Description:FIELD OF INVENTION

[001] The present invention relates to a system for evaluating brand value metric of an organization and a method thereof. Particularly, the present invention relates to evaluate brand value metric of an organization, by a system integrated through a machine learning model-based system.
BACKGROUND OF THE INVENTION

[002] In today’s competitive marketplace, understanding and quantifying brand value is essential for companies. Brand value encompasses various intangible assets that contribute to a company’s overall worth, such as brand loyalty, customer perception, and market positioning. The significance of brand value has increased with the advent of digital marketing and social media. These platforms have amplified the ways brand interact with customers, further complicating the assessment of brand impact. Existing systems of calculating brand value often involve complex financial models and subjective assessments, making it challenging for companies to obtain accurate and consistent evaluations.

[003] Existing methods often fails to integrate diverse data sources comprehensively and this narrow focus results in incomplete assessment of brand value. Additionally, the manual nature of traditional brand value calculators is time-consuming and prone to error, leading to delays and inaccuracies that reduce the effectiveness of the evaluation process. Further, these methods typically provide a static view of brand value at a specific point in time, lacking the capability of real-time insights or track changes dynamically.

[004] Furthermore, the implementation of traditional brand value assessment systems and methods is expensive requiring specialized expertise, extensive data collection, and comprehensive analysis, which drives up cost and make these methods less assessable to smaller companies. Traditional methods are not easily adaptable to new data sources or evolving market conditions, struggling to remain relevant in the face of changing customer behaviour and emerging digital trends.

[005] Moreover, the presently available diverse tools and software significantly enhance analysis and decision-making processes. Prominent examples include Microsoft Excel, QuickBooks, SAP (Systems, Applications, and Products), Bloomberg Terminal, and Power BI (Business Intelligence), each serving distinct purposes to facilitate comprehensive data analysis. Analytical calculators and spreadsheets, while useful, often fall short in automating integrating diverse data sources, making obtained metrics prone to error and inefficiencies. Furthermore, traditional tool lack contextual analysis and predictive analytics capabilities, hindering their ability to generate accurate insights and forecasts.

[006] There are several patent applications that disclose a system and method to obtain different metrics. One such Indian patent application IN201914026859 discloses a calculator including a processor configured to: execute a first calculation using a numerical value in response to an input of an execution instruction of the first calculation, store a first numerical value relating to a result of the executed first calculation in a first memory, and display the first numerical value on a display; execute a second calculation which is different from the first calculation using a numerical value in response to an input of an execution instruction of the second calculation, store a second numerical value relating to a result of the executed second calculation in a second memory, and display the second numerical value on the display; display the first numerical value stored in the first memory on the display, when a first read instruction is input while a numerical value indicating a result of the executed first calculation is displayed on the display; and display the second numerical value stored in the second memory on the display, when the first read instruction is input while a numerical value indicating a result of the executed second calculation is displayed on the display. However, the cited invention does not provide advanced metrics to assess brand value of an organization using advance analytical capabilities.

[007] In order to overcome the problem associated with a state of arts, there is a need for the development of an efficient system for determining the brand value metric of an organization.

OBJECTIVE OF THE INVENTION

[008] The primary objective of the present invention is to provide a system for determining the brand value metric of an organization and the method thereof.

[009] Another objective of the present invention is to provide a streamlined and automated assessment of the brand value metric of an organization.

[0010] Another objective of the present invention is to leverage advanced machine learning models to analyze complex datasets, identify patterns, and generate precise brand value metrics.

[0011] Another objective of the present invention is to provide an extensive brand value metric based on a wide range of parameters.

[0012] Another objective of the present invention is to utilize machine learning models that indicate and forecast future performance trends.

[0013] Another objective of the present invention is to assist organizations to improve the precision of brand value metric evaluation and reduce manual effort by leveraging advanced analytical capabilities.

[0014] Yet another objective of the present invention is to provide comprehensive assessment that support strategic planning and resource allocation.

