KR20230110221A - Method and apparatus for providing solutions for brand improvement - Google Patents

Abstract

A method for providing a solution for improving a brand according to an embodiment includes evaluating, by a processor, a user brand of a user and obtaining an evaluation result; determining, by the processor, a bottleneck based on an evaluation result of the user brand; retrieving, by the processor, a database for a solution for improving the user brand based on the bottleneck; and outputting, by the processor, the searched solution.

Description

Method and apparatus for providing solutions for brand improvement {METHOD AND APPARATUS FOR PROVIDING SOLUTIONS FOR BRAND IMPROVEMENT}

Embodiments of the present invention relate to a technology for providing a solution for brand improvement, and to a technology for providing a solution for brand improvement using various evaluation models.

Just because they've been in consumers' heads for a long time doesn't mean they all play a leading role in the market. Some brands are in a difficult situation, while others remain only in memory, and the corresponding products often disappear from the market. The reason why the fortunes of brands differ in this way depends on how the brands are managed and improved. If you don't properly manage and improve your brand, the brand will eventually go out of business.

When sales decline according to a brand continues, companies tend to reduce marketing investment and switch to a new brand rather than trying to revive the brand itself. However, launching a new brand comes with new costs and risks, and improving existing brands based on market feedback can be more effective.

As such, in order to improve a brand, it is necessary to diagnose the brand first, and an automated system for determining the bottleneck of the brand through various evaluation indicators is required.

In embodiments, a system that provides solutions for brand improvement may diagnose the condition of a brand. The system can continuously track the performance of marketing activities against the desired image. The system may measure image association and strength between the company's parent and sub-brands. Based on the results of these diagnosis, tracking, and measurement, it is possible to provide a method to provide a brand management system that can increase the activity and potential of the brand portfolio.

Technical tasks to be achieved in the embodiments are not limited to those mentioned above, and other technical tasks not mentioned may be considered by those skilled in the art from various embodiments to be described below.

A method for providing a solution for improving a brand according to an embodiment includes evaluating, by a processor, a user brand of a user and obtaining an evaluation result; determining, by the processor, a bottleneck based on an evaluation result of the user brand; retrieving, by the processor, a database for a solution for improving the user brand based on the bottleneck; and outputting, by the processor, the searched solution.

Obtaining the evaluation result may include obtaining a health evaluation result by evaluating the brand health of the user brand; evaluating brand assets of the user brand to obtain an asset evaluation result; obtaining a portfolio evaluation result by evaluating a brand portfolio of the user brand; and obtaining a persona evaluation result by evaluating the brand persona of the user brand.

Obtaining the health level evaluation result may include obtaining the health level evaluation result by evaluating the brand health level of the user brand using a brand health level measurement model.

Obtaining the asset evaluation result may include obtaining the asset evaluation result by evaluating brand assets of the user brand using a brand equity evaluation model.

According to an embodiment, the operation of evaluating the brand equity of the user brand using the brand equity evaluation model to obtain the asset evaluation result may include setting a brand structure model of the user brand; calculating one or more fitness indices of the brand structure model; improving the brand structure model in a direction of improving a fitness index that does not satisfy a fitness criterion among the one or more fitness indices; and evaluating brand assets of the user brand and one or more competitor brands using the improved brand structure model to obtain an asset evaluation result.

The operation of setting the brand structure model of the user brand includes an operation of setting factors, variables, paths, and coefficients of the brand structure model, and the variables may include awareness, penetration rate, emotional loyalty, and behavioral loyalty.

Setting the factors, variable paths, and coefficients of the brand structure model may include measuring behavioral loyalty of the user brand based on a repurchase rate of one or more products related to the user brand.

Measuring the behavioral loyalty of the user brand based on the repurchase rate of the one or more products may include clustering the one or more products into a plurality of groups according to a plurality of criteria; calculating a repurchase score of each group based on repurchase rates of products belonging to different groups; and measuring the behavioral loyalty of the user brand based on the repurchase score of each group.

The operation of improving the brand structure model may include an operation of changing a factor, variable, path, or coefficient of the brand structure model in a direction of improving a fitness index that does not satisfy the fitness criterion among the one or more fitness indices.

The operation of evaluating the brand persona of the user brand and obtaining a persona evaluation result may include an operation of obtaining a persona evaluation result by evaluating the brand persona based on a multidimensional scale.

The determining of the bottleneck may include determining the bottleneck by comparing an asset evaluation result of the user brand with an asset evaluation result of one or more competitor brands.

According to another embodiment, the brand equity evaluation model includes an input layer, one or more hidden layers, and an output layer, and each learning data composed of awareness, penetration, emotional loyalty, and behavioral loyalty is input to the input layer of the brand equity evaluation model, passes through the one or more hidden layers and output layers, and outputs an output vector. output, and parameters of the brand equity evaluation model may be learned in a direction in which the loss value becomes smaller.

A server according to an embodiment includes at least one processor; and a database, wherein the processor evaluates a user brand of a user to obtain an evaluation result, determines a bottleneck based on the evaluation result of the user brand, searches the database for a solution for improving the user brand based on the bottleneck, and outputs the searched solution.

A computer program according to an embodiment may be combined with hardware and stored in a computer readable recording medium to execute the method of claim 1 .

A computer readable recording medium according to an embodiment may store a computer program that executes the method of claim 1 by being combined with hardware.

According to embodiments, a system that provides solutions for brand improvement may diagnose the condition of a brand. The system can continuously track the performance of marketing activities against the desired image. The system may measure image association and strength between the company's parent and sub-brands. Based on the results of these diagnosis, tracking, and measurement, the system can provide a brand management system that can increase the activity and potential of the brand portfolio.

Effects obtainable from the embodiments are not limited to the effects mentioned above, and other effects not mentioned can be clearly derived and understood by those skilled in the art based on the detailed description below.

BRIEF DESCRIPTION OF THE DRAWINGS Included as part of the detailed description to aid understanding of the embodiments, the accompanying drawings provide various embodiments and, together with the detailed description, describe technical features of the various embodiments.
1 is a diagram illustrating a configuration of an electronic device according to an exemplary embodiment.
2 is a diagram showing the configuration of a program according to an embodiment.
3 is a diagram showing the entire configuration of a system for providing a solution for brand improvement according to an embodiment.
4 is a flow diagram illustrating the operation of a method for providing a solution for brand improvement according to one embodiment.
5 is a diagram illustrating types of brand evaluation indicators according to an exemplary embodiment.
6 is an exemplary diagram of a brand equity valuation model for evaluating brand equity according to one embodiment.
7 is a diagram illustrating an example of a brand equity evaluation result according to an embodiment.
8 is an example of a graph for determining a bottleneck based on the brand equity evaluation result according to FIG. 7 .
9 is an example of a graph in which one or more products are clustered into a plurality of groups according to a plurality of criteria in order to measure behavioral loyalty based on repurchase rates of one or more products.
FIG. 10 is a diagram showing the configuration of a server included in the system of FIG. 3 .

