DESC:FIELD
The present disclosure generally relates to systems and methods for analyzing data. More particularly, the present disclosure relates to a system and a method for client sentiment profiling.
DEFINITIONS
As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used indicate otherwise.
Identification Bias – The term “identification bias” hereinafter refers to a subjective bias in understanding client connect.
User – The term “user” hereinafter refers to a person, a group of people, or an entity which uses the system of the present disclosure for client sentiment profiling.
Client – The term “client” hereinafter refers to a prospective/potential customer who is interested in buying products or services offered by the user of the present disclosure and has the necessary financial resources to buy it.
BACKGROUND
The background information herein below relates to the present disclosure but is not necessarily prior art.
Large account planning embeds as its core the identification of strategic players within the client hierarchy as well as understanding the client’s perception along the buy-sell horizon. Moreover, with leadership connect becoming an increasingly pertinent aspect of any sales outreach, such mapping becomes increasingly critical. This process, at large across the industry, is still being driven manually, thereby inviting heavy chances of subjective errors and bias as the primary call remains that of the account manager.
For example, in B2B sales, the relationship that an organization has with the key executives within the client is of utmost importance. There will always be some executives, namely sponsors and strategic coaches, who would want to favour a particular organization against other competitors and continue or increase the business with that organization. At the same time, there will always be people who are at the other end of the spectrum – people who are unhappy and in favour of the competitors. The ideal scenario for an organization is where the sponsors and strategic coaches vastly outnumber the detractors. In such scenarios, the business of the organization would be more stable and any new opportunities will be given to that organization over anybody else. Accounts with high sponsors tend to grow exponentially and thus are key to any organization.
One of the largest problems faced is in trying to rightly map all players in the buying organization based on their connect with the sales executives. Typically, the sales executives tend to ensure that they not only maintain stable relationships with their sponsors, but also push them within an account to even more strategic roles such that they gain further. At the same time, the sales executives would want to interact more with the detractors to try and win them over and convert them into sponsors. If not, the aim then becomes to ensure that they remain neutral and not sponsor any other organizations. All this begins with identifying the current connect of the executives of buying organization.
As of today, most sales executives don’t have any scientific mapping technique that they can rely upon to ensure that they are well placed within an account. Moreover, the conventional mapping process is manual and largely based on the understanding and the perception of the Sales Executive and client satisfaction surveys. This process is highly susceptible to ‘identification bias’. Further, in case of executive movement, the knowledge transfer always tends to be broken as incoming person has only a limited knowledge about the key executives within the account. Due to this, the companies lose precious connects and thus are challenged by the competitors.
Thus, the prevailing client sentiment mapping processes are largely manual in nature and are based on the intuition of certain individuals. Further, the surveys are conducted once every few years and thus are not dynamic in nature.
There is, therefore, felt a need for a real-time, scientific, and rule based system and method for client sentiment profiling that eliminates the above-mentioned drawbacks.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative.
An object of the present disclosure is to provide a system for client sentiment profiling.
Another object of the present disclosure is to provide a system for client sentiment profiling that can pick up inferences from qualitative inputs provided by a user.
Still another object of the present disclosure is to provide a system for client sentiment profiling that helps in improving client meeting impact.
Yet another object of the present disclosure is to provide a system for client sentiment profiling that helps in improving connect with key executives within a client organization.
Still another object of the present disclosure is to provide a system for client sentiment profiling that helps users in putting together an improved contingency plan.
Yet another object of the present disclosure is to provide a system for accurately mapping sentiment of key executives within an organization.
Still another object of the present disclosure is to provide a system for client sentiment profiling that is dynamic in nature.
Yet another object of the present disclosure is to provide a rules-based system for client sentiment profiling that improves visibility across multiple users in an organization.