[0015] Yet another objective of the present invention is to help organizations track success of marketing campaigns.

[0016] Other objectives and advantages of the present invention will become apparent from the following description taken in connection with the accompanying drawings, wherein, by way of illustration and example, the aspects of the present invention are disclosed.

BRIEF DESCRIPTION OF DRAWINGS
[0017] The present invention will be better understood after reading the following detailed description of the presently preferred aspects thereof with reference to the appended drawings, in which the features, other aspects and advantages of certain exemplary embodiments of the invention will be more apparent from the accompanying drawing in which:

[0018] Figure 1 illustrates a block diagram of a system for determining brand value metric of an organization.
SUMMARY OF THE INVENTION
[0019] The present invention relates to a system for evaluation of brand value metric of an organization and a method thereof. The system for evaluation of brand value of organizations, comprising: a computing device to receive input data and instructions by a user; an input device connected to the computing device and communicably connected to the processor for the user to provide input data and instructions to initiate analysis of the input data for evaluation of brand value metric; a processor integrated into the computing device to process the input data received from the user; a server wirelessly connected to the processor to receive and store the processed input data; a machine learning module installed in the server to evaluate brand value metric based on the processed input data; a display connected to the computing device to display the evaluated brand value metric; and a memory integrated into the computing device and connected to the processor to store the input data and evaluated brand value metric’s data. The present invention also provides a method for operating the system for evaluation of brand value metric. The method comprising steps of: providing input data and instructions to the computing device through the input device; storing the input data in the memory integrated into the computing device; transmitting the input data to the processor through the computing device to process/analyze the input data based on the instructions received by the user; transmitting the processed input data through the processor integrated in the computing device to the server wirelessly connected to the computing device; analyzing the processed input data received by the server through the machine learning module installed in the server to evaluate brand value metric; storing the evaluated brand value metric by the server; transmitting the evaluated brand value metric data from the server to the computing device wirelessly to display the evaluated brand value metric on the display connected to the computing device. The present invention assists organizations to improve the precision of brand value metric evaluation and reduce manual effort by leveraging advanced analytical capabilities.

DETAILED DESCRIPTION OF INVENTION
[0020] The following detailed description and embodiments set forth herein below are merely exemplary out of the wide variety and arrangement of instructions which can be employed with the present invention. The present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. All the features disclosed in this specification may be replaced by similar other or alternative features performing similar or same or equivalent purposes. Thus, unless expressly stated otherwise, they all are within the scope of the present invention.

[0021] Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

[0022] The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention.

[0023] It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.

[0024] It should be emphasized that the term “comprises/comprising” when used in this specification is taken to specify the presence of stated features, integers, steps, or components but does not preclude the presence or addition of one or more other features, integers, steps, components, or groups thereof.

[0025] Accordingly, the present invention relates to a system for determining the brand value metric of an organization and a method thereof. Particularly, the present invention relates to determining the brand value metric of an organization, by a system integrated through a machine learning model-based system.

[0026] In an embodiment, as shown in Figure 1, the system (100) comprises a computing device (102), a processor (104) integrated into the computing device (102), a display (106) connected to the computing device (102); an input device (108) connected to the computing device (102) and communicably connected to the processor (104), a memory (110) integrated into the computing device (102) and connected to the processor (104), a server (112) wirelessly connected to the processor (104), and a machine learning module (114) installed in the server (112).

