Claims:1. A processor-implemented method for brand valuation, the method comprising:
aggregating, via one or more hardware processors, qualitative data associated with a plurality of brands from a first plurality of sources;
identifying, via the one or more hardware processors, one or more discriminative patterns corresponding to a plurality of value chain stages based on sampling of the qualitative data;
computing, via the one or more hardware processors, a Brand Value Chain (BVC) score based on the one or more discriminative patterns corresponding to the plurality of value chain stages;
deriving, via the one or more hardware processors, royalty rates associated with the plurality of brands from a second plurality of sources, the royalty rate indicative of potential earnings of a brand based on brand equity;
determining, via the one or more hardware processors, a covariance between the BVC score and the royalty rates based on a statistical method to estimate a discounted rate associated with the plurality of brands, the discounted rate associated with a brand indicative of time value of money of the brand inclusive of risk of future cash flows; and
identifying, via the one or more hardware processors, based at least on the discounted rate associated with the plurality of brands, a Net Present Value (NPV) of future earnings from the plurality of brands, the NPV indicative of the brand value associated with the plurality of brands.

2. The method as claimed in claim 1, wherein the sampling of the qualitative data performed based on one or more sampling method such as simple random sampling, cluster sampling, systematic sample, and stratified sampling so as to exclude survey bias.

3. The method as claimed in claim 1, wherein the BVC score is computed by using Analytical Hierarchical Process (AHP) method.

4. The method as claimed in claim 3, wherein computing the BVC score based on the discriminative patterns using the AHP method comprises:
structuring a decision hierarchy comprising a plurality of levels, each level comprising a plurality of parameters and a plurality of sub-parameters corresponding to each level;
computing a plurality of comparison matrices based on a set of pairwise comparisons between parameters in upper levels of the plurality of levels with the parameters in the levels immediately below in the decision hierarchy; and
determining the BVC score based on the plurality of comparison matrices.

5. The method as claimed in claim 1, wherein the first plurality of sources comprises customer surveys, social media sources, websites and video traffic, number of people contacting and interacting, and blog feedbacks.

6. The method as claimed in claim 1, wherein the second plurality of sources comprises agencies and non-singular entities.

7. The method as claimed in claim 1, wherein the covariance is computed based on the expression:
Cor(x,y) = ?xy/sx sy
where,
?xy is the covariance between x and y
sx is standard deviation of X,
sy is standard deviation of X
where,
xi and yi = ith term in X variables, and Y variables respectively.
x ¯ and y ¯ are Average of X variables and Y variables, respectively
n is sample size.
where,

8. A processor-implemented system for administering a secured assessment, the system comprising:
one or more memories storing instructions; and
one or more hardware processors coupled to said one or more memories, wherein the one or more hardware processors configured by said instructions to:
aggregate qualitative data associated with a plurality of brands from a first plurality of sources;
identify one or more discriminative patterns corresponding to a plurality of value chain stages based on sampling of the qualitative data;
compute a Brand Value Chain (BVC) score based on the one or more discriminative patterns corresponding to the plurality of value chain stages;
deriving royalty rates associated with the plurality of brands from a second plurality of sources, the royalty rate indicative of potential earnings of a brand based on brand equity;
determine a covariance between the BVC Score and the royalty rate based on a statistical method to estimate a discounted rate associated with the plurality of brands, the discounted rate associated with a brand indicative of time value of money of the brand inclusive of risk of future cash flows; and
identify, based at least on the discounted rate associated with the plurality of brands, a Net Present Value (NPV) of future earnings from the plurality of brands, the NPV indicative of the brand value associated with the plurality of brands.

9. The system as claimed in claim 8, wherein said one or more hardware processors are further configured by the instructions to sample the qualitative data based on one or more sampling method such as simple random sampling, cluster sampling, systematic sample, and stratified sampling.

10. The system as claimed in claim 8, wherein said one or more hardware processors computes the BVC score by using Analytical Hierarchical Process (AHP) method.