Still another object of the present disclosure is to provide a method for client sentiment profiling.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure envisages a system for sentiment profiling of key executives within a client organization into a plurality of categories. The system comprises a server that is communicatively coupled to at least one user interface and at least one client database. The client database includes at least one of archived meeting notes corresponding to the client organization and results of internal and external client surveys. The server is configured to receive an input note associated with a client meeting from a user via the user interface. The server comprises a keyword-based scoring engine, an internal survey based scoring engine, an external survey based scoring engine, and a profiling module. The keyword-based scoring engine is configured to receive at least one of the meeting notes and the input note, and is further configured to traverse through the received notes to identify a pre-determined set of keywords and generate a first score for each of the categories for each of the key executives based on the identified keywords. The internal survey based scoring engine is configured to receive the results of the internal surveys from the client database, and is further configured to generate a second score for each of the categories for each of the key executives based on the received results. The external survey based scoring engine is configured to receive the results of the external surveys, and is further configured to generate a third score for each of the categories for each of the key executives based on the received results. The profiling module is configured to cooperate with the keyword-based scoring engine, the internal survey based scoring engine, and the external survey based scoring engine to receive the first, second and third scores respectively. The profiling module is further configured to perform summation of the received scores to generate a sentiment profile of each of the key executives. The profile can be generated in the form of at least one of a chart, a drawing, a diagram, a table, and a graph.
In an embodiment, the keyword-based scoring engine, the internal survey based scoring engine, the external survey based scoring engine, and the profiling module are implemented using one or more processor(s).
In an embodiment, the input note is received in the form of a text stream. Alternatively, the input note is received in the form of a voice recording. The server includes a voice to text converter configured to convert the received voice recording into a text stream.
In an embodiment, the keyword-based scoring engine includes a repository, a keyword identifier module, and a scoring module. The repository is configured to store the categories, the pre-determined set of keywords associated with each of the categories, and an intensity score corresponding to each of the keywords in the categories. The keyword identifier module is configured to traverse through the received input note and meeting notes, and is further configured to cooperate with the repository to identify the pre-determined set of keywords in the notes. The scoring module is configured to cooperate with the keyword identifier module and the repository to receive the identified keywords and determine intensity score of each of the identified keywords corresponding to each of the categories. The scoring module is further configured to generate the first score for each of the categories for each of the key executives based on the determined intensity scores.
In an embodiment, the input note and the sentiment profiles of each of the executives corresponding to the client organization are stored in the database.
Advantageously, the keyword-based scoring engine, the internal survey based scoring engine, and the external survey based scoring engine employ a machine learning model for strengthening the accuracy of the sentiment profiles based on the sentiment profiles and the input notes stored iteratively after every client meeting in the database.
Advantageously, the system includes a triggering module configured to cooperate with the profiling module to receive the sentiment profiles of each of the key executives, and further configured to generate at least one notification when the sentiment profile of at least one of the key executives changes.
The present disclosure also envisages a method for sentiment profiling of key executives within a client organization into a plurality of categories. The method comprises the following steps:
• communicatively coupling, a server, to at least one user interface and at least one client database, wherein the client database includes at least one of archived meeting notes corresponding to the client organization and results of internal and external client surveys;
• receiving, by the server, an input note associated with a client meeting from a user via the user interface;
• receiving, by a keyword-based scoring engine of the server, at least one of the meeting notes and the input note;
• generating, by the keyword-based scoring engine, a first score for each of the categories for each of the key executives based on the received notes;
• receiving, by an internal survey based scoring engine of the server, the results of the internal surveys;
• generating, by the internal survey based scoring engine, a second score for each of the categories for each of the key executives based on the received results;
• receiving, by an external survey based scoring engine of the server, the results of the external surveys;
• generating, by the external survey based scoring engine, a third score for each of the categories for each of the key executives based on the received results;
• receiving, by a profiling module of the server, the first, second and third scores from the keyword-based scoring engine, the internal survey based scoring engine, and the external survey based scoring engine respectively; and
• performing, by the profiling module, summation of the received scores to generate a sentiment profile of each of the key executives.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
A system and a method for client sentiment profiling of the present disclosure will now be described with the help of the accompanying drawing, in which:
Figure 1 illustrates a block diagram of a system for client sentiment profiling;
Figure 2 illustrates a flow diagram depicting steps involved in a method for client sentiment profiling; and
Figure 3 illustrates a logic flow diagram depicting steps involved in client sentiment profiling.
LIST OF REFERENCE NUMERALS
100 – System
10 – User
20 – User interface
30 – Client database
102 – Server
104 – Keyword-based scoring engine
104a – Repository
104b – Keyword identifier module
104c – Scoring module
106 – Internal survey based scoring engine
108 – External survey based scoring engine
110 – Profiling module
112 – Triggering module
DETAILED DESCRIPTION
Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.
Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details, are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.
The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a,” "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms "comprises," "comprising," “including,” and “having,” are open ended transitional phrases and therefore specify the presence of stated features, elements, modules, units and/or components, but do not forbid the presence or addition of one or more other features, elements, components, and/or groups thereof. The particular order of steps disclosed in the method and process of the present disclosure is not to be construed as necessarily requiring their performance as described or illustrated. It is also to be understood that additional or alternative steps may be employed.
The terms first, second, third, etc., should not be construed to limit the scope of the present disclosure as the aforementioned terms may be only used to distinguish one element, component, region, layer or section from another component, region, layer or section. Terms such as first, second, third etc., when used herein do not imply a specific sequence or order unless clearly suggested by the present disclosure.
One of the largest problems faced in B2B industries is in accurately mapping the sentiment of key executives (i.e. enterprise buyers) within a client organization based on their connect with the sales team. Currently, most sales executives do not have any scientific mapping techniques that they can rely upon to ensure that they are well placed within an account. The conventional processes are mostly manual and based on the understanding and the perception of the sales executive, which is likely to introduce ‘identification bias’. Moreover, in case of executive movement, the knowledge transfer tends to be broken as the incoming person will only have a limited knowledge about the key executives within the account. Due to this, the companies may lose precious connects and be challenged by the competitors. To avoid this, a system (hereinafter referred to as “system 100”) and a method (hereinafter referred to as “method 200”) for real-time client sentiment profiling, of the present disclosure is now being described with reference to Figure 1 through Figure 3. The system 100 and method 200 of the present disclosure helps in identifying the sentiments of key executives within a buyer account and profiling the sentiments of key executives into a plurality of categories in order to identify prospects who will support and sponsor the client into the account and help them close the deal. Accordingly, the categories include (i) strategic coach, (ii) sponsor, (iii) neutral, and (iv) detractor/ anti-sponsor. A strategic coach is the one who provides to the user 10, guidance on strategic direction of Field of Play, information on other strategic players, and information/guidance on elements of situation appraisal and strategy. A sponsor is the one who promotes user’s tenure in Field of Play, champions the user 10, and counters competition’s efforts to develop relationship. A neutral executive is the one who neutral who plays a significant part in user’s relationship with the Field of play. A detractor is the one who works against user’s position in Field of Play and can possibly sponsor user’s competition.
Referring to Figure 1, the system 100 comprises a server 102 communicatively coupled to at least one user interface 20 and at least one client database 30. The client database 30 includes at least one of archived meeting notes corresponding to the client organization and results of internal and external client surveys. The client database 30 can be a client meeting platform (such as Webex, Corus, etc.) that contains the archived meeting notes. Alternatively, the client database 30 can be a backend project management system that stores results of internal and external surveys.
The server 102 is configured to receive an input note associated with a client meeting from a user 10 via the user interface 20. The input note may be a pre-meeting or a post-meeting note. The input note can be received in the form of a text stream. Alternatively, the input note can be received in the form of a voice recording. If the input note is a voice recording, the system 100 triggers a voice to text converter for converting the received voice recording into a text stream.
The server 102 comprises a keyword-based scoring engine 104, an internal survey based scoring engine 106, an external survey based scoring engine 108, and a profiling module 110. The keyword-based scoring engine 104 is configured to receive at least one of the meeting notes and the input note, and is further configured to traverse through the received notes to identify a pre-determined set of keywords and generate a first score for each of the categories for each of the key executives based on the identified keywords. In other words, the keyword-based scoring engine 104 picks up inferences from the qualitative inputs i.e. the input note or the text stream of the audio recording that a user 10 (for example, a sales person) records and generates first scores for each category for each of the executives. The keyword-based scoring engine 104 thus establishes primary landscaping of each executive into the aforementioned given categories based on the presence of pre-determined keywords/phrases in the received notes.
In an embodiment, the keyword-based scoring engine 104 includes a repository 104a, a keyword identifier module 104b, and a scoring module 104c. The repository 104a is configured to store the categories, the pre-determined set of keywords associated with each of the categories, and an intensity score corresponding to each of the keywords in the categories. Thus, each category has its own unique grouping of keywords which are associated with an intensity score. For example, the intensity score associated with a particular word in a particular category may be represented as below.