[0027] The computing device (102) may be configured to receive input data as well as the instructions for the processor (104) to initiate analysis of the input data for determining brand value of the organization, wherein the data is provided by a user through an input device (108). In an exemplary embodiment, the computing device (102) may be selected from a group consisting of, such as, but not limited to, a computer, laptop, calculator, etc.
[0028] The input data provided by the user comprises of a plurality of pre-defined parameters that are used for determining the brand value of the organization. In an exemplary embodiment, the input data may be selected from a group consisting of, such as, but not limited to, number of active users per product/service; customer acquisition rate (monthly/quarterly); customer retention rate; net promoter score (NPS); customer lifetime value (CLV); revenue per customer; average revenue per customer; time to achieve specific mission milestones; percentage of target market reached; brand awareness percentage; customer satisfaction score; number of positive customer testimonials; social media engagement rate for brand related content; product/service usage frequency; number of customer needs addressed by the product/service; time saved or value added for customers (quantified); number of social or environmental goals achieved (if applicable to the vision); market share percentage; year-over-year growth rate; number of new products/services launched aligned with the vision; percentage of employees who can articulate the company's vision and mission; size of professional network (total connections); network growth rate (new connections per month); number of industry events attended; number of speaking engagements at relevant forums; frequency of interactions with key network members; number of successful referrals received; number of referrals given; conversion rate of network connections to business opportunities; number of partnerships formed; revenue generated through network connections; number of mentorship relationships established; diversity of network (number of different industries/roles represented); engagement rate on professional social media platforms; number of collaborative projects initiated with network members; frequency of network newsletter or updates sent; open and response rates for network communications; number of introductions made within the network; average response time to network communications; number of categories of strategic connections identified; percentage of identified categories with established connections; inbound leads and their growth rate; number of referred customers; total customers; net sales; brand metrics; brand impression formation time; number of brand impressions before recognition; emotional response scores to brand elements; brand consistency across touchpoints, or a combination thereof.

[0029] In an exemplary embodiment, the input device (108) may be selected from a group consisting of, such as, but not limited to, a physical keyboard, touch-screen keyboard, mouse, etc.

[0030] The processor (104) may be configured to process/analyze input data on receiving the user’s instructions from the computing device (102) and thereafter wirelessly transmit data to the server (112) for evaluation of the brand value metric of the organization by the machine learning module (114). In an exemplary embodiment, the processed input data may be transmitted to the server (112) through Wi-Fi, Bluetooth, Ethernet, GPRS module, and the like.
[0031] The server (112) receives processed data from the processor (104) and stores the processed input data through architecture. In an exemplary embodiment, the architecture may be selected from, but not limited to, Recurrent Neural Network (RNN), Autoencoder, and the like. The machine learning module (114) installed in the server (112) evaluates brand value metrics based on the processed input data. The server (112) wirelessly transmits the determined brand value metric to the computing device (102) through such as, but not limited to, Wi-Fi, Bluetooth, etc., for displaying the determined brand value metric through the display (106) for the user.
[0032] The machine learning module (114) may be trained on datasets encompassing a wide range of pre-defined parameters to determine/evaluate brand value metrics, thereby providing an assessment of the brand value of the organizations among the consumers. The machine learning module (114) is trained to assign weightage to each pre-defined parameter, which involves segregating the data for training the machine learning module (114) into training data, which is used to train the machine learning module (114) and testing data, which is used to determine the performance of the trained machine learning module (114). The machine learning module (114) may also be configured In an exemplary embodiment, the machine learning module (114) may be selected from a group consisting of such as, but not limited to, linear regression, polynomial regression, random forest model, and the like, thereby making the process of assigning weight dynamic and automatic.

[0033] The machine learning module (114) also provides data-driven recommendations based on the extensive reservoir of quantitative pre-defined parameter data. The machine learning module (114) is trained on training data of various industries and companies so as to evaluate a calibrated score reflective of the organization’s brand value. In an exemplary embodiment, the machine learning module (114) is configured to evaluate the calibrated score based upon assigned weight, through a model selected from a group consisting of such as, but not limited to, linear regression, polynomial regression, random forest model, and the alike.

[0034] The memory (110) may be configured to store the data and instructions provided by the user and the evaluated brand value metric. In an exemplary embodiment, the memory may be selected from a group consisting of, such as, but not limited to, flash memory, secondary memory, cache memory, and the like.