11. The system as claimed in claim 10, wherein to compute the BVC score based on the discriminative patterns using the AHP method, said one or more hardware processors are further configured by the instructions to:
structure a decision hierarchy comprising a plurality of levels, each level comprising a plurality of parameters and a plurality of sub-parameters corresponding to each level;
compute a plurality of comparison matrices based on a set of pairwise comparisons between parameters in upper levels of the plurality of levels with the parameters in the levels immediately below in the decision hierarchy; and
determine the BVC score based on the plurality of comparison matrices.
12. The system as claimed in claim 8, wherein the first plurality of sources comprises customer surveys, social media sources, websites and video traffic, number of people contacting and interacting, and blog feedbacks.

13. The system as claimed in claim 8, wherein the second plurality of sources comprises agencies and non-singular entities.

14. The system as claimed in claim 8, wherein said one or more hardware processors are further configured by the instructions to compute the covariance based on the expression:
Cor(x,y) = ?xy/sx sy
where,
?xy is the covariance between x and y
sx is standard deviation of X,
sy is standard deviation of X
where,
xi and yi = ith term in X variables, and Y variables respectively.
x ¯ and y ¯ are Average of X variables and Y variables, respectively
n is sample size.
where,
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEM AND METHOD FOR BRAND VALUATION

Applicant:
Tata Consultancy Services Limited
A Company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India

The following specification describes the invention and the manner in which it is to be performed.
TECHNICAL FIELD
[001] The present disclosure in general relates to brand valuation, and particularly, but not exclusively, to systems and methods for brand valuation.

BACKGROUND
[002] Brands are core to Consumer Package Goods (CPG) and other businesses. Many CPG companies frequently buy or sell or build brands, it is inevitable that these CPG companies spend millions in developing a brand. Brand Valuation (BV) can have a major impact on CPG company's financials. The BV solution computes BV for a CPG business as well other industries that deal in brands. Computation of BV is important since it not only forms a big portion of the intangible assets reported in the Annual reports (specifically the ‘Balance sheet’) but also forms the basis of mergers and acquisition (M&A) valuation.
[003] Currently, there are few approaches that are utilized for computation of BV. However, such approaches are quiet complex. Moreover the existing methods lack accuracy, because of complexity involved in tracking consumer behaviour due to the fecundity of media drivers and the exponential increase in spending that is required for incremental growth in brand resonance.

SUMMARY
[004] This summary is provided to introduce aspects related to systems and methods for brand valuation and the aspects are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[005] In one implementation, a processor-implemented method for brand valuation is provided. The method includes aggregating, via one or more hardware processors, qualitative data associated with a plurality of brands from a first plurality of sources. Further, the method includes identifying, via the one or more hardware processors, one or more discriminative patterns corresponding to a plurality of value chain stages based on sampling of the qualitative data. The sampling of the qualitative data facilitates in removing any bias in the qualitative data. Furthermore, the method includes computing, via the one or more hardware processors, a Brand Value Chain (BVC) score based on the discriminative patterns corresponding to the plurality of value chain stages. Moreover, the method includes deriving, via the one or more hardware processors, royalty rates associated with the plurality of brands from a second plurality of sources. The royalty rate is indicative