Intensity Score (A)
Low 1
Medium 2
High 3
Very High 5
The keyword identifier module 104b is configured to traverse through the received input note and meeting notes, and is further configured to cooperate with the repository 104a to identify the pre-determined set of keywords in the notes. The scoring module 104c is configured to cooperate with the keyword identifier module 104b and the repository 104a to receive the identified keywords and determine intensity score of each of the identified keywords corresponding to each of the categories. The scoring module 104c is further configured to generate the first score for each of the categories for each of the key executives based on the determined intensity scores.
The generated first score is a score which is rationalized on a simple index of 10 based on the following formula –
Category score = (Addition of all intensity scores of respective words or phrases/ No of total words or phrases picked) *2
The second part of the system 100 is the internal survey based scoring engine 106. The internal survey based scoring engine 106 is integrated with the backend project management system. The internal survey based scoring engine 106 is configured to receive the results of the internal surveys from the backend project management system, and is further configured to generate a second score for each of the categories for each of the key executives based on the received results. These surveys are conducted in two parts – quantitative with questions that will have a numerical score (say between 1 to 5) as well as MCQ choice questions. The answers and choices to these questions are so mapped such that it creates a persona of the client executive along the aforementioned categories. The options are marked on two aspects:
1. The category that the answers belong to – strategic coach, sponsor, neutral, detractor/anti-sponsor; and
2. The intensity of choice of answers – This parameter is for determining where the selected option/answer lies on an intensity scale from 1 to 5.
Similar to the keyword-based scoring engine 104, the internal survey based scoring engine 106 rationalizes individually across categories and brings the second score on the base of 10. The formula used for determining the second score is – (cumulative intensity score across questions in a respective category/ highest achievable score in a category) * 10.’
The second score includes multiple internal voices rather than just one. In most cases, the sales executive maps a client representative based on his own understanding and without any input from the delivery team that works continuously and on a day-to-day basis with the client organization. This module enables and brings into play this unheard input from the delivery team.
The third part of the system 100 is the external survey based scoring engine 108. The external survey based scoring engine 108 is similar to the internal survey based scoring engine 106 but differs in one large aspect – this is the actual voice of the client and thus carries a lot of weight. Almost all the organizations in the services field often conduct externally monitored Customer Satisfaction Surveys that are done through neutral agencies so as to get a fair feedback from their clients/customers. Unfortunately, this is done only once a year and thus is static in nature. However, due its direct importance, it is plugged into the system 100 and factored in. The external survey based scoring engine 108 receives the results of the external surveys and generates a third score for each of the categories for each of the key executives based on the received results.
These surveys are very similar to the internal surveys. They are also conducted in two parts – quantitative with questions that will have a numerical score (say between 1 to 5) as well as MCQ choice questions. The answers and choices to these questions are so mapped such that it creates a persona of the client executive along the aforementioned categories. The options are marked on two aspects:
1. The category that the answers belong to – strategic coach, sponsor, neutral, detractor/anti-sponsor; and
2. The intensity of choice of answers – This parameter is for determining where the selected option/answer lies on an intensity scale from 1 to 5.
The external survey based scoring engine 108 also rationalizes individually across categories and brings the third score on the base of 10. The formula used for determining the third score is – (cumulative intensity score across questions in a respective category/ highest achievable score in a category) * 10.
The profiling module 110 is configured to cooperate with the keyword-based scoring engine 104, the internal survey based scoring engine 106, and the external survey based scoring engine 108 to receive the first, second and third scores respectively. The profiling module 110 is further configured to perform summation of the received scores to generate a sentiment profile of each of the key executives. The addition across of the first, second and third scores for each of the categories provides a primary score for each category out of 30. In an embodiment, the system 100 charters these primary scores of each of the categories in a spider chart to generate the sentiment profile of a client executive. Alternatively, the profile can be generated in the form of at least one of a drawing, a diagram, a table, and a graph.
