present invention for achieving the above-mentioned purpose, a method for providing franchise startup-related information is provided, comprising: a step in which a franchise startup information provision service server receives franchise business information from a plurality of franchise headquarters servers and classifies and stores it by category; a step in which information on prospective entrepreneurs, information on franchise startups, and information on startup results are collected, and a step in which a franchise success matching algorithm is constructed based on the collected data; a step in which condition data for a desired startup is received from a prospective entrepreneur terminal; a step in which the received condition data is input into the franchise success matching algorithm to calculate a franchise startup optimization matching rate; and a step in which the calculated franchise startup optimization matching rate information is transmitted to the prospective entrepreneur terminal.

[0011] The step of calculating the above franchise optimization matching rate may include a step of applying different weights or priorities for each franchise brand based on franchise startup region characteristic information and prospective entrepreneur information.

[0012] The step of calculating the above franchise optimization matching rate may include a step of increasing the weight or priority of the franchise in order of the number of nationwide stores as the entrepreneurial experience decreases, by referring to the above prospective entrepreneur information. Effects of the invention

[0013] According to an embodiment of the present invention, an optimized franchise brand can be selected based on the characteristics of the planned franchise location and the prospective entrepreneur. Brief explanation of the drawing

[0014] FIG. 1 is a configuration diagram of a franchise startup-related information provision system according to one embodiment of the present invention. FIG. 2 is a block diagram illustrating the configuration of a franchise startup-related information provision system according to an embodiment of the present invention. FIG. 3 is a flowchart illustrating the process of a franchise startup information provision service according to one embodiment of the present invention. Specific details for implementing the invention

[0015] The objectives and effects of the present invention, and the technical configurations for achieving them, will become clear by referring to the embodiments described in detail below in conjunction with the accompanying drawings. In describing the present invention, if it is determined that a detailed description of known functions or configurations may unnecessarily obscure the essence of the invention, such detailed description will be omitted. Furthermore, the terms described below are defined considering the structure, role, and function, etc., in the present invention, and these may vary depending on the intentions or conventions of the user or operator.

[0016] However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined solely by the scope of the claims set forth in the patent claims. Therefore, such definition must be based on the content throughout this specification.

[0017] Throughout the specification, when a part is described as "including" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0018] Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the attached drawings.

[0019] Meanwhile, in an embodiment of the present invention, combinations of each block of the attached block diagram and each step of the flowchart may be performed by computer program instructions. Since these computer program instructions can be loaded into the processor of a general-purpose computer, a special-purpose computer, a portable notebook computer, a network computer, or other programmable data processing equipment, the instructions executed through the processor of the computer device or other programmable data processing equipment create means for performing the functions described in each block of the block diagram or each step of the flowchart described below. Since these computer program instructions may also be stored in memory or computer-readable memory available to a computer device capable of directing the computer device or other programmable data processing equipment to implement functions in a specific manner, it is also possible to produce a manufactured product containing means of instructions for performing the functions described in each block of the block diagram or each step of the flowchart. Since computer program instructions can be loaded onto a computer device or other programmable data processing equipment, it is also possible to create a process for performing a series of operation steps on a computer device or other programmable data processing equipment and to provide steps for executing the functions described in each block of the block diagram and each step of the flowchart.

[0020] Additionally, each block or each step may represent a module, segment, or part of code containing one or more executable instructions for executing a specified logical function(s). Also, it should be noted that in some alternative embodiments, the functions mentioned in the blocks or steps may occur out of order. For example, two blocks or steps described in succession may actually be performed substantially simultaneously, or the blocks or steps may sometimes be performed in reverse order according to the corresponding function.

[0021] FIG. 1 is a configuration diagram of a franchise startup-related information provision system according to an embodiment of the present invention, and FIG. 2 is a block diagram illustrating the configuration of a franchise startup-related information provision system according to an embodiment of the present invention.

[0022] Referring to FIGS. 1 and 2, a system according to one embodiment of the present invention includes a franchise headquarters server (101~100n: 100), a franchise startup information provision service server (200), and a prospective entrepreneur terminal (301~300n: 300).

[0023] The franchise headquarters server (100) transmits information intended for franchise business explanation and guidance, recruitment of franchise stores, franchise store management, marketing, etc. from the franchise headquarters to the franchise startup information provision service server (200).

[0024] This franchise headquarters server (100) can receive inquiries and requests from prospective entrepreneurs who wish to open a franchise from the franchise startup information provision service server (200), and can connect with the prospective entrepreneur terminal (300) to proceed with cooperation for franchise startup.

[0025] The franchise startup information provision service server (200) generates a franchise startup information provision service application and enables prospective entrepreneur terminals (300) to receive and execute it. In addition, the franchise startup information provision service server (200) enables franchise headquarters servers (100) to transmit information that allows them to market franchise startups, franchise promotional information, etc., and enables them to receive and execute a dedicated application that can be connected to a prospective entrepreneur terminal (300) matched by a franchise success matching algorithm.