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form not combined with other components or features. In addition, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment.

In the description of the drawings, procedures or steps that may obscure the gist of various embodiments have not been described, and procedures or steps that can be understood by those skilled in the art have not been described either.

Throughout the specification, when a part is said to "comprising" or "including" a certain element, it means that it may further include other elements, not excluding other elements unless otherwise stated. In addition, terms such as “… unit”, “… unit”, and “module” described in the specification refer to a unit that processes at least one function or operation, which may be implemented by hardware, software, or a combination of hardware and software. Also, "a or an", "one", "the" and similar related words in the context of describing various embodiments (particularly in the context of the claims below) may be used in the meaning of both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The detailed description set forth below in conjunction with the accompanying drawings is intended to describe exemplary embodiments of various embodiments, and is not intended to represent a single embodiment.

In addition, specific terms used in various embodiments are provided to help understanding of various embodiments, and the use of these specific terms may be changed into other forms without departing from the technical spirit of various embodiments.

1 is a diagram illustrating a configuration of an electronic device according to an exemplary embodiment.

1 is a block diagram of an electronic device 101 within a network environment 100, according to various embodiments. Referring to FIG. 1 , in a network environment 100, an electronic device 101 may communicate with the electronic device 102 through a first network 198 (eg, a short-distance wireless communication network), or may communicate with at least one of the electronic device 104 and the server 108 through a second network 199 (eg, a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108 . According to an embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, a sound output module 155, a display module 160, an audio module 170, a sensor module 176, an interface 177, a connection terminal 178, a haptic module 179, a camera module 180, a power management module 188, a battery 189, a communication module ( 190), a subscriber identification module 196, or an antenna module 197. In some embodiments, in the electronic device 101, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added. In some embodiments, some of these components (eg, sensor module 176, camera module 180, or antenna module 197) may be integrated into one component (eg, display module 160). The electronic device 101 may also be referred to as a client, terminal, or peer.

The processor 120 may, for example, execute software (eg, program 140) to control at least one other component (eg, hardware or software component) of the electronic device 101 connected to the processor 120, and may perform various data processing or calculations. According to one embodiment, as at least part of data processing or operation, processor 120 may store commands or data received from other components (e.g., sensor module 176 or communication module 190) in volatile memory 132, process the commands or data stored in volatile memory 132, and store resultant data in non-volatile memory 134. According to one embodiment, the processor 120 may include a main processor 121 (eg, a central processing unit or an application processor) or a secondary processor 123 (eg, a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that may operate independently or together with the main processor 121. For example, when the electronic device 101 includes the main processor 121 and the auxiliary processor 123, the auxiliary processor 123 may use less power than the main processor 121 or may be set to be specialized for a designated function. The secondary processor 123 may be implemented separately from or as part of the main processor 121 .

The auxiliary processor 123 functions related to at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) along with the main processor 121 while the main processor 121 is in an active (eg, application execution) state or instead of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state. Alternatively, at least some of the states may be controlled. According to one embodiment, the auxiliary processor 123 (eg, an image signal processor or a communication processor) may be implemented as a part of other functionally related components (eg, the camera module 180 or the communication module 190). According to an embodiment, the auxiliary processor 123 (eg, a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model. AI models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself where the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the above examples. The artificial intelligence model may include a plurality of artificial neural network layers. The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), deep Q-networks, or a combination of two or more of the above, but is not limited to the above examples. The artificial intelligence model may include, in addition or alternatively, software structures in addition to hardware structures.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101 . The data may include, for example, input data or output data for software (eg, program 140) and commands related thereto. The memory 130 may include volatile memory 132 or non-volatile memory 134 .

The program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142 , middleware 144 , or an application 146 .

The input module 150 may receive a command or data to be used by a component (eg, the processor 120) of the electronic device 101 from the outside of the electronic device 101 (eg, a user). The input module 150 may include, for example, a microphone, a mouse, a keyboard, a key (eg, a button), or a digital pen (eg, a stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101 . The sound output module 155 may include, for example, a speaker or a receiver. The speaker can be used for general purposes such as multimedia playback or recording playback. A receiver may be used to receive an incoming call. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.

The display module 160 may visually provide information to the outside of the electronic device 101 (eg, a user). The display module 160 may include, for example, a display, a hologram device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module 160 may include a touch sensor set to detect a touch or a pressure sensor set to measure the intensity of force generated by the touch.

The audio module 170 may convert sound into an electrical signal or vice versa. According to an embodiment, the audio module 170 may obtain sound through the input module 150, output sound through the sound output module 155, or an external electronic device (e.g., electronic device 102) (e.g., speaker or headphone) connected directly or wirelessly to the electronic device 101.

The sensor module 176 may detect an operating state (eg, power or temperature) of the electronic device 101 or an external environmental state (eg, a user state), and generate an electrical signal or data value corresponding to the detected state. According to one embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an infrared (IR) sensor, a bio sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

The interface 177 may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device 101 to an external electronic device (eg, the electronic device 102). According to one embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 may be physically connected to an external electronic device (eg, the electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 may convert electrical signals into mechanical stimuli (eg, vibration or motion) or electrical stimuli that a user may perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 may capture still images and moving images. According to one embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 may manage power supplied to the electronic device 101 . According to one embodiment, the power management module 188 may be implemented as at least part of a power management integrated circuit (PMIC), for example.

The battery 189 may supply power to at least one component of the electronic device 101 . According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary cell, a rechargeable secondary cell, or a fuel cell.