In an embodiment, the keyword-based scoring engine 104 may be used in conjunction with the sentiment analyzers available in the market that operate on strings. The sentiment analyzers can provide a learning feedback in order to reiterate the finding of the keyword-based scoring engine 104. If the sentiment analyzers give an extremely varied outcome, the feedback mechanism may trigger Machine Learning (ML) based internal survey and external survey based scoring engines (106, 108) that identify the key variations based on the results of internal as well as external client surveys. Thus, a more secure result is obtained while the system 100 continuously learns from the inputs and variances that comes its way.
In an embodiment, the keyword-based scoring engine 104, the internal survey based scoring engine 106, the external survey based scoring engine 108, and the profiling module 110 are implemented using one or more processor(s). The processor may be a microprocessor, a controller, a microcontroller, or a state machine. The processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
In an embodiment, the input note and the sentiment profiles of each of the executives corresponding to the client organization are stored in the database 30. In an embodiment, the keyword-based scoring engine 104, the internal survey based scoring engine 106, the external survey based scoring engine 108, and the profiling module 110 employ a machine learning (ML) model for strengthening the accuracy of the sentiment profiles based on the sentiment profiles and the input notes stored iteratively after every client meeting in the database 30.
The system 100 can be employed to determine client importance. The client importance factor helps users to focus on important executives in the client hierarchy. For determining important client executives within a client organization, the deals are divided into two buckets. Client importance is largely determined based on two categories – Sponsors and Detractors. The following table/matrix may be used for determination of client importance –

A deal won indicates presence of a strong sponsor or strategic coach in the client hierarchy, whereas a deal lost indicates the presence of a stronger detractor. Using this matrix, organizations can ensure that they maintain stable relationships with their sponsors and interact more with the detractors to try and win them over and convert them into sponsors.
Advantageously, the system 100 includes a triggering module 112 configured to cooperate with the profiling module 110 to receive the sentiment profiles of each of the key executives, and further configured to generate at least one notification when the sentiment profile of at least one of the key executives changes.
In an embodiment, the system 100 is implemented in three layers, a front end layer, a middle layer, and a back end layer. The front end layer includes the user interface 20 for receiving the input note from the user 10. The front end layer may be implemented using an installable application. The back end layer includes the keyword-based scoring engine 104, the internal survey based scoring engine 106, the external survey based scoring engine 108, and the profiling module 110. The back end layer processes and analyses the received input note and performs sentiment profiling of client executives based on the received input note, prior meeting notes and the results of internal and external surveys. The middle layer is implemented as a virtual server which may be configured as a gateway that manages traffic between the front end layer and the back end layer.
The present disclosure also discloses a method 200 for sentiment profiling of key executives within a client organization into a plurality of categories. Referring to Figure 2, the method 200 comprising the following steps:
At step 202, communicatively coupling, a server 102, to at least one user interface 20 and at least one client database 30, wherein the client database 30 includes at least one of archived meeting notes corresponding to the client organization and results of internal and external client surveys;
At step 204: receiving, by the server 102, an input note associated with a client meeting from a user 10 via the user interface 20;
At step 206, receiving, by a keyword-based scoring engine 104 of the server 102, at least one of the meeting notes and the input note;
At step 208, generating, by the keyword-based scoring engine 104, a first score for each of the categories for each of the key executives based on the received notes, wherein the step of generating, by the keyword-based scoring engine 104, the first score includes storing, in a repository 104a the categories, a pre-determined set of keywords associated with each of the categories, and an intensity score corresponding to each of the keywords in the categories; traversing, by a keyword identifier module 104b, through the received input note and meeting notes to identify the pre-determined set of keywords in the notes; receiving, by a scoring engine 104c, the identified keywords from the keyword identifier module 104b; cooperating, by the scoring module 104c, with the repository 104a to determine the intensity score of each of the identified keywords corresponding to each of the categories; and performing, by the scoring module 104c, summation of the determined intensity scores, to generate the first score for each of the categories for each of the key executives.