of potential earnings of a brand based on brand equity. Also, the method includes determining, via the one or more hardware processors, a covariance between the BVC Score and the royalty rate based on a statistical method to estimate a discounted rate associated with the plurality of brands. The discounted rate is associated with a brand indicative of time value of money of the brand inclusive of risk of future cash flows. In addition, the method includes identifying, via the one or more hardware processors, and based at least on the discounted rate associated with the plurality of brands, a Net Present Value (NPV) of Future Earnings from the plurality of brands, the NPV indicative of the brand value associated with the plurality of brands.
[006] In one implementation, a computer-implemented system for brand valuation is provided. The system includes one or more memories storing instructions and one or more hardware processors coupled to said one or more memories. The one or more hardware processors are configured by said instructions to aggregate qualitative data associated with a plurality of brands from a first plurality of sources. Further, the one or more hardware processors are configured by said instructions to identify one or more discriminative patterns corresponding to a plurality of value chain stages based on sampling of the qualitative data. Furthermore, the one or more hardware processors are configured by said instructions to compute a Brand Value Chain (BVC) score based on the discriminative patterns corresponding to the plurality of value chain stages. Also, the one or more hardware processors are configured by said instructions to derive royalty rates associated with the plurality of brands from a second plurality of sources, the royalty rate indicative of potential earnings of a brand based on brand equity. Moreover, the one or more hardware processors are configured by said instructions to determine a covariance between the BVC Score and the royalty rate based on a statistical method to estimate a discounted rate associated with the plurality of brands. The discounted rate associated with a brand is indicative of time value of money of the brand inclusive of risk of future cash flow. Also, the one or more hardware processors are configured by said instructions to identify, based at least on the discounted rate associated with the plurality of brands, a Net Present Value (NPV) of Future Earnings from the plurality of brands. The NPV is indicative of the brand value associated with the plurality of brands.
[007] In yet another implementation, a non-transitory computer-readable medium having embodied thereon a computer program for executing a method for brand valuation is provided. The method includes aggregating qualitative data associated with a plurality of brands from a first plurality of sources. Further, the method includes identifying one or more discriminative patterns corresponding to a plurality of value chain stages based on sampling of the qualitative data. The sampling of the qualitative data facilitates in removing any bias in the qualitative data. Furthermore, the method includes computing, a Brand Value Chain (BVC) score based on the discriminative patterns corresponding to the plurality of value chain stages. Moreover, the method includes deriving royalty rate associated with the plurality of brands from a second plurality of sources. The royalty rate is indicative of potential earnings of a brand based on brand equity. Also, the method includes determining a covariance between the BVC Score and the royalty rates based on a statistical method to estimate a discounted rate associated with the plurality of brands. The discounted rate is associated with a brand indicative of time value of money of the brand inclusive of risk of future cash flows. In addition, the method includes identifying based at least on the discounted rate associated with the plurality of brands, a Net Present Value (NPV) of Future Earnings from the plurality of brands, the NPV indicative of the brand value associated with the plurality of brands.

