DESC:FIELD
The present disclosure relates to the field of Natural Language Processing (NLP). More particularly, the present disclosure relates to a system and a method for automatically managing opportunities and determining health of opportunities using NLP techniques.
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.
Opportunity – The term “opportunity” hereinafter refers to a deal in progress and can include sales prospect, requested service or product, sales volume and a sales probability. The opportunities are typically tracked using Customer Resource Management (CRM) tools.
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 managing opportunities and determining health of the opportunities.
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.
One of the largest problems faced by the Sales Operation teams across all companies is the discipline of data entry by the Sales team. Most core Sales back end systems drive on pure quantitative inputs that are largely driven through entries from legacy systems/methods such as manual entry through excel or manual entry through a Customer Relationship Management (CRM) tool on a web or a mobile platform. As the Sales team is continuously on the move, entry and proper tracking of data is often considered the least important task.
However, this completely breaks the visibility of the supply side and makes it largely a reactive function, thereby preventing them from actively preparing for any incoming orders. Thus, the entire hiring chain gets delayed.
As a direct result, especially in the IT Services market, this leads to drop in revenue as the project timeline gets delayed as the hiring cycle does not get completed within due time. Hence the company ends up leaving not just revenue on the table but also creates discontent within the customer. This makes delivery of the project even more difficult.
Lastly, as there is no data available in the system, no intelligence can be applied by the Sales Operations team – be it finding the probability of winning a deal, the performance of Sales Executives, forecasted revenue and booking among many others such tasks. This only means that most decisions, when it comes to the Sales Engine, are thus not driven by any data backed analytics.
There is, therefore, felt a need for a system and a method for automatically managing opportunities and determining health of opportunities 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 automatically managing opportunities and determining health of opportunities.
Another object of the present disclosure is to provide a system that can pick up inferences from qualitative inputs that a sales person records.
Still another object of the present disclosure is to provide a system that automatically updates fields in a core back end sales engine based on conversations being recorded in meeting notes without any manual interaction.
Yet another object of the present disclosure is to provide a system that facilitates seamless data entry in the core back end sales engine.
Still another object of the present disclosure is to provide a system that saves data entry time.
Still another object of the present disclosure is to provide a dynamic system that can determine health of an opportunity on the basis of audio meeting notes during all the stages of the sales cycle.
Yet another object of the present disclosure is to provide a system for managing opportunities and determining health of opportunities that improves supply side visibility.
Still another object of the present disclosure is to provide a system for managing opportunities and determining health of opportunities that allows sales executives to generate performance metrics for opportunities using data analytics techniques.
Yet another object of the present disclosure is to provide a system for managing opportunities and determining health of opportunities that is error free.
Still another object of the present disclosure is to provide a system for managing opportunities and determining health of opportunities that results in faster revenue realization.
Yet another object of the present disclosure is to provide a system for managing opportunities and determining health of opportunities that improves responsiveness of the entire accounts team.
Still another object of the present disclosure is to provide a system for managing opportunities and determining health of opportunities that improves staffing for Talent Acquisition (TA)/Resource Management Group (RMG) functions due to increased dynamic visibility.
Yet another object of the present disclosure is to provide a method for managing opportunities and determining health of opportunities.
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 managing opportunities in a core back end customer management engine and determining health of the opportunities. The customer management engine includes a database comprising a list of opportunities and a plurality of data fields defining each of the opportunities. The system comprises a server communicatively coupled to at least one user interface. The server is configured to receive an audio recording of a client meeting associated with an opportunity from a user via the user interface. The server comprises a transcription module and a processing engine. The transcription module is configured to generate a transcript of the received recording. The processing engine is configured to cooperate with the transcription module to receive the transcript. The processing engine comprises a keyword identifier, a data manager, and an opportunity health tracker. The keyword identifier is configured to traverse through the received transcript to identify a pre-determined set of keywords in the transcript. The keywords are selected from the group consisting of “new opportunity”, “Total Contract Value (TCV)”, “Date”, “Closure”, “Quarter”, “Postpone”, “Reminder”, “Reduce”, “Increase”, “Margin value”, “Percentage”, and “Account team names”. The data manager is configured to identify the opportunity and manage data fields associated with the identified opportunity in the database based on the identified keywords. The opportunity health tracker is configured to evaluate the health of the opportunity based on the identified keywords. In an embodiment, the transcription module and the processing engine are implemented using one or more processor(s).
In an embodiment, the data manager includes an opportunity identifier and creator module and an updation module. The opportunity identifier and creator module is configured to receive the identified keywords from the keyword identifier, and is further configured to, (i) identify the opportunity based on the received keywords, (ii) determine whether the identified opportunity exists within the database, and (iii) trigger creation of the opportunity if the opportunity does not already exist in the database. The updation module is configured to receive the identified keywords from the keyword identifier, and is further configured to cooperate with the opportunity identifier and creator module to, (i) identify data fields associated with the identified opportunity to be updated in the database based on the received keywords, and (ii) trigger updating of the identified data fields associated with the identified opportunity.
In an embodiment, the opportunity health tracker includes a repository, an extractor unit, a scoring engine, and a health tracking unit. The repository is configured to store a pre-determined set of parameters and scores associated with a plurality of pre-determined values corresponding to each of the parameters. The parameters are selected from the group consisting of “designation of client executive”, “frequency of meetings”, “time between consecutive opportunity levels”, “Customer Satisfaction (CSAT) score”, “Click through rate”, “Change in Total Contract Value (TCV) and Gross Margin (GM)”, and “Number of active competitors”. The extractor unit is configured to extract the values associated with the pre-determined parameters based on the identified keywords from the received transcript. The scoring engine is configured to cooperate with the repository to generate the score for each of the parameters based on the extracted values. The health tracking unit is configured to cooperate with the scoring engine to perform summation of the scores to evaluate the health of the opportunity.