The communication module 190 may support establishment of a direct (e.g., wired) communication channel or wireless communication channel between the electronic device 101 and an external electronic device (e.g., the electronic device 102, the electronic device 104, or the server 108), and communication through the established communication channel. The communication module 190 may include one or more communication processors that operate independently of the processor 120 (eg, an application processor) and support direct (eg, wired) communication or wireless communication. According to one embodiment, the communication module 190 may include a wireless communication module 192 (eg, a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (eg, a local area network (LAN) communication module or a power line communication module). A corresponding communication module among these communication modules may communicate with the external electronic device 104 through a first network 198 (eg, a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or an infrared data association (IrDA)) or a second network 199 (eg, a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a long-distance communication network such as a computer network (eg, a LAN or a WAN)). These various types of communication modules may be integrated as one component (eg, a single chip) or implemented as a plurality of separate components (eg, multiple chips). The wireless communication module 192 may identify or authenticate the electronic device 101 within a communication network such as the first network 198 or the second network 199 using subscriber information (eg, International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196.

The wireless communication module 192 may support a 5G network after a 4G network and a next-generation communication technology, for example, NR access technology (new radio access technology). NR access technology can support high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and access to multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module 192 may support a high frequency band (eg, mmWave band) to achieve a high data rate, for example. The wireless communication module 192 may support various technologies for securing performance in a high frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), an array antenna, analog beam-forming, or a large scale antenna. The wireless communication module 192 may support various requirements defined for the electronic device 101, an external electronic device (eg, the electronic device 104), or a network system (eg, the second network 199). According to an embodiment, the wireless communication module 192 may support peak data rate (eg, 20 Gbps or more) for eMBB realization, loss coverage (eg, 164 dB or less) for mMTC realization, or U-plane latency (eg, downlink (DL) and uplink (UL) 0.5 ms or less, or round trip 1 ms or less) for realizing URLLC.

The antenna module 197 may transmit or receive signals or power to the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 may be selected from the plurality of antennas by, for example, the communication module 190. A signal or power may be transmitted or received between the communication module 190 and an external electronic device through the selected at least one antenna. According to some embodiments, other components (eg, a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module 197 in addition to the radiator.

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to an embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent to a first surface (eg, bottom surface) of the printed circuit board and capable of supporting a designated high frequency band (eg, mmWave band), and a plurality of antennas (eg, array antennas) disposed on or adjacent to a second surface (eg, top surface or side surface) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band.

At least some of the components are connected to each other through a communication method between peripheral devices (e.g., a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signals (e.g., commands or data) can be exchanged with each other.

According to an embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199 . Each of the external electronic devices 102 or 104 may be the same as or different from the electronic device 101 . According to an embodiment, all or part of operations executed in the electronic device 101 may be executed in one or more external electronic devices among the external electronic devices 102 , 104 , or 108 . For example, when the electronic device 101 needs to automatically perform a certain function or service or in response to a request from a user or another device, the electronic device 101 may request one or more external electronic devices to perform the function or at least part of the service, instead of or in addition to executing the function or service by itself. One or more external electronic devices receiving the request may execute at least a part of the requested function or service or an additional function or service related to the request, and deliver the execution result to the electronic device 101 . The electronic device 101 may provide the result as at least part of a response to the request as it is or additionally processed. To this end, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an internet of things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199 . The electronic device 101 may be applied to intelligent services (eg, smart home, smart city, smart car, or health care) based on 5G communication technology and IoT-related technology.

The server 108 is connected to the electronic device 101 and can provide a service to the connected electronic device 101 . In addition, the server 108 may proceed with a membership sign-up procedure, store and manage various types of information of users subscribed as members, and provide various purchase and payment functions related to services. In addition, the server 108 may share execution data of service applications executed in each of the plurality of electronic devices 101 in real time so that the service can be shared among users. This server 108 may have the same configuration as a conventional web server or WAP server in terms of hardware. However, in terms of software, it may include a program module that is implemented through any language such as C, C++, Java, Visual Basic, or Visual C and performs various functions. In addition, the server 108 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, receives a request for performing a task from a client or other server, derives and provides a result of the task, and refers to a computer system and computer software (server program) installed therefor. In addition, the server 108, in addition to the above-described server program, should be understood as a broad concept including a series of application programs operating on the server 108 and various databases (DB: Database, hereinafter referred to as “DB”) built inside or outside depending on the case. Accordingly, the server 108 classifies member registration information and various information and data about games, stores them in a DB, and manages them. This DB may be implemented inside or outside the server 108 . In addition, the server 108 may be implemented using server programs that are variously provided according to operating systems such as DOS, Windows, Linux, UNIX, Macintosh, etc. on general server hardware. As representative examples, a website used in a Windows environment, Internet Information Server (IIS), and CERN, NCSA, APPACH, etc. used in a Unix environment can be used. In addition, the server 108 may interoperate with an authentication system and a payment system for user authentication of services or payment for purchases related to services.

The first network 198 and the second network 199 refer to a connection structure capable of exchanging information between nodes such as terminals and servers or a network connecting the server 108 and the electronic devices 101 and 104. The first network 198 and the second network 199 include the Internet, a local area network (LAN), a wireless local area network (Wireless Local Area Network), a wide area network (WAN), a personal area network (PAN), 3G, 4G, LTE, 5G, Wi-Fi, etc. are included, but are not limited thereto. The first network 198 and the second network 199 may be closed first networks 198 and second networks 199 such as LAN and WAN, but are preferably open such as the Internet. The Internet refers to a structure of a worldwide open computer first network 198 and a second network 199 that provides the TCP/IP protocol and various services existing in its upper layers, that is, HyperText Transfer Protocol (HTTP), Telnet, File Transfer Protocol (FTP), Domain Name System (DNS), Simple Mail Transfer Protocol (SMTP), Simple Network Management Protocol (SNMP), Network File Service (NFS), and Network Information Service (NIS).

The database may have a general data structure implemented in a storage space (hard disk or memory) of a computer system using a database management program (DBMS). The database may have a data storage form in which data can be freely searched for (extracted), deleted, edited, added, and the like. The database is a relational database management system (RDBMS) such as Oracle, Informix, Sybase, and DB2, an object-oriented database management system (OODBMS) such as Gemston, Orion, and O2, and an XML Native Database such as Excelon, Tamino, and Sekaiju. and may have appropriate fields or elements to achieve its own function.

2 is a diagram showing the configuration of a program according to an embodiment.

2 is a block diagram 200 illustrating a program 140 according to various embodiments. According to one embodiment, the program 140 may include an operating system 142, middleware 144, or an application 146 executable in the operating system 142 for controlling one or more resources of the electronic device 101. The operating system 142 may include, for example, Android™, iOS™, Windows™, Symbian™, Tizen™, or Bada™. At least some of the programs 140 may be preloaded in the electronic device 101 during manufacture, or may be downloaded or updated from an external electronic device (e.g., the electronic device 102 or 104 or the server 108) when used by a user. All or part of program 140 may include a neural network.