BRIEF DESCRIPTION OF DRAWINGS
[008] The detailed description is described with reference to the accompanying Figures. In the Figures, the left-most digit(s) of a reference number identifies the Figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like/similar features and components.
[009] FIG. 1 illustrates a networking environment implementing a brand valuation system, in accordance with an embodiment of the present subject matter.
[010] FIG. 2 illustrates a block diagram of a system for brand valuation, in accordance with an embodiment of the present subject matter.
[011] FIGS. 3A – 3D illustrate an example representation of a qualitative framework for brand valuation, in accordance with an embodiment of the present subject matter.
[012] FIG. 4 illustrates a flowchart of a method for brand valuation, in accordance with an embodiment of the present subject matter.
[013] FIGS. 5A-5B illustrates a representative example for computation of brand value chain (BVC) score, in accordance with an embodiment of the present subject matter.
DETAILED DESCRIPTION
[014] Currently, most widely used method of brand valuation includes determining a fair value of the brand based on present value of the future royalty receipts that may be obtained from licensing the brand to another party. This method of determining brand value is referred to as “Royalty relief method”. Said method for determining brand value is capable of measuring quantitative aspects of brand value correctly. However, such methodology ignores the quantitative aspects of brand valuation. Due to the dependence of brand value computation only on the quantitative aspects of BV, the computed BV is devoid of the qualitative aspects, and hence may not accurately predict the brand value.
[015] In accordance with the present subject matter, systems and methods are provided for brand valuation, wherein the brand valuation is determined based on both the qualitative and quantitate aspects of the brands. The combination of the both, the qualitative and the quantitative aspects of the brand, to compute the brand-valuation facilitates in accurately determining the brand valuation of the brands. In an embodiment, the disclosed system combines Royalty relief methodology with qualitative aspects of brand valuation correctly weighed to compute the Brand Values accurately. In addition, the disclosed methods and system facilitates in scoring the qualitative aspects for computing Brand Values based on proper segmentation and sub-segmentation of non-tangible psychometric parameters obtained from the qualitative analysis.
[016] The above method(s) and system(s) are further described in conjunction with the following figures. It should be noted that the description and figures merely illustrate the principles of the present subject matter. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody the principles of the present subject matter and are included within its spirit and scope. Furthermore, all examples recited herein are principally intended expressly to be only for pedagogical purposes to aid the reader in understanding the principles of the present subject matter and the concepts contributed by the inventor(s) to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the present subject matter, as well as specific examples thereof, are intended to encompass equivalents thereof.
[017] FIG. 1 illustrates a network environment 100 implementing a brand valuation system 102, according to an embodiment of the present subject matter. The brand valuation system 102, hereinafter referred to as the system 102, is configured for computing the brand valuation. The system 102 may be embodied in a computing device, for instance a computing device 104. In an implementation, the system 102 is implemented at a BV determination center. Alternatively, the system 102 can be implemented outside of the BV determination center. The BV determination center can be an Information Technology (IT)/software firm or a specialized agency involved in such practice.
[018] The system 102 is communicatively coupled, over a network 106, to user devices, for example machine 108-1. In an implementation the system 102 may be connected to the machine 108-1 over a secured Virtual Private Network (VPN). The machine 108-1 may be further connected to another user device, for example a machine 108-2 over a local area network (LAN). The machines 108-1, 108-2 enables users to provide details associated with qualitative analysis thereof for brand valuation. It is to be noted herein that the system 200 may enable in provisioning of a user interface, such as a graphic user interface (GUI), which may be used by the candidate for registration.
[019] In an embodiment, the network 106 may be a wireless or a wired network, or a combination thereof. In an example, the network 106 can be implemented as a computer network, as one of the different types of networks, such as virtual private network (VPN), intranet, local area network (LAN), wide area network (WAN), the internet, and such. The network 106 may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), and Wireless Application Protocol (WAP), to communicate with each other. Further, the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices. The network devices within the network 106 may interact with the system 102 through communication links.
[020] As discussed above, the system 102 may be implemented in a computing device 104, such as a hand-held device, a laptop or other portable computer, a tablet computer, a mobile phone, a PDA, a smartphone, and a desktop computer. The system 102 may also be implemented in a workstation, a mainframe computer, a server, and a network server. In an embodiment, the system 102 may be coupled to a data repository, for example, a repository 112. The repository 112 may store data processed, received, and generated by the system 102. In an alternate embodiment, the system 102 may include the data repository 112. The components and functionalities of the system 102 are described further in detail with reference to FIG. 2.