At step 210, receiving, by an internal survey based scoring engine 106 of the server 102, the results of the internal surveys;
At step 212, generating, by the internal survey based scoring engine 106, a second score for each of the categories for each of the key executives based on the received results;
At step 214, receiving, by an external survey based scoring engine 108 of the server 102, the results of the external surveys;
At step 216, generating, by the external survey based scoring engine 108, a third score for each of the categories for each of the key executives based on the received results;
At step 218, receiving, by a profiling module 110 of the server 102, the first, second and third scores from the keyword-based scoring engine 104, the internal survey based scoring engine 106, and the external survey based scoring engine 108 respectively; and
At step 218, performing, by the profiling module 110, summation of the received scores to generate a sentiment profile of each of the key executives.
Referring to the logic diagram of Figure 3, an exemplary embodiment depicting the working of the system 100 is described below. After a meeting with an enterprise buyer ‘X’, a user ‘Y’ (Sales lead) opens the front end application of the system 100 (step 302), logins to his account (step 304), and records (step 306) a note on the meeting. The system 100 generates a transcript (step 308) of the received recording and analyses (step 310) the text in the generated transcript to generate first scores corresponding to each of the categories (sponsor, anti-sponsor, neutral, and strategic coach) for the enterprise buyer ‘X’. The system 100 then fetches the results of internal and external client surveys and generates second and third scores (step 312) corresponding to each of the categories for the enterprise buyer ‘X’. Based on the generated scores, the system 100 generates a sentiment profile of the enterprise buyer ‘X’ and triggers a dash boarding matrix (step 314) for displaying the same. The generated sentiment profile helps the user ‘Y’ to ensure that the buyer’s sentiment slowly and strategically moves from the detractor/anti-sponsor space to neutral and finally to the sponsor/strategic coach space. The system 100 further generates a notification (step 316) for the user ‘Y’ when a change in the sentiment of the enterprise buyer ‘X’ is detected. Accordingly, the system 100 updates the dashboard (step 318) with the new sentiment profile.
Thus, the system 100 and the method 200 make the process of sentiment mapping accurate, dynamic, and rule based and bring in a scientific flavor to the entire process. The subjectivity from the process of sentiment mapping is eliminated.
The foregoing description of the embodiments has been provided for purposes of illustration and not intended to limit the scope of the present disclosure. Individual components of a particular embodiment are generally not limited to that particular embodiment, but, are interchangeable. Such variations are not to be regarded as a departure from the present disclosure, and all such modifications are considered to be within the scope of the present disclosure.
TECHNICAL ADVANCEMENTS
The present disclosure described herein above has several technical advantages including, but not limited to, the realization of a system and a method for client profiling that:
• can pick up inferences from qualitative inputs provided by a user;
• helps in improving client meeting impact;
• helps in improving connect with key executives within a client organization;
• helps users in putting together an improved contingency plan;
• helps in accurately mapping sentiment of key executives within an organization;
• is dynamic in nature; and
• improves visibility across multiple users in an organization.
The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The foregoing description of the specific embodiments so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.
The numerical values mentioned for the various physical parameters, dimensions or quantities are only approximations and it is envisaged that the values higher/lower than the numerical values assigned to the parameters, dimensions or quantities fall within the scope of the disclosure, unless there is a statement in the specification specific to the contrary.
,CLAIMS:WE CLAIM:

1. A system (100) for sentiment profiling of key executives within a client organization into a plurality of categories, said system (100) comprising:
a server (102) communicatively coupled to at least one user interface (20) and at least one client database (30), said client database (30) having at least one of archived meeting notes corresponding to said client organization and results of internal and external client surveys, said server (102) configured to receive an input note associated with a client meeting from a user (10) via said user interface (20), said server (102) comprising:
a keyword-based scoring engine (104) configured to receive at least one of said meeting notes and said input note, and further configured to traverse through said received notes to identify a pre-determined set of keywords and generate a first score for each of said categories for each of said key executives based on said identified keywords;
an internal survey based scoring engine (106) configured to receive the results of said internal surveys, and further configured to generate a second score for each of said categories for each of said key executives based on said received results;
an external survey based scoring engine (108) configured to receive the results of said external surveys, and further configured to generate a third score for each of said categories for each of said key executives based on said received results; and
a profiling module (110) configured to cooperate with said keyword-based scoring engine (104), said internal survey based scoring engine (106), and said external survey based scoring engine (108) to receive said first, second and third scores respectively, said profiling module (110) further configured to perform summation of said received scores to generate a sentiment profile of each of said key executives.
wherein said keyword-based scoring engine (104), said internal survey based scoring engine (106), said external survey based scoring engine (108), and said profiling module (110) are implemented using one or more processor(s).