The operating system 142 may control management (eg, allocation or reclamation) of one or more system resources (eg, process, memory, or power) of the electronic device 101 . Operating system 142 may additionally or alternatively include other hardware devices of electronic device 101, such as input module 150, sound output module 155, display module 160, audio module 170, sensor module 176, interface 177, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 or one or more driver programs for driving the antenna module 197.

The middleware 144 may provide various functions to the application 146 so that the function or information provided from one or more resources of the electronic device 101 may be used by the application 146 . Middleware 144 includes, for example, application manager 201, window manager 203, multimedia manager 205, resource manager 207, power manager 209, database manager 211, package manager 213, connectivity manager 215, notification manager 217, location manager 219, graphic manager 221, security manager 223, call manager ( 225), or a voice recognition manager 227.

The application manager 201 may manage the life cycle of the application 146 , for example. The window manager 203 may manage one or more GUI resources used in a screen, for example. The multimedia manager 205, for example, identifies one or more formats necessary for reproducing media files, and encodes or decodes a corresponding media file among the media files using a codec suitable for a selected corresponding format. The resource manager 207 may manage a source code of the application 146 or a memory space of the memory 130 . The power manager 209 may manage, for example, the capacity, temperature, or power of the battery 189, and determine or provide related information necessary for the operation of the electronic device 101 by using corresponding information among them. According to an embodiment, the power manager 209 may interoperate with a basic input/output system (BIOS) (not shown) of the electronic device 101 .

The database manager 211 may create, search, or change a database to be used by the application 146, for example. The package manager 213 may manage installation or update of applications distributed in the form of package files, for example. The connectivity manager 215 may manage, for example, a wireless connection or a direct connection between the electronic device 101 and an external electronic device. The notification manager 217 may provide a function for notifying a user of occurrence of a designated event (eg, an incoming call, message, or alarm), for example. The location manager 219 may manage location information of the electronic device 101, for example. The graphic manager 221 may manage, for example, one or more graphic effects to be provided to a user or a user interface related thereto.

Security manager 223 may provide system security or user authentication, for example. The telephony manager 225 may manage, for example, a voice call function or a video call function provided by the electronic device 101 . The voice recognition manager 227 may transmit, for example, the user's voice data to the server 108, and receive a command corresponding to a function to be performed in the electronic device 101 based at least in part on the voice data, or text data converted based on at least a portion of the voice data from the server 108. According to one embodiment, the middleware 244 may dynamically delete some existing components or add new components. According to one embodiment, at least part of the middleware 144 may be included as part of the operating system 142 or may be implemented as separate software different from the operating system 142 .

Applications 146 include, for example, home 251, dialer 253, SMS/MMS 255, IM (instant message) 257, browser 259, camera 261, alarm 263, contact 265, voice recognition 267, e-mail 269, calendar 271, media player 273, album 275, watch 27 7), health 279 (eg, measurement of vital information such as exercise or blood sugar), or environment information 281 (eg, measurement of atmospheric pressure, humidity, or temperature information). According to an embodiment, the application 146 may further include an information exchange application (not shown) capable of supporting information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to transfer designated information (eg, a call, message, or alarm) to an external electronic device, or a device management application configured to manage the external electronic device. The notification relay application, for example, can deliver notification information corresponding to a designated event (eg, mail reception) generated in another application (eg, the email application 269) of the electronic device 101 to an external electronic device. Additionally or alternatively, the notification relay application may receive notification information from an external electronic device and provide the notification information to the user of the electronic device 101 .

The device management application may control, for example, power (eg, turn-on or turn-off) or function (eg, brightness, resolution, or focus) of an external electronic device that communicates with the electronic device 101 or some component thereof (eg, a display module or a camera module of the external electronic device). The device management application may additionally or alternatively support installation, deletion, or update of an application operating in an external electronic device.

Throughout this specification, a neural network, a neural network, and a network function may be used interchangeably. A neural network may consist of a set of interconnected computational units, which may be generally referred to as “nodes”. These “nodes” may also be referred to as “neurons”. A neural network includes at least two or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more “links”.

In a neural network, two or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, the input node to output node relationship can be created around the link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting the input node and the output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node may determine an output node value based on values input to input nodes connected to the output node and a weight set for a link corresponding to each input node.

As described above, in a neural network, two or more nodes are interconnected through one or more links to form an input node and output node relationship in the neural network. Characteristics of the neural network may be determined according to the number of nodes and links in the neural network, an association between the nodes and links, and a weight value assigned to each link. For example, when there are two neural networks having the same number of nodes and links and different weight values between the links, the two neural networks may be recognized as different from each other.

3 is a diagram showing the overall configuration of a system for providing a solution for brand improvement according to an embodiment.

According to one embodiment, a system providing solutions for brand improvement may diagnose the condition of a brand. The system can continuously track the performance of marketing activities against the desired image. The system may measure image association and strength between the company's parent and sub-brands. Based on the results of these diagnosis, tracking, and measurement, the system can provide a brand management system that can increase the activity and potential of the brand portfolio. Through this, the system can assist the user's brand to secure and strengthen image leadership. In fact, many companies are diagnosing a brand and establishing a brand strategy through a methodology of a system according to an embodiment.

To this end, the system may include a server 300 , a network 330 , a database 310 and a user terminal 320 . However, the structure of FIG. 3 is exemplary, and the database 310 may be included in the server 300 .

The server 300 may identify problems faced by a company having a brand, focusing on the brand. The server 300 may evaluate a brand of a specific company using various brand evaluation models and derive an index corresponding to the model. For example, the brand evaluation model may include a design evaluation model, a brand equity evaluation model, or a brand health measurement model. However, these models are exemplary and various models developed to evaluate the brand may be used.

The server 300 may quantitatively determine the bottleneck using these models. The bottleneck means the bottleneck or the cause of the problem, and it means the cause of the problem that causes the gap between the brand's goal and the current state. Bottlenecks can have both verbal and non-verbal causes. For example, the server 300 may find a reason why sales of a specific company are low compared to product performance and marketing execution costs based on indicators derived using evaluation models from the brand name. Here, the brand name becomes the bottleneck of the case. As another example, the server 300 may find the reason why sales of a specific company are low compared to product performance and marketing execution costs based on the color of the brand based on indicators derived using evaluation models. Here, the color of the brand becomes the bottleneck of the case.