[021] FIG. 2 illustrates a block diagram of a brand valuation system 200, in accordance with an example embodiment. The brand valuation system 200 (hereinafter referred to as system 200) may be an example of the system 102 (FIG. 1). In an example embodiment, the system 200 may be embodied in, or is in direct communication with the system, for example the system 102 (FIG. 1). In an embodiment, the system 200 facilitates in computation of the brand value in a secured manner. The system 200 includes or is otherwise in communication with one or more hardware processors such as a processor 202, one or more memories such as a memory 204, and an I/O interface 206. The processor 202, memory 204, and the I/O interface 206 may be coupled by a system bus such as a system bus 208 or a similar mechanism.
[022] The I/O interface 206 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like The interfaces 206 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a camera device, and a printer. Further, the interfaces 206 may enable the system 102 to communicate with other devices, such as web servers and external databases. The interfaces 206 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the interfaces 206 may include one or more ports for connecting a number of computing systems with one another or to another server computer. The I/O interface 206 may include one or more ports for connecting a number of devices to one another or to another server.
[023] The hardware processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the hardware processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204.
[024] The memory 204 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory 204 includes a plurality of modules 220 and a repository 240 for storing data processed, received, and generated by one or more of the modules 220. The modules 220 may include routines, programs, objects, components, data structures, and so on, which perform particular tasks or implement particular abstract data types. In one implementation, the modules 220 may include programs or coded instructions that supplement applications and functions of the system 200. The repository 240, amongst other things, includes a system database 242 and other data 244. The other data 244 may include data generated as a result of the execution of one or more modules in the other modules 230.
[025] According to the present subject matter, the system 200 is configured to logically aggregate the qualitative parameters and the quantitative parameters associated with brands in a unique way so as to calculate brand value individually from the qualitative parameters and the quantitative parameters. Further, the system 200 is capable of identifying a Net Present Value (NPV) associated with the brands based on the qualitative parameters and the quantitative parameters, where the NPV is indicative of the brand value associated with the plurality of brands. In other words, NPV is the present value of an investment by discounted sum of all cash flows received from a project.
[026] In an embodiment, the system 200 determines qualitative data associated with the plurality of brands by deriving qualitative parameters from a qualitative framework. The qualitative framework includes a plurality of Value Chain Stages and each of the Value Chain stage is divided into number of sub-parameters which define a particular Value Chain Stage. The system 200 is caused to compute a Brand Value Chain (BVC) Score associated with the plurality of Value Chain Stages. In an embodiment, a logical survey methodology may be utilized in computation of the BVC score based on the qualitative parameters.
[027] It will be noted that utilization of a robust survey methodology based on empirical methods facilitates in increasing accuracy and improve the quality of computation. The parameters and the sub-parameters may be based on important attributes of the brand such as the length of the interview/questionnaire based on channel of survey, such as manual, Computer Aided, email and mobile devices. Various modes of surveys may include, Self-Administered Mails, Online surveys, Mobile surveys, and the time allocation for the modes. Some of the methodologies that can be used in designing the survey questions includes, but are not limited to Likert Scale, Reverse Recoding, and Semantic Differential, and so on. The customer awareness can be measured by Likert Scale and Reverse Recoding. Customer attachment and associations to the brand can be measured by Semantic Differential. With the collaboration of right survey method and optimum designing of the length of the questionnaire, the system 200 is able to compute an accurate BVC Score.
[028] In particular, the responses to the survey questions may be in form of general English language, and/or certain ratings. In other words, the responses to the surveys would provide intangible information regarding the brands. The system facilitates in deriving the qualitative parameters from the plurality of sources for the purpose of valuation of brands. Herein, it will be noted that a significant contribution of the system is to convert the intangible responses received from the surveys into meaningful tangible scores. The scores of the parameters are indicative of the contribution of the each of said parameters towards the evaluation of the brands.
[029] Also, for the purpose of valuation of brands, a plurality of criteria and sub-criteria are utilized for ranking alternatives of a decision. In an embodiment, the plurality of criteria and sub-criteria may be provided by the experts of the technology for which brand valuation is to be performed. Here, the criteria are intangible and in order for the criteria to serve as a guide for scoring the alternatives, the system 200 is caused to provide scoring to the said criteria and sub-criteria for the plurality of brands.