[0026] This franchise startup information provision service server (200) is a platform created by the developer server, and it can provide services needed by franchise headquarters and prospective entrepreneurs in both directions or receive requests for services needed from them, and acts as a mediator connecting the two media.

[0027] Specifically, the franchise startup information provision service server (200) may receive and store information regarding the franchise business status of the company from the franchise headquarters server (100), such as startup costs based on unit area, monthly average sales, monthly average sales per unit area, franchise status, and possible startup types. Here, the information regarding startup types may include information regarding whether delivery operations for products are possible, the type of store, whether a shop-in-shop is possible, whether a shared kitchen is available, etc.

[0028] In addition, the franchise startup information provision service server (200) can receive and store brand information, such as the franchise's brand introduction, brand competitiveness, brand menu, etc., and headquarters information, such as headquarters corporate information and headquarters sales information, from the franchise headquarters server (100), and can receive and update the changed information whenever the information changes.

[0029] In addition, the franchise startup information provision service server (200) can receive and store information regarding successful franchise startup cases from the franchise headquarters server (100).

[0030] The franchise startup information provision service server (200) can receive franchise business information from the aforementioned franchise headquarters servers (100) and classify and store it by category. For example, in the case of a franchise in the food industry, the classification of franchises by category can be broadly divided into upper categories such as Korean food, snack food, Western food, Chinese food, Japanese food, and desserts. As lower categories of each upper category, Japanese food can be matched with pork cutlets, sashimi restaurants, fast food, etc., and desserts can be matched with cafes, bread shops, cake shops, etc.

[0031] In addition, the franchise startup information provision service server (200) can match a franchise optimized for prospective entrepreneurs based on an artificial intelligence algorithm, provide information regarding the matched franchise startup, and connect the prospective entrepreneur to the headquarters server (100) of the franchise.

[0032] To this end, the franchise startup information provision service server (200) collects information on prospective entrepreneurs, startup franchise information, and startup result information, and can build a franchise success matching algorithm based on the collected data.

[0033] Here, the franchise success matching algorithm first collects and converts into big data information including information on multiple prospective entrepreneurs, information on the franchises established by each prospective entrepreneur, and operational status after establishment. Then, the collected data is subjected to machine learning, and the collected data is analyzed through machine learning to produce information recommending franchise startups for prospective entrepreneurs and information predicting startup results. Here, the information on prospective entrepreneurs may include at least one of the prospective entrepreneur's startup budget, business type, startup model, startup location, and startup area.

[0034] By learning from millions of data points, this franchise success matching algorithm can theoretically predict results with a high probability of startup success for pairs of new prospective entrepreneur information and franchise recruitment.

[0035] For example, when reviewing actual information on prospective entrepreneurs, it is determined whether they possess sufficient elements for starting a business by comparing factors such as their industry of interest and limits on startup costs. In the franchise success matching algorithm, a machine learning model learns patterns regarding which factors influence successful operational outcomes after launch, based on the prospective entrepreneur's condition information and the franchise startup conditions corresponding to successful cases.

[0036] To this end, weights can be assigned to each condition element, and importance can be calculated for each element. Since successful franchise operations are influenced by many factors in addition to the prospective entrepreneur's qualifications, the diverse data regarding prospective entrepreneurs and franchises held by the franchise success matching algorithm can also be utilized to enhance the accuracy of machine learning. For example, data such as the regional environment where the franchise is established, population density, pedestrian traffic, distance from manufacturers, and product trends can be used.

[0037] In this way, the franchise startup information provision service server (200) inputs condition data input from the prospective entrepreneur terminal (300) into the franchise success matching algorithm to calculate the franchise affiliation optimization matching rate, and can transmit the calculated franchise affiliation optimization matching rate information to the prospective entrepreneur terminal (300).

[0038] After receiving franchise affiliation optimization matching rate information through the franchise startup information provision service server (200), the prospective entrepreneur terminal (300) can receive a consultation inquiry regarding the results of the franchise opening prospective entrepreneur data, and in response to the consultation inquiry, can receive counseling provided by the franchise headquarters server (100).

[0039] Meanwhile, the franchise startup information provision service server (200) divides franchise optimization information for each prospective entrepreneur into a general mode and an augmented reality mode, and in the general mode, provides matching rate information and corresponding franchise information as described in FIG. 1.

[0040] In contrast, the franchise startup information provision service server (200) can implement augmented reality in which franchise startup items can be output in the form of a 3D virtual image based on condition information entered by a prospective entrepreneur when in augmented reality mode. At this time, the server can design and arrange the store considering the region, area, and type of business desired by the prospective entrepreneur and output a 3D virtual image store. In addition to the 3D virtual image store, the server also outputs the movement of customers entering and leaving the store according to the number of people moving in and out over time, taking into account the surrounding environment and population density.