Server 300 may provide a solution to overcome the identified problem. The database 310 may store information on various evaluation indicators. The database 310 may store solutions in advance for each of the various bottlenecks defined based on the evaluation index. This bottleneck-solution matching information may be set in advance based on data verified in the relevant industry. For example, when the brand name is bottleneck, the server 300 may retrieve and output a brand name change direction as a solution corresponding to the corresponding bottleneck from the database 310 . As another example, when the color of the brand is a bottleneck, the server 300 may retrieve and output items to be considered for changing the color of the brand as a solution corresponding to the corresponding bottleneck from the database 310 .

In the following, terms are defined as follows.

The user brand means a brand of a company or product or service operated by the user. A competitor brand means a brand of a company or product or service that competes with a company or product or service operated by the user.

Brand health is an index that evaluates the strength and vitality of a user's brand, and indicates the relative position of the user's brand compared to competing brands. For example, Global Brand Health Rankings announced by 'YouGov', a global polling company, may correspond to this. The brand health measurement model refers to a model developed and used to measure brand health. The health evaluation result means a brand health expressed as a relative position of a user brand to a competitor brand with respect to a specific brand.

Brand Equity is an index that evaluates the value of the assets that a brand has comprehensively at the corporate level. A brand equity evaluation model refers to a model developed and used to measure brand equity. The asset evaluation result means brand equity that represents the value of the assets that the brand holds comprehensively for a specific brand.

The fit index is a specific example of asset evaluation results and refers to various indicators measured through a brand structure model. For example, the fitness index may include a chi-square statistic, SRMR, CFI, NNFI, RFI, etc., but these are only examples and may include various indicators discussed or developed in academic societies.

The brand structure model is a model for measuring brand equity of a user brand used in a specific model among brand equity evaluation models, and is composed of factors, variables, paths, and coefficients. For example, confirmatory factor analysis (CFA) assumes a structural model of factors in advance and determines how well this structural model fits the data. A factor is an element that affects a variable, and a relationship between a factor and a variable is defined through a path, and each path corresponds to a coefficient to determine the effect of a factor on a variable, and a variable refers to an element to be observed.

For example, variables may include awareness, penetration, emotional loyalty, and behavioral loyalty. Behavioral loyalty is an index expressed by general consumers through active behaviors such as purchasing toward a brand. Behavioral loyalty can be measured in a variety of ways. For example, behavioral loyalty can be measured based on repeat purchase rates. Here, the repurchase rate means the degree to which the same product or service is purchased again after the first purchase.

Brand Portfolio refers to an index that evaluates the influence, importance, and image relevance of top brands and sub-brands included in user brands. The portfolio evaluation result means an index that evaluates the influence, importance, and image relevance of a top brand and a bottom brand included in a user brand for a specific brand.

Brand Persona (Brand Personality Scale, BPS) means the concept of looking at a brand from the consumer's point of view. The persona evaluation result means the concept of looking at a brand from the consumer's point of view with respect to a specific brand.

In the following, the overall process is described in units of operations.

The server 300 may obtain an evaluation result by evaluating the user brand of the user. The server 300 may evaluate the user brand using various indicators. For example, the server 300 may obtain a health evaluation result, an asset evaluation result, a portfolio evaluation result, and a persona evaluation result by evaluating brand health, brand equity, brand portfolio, and brand persona.

The server 300 may obtain a health evaluation result by evaluating the brand health of the user brand. The server 300 may obtain a health evaluation result by evaluating the brand health of the user brand using the brand health measurement model. Brand health measurement models may include models discussed at conferences or used in the market. For example, the brand health measurement model may include a model used in 'YouGov'.

The server 300 may obtain an asset evaluation result by evaluating the brand equity of the user brand. The server 300 may continuously track the performance of the marketing activity against the target image based on the asset evaluation result. The server 300 may obtain an asset evaluation result by evaluating the brand equity of the user brand using the brand equity evaluation model.

According to one embodiment, server 300 may evaluate the brand equity of a user brand using a confirmatory factor analysis (CFA) method. The server 300 may set a brand structure model of the user brand. The server 300 may determine how faithfully the brand structure model describes actual data by using the covariance matrix. The server 300 may determine the fit between the brand structure model and the data by comparing the covariance matrix of the data and the brand structure model including information on each variable.

The server 300 may set factors, variable paths, and coefficients of the brand structure model. Here, the variables may include Awareness, Penetration, Emotional Loyalty, and Behavioral Loyalty.

Awareness refers to an indicator of whether customers can remember, recognize, or simply know a brand. Penetration is a measure of a brand's popularity and is defined as the number of people who have purchased a particular brand or product category divided by the population in that market over a given period of time. Emotional loyalty is an indicator of the degree of commitment to a brand with unique values. Behavioral loyalty is an index measured by considering the customer's repeated purchase intention, willingness to purchase organizations and services, and insensitivity to price. Behavioral loyalty can be measured based on purchase frequency and purchase rate through repeat purchase behavior based on frequency, intensity, rate, order, etc. for an organization.

The server 300 may measure the behavioral loyalty of the user brand based on the repurchase rate of one or more products related to the user brand. For example, the server 300 may measure behavioral loyalty of a user brand based on a repurchase rate of one or more products related to the user brand and a repurchase score reflecting an evaluation of the product.

The server 300 may cluster one or more products into a plurality of groups according to a plurality of criteria. A group of products may be determined as a plurality of groups through a clustering technique using a neural network based on product information and sales information on the product. Product information refers to information about the characteristics of the product itself, such as the type of product, the price of the product, and the average period of use of the product. The sales information includes product sales volume and product review information. The server 300 may map a plurality of pieces of data regarding product information and sales information to spatial coordinates. The server 300 may determine one or more density areas for a plurality of data. The server 300 may group products corresponding to cores and boundaries of one or more density areas. For example, the server 300 may group products into a plurality of groups including a first group and a second group through the above process.

The server 300 may calculate repurchase scores for each group based on repurchase rates of products belonging to different groups. The server 300 may measure the behavioral loyalty of the user brand based on the repurchase score of each group. For example, the server 300 may determine the weight of each group based on the average sales of each group and measure the behavioral loyalty of the user's brand by averaging the repurchase scores of each group.

Various products related to the user brand may exist, and each product may belong to a plurality of different groups determined through the clustering. However, if the sample of data for each group, for example, data related to repurchase rate or user evaluation is small, reliability may be a problem in calculating the repurchase score of the corresponding group. If a group with a large sample and a group with a small sample exist, the server 300 may determine whether to use the repurchase score of the group with a large sample as the repurchase score for the group with a small sample.

For example, the server 300 may calculate the repurchase score of each group through Equation 1, and determine whether to infer the repurchase score for a group with a small sample to the repurchase score for a group with a large sample.

Statistically, the higher the price, the more consumers tend to score with more consideration. In consideration of this, the server 300 may assign a higher weight to a higher price. For example, for the first product of the first group, a weight of 1, 2, 4, 8, and 16 may be applied when the price class i = 1, 2, 3, 4, and 5 classified according to the price sold at one time.

[Equation 1]

In Equation 1, the repurchase score for the first group is can be defined as Here, m is the number of sample data of each class, j is the identification number of sample data, xi,j = 0 (not purchased), 1 (purchased in a smaller quantity than before), and 2 (purchased in a larger quantity than before). n is the number of classes, for example, n = 5 (i is a class identification number, 5 = A, 4 = B ?? 1 = E), is a set of sample data of the i-th rank, denotes a value obtained by standardizing the value of each sample data with the maximum value of the i-th grade sample data set, and cannot exceed a maximum of 1. Represents the repurchase score for each grade as the average value of the standardized values of the sample data. In , n differs by 1 for each grade, cannot exceed a maximum of 1, so the higher the repurchase score of the higher grade, the larger the value is. in other words, denotes the weight for the class.

In Equation 1, weights of 1, 2, 4, 8, and 16 may be applied to the second product of the second group when price class i = 1, 2, 3, 4, and 5. The repurchase score for the second group is can be defined as q is the number of sample data of each level, l is the identification number of the sample data, xk,l = 0 (not purchased), 1 (purchased in less quantity than before), 2 (purchased in greater quantity than before), p is the number of grades, p = 5 (k is the grade identification number, 5 = A, 4 = B ?? 1 = E). is a set of sample data of the kth rank, is a value obtained by standardizing the value of each sample data with the maximum value of the set of sample data of the kth rank, and cannot exceed a maximum of 1. Represents the repurchase score for each grade as the average value of the standardized values of the sample data. In , p differs by 1 for each grade, cannot exceed a maximum of 1, so the higher the repurchase score of the higher grade, the larger the value is. in other words, denotes the weight for the class.

In Equation 1, Means the absolute value of the difference between the repurchase score for the first group and the repurchase score for the second group. The functions of y and z are:

Depending on the value of y, z will be distributed between 0.5 and 1.5. The server 300 estimates that the score of the first group will be similar to the score of the second group for the same product if z is less than 1.0 based on the threshold value of 1.0. However, additional costs may be incurred to ensure reliability.

The server 300 may calculate one or more fitness indices of the brand structure model. For example, the fitness index may include a chi-square statistic, SRMR, CFI, NNFI, RFI, etc., but these are only examples and may include various indicators discussed or developed in academic societies.

The server 300 may improve the brand structure model in a direction of improving a fitness index that does not satisfy the fitness criterion among one or more fitness indices. The server 300 may change factors, variables, paths, or coefficients of the brand structure model in a direction of improving a fitness index that does not satisfy the fitness criterion among one or more fitness indices. The server 300 may obtain an asset evaluation result by evaluating the brand assets of the user brand and one or more competitor brands using the improved brand structure model.

According to another embodiment, the server 300 may evaluate the brand equity of the user brand using a brand equity evaluation model configured with a neural network. A brand equity evaluation model may include an input layer, one or more hidden layers, and an output layer.

Each learning data consisting of awareness, penetration rate, emotional loyalty, and behavioral loyalty is input to the input layer of the brand equity evaluation model, passes through one or more hidden layers and output layers, and outputs an output vector, and the output vector is input to the loss function layer connected to the output layer.

For example, Equation 2 may be used as a loss function used for learning a neural network.

[Equation 2]

In Equation 2, n is the number of training data per class, y and j are identifiers representing classes, C is a constant value, M is the number of classes, x_y is the probability that the training data belongs to class y, x_j is the probability that the training data belongs to class j, and L can mean a loss value. Since Equation 2 reflects the number of learning data per class, a class with a small number of training data may have a small effect on learning, and a class with a large number of training data may have a large effect on learning.

The server 300 may obtain a portfolio evaluation result by evaluating the brand portfolio of the user brand. The server 300 may measure image association and strength between the company's upper and lower brands. The server 300 may provide a brand management system capable of increasing the activity and potential of the brand portfolio based on the result of the measurement.

The server 300 may obtain a persona evaluation result by evaluating the brand persona of the user brand. The server 300 may obtain a persona evaluation result by evaluating the brand persona based on the multidimensional scale. For example, the multidimensional scale may include sincerity, interest, competence, and sophistication.

The server 300 may determine the bottleneck based on the user brand evaluation result. The server 300 may determine the bottleneck by comparing the asset evaluation result of the user brand with the asset evaluation result of one or more competitor brands. The server 300 may determine a bottleneck corresponding to a relative weakness based on the evaluation result of the user brand compared to competing brands.

The server 300 may search the database for a solution to improve the user brand based on the bottleneck. The server 300 may output the searched solution. The database 310 may store solutions in advance for each of the various bottlenecks defined based on the evaluation index. This bottleneck-solution matching information may be set in advance based on data verified in the relevant industry.

4 is a flow diagram illustrating the operation of a method for providing a solution for brand improvement according to one embodiment.

According to one embodiment, in operation 401, the processor of the server 300 may evaluate the user brand of the user and obtain an evaluation result. The server 300 may evaluate a brand of a specific company using various brand evaluation models and derive an index corresponding to the model. For example, the brand evaluation model may include a design evaluation model, a brand equity evaluation model, or a brand health measurement model.

According to one embodiment, in operation 403, the processor of server 300 may determine a bottleneck based on the evaluation result of the user's brand. The server 300 may quantitatively determine the bottleneck using the models. Bottlenecks can have both verbal and non-verbal causes. The server 300 may determine the bottleneck by comparing the asset evaluation result of the user brand with the asset evaluation result of one or more competitor brands.

According to one embodiment, at operation 405, the processor of server 300 may search a database for a solution to improve the user brand based on the bottleneck. The database 310 may store solutions in advance for each of the various bottlenecks defined based on the evaluation index. This bottleneck-solution matching information may be set in advance based on data verified in the relevant industry.

According to one embodiment, at operation 407, the processor of server 300 may output the retrieved solution. Users can establish strategies for brand improvement offline based on the printed solutions.