[0035] In an embodiment, the present invention also provides a method for determining brand value metrics using the system of the present invention. The method comprises the following steps:
• providing input data and instructions to a computing device (102) through an input device (108);
• storing the input data in a memory (110) integrated into the computing device (102);
• transmitting the input data to the processor (104) through the computing device (102) to process/analyze the input data based on the instructions received by the user;
• transmitting the processed input data through the processor (104) integrated in the computing device (102) to a server (112) wirelessly connected to the computing device (102);
• analyzing the processed input data received by the server (112) through a machine learning module (114) installed in the server (112) to evaluate brand value metric;
• storing the evaluated brand value metric by the server (112);
• transmitting the evaluated brand value metric from the server (112) to the computing device (102) wirelessly to display the evaluated brand value metric on a display (106) connected to the computing device (102).
[0036] The input data provided by the user comprises of a plurality of pre-defined parameters that are used for determining brand value of the organization. In an exemplary embodiment, the input data may be selected from a group consisting of, such as, but not limited to, number of active users per product/service; customer acquisition rate (monthly/quarterly); customer retention rate; net promoter score (NPS); customer lifetime value (CLV); revenue per customer; average revenue per customer; time to achieve specific mission milestones; percentage of target market reached; brand awareness percentage; customer satisfaction score; number of positive customer testimonials; social media engagement rate for brand related content; product/service usage frequency; number of customer needs addressed by the product/service; time saved or value added for customers (quantified); number of social or environmental goals achieved (if applicable to the vision); market share percentage; year-over-year growth rate; number of new products/services launched aligned with the vision; percentage of employees who can articulate the company's vision and mission; size of professional network (total connections); network growth rate (new connections per month); number of industry events attended; number of speaking engagements at relevant forums; frequency of interactions with key network members; number of successful referrals received; number of referrals given; conversion rate of network connections to business opportunities; number of partnerships formed; revenue generated through network connections; number of mentorship relationships established; diversity of network (number of different industries/roles represented); engagement rate on professional social media platforms; number of collaborative projects initiated with network members; frequency of network newsletter or updates sent; open and response rates for network communications; number of introductions made within the network; average response time to network communications; number of categories of strategic connections identified; percentage of identified categories with established connections; inbound leads and their growth rate; number of referred customers; total customers; net sales; brand metrics; brand impression formation time; number of brand impressions before recognition; emotional response scores to brand elements; brand consistency across touchpoints.

[0037] In an exemplary embodiment, the computing device may be selected from a group consisting of such as, but not limited to, a computer, laptop, calculator, etc.

[0038] In another exemplary embodiment, the input device (108) may be selected from a group consisting of, such as, but not limited to, a physical keyboard, touch-screen keyboard, mouse, etc.

[0039] In yet another exemplary embodiment, the memory may be selected from a group consisting of, such as, but not limited to, flash memory, secondary memory, cache memory, and the alike.

[0040] The instructions provided by the user include the instructions for analyzing the input data by the processor (104) for evaluating/determining brand value metric.

[0041] In yet another exemplary embodiment, the server (112) may store data in an architecture selected from a group consisting of, such as, but not limited to, Recurrent Neural Network (RNN) model architecture, an Autoencoder, and the like.

[0042] The advantages of the present invention are enlisted herein:
• The present invention provides a streamlined and automated assessment of the products and services of an organization among customers.
• The present invention leverages advanced machine learning models to analyse complex datasets, identify patterns, and generate precise brand value metric.
• The present invention provides an extensive brand value metric based on a wide range of parameters.
• The present invention utilizes machine learning models that indicate and forecast future performance trends.
• The present invention assists organizations to improve the precision of brand value metric evaluation and reduce manual effort by leveraging advanced analytical capabilities.
• The present invention provides a comprehensive assessment that supports strategic planning and resource allocation.
• The present invention helps organizations to develop products/services aligning well with the needs of consumers.