[0041] In addition, information regarding stores of the same or similar business type located in the area requested by the prospective entrepreneur terminal (200) can be displayed in conjunction with the GPS receiving function or map information display function stored within the terminal. That is, additional information regarding the business type of stores located within a set distance centered on the planned franchise startup area, or distribution information regarding business types similar to the franchise, can be provided.

[0042] In addition, when an image of one of the display items among the three-dimensional virtual image stores is selected by the prospective entrepreneur terminal (300), a text information window regarding cost information, management information, function information, etc. of the selected display item is displayed.

[0043] In addition, an image of one of the display items is selected by the prospective entrepreneur terminal (300), and options such as changing the position of the item or changing the product can be selected. Accordingly, the prospective entrepreneur can experience a simulation service that allows them to change necessary items, such as various fixtures included in the startup, in detail according to price, material, interior, etc.

[0044] When all modifications and changes to the 3D virtual image store are completed by the prospective entrepreneur terminal (300), information regarding the completed 3D virtual image store can be transmitted to the franchise headquarters server (100), and the franchise headquarters server (100) can use this as reference material when counseling regarding the franchise.

[0045] The prospective entrepreneur terminal (300) can be connected to the webpage of the franchise through a link provided in the application, and can also obtain information on successful franchise startup cases from the content available on the webpage.

[0046] In addition, the prospective entrepreneur terminal (300) can conduct marketing activities such as promoting my store through an application.

[0047] Through the operation of such a platform, the franchise startup information provision service system can obtain compensation-based revenue from the franchise headquarters when prospective entrepreneurs sign franchise agreements, and can generate revenue from advertising franchise stores and from business owners.

[0048] In conclusion, by entrusting marketing to a professional franchise platform, franchise headquarters can reduce costs, and prospective entrepreneurs can not only obtain the information they want but also utilize franchise recommendations that suit their conditions and have a high probability of success.

[0049] FIG. 3 is a flowchart illustrating the process of a franchise startup information provision service according to one embodiment of the present invention.

[0050] Referring to FIG. 3, a franchise startup information provision service method according to one embodiment of the present invention first receives franchise business information from a plurality of franchise headquarters servers and classifies and stores it by category (S110).

[0051] For example, in the case of franchises in the food industry, the classification of franchises by category can be broadly divided into upper categories such as Korean food, snack food, Western food, Chinese food, Japanese food, and desserts. As subcategories of each of the above upper categories, Japanese food can be matched with pork cutlets and sashimi restaurants, while desserts can be matched with cafes, bakeries, and cake shops.

[0052] Next, the franchise startup information provision service server collects information on prospective entrepreneurs, startup franchise information, and startup result information, and can build a franchise success matching algorithm based on the collected data (S120).

[0053] Next, the franchise startup information provision service server receives condition data for the desired startup from the prospective entrepreneur's terminal (S130). That is, it can receive information regarding the desired location for opening a franchise, startup budget, business type, startup form, and startup area from the prospective entrepreneur's terminal, and can also receive information about the prospective entrepreneur, such as the prospective entrepreneur's name, address, and phone number.

[0054] Next, the franchise startup information provision service server inputs the above-mentioned condition data into the above-mentioned franchise success matching algorithm to calculate the franchise affiliation optimization matching rate (S140).

[0055] To this end, the franchise startup information provision service server can match a franchise optimized for prospective entrepreneurs based on an artificial intelligence algorithm and provide information regarding the matched franchise startup. In one embodiment, the franchise startup information provision service server collects information on prospective entrepreneurs, information on franchise startups, and startup results, and can construct a franchise success matching algorithm based on the collected data.

[0056] Here, the franchise success matching algorithm first collects and converts into big data information including information on multiple prospective entrepreneurs, information on the franchises established by each prospective entrepreneur, and operational status after establishment. Then, the collected data is subjected to machine learning, and the collected data is analyzed through machine learning to produce information recommending franchise startups for prospective entrepreneurs and information predicting startup results. Here, the information on prospective entrepreneurs may include at least one of the prospective entrepreneur's startup budget, business type, startup model, startup location, and startup area.

[0057] By learning from millions of data points, this franchise success matching algorithm can theoretically predict results with a high probability of startup success for pairs of new prospective entrepreneur information and franchise recruitment.

[0058] For example, when reviewing actual information on prospective entrepreneurs, it is determined whether they possess sufficient elements for starting a business by comparing factors such as their industry of interest and limits on startup costs. In the franchise success matching algorithm, a machine learning model learns patterns regarding which factors influence successful operational outcomes after launch, based on the prospective entrepreneur's condition information and the franchise startup conditions corresponding to successful cases.