5 is a diagram illustrating types of brand evaluation indicators according to an exemplary embodiment.

Referring to FIG. 5 , an example of indicators evaluated by a system providing a solution for brand improvement according to one embodiment is shown.

Brand health is an index that evaluates the strength and vitality of a user's brand, and indicates the relative position of the user's brand compared to competing brands. Brand health may be measured by a plurality of indicators, and each of the plurality of indicators may be expressed on a different scale.

Brand Equity is an index that evaluates the value of the assets that a brand has comprehensively at the corporate level. The server 300 may evaluate the degree of assets possessed by the brand, which is the final goal of the brand, at the corporate level. Brand equity can be measured by a plurality of indicators, and each of the plurality of indicators can be expressed on a different scale. The server 300 may analyze correlation with marketing activities through evaluation of brand equity.

Brand Portfolio refers to an index that evaluates the influence, importance, and image relevance of top brands and sub-brands included in user brands. A brand portfolio may be measured with a plurality of indicators, and each of the plurality of indicators may be expressed on a different scale.

Brand Persona (Brand Personality Scale, BPS) means the concept of looking at a brand from the consumer's point of view. The server 300 may periodically measure the achievement level of the brand persona to evaluate the influence of the brand persona on brand equity. A brand persona can be measured by a plurality of indicators, and each of the plurality of indicators can be expressed on a different scale. The server 300 may extract and continuously track sub-images according to core values through evaluation of the brand persona.

6 is an exemplary diagram of a brand equity valuation model for evaluating brand equity according to one embodiment.

Referring to FIG. 6 , a brand structure model assuming one factor and four variables for evaluating the brand equity of a user's brand is shown. Variables include awareness, penetration, emotional loyalty and behavioral loyalty.

The brand structure model consists of factors, variables, paths and coefficients. The server 300 assumes a structural model in advance and determines how well the structural model fits the data. A factor is an element that affects a variable, and a relationship between a factor and a variable is defined through a path, and each path corresponds to a coefficient to determine the effect of a factor on a variable, and a variable refers to an element to be observed.

Variables of the brand structure model may include awareness, penetration rate, emotional loyalty, and behavioral loyalty. Here, behavioral loyalty is an index expressed by general consumers through active behaviors such as purchasing for a brand. Behavioral loyalty can be measured in a variety of ways. For example, behavioral loyalty can be measured based on repeat purchase rates. Here, the repurchase rate means the degree to which the same product or service is purchased again after the first purchase.

The fit index is a specific example of asset evaluation results and refers to various indicators measured through a brand structure model. For example, the fitness index may include a chi-square statistic, SRMR, CFI, NNFI, RFI, etc., but these are only examples and may include various indicators discussed or developed in academic societies.

For example, the server 300 may determine whether each fitness index meets the criteria of the table of FIG. 6 . The server 300 may change one or more of the factors, variables, paths, and coefficients of the brand structure model in order to improve the fit index that does not meet the criteria. The server 300 may repeat this process until all fitness indices meet the criterion.

The server 300 may derive an asset evaluation result through a brand structure model. The server 300 may evaluate a key factor that most influences the formation of brand equity among recognition, association, quality, and loyalty factors based on the asset evaluation results of the user's brand and competing brands. The server 300 may evaluate the direction of brand management based on the relative influence of factors constituting brand assets. The server 300 may evaluate marketing activities to be strengthened and maintained based on a strategic direction contributing to the formation of brand equity.

7 is a diagram illustrating an example of a brand equity evaluation result according to an embodiment.

Referring to FIG. 7 , asset evaluation results of a user brand and a competitor brand are shown. Here, the user brand is denoted by A, and B to E represent competing brands. The user brand represents a third-level position in the asset evaluation result. The user brand has a high brand advocacy rate (1.035, D = 1.040) and is evaluated as having a good overall asset accumulation structure. Accordingly, it may be first proposed for the user brand to reinforce association image assets that serve as a reason for brand extension and purchase.

For example, when brand D is requested as a user brand, the server 300 may suggest copywriting adjustments and performance marketing for brand D for TPO association positioning in order to increase the keyword search volume and the number of uses as a direction to increase the penetration rate. In order to increase brand preference, the server 300 may suggest reviewing rebranding in terms of content/personality/design. In addition, the server 300 may reorganize the member loyalty program and review reinforcement of rewards in order to increase repurchase intention and NPS.

8 is an example of a graph for determining a bottleneck based on the brand equity evaluation result according to FIG. 7 .

Referring to FIG. 8 , an example of determining a bottleneck is shown. In the graph on the right, in the case of brand D, the premium index and recommendation index are the best, but the purchase intention rate and likeability rate are relatively low. In the graph on the left, brand D shows relatively poor recognition and recall values compared to A to C. Accordingly, the server 300 may determine a point having a lower purchase intention rate and a lower liking rate compared to the premium index and the recommendation index as the bottleneck. The server 300 may retrieve a previously prepared solution corresponding to the bottleneck from the database 310 and transmit it to the user terminal 320 .

9 is an example of a graph in which one or more products are clustered into a plurality of groups according to a plurality of criteria in order to measure behavioral loyalty based on repurchase rates of one or more products.

Products related to the user's target brand may be determined as a plurality of groups based on product information and sales information through a clustering technique using a neural network. Here, the product information refers to information about characteristics of the product itself, such as the type of product, the price of the product, and the average period of use of the product. The sales information may include product sales volume and product review information. Clustering may refer to unsupervised learning that groups data having similar properties into a certain number of clusters. Groups may be determined as n types through a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique. DBSCAN assumes that if a specific point belongs to a cluster, it must be located close to many other points in the cluster, and radius and minimum points can be used for this calculation. Additionally, the element is a value obtained by digitizing product information and sales information and converting them into two-dimensional coordinates. The 2D coordinates may be [product information values, sales information values]. For example, the diameter may be a radius based on a specific data element, and may be referred to as a dense area. The minimum element may indicate how many elements are required around the core point to specify a core point. In addition, each element of the data set can be divided into a core, a border, and an outlier point. Through the neural network, the processor can check the size of the diameter of each element and search for how many elements there are. Thereafter, if m or more elements exist within the diameter range, the processor may determine the corresponding element as a core element. Also, the processor may determine an element included within a range of a diameter from the core element as a boundary element. Further, the processor may determine elements that are not included within a diameter range from the core elements as outlier elements, and the outlier elements may be excluded from the corresponding cluster. Also, if the distance between the core elements is smaller than the diameter, the processor may classify the corresponding elements into the same cluster. As a result, the responsiveness is not determined as fixed types, but is dynamically changed according to the game log collected by the current processor to determine the responsiveness classified as a section more suitable for the user.