[030] In an embodiment, the qualitative data is derived from a plurality of sources. For example, the qualitative data may be derived from channel of survey, such as manual, Computer aided, Email and Mobile. The system 200 is caused to sample the qualitative data associated with a plurality of brands so as to remove bias, if any, from the data. In an embodiment, the system 200 may sample the qualitative data using one or more sampling techniques, including but not limited to, Simple Random Sampling, Cluster Sampling, Systematic sample, and Stratified sampling. Based on sampling, the system 200 may identify one or more discriminative patterns corresponding to a plurality of value chain stages associated with the qualitative analysis. Herein, Simple Random Sampling refers to sampling when a simple random sample of size n (commonly referred to as an SRS) is taken from a population, all possible samples of size n in the population have an equal probability of being selected for the sample. The term ‘Cluster Sampling’ refers to a random selection is made of primary, intermediate and final units from a given population or stratum. There are several stages in which the sampling process can be carried out. The term ‘Systematic sample’ implies sampling formed by selecting one unit at random and then selecting additional units at evenly spaced intervals until the sample has been formed. Stratified sampling refers to sampling, where (1) the universe to be sampled is sub-divided (or stratified) into groups which are mutually exclusive and include all items in the universe, and (2) A simple random sample is then chosen independently from each group.
[031] For the purpose of sampling, the value chain stages may be categorised into various categories, including, awareness, associations, attitudes and familiarity, attachment and activity. In an embodiment, the system may utilize machine learning techniques to identify the discriminative patterns from the qualitative data. Additionally or alternatively, Clustering Methods and Dimensionality Reduction techniques can help in reducing the size of the sample or rather put them in clusters. It organizes the data and helps in modelling.
For example,
?? = ?? (??1, ??2, ??3, …. ????), i=1 to n
[032] Here, X is the output parameter to measure a criteria, for example ‘awareness’ and Y can be ‘input variables’. For a huge amount of qualitative data, clustering techniques facilitates in identifying patterns by identifying clusters in the qualitative data. In certain scenarios, the data collected can be labelled data as well as unlabelled data. The system may be caused to retrieve the labelled data and the unlabelled data from various resources. For example, the system may retrieve qualitative data such as engagement activities of customers of the brands on social networking media; and patterns of Price Premium, Price Elasticity and Market Share. Additionally or alternatively, the system may facilitate in setting up of alerts for brands, information regarding a number of elements of website of the brands, and completing the “missing data” which can be possible when doing surveys.
[033] In an embodiment, the system 200 may identify discriminative patterns corresponding to a plurality of value chain stages based on sampling of the qualitative data from the qualitative data. In an embodiment, the discriminative patterns may be identified by using an Analytical Hierarchal process (AHP) model. The AHP model may be utilized to evaluate score for each comparison of criteria, sub-criteria and brands. In an embodiment, the system may facilitate in creating priority matrices and criteria weights as a part of the AHP model.
[034] In an embodiment, computing the BVC score using the AHP method includes structuring a decision hierarchy comprising a plurality of levels, such that each level includes a plurality of parameters and plurality of sub-parameters corresponding to each level. The system computes a plurality of comparison matrices based on a set of pairwise comparisons between parameters in upper levels of the plurality of levels with the parameters in the levels immediately below in the decision hierarchy. The BVC score is determined based on the plurality of comparison matrices. An example of various brand value chain stages, parameters and sub-parameters is described further with reference to FIGS. 3A-3D.
[035] In addition to the qualitative parameters, the system 200 is caused to compute the quantitative parameters for brand valuation. In an embodiment, in order to compute the quantitative parameters, the system is caused to derive royalty rates associated with the plurality of brands from the second plurality of sources. Herein, the second plurality of sources may include non-singular entities, corporates, agencies, and the like. Herein, the royalty rate is indicative of the potential a brand can earn in future. It is the revenue paid as royalty to the owner of an Intellectual property by a third party who is allowed by the owner to use the Intellectual property. The royalty rate is determined based on the brand equity and is derived from one or more external sources. It will be noted that the fair value of the brand is determined on the basis of the present value of the future royalty receipts that could be obtained from licensing the brand to another party.
[036] Further, the system 200 is caused to determine a covariance between the BVC Score and the royalty rate based on a statistical method to determine a discounted rate associated with the plurality of brands. The discounted rate is indicative of time value of money of the brand inclusive of the risk of the future cash flows. In other words, the discounted rate is the interest that can be earned from a particular investment in future after accounting all the considerable risk. In an embodiment, the covariance between the BVC score and the royalty rate is determined based on the following expression:
Cor(x,y) = ?xy/sx sy
where,
?xy is the covariance between x and y
sx is standard deviation of X,
sy is standard deviation of X
where,
xi and yi = ith term in X variables, and Y variables respectively.
and are Average of X variables and Y variables, respectively
n is sample size.
where,