[0059] To this end, weights can be assigned to each condition element, and importance can be calculated for each element. Since successful franchise operations are influenced by many factors beyond just the prospective entrepreneur's qualifications, the diverse data regarding prospective entrepreneurs and franchises held by the franchise success matching algorithm can also be utilized to enhance the accuracy of machine learning. For example, data such as the regional environment where the franchise is established, population density, pedestrian traffic, patterns of increase and decrease over time, distance from manufacturers, and product trends can be used. Additionally, data regarding prospective entrepreneur information, such as business experience (number of startups, duration of store operation after establishment, etc.), age, and gender, can also be utilized.

[0060] When data is learned and input into a matching algorithm obtained from the learned results, a specific type of franchise industry or brand with the highest matching rate can be derived as a result.

[0061] In this process, the correlation between characteristic information regarding the region where the franchise is established (regional environment, population density, pedestrian traffic, patterns of increase or decrease in pedestrian traffic over time, distance from the manufacturer, product trends, etc.) and information on prospective entrepreneurs (startup experience, age, gender, etc.) and each franchise brand—that is, the probability of success for each brand when starting a franchise based on the aforementioned information—can be calculated.

[0062] At this time, when calculating the matching rate for each franchise brand, different weights or priority values ​​may be assigned based on characteristic information regarding the region where the franchise is established and information regarding prospective entrepreneurs.

[0063] According to one embodiment, in the case of a franchise startup area where the pedestrian traffic peaks during lunchtime (12:00 PM to 1:00 PM), the weight or priority of a cafe can be increased. Additionally, if the pedestrian traffic peaks during lunchtime and dinnertime (7:00 PM to 10:00 PM), or ranks first and second, and the similarity of pedestrian traffic between the two time periods is above a threshold, the weight or priority value of a Korean food franchise suitable for company dinners can be increased, as there is a high probability that the area has many office workers. Additionally, if the pedestrian traffic peaks during dinnertime and the similarity of pedestrian traffic with lunchtime is below a threshold, the weight or priority value of franchises such as Western food, Japanese food, or pubs can be increased.

[0064] Meanwhile, based on information regarding prospective entrepreneurs, the weighting or priority of franchises can be increased for those with less entrepreneurial experience (such as the number of startups) or those with the shortest maximum period of store operation after opening, regardless of the number of startups, in order of the number of nationwide stores. This is intended to minimize the failure rate after starting a business.

[0065] In addition, based on the information of prospective entrepreneurs, younger prospective entrepreneurs may be given higher weight or priority for dessert franchises.

[0066] In this way, the above matching rate can be calculated, and a final matching rate with the above weight or priority applied can be calculated for each franchise.

[0067] Next, the franchise startup information provision service server transmits the calculated franchise optimization matching rate information to the prospective entrepreneur's terminal (S150). That is, it can provide franchise information with the highest matching rate.

[0068] In this way, the franchise startup information provision service server inputs condition data entered from the prospective entrepreneur's terminal into the franchise success matching algorithm to calculate the franchise optimization matching rate, and can transmit the calculated franchise optimization matching rate information to the prospective entrepreneur's terminal.

[0069] Accordingly, the prospective entrepreneur terminal can receive franchise optimization matching rate information through the franchise startup information provision service server, receive a consultation inquiry regarding the data results of prospective franchisees, and receive counseling input from the franchise headquarters server in response to the above consultation inquiry.

[0070] The present specification and drawings disclose preferred embodiments of the present invention. Although specific terms have been used, they are used merely in a general sense to facilitate the explanation of the technical content of the present invention and to aid in understanding the invention, and are not intended to limit the scope of the present invention. It is obvious to those skilled in the art that, in addition to the embodiments disclosed herein, other variations based on the technical concept of the present invention are possible.

Claims

Claim 1 A method for providing franchise startup-related information, comprising: a step in which a franchise startup information provision service server receives franchise business information from multiple franchise headquarters servers and classifies and stores it by category; a step in which prospective entrepreneur information, startup franchise information, and startup result information are collected, and a franchise success matching algorithm is constructed based on the collected data; a step in which condition data for a desired startup is received from a prospective entrepreneur terminal; a step in which the received condition data is input into the franchise success matching algorithm to calculate a franchise startup optimization matching rate; and a step in which the calculated franchise startup optimization matching rate information is transmitted to the prospective entrepreneur terminal. Claim 2 A method for providing franchise startup-related information, wherein the step of calculating the franchise optimization matching rate in claim 1 includes the step of applying different weights or priorities according to franchise startup region characteristic information and prospective entrepreneur information for each franchise brand. Claim 3 A method for providing franchise startup-related information, wherein the step of calculating the franchise optimization matching rate in paragraph 2 includes the step of increasing the weight or priority of the franchise in order of the number of nationwide stores as the startup experience decreases, by referring to the prospective entrepreneur information.

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