Referring to FIG. 5 , the 2D coordinates may be [product information values, sales information values]. The product information value may be distributed between 0.1, 0.2, 0.3, 0.4, and 0.5, and the sales information value may be distributed between 1 and 7. On the graph of FIG. 5 , [product information values and sales information values] can be clustered into a total of four groups. However, this is only an example, and a plurality of products may be clustered using multi-dimensional coordinates of sub-elements of each product information value and sales information value, for example, product type, product price, average period of use of the product, sales volume of the product, and product review information (e.g., purchase rating).

FIG. 10 is a diagram showing the configuration of a server included in the system of FIG. 3 .

According to one embodiment, the server 300 may include a memory 1010, a processor 1020, and a communication unit 1030.

According to an embodiment, the processor 1020 may obtain an evaluation result by evaluating the user brand of the user. The processor 1020 may evaluate a brand of a specific company using various brand evaluation models and derive an index corresponding to the model. For example, the brand evaluation model may include a design evaluation model, a brand equity evaluation model, or a brand health measurement model.

According to an embodiment, the processor 1020 may determine the bottleneck based on the user brand evaluation result. The processor 1020 may quantitatively determine the bottleneck using the models. Bottlenecks can have both verbal and non-verbal causes. Processor 1020 may determine a bottleneck by comparing the user's brand's asset evaluation results with one or more competitor brand's asset evaluation results.

According to one embodiment, processor 1020 may search a database for solutions to improve the user brand based on the bottleneck. The database 310 may store solutions in advance for each of the various bottlenecks defined based on the evaluation index. This bottleneck-solution matching information may be set in advance based on data verified in the relevant industry.

According to one embodiment, the processor 1020 may output the searched solution. Users can establish strategies for brand improvement offline based on the printed solutions.

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

Software may include a computer program, code, instructions, or a combination of one or more of these, and may configure a processing device to operate as desired, or may independently or collectively direct a processing device. Software and/or data may be permanently or temporarily embodied in any tangible machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave, to be interpreted by, or to provide instructions or data to, a processing device. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program instructions such as ROM, RAM, and flash memory. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

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

Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the following claims.

Claims (15)

evaluating, by the processor, the user brand of the user and obtaining an evaluation result;
determining, by the processor, a bottleneck based on an evaluation result of the user brand;
retrieving, by the processor, a database for a solution for improving the user brand based on the bottleneck; and
Outputting, by the processor, the searched solution
A method for providing solutions for brand improvement, including. According to claim 1,
The operation of obtaining the evaluation result,
obtaining a health evaluation result by evaluating the brand health of the user brand;
evaluating brand assets of the user brand to obtain an asset evaluation result;
obtaining a portfolio evaluation result by evaluating a brand portfolio of the user brand; and
Evaluating the brand persona of the user brand and obtaining a persona evaluation result
How to provide solutions for the improvement of brands, including. According to claim 1,
The operation of obtaining the health evaluation result,
Evaluating the brand health of the user brand using a brand health measurement model to obtain the health evaluation result.
How to provide solutions to improve your brand. According to claim 1,
The operation of obtaining the asset evaluation result,
Evaluating the brand equity of the user brand using a brand equity evaluation model to obtain an asset evaluation result.
How to provide solutions to improve your brand. According to claim 4,
The operation of evaluating the brand equity of the user brand using the brand equity evaluation model to obtain the asset evaluation result,
setting a brand structure model of the user brand;
calculating one or more fitness indices of the brand structure model;
improving the brand structure model in a direction of improving a fitness index that does not satisfy a fitness criterion among the one or more fitness indices; and
Evaluating the brand assets of the user brand and one or more competitor brands using the improved brand structure model to obtain the asset evaluation result.
A method for providing solutions for brand improvement, including. According to claim 5,
The operation of setting the brand structure model of the user brand,
Including the operation of setting the factors, variables, paths and coefficients of the brand structure model,
The variable includes awareness, penetration rate, emotional loyalty and behavioral loyalty,
How to provide solutions to improve your brand. According to claim 6,
The operation of setting the factors, variable paths and coefficients of the brand structure model,
Measuring the behavioral loyalty of the user brand based on the repurchase rate of one or more products related to the user brand.
How to provide solutions to improve your brand. According to claim 7,
The operation of measuring the behavioral loyalty of the user brand based on the repurchase rate of the one or more products,
clustering the one or more products into a plurality of groups according to a plurality of criteria;
calculating a repurchase score of each group based on repurchase rates of products belonging to different groups; and
An operation of measuring the behavioral loyalty of the user brand based on the repurchase score of each group
A method for providing solutions for brand improvement, including. According to claim 5,
The operation of improving the brand structure model,
Changing factors, variables, paths, or coefficients of the brand structure model in a direction of improving a fitness index that does not satisfy a fitness criterion among the one or more fitness indices,
How to provide solutions to improve your brand. According to claim 1,
The operation of evaluating the brand persona of the user brand and obtaining a persona evaluation result,
Evaluating the brand persona based on a multidimensional scale to obtain a persona evaluation result
How to provide solutions for the improvement of brands, including. According to claim 1,
The operation of determining the bottleneck,
Comprising an operation of determining the bottleneck by comparing an asset evaluation result of the user brand with an asset evaluation result of one or more competing brands.
How to provide solutions to improve your brand. According to claim 4,
The brand equity evaluation model includes an input layer, one or more hidden layers and an output layer;
Each learning data consisting of awareness, penetration rate, emotional loyalty, and behavioral loyalty is input to the input layer of the brand equity evaluation model and passes through the one or more hidden layers and output layers to output an output vector, the output vector is input to a loss function layer connected to the output layer, the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each learning data, and the parameters of the brand equity evaluation model are learned in a direction in which the loss value becomes smaller,
How to provide solutions to improve your brand. at least one processor; and
contains a database;
the processor,
Evaluate the user's brand of the user to obtain an evaluation result;
Determine a bottleneck based on the evaluation result of the user brand;
Searching the database for a solution for improving the user brand based on the bottleneck;
Outputting the searched solution,
server. A computer program stored in a computer readable recording medium in order to execute the method of claim 1 in combination with hardware. A computer readable recording medium storing a computer program combined with hardware to execute the method of claim 1.

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