[037] The system identifies, based on the discounted rate associated with the plurality of brands, a Net Present Value (NPV) of future earnings from the plurality of brands. The NPV is indicative of the brand value associated with the plurality of brands. In an embodiment, the system 200 may be caused to determine the NPV using Royalty relief method. Various steps that may facilitate in calculating brand value from the royalty relief method, includes forecasting an expected revenue based on a market share of a competitor brand and private label data in both domestic and international market. The private label data may be data related to private label sales such as revenue, growth, penetration, and so on. The private label data may be determined based on sales for a category, for instance, in case of instant coffee category, and then comparing said sales with sales of different brands of the instant coffee.
[038] At second step, length of the economic Benefit of the brand is determined. At third step, appropriateness of the comparable royalty rate, which can be obtained from the Third party vendors who have expertise in maintaining the latest royalty rates of the brands, is determined. Further, at step four, risk premiums are included in the royalty rate. Based on the above method, the Quantitative Brand Value is determined.
[039] FIGS. 3A-3D illustrates an example representation of a qualitative framework for brand valuation, in accordance with an example embodiment. As already explained with reference to FIG. 2, the brand value chain framework is illustrative of various value chain stages, criteria, and sub-criteria for various brands.
[040] As previously discussed, a BVC score is computed using the qualitative framework 300. In an embodiment, the system utilized an AHP methodology for computing the BVC score. In an embodiment, the system structures a decision hierarchy comprising a plurality of levels, such that each level includes a plurality of parameters and plurality of sub-parameters corresponding to each level. An example of decision hierarchy having value chain stages, and corresponding parameters and sub-parameters is described below.
[041] For example, the illustrative framework may include value chain stages namely, marketing program investment, customer mind-set, market place conditions, market performance, investor sentiments, and shareholder value. The marketing program investment stage includes parameters such as product, communication, personnel, ad-stock effect, design, advertising, promotion/sponsorship, direct and interactive marketing, and personnel training.
[042] The value chain stage namely, customer mind set may include parameters such as awareness, associations, attitudes and familiarity, attachment, activity; and sub-parameters such as active vs. passive awareness strength, favourability, uniqueness, quality, satisfaction, loyalty, trust, talk about the brand, suggest friends and relatives, follow the promotion.
[043] The value chain stage namely market place conditions, may include parameters such as competitive reactions channel support customer size and profile, and sub-parameters such as effectiveness of the marketing investments of the competitive brands, brand reinforcement and selling effort put forth by selling partners, number and type of customers attracted to the brand, and profitability.
[044] the value chain stage namely market performance, may include parameters such as price premiums, price elasticity, market share, expansion success, cost structure, and profitability; and sub-parameters such as additional price consumers are willing to pay when compared to a non-branded product, demand increase/decrease when the price rise/falls (more elastic responses to the price decrease and inelastic responses to the price increase, market share of the brand in the product category, brand elasticity, entry into new markets, inbuilt plant, and outsourced profit margin.
[045] the value chain stage namely investor sentiments, may include parameters such as market dynamics, growth potential, risk profile, and brand contribution; and sub-parameters such as dynamics of the financial market as a whole (interest rate, investor sentiments etc.), external factors like economic, social, physical and legal environment, brand vulnerability to the external factors, and brand importance to the firm's portfolio.
[046] The value chain stage namely Shareholder Value, may include parameters such as contribution to P/E and Brand contribution to the company's P/E. In an embodiment, upon structuring the decision hierarchy, the system computes a plurality of comparison matrices based on a set of pairwise comparisons between parameters in upper levels of the plurality of levels with the parameters in the levels immediately below in the decision hierarchy. An example of comparison matrix is described further with reference to FIG. 3A.
[047] Referring to FIG. 3A, 3B and 3C, the set of pairwise comparisons between parameters in upper levels of the plurality of levels with the parameters in the levels immediately below in the decision hierarchy, are illustrated. For instance, FIG. 3A illustrates a comparison model illustrating the set of pairwise comparisons between parameters in upper levels of the plurality of levels with the parameters in the levels immediately below in the decision hierarchy. FIG. 3B illustrates the pairwise criteria comparisons between the upper levels and levels immediately below thereof. FIG. 3C illustrates the pairwise sub-criteria comparisons between the upper levels and levels immediately below thereof. Herein, only one set of sub-criteria is shown for the brevity of description. However it will be noted that the sub-criteria comparison is repeated for sub-criteria of each of the brand value chain stages. In the instant example, the sub-criteria pairwise comparison need to be performed for four more value chain stages.
[048] FIG. 3D illustrates brand comparison between the upper levels and levels immediately below thereof. Herein, Brand pairwise comparison is to be repeated for each sub-criteria. For example; under criteria 1 (Brand Awareness) brand wise comparison should be done for Sub-Criteria 1 and Sub-Criteria 2 (i.e., Brand Recall & Brand Recognition). Based on the plurality of comparison matrices, the BVC score is determined. An example of BVC score computed based on the plurality of comparison matrices is illustrated with reference to FIG. 5A-5B. A flow-chart of a method for brand valuation is described further with reference to FIG. 4