2. The system (100) as claimed in claim 1, wherein said input note is received in the form of text stream or voice recording.
3. The system (100) as claimed in claim 2, which includes a voice to text converter (not shown in figures) configured to convert said received voice recording into a text stream.
4. The system (100) as claimed in claim 1, wherein said keyword-based scoring engine (104) includes:
• a repository (104a) configured to store said categories, said pre-determined set of keywords associated with each of said categories, and an intensity score corresponding to each of said keywords in said categories;
• a keyword identifier module (104b) configured to traverse through the received input note and meeting notes, and further configured to cooperate with said repository (104a) to identify said pre-determined set of keywords in said notes; and
• a scoring module (104c) configured to cooperate with said keyword identifier module (104b) and said repository (104a) to receive said identified keywords and determine the intensity score of each of said identified keywords corresponding to each of said categories, said scoring module (104c) further configured to generate said first score for each of said categories for each of said key executives based on said determined intensity scores.
5. The system (100) as claimed in claim 1, wherein said input note and said sentiment profiles of each of said executives corresponding to said client organization are stored in said database (30).
6. The system (100) as claimed in claim 1, wherein said profile is generated in the form of at least one of a chart, a drawing, a diagram, a table, and a graph.
7. The system (100) as claimed in claim 1, which includes a triggering module (112) configured to cooperate with said profiling module (110) to receive said sentiment profiles of each of said key executives, and further configured to generate at least one notification when the sentiment profile of at least one of said key executives changes.
8. The system (100) as claimed in claim 1, wherein said keyword-based scoring engine (104), said internal survey based scoring engine (106), and said external survey based scoring engine (108) employ a machine learning model for strengthening the accuracy of said sentiment profiles based on said sentiment profiles and said input notes stored iteratively after every client meeting in said database (30).
9. A method (200) for sentiment profiling of key executives within a client organization into a plurality of categories, said method (200) comprising the following steps:
• communicatively coupling (202), a server (102), to at least one user interface (20) and at least one client database (30), wherein said client database (30) includes at least one of archived meeting notes corresponding to said client organization and results of internal and external client surveys;
• receiving (204), by said server (102), an input note associated with a client meeting from a user (10) via said user interface (20);
• receiving (206), by a keyword-based scoring engine (104) of said server (102), at least one of said meeting notes and said input note;
• generating (208), by said keyword-based scoring engine (104), a first score for each of said categories for each of said key executives based on said received notes;
• receiving (210), by an internal survey based scoring engine (106) of said server (102), the results of said internal surveys;
• generating (212), by said internal survey based scoring engine (106), a second score for each of said categories for each of said key executives based on said received results;
• receiving (214), by an external survey based scoring engine (108) of said server (102), the results of said external surveys;
• generating (216), by said external survey based scoring engine (108), a third score for each of said categories for each of said key executives based on said received results;
• receiving (218), by a profiling module (110) of said server (102), said first, second and third scores from said keyword-based scoring engine (104), said internal survey based scoring engine (106), and said external survey based scoring engine (108) respectively; and
• performing (220), by said profiling module (110), summation of said received scores to generate a sentiment profile of each of said key executives,
wherein said keyword-based scoring engine (104), said internal survey based scoring engine (106), said external survey based scoring engine (108), and said profiling module (110) are implemented using one or more processor(s).
10. The method (100) as claimed in claim 9, wherein the step of generating (208), by said keyword-based scoring engine (104), said first score includes the following sub-steps:
• storing, in a repository (104a) said categories, a pre-determined set of keywords associated with each of said categories, and an intensity score corresponding to each of said keywords in said categories;
• traversing, by a keyword identifier module (104b), through the received input note and meeting notes to identify said pre-determined set of keywords in said notes;
• receiving, by a scoring module (104c), said identified keywords from said keyword identifier module (104b);
• cooperating, by said scoring module (104c), with said repository (104a) to determine the intensity score of each of said identified keywords corresponding to each of said categories; and
• performing, by said scoring module (104c), summation of said determined intensity scores, to generate said first score for each of said categories for each of said key executives.