[0043] While this invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims. , Claims:WE CLAIM:

1. A system for evaluation of brand value metric of an organization, comprising:
• a computing device (102) to receive input data and instructions by a user;
• an input device (108) connected to the computing device (102) and communicably connected to the processor (104) for the user to provide input data and instructions to initiate analysis of the input data for evaluation of brand value metric;
• a processor (104) integrated into the computing device (102) to process the input data received from the user;
• a server (112) wirelessly connected to the processor (104) to receive and store the processed input data;
• a machine learning module (114) installed in the server (112) to evaluate brand value metric based on the processed input data;
• a display (106) connected to the computing device (102) to display the evaluated brand value metric; and
• a memory (110) integrated into the computing device (102) and connected to the processor (104) to store the input data and evaluated brand value metric data.
2. The system (100) as claimed in claim 1, wherein the computing device (102) is selected from a group consisting of a computer, calculator, and laptop.
3. The system (100) as claimed in claim 1, wherein the input data comprises of number of active users per product/service; customer acquisition rate (monthly/quarterly); customer retention rate; net promoter score (NPS); customer lifetime value (CLV); revenue per customer; average revenue per customer; time to achieve specific mission milestones; percentage of target market reached; brand awareness percentage; customer satisfaction score; number of positive customer testimonials; social media engagement rate for brand related content; product/service usage frequency; number of customer needs addressed by the product/service; time saved or value added for customers (quantified); number of social or environmental goals achieved (if applicable to the vision); market share percentage; year-over-year growth rate; number of new products/services launched aligned with the vision; percentage of employees who can articulate the company's vision and mission; size of professional network (total connections); network growth rate (new connections per month); number of industry events attended; number of speaking engagements at relevant forums; frequency of interactions with key network members; number of successful referrals received; number of referrals given; conversion rate of network connections to business opportunities; number of partnerships formed; revenue generated through network connections; number of mentorship relationships established; diversity of network (number of different industries/roles represented); engagement rate on professional social media platforms; number of collaborative projects initiated with network members; frequency of network newsletter or updates sent; open and response rates for network communications; number of introductions made within the network; average response time to network communications; number of categories of strategic connections identified; percentage of identified categories with established connections; inbound leads and their growth rate; number of referred customers; total customers; net sales; brand metrics; brand impression formation time; number of brand impressions before recognition; emotional response scores to brand elements; brand consistency across touchpoints, or a combination thereof.
4. The system (100) as claimed in claim 1, wherein the input device (108) is selected from a group consisting of, a physical keyboard, touch-screen keyboard, mouse, or a combination thereof.
5. The system (100) as claimed in claim 1, wherein the machine learning module (114) is configured for continuous refinement through iterative training to alleviate reliance on human intervention.
6. The system (100) as claimed in claim 1, wherein the machine learning module (114) is configured to attribute weightage to pre-defined parameters based on a machine learning model.
7. The system (100) as claimed in claim 1, wherein the machine learning model is trained on datasets encompassing a wide range of pre-defined parameters to evaluate brand value metric of the organization.
8. The system (100) as claimed in claim 7, wherein the machine learning model is selected from a group consisting of linear regression, polynomial regression, and random forest model.
9. A method for evaluation of brand value metric using the system as claimed in claim 1, comprising steps of:
• providing input data and instructions to the computing device (102) through the input device (108);
• storing the input data in the memory (110) integrated into the computing device (102);
• transmitting the input data to the processor (104) through the computing device (102) to process/analyze the input data based on the instructions received by the user;
• transmitting the processed input data through the processor (104) integrated in the computing device (102) to the server (112) wirelessly connected to the computing device (102);
• analyzing the processed input data received by the server (112) through the machine learning module (114) installed in the server (112) to evaluate brand value metric;
• storing the evaluated brand value metric by the server (112);
• transmitting the evaluated brand value metric data from the server (112) to the computing device (102) wirelessly to display the evaluated brand value metric on the display (106) connected to the computing device (102).
10. The method as claimed in claim 9, wherein the method comprise a step of providing iterative training to machine learning module (114) for continuous refinement.