[049] FIG. 4 illustrates a flowchart of a method 400 for brand valuation, in accordance with an example embodiment. The method 400 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 400 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. The order in which the method 400 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 400, or an alternative method. Furthermore, the method 400 can be implemented in any suitable hardware, software, firmware, or combination thereof.
[050] In an embodiment, the method 400 depicted in the flow chart may be executed by a system, for example, the system 200 of FIG. 2. In an example embodiment, the system 200 may be embodied in a computing device, for example, the computing device 104 (FIG. 1).
[051] At 402, the method 400 includes aggregating qualitative data and the quantitative data associated with a plurality of brands. The qualitative data may be derived from a first plurality of sources. The first plurality of sources may include surveys, and other online resources. At 404, the method 400 includes identifying, from the qualitative data, one or more discriminative patterns corresponding to a plurality of value chain stages based on sampling of the qualitative data. At 406, the method 400 includes computing a BVC score based on the discriminative patterns corresponding to the plurality of value chain stages. At 408, the method 400 deriving royalty rates associated with the plurality of brands from a second plurality of sources such as such agencies and non-singular entities including data vendors and third party. The royalty rates are indicative of potential earnings of brands, and is determined based on brand equity and. At 410, the method 400 includes determining a covariance between the BVC Score and the royalty rate based on a statistical method to estimate a discounted royalty rate associated with the plurality of brands. The discounted rate associated with a brand is indicative of time value of money of the brand inclusive of risk of the future cash flows. At 412, the method 400 includes identifying, based at least on the discounted royalty rate associated with the plurality of brands, a Net Present Value (NPV) of Future Earnings from the plurality of brands, where the NPV is indicative of the brand value associated with the plurality of brands.
Example scenario:
[052] An example scenario for computing brand value chain score is described here with reference to FIGS. 5A and 5B. In the instant example various criteria for comparison are considered, for instance, awareness, association, and attitudes and familiarity are considered for computing the BVC score, as is illustrated in Table 1. Various sub-criteria for the criteria awareness include Active awareness and passive awareness. Similarly, various sub-criteria for the criteria associations may include strength, favourability and uniqueness. Likewise various sub-criteria for the criteria attitudes and familiarity include quality and satisfaction. Also, a common scale is determined for assigning scores for pairwise comparison. For instance, while performing comparison, the brand that may be equally preferred may be given a score of 1. Equally to moderately preferred brands may be given a score of 2, moderately preferred brands may be given a score of 3, moderately to strongly preferred brands may be given a score of 4, Strongly Preferred brands may be given a score of 5, Strongly to very strongly preferred brands may be given a score of 6, Very Strongly preferred brands may be given a score of 7, Very to extremely Strongly preferred brands may be given a score of 8, Extremely preferred brands may be given a score of 9, and so on.
[053] Now, for criteria comparison, first step is to develop pairwise comparison matrices for the criteria. An example pairwise matrix and corresponding scores for the comparisons are illustrated in Table 2. In the second step, the values in each row are multiplied together and the nth root of the said product is calculated, as in presented in Table 2. In step 3, the aforementioned nth root of product is normalized to obtain appropriate weights. In step 4, priority vectors are computed based on the weights, as shown in Table 2. At step 5, the three matrices to determine the ratings for each decision alternative (or Brand) for each criteria are computed, as illustrated in Table 4. Thereafter, a weighted average rating for each decision alternatives is computed. As shown in Table 5, the priority vectors are multiplied with the values obtained from the qualitative data to determine the final BVC Score. Table 5 illustrates BVC score for three brands, B1, B2 and B3. The brand having highest score is selected as the most preferred brand.
[054] The proliferation of data in size and diversity has made computation of Brand Value very complex. The simplification can only be done through logical and method based analysis using Machine Learning. The CPG domain is vast covering FMCG, Apparel Industry, Consumer Electronics etc. Machine Learning can be very helpful for the brands specifically which have been utilizing the internet based marketing and has vast consumer touch points. In this regard, the disclosed method and system enables in computing brand valuation in a robust manner. A significant outcome of the disclosed method is that the disclosed system enables in efficient integration of qualitative data as well as quantitative data associated with various brands to compute brand valuation. In addition, the disclosed method and system enables combining the qualitative data and the quantitative data in correct proportion, which can be achieved by assigning suitable weights to respective scores obtained from the qualitative data and the quantitative data. The disclosed method and system further enables provisioning of reports and dashboards which may facilitate in comparing brand values, changes to brand values with passage of time, and brand value benchmarking.
[055] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
[056] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[057] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[058] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, non-volatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[059] It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.