In an embodiment, the processing engine includes a trigger reminder module configured to cooperate with an email scheduler tool, wherein the email scheduler tool includes a schedule of upcoming events and activities. The trigger reminder module is configured to generate at least one trigger based on the schedule to remind the user about the upcoming events and activities.
In an embodiment, the processing engine employs any of an artificial intelligence, a machine learning, or natural language processing technique for managing the data fields of opportunities in the customer management engine and for determining the health of opportunities.
The present disclosure also envisages a method for managing opportunities in a core back end customer management engine and for determining health of the opportunities, wherein the customer management engine includes a database comprising a list of opportunities and a plurality of data fields defining each of the opportunities. The method comprises:
communicatively coupling, a server to at least one user interface;
receiving, by the server, an audio recording of a client meeting associated with an opportunity from a user via the user interface;
generating, by a transcription module of the server, a transcript of the received recording;
receiving, by a processing engine of the server, the transcript from the transcription module;
identifying, by a keyword identifier of the processing engine, a pre-determined set of keywords in the received transcript;
identifying, by a data manager of the processing engine, the opportunity in the database;
managing, by the data manager, the data fields associated with the opportunity based on the identified keywords; and
evaluating, by an opportunity health tracker, the health of the opportunity based on the identified keywords.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
A system and a method for managing opportunities and determining health of opportunities 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 managing and determining health of opportunities;
Figure 2 illustrates a flow chart depicting steps involved in a method for managing and determining health of opportunities; and
Figure 3 illustrates a logic diagram for automatically managing opportunities in a customer management engine.
LIST OF REFERENCE NUMERALS
100 – System
10 – User
20 – User interface
30 – Customer management engine
30a – Database
102 – Server
104 – Transcription module
106 – Processing engine
108 – Keyword identifier
110 – Data Manager
110a – Opportunity identifier and creator module
110b – Updation module
112 – Opportunity health tracker
112a – Repository
112b – Extractor unit
112c – Scoring engine
112d – Health tracking unit
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.
Most core Sales back end engines typically function on pure quantitative inputs that are largely driven through entries from legacy systems/methods such as manual entry through excel or manual entry through a Customer Relationship Management (CRM) tool on a web or a mobile platform. As the back end systems are not updated regularly, proper tracking of data becomes difficult. This completely breaks the visibility of the supply side and prevents them from actively preparing for any incoming orders. Thus, the entire hiring chain gets delayed, making the delivery of the project even more difficult. Further, as there is no data available in the back end engine, no intelligence can be applied, be it for finding the probability of winning a deal, the performance of Sales Executives, forecasting revenue and booking among many others such tasks. This only means that most decisions, when it comes to the Sales Engine, are thus not driven by any data backed analytics. To avoid this, a system (hereinafter referred to as “system 100”) and a method (hereinafter referred to as “method 200”) for automatically managing opportunities in a core back end customer management engine and for determining health of the opportunities, of the present disclosure is now being described with reference to Figure 1 through Figure 3.
The customer management engine 30 includes a database 30a comprising a list of opportunities and a plurality of data fields defining each of the opportunities. The system 100 allows users to automatically update fields in the customer management engine 30. Referring to Figure 1, the system 100 comprises a server 102 communicatively coupled to at least one user interface 20. The server 102 is configured to receive an audio recording of a client meeting associated with an opportunity from a user 10 via the user interface 20. The server 102 comprises a transcription module 104 and a processing engine 106. The transcription module 104 is configured to generate a transcript of the received recording. The processing engine 106 is configured to cooperate with the transcription module 104 to receive the transcript. The processing engine 106 comprises a keyword identifier 108, a data manager 110, and an opportunity health tracker 112. The keyword identifier 108 is configured to traverse through the received transcript to identify a pre-determined set of keywords in the transcript. The keywords can be selected from the group consisting of, but not limited to, “new opportunity”, “Total Contract Value (TCV)”, “Date”, “Closure”, “Quarter”, “Postpone”, “Reminder”, “Reduce”, “Increase”, “Margin value”, “Percentage”, “Account team names”, and the like. The data manager 110 is configured to identify the opportunity associated with the recording and manage data fields associated with the identified opportunity in the database 30a based on the identified keywords. The opportunity health tracker 112 is configured to evaluate the health of the opportunity based on the identified keywords.
In other words, the processing engine 106 picks up inferences from the qualitative inputs i.e. the transcript of the audio recording that a user 10 (for example, a sales person) records and automatically updates the fields in the core back end sales engine 30 without any manual interaction. This enables seamless entry of data and saves a lot of data entry time, especially for on the move Sales executives while ensuring the sales engine 30 stays updated and aligned. Further, the processing engine 30 picks up keywords from the transcript to judge the opportunity health.
In an embodiment, the transcription module 104 and the processing engine 106 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 another embodiment, the processing engine 106 employs any of an artificial intelligence (AI), a machine learning (ML), or natural language processing (NLP) technique for managing the data fields of opportunities in the customer management engine 30 and for determining the health of opportunities.
In an embodiment, the data manager 110 includes an opportunity identifier and creator module 110a and an updation module 110b. The opportunity identifier and creator module 110a is configured to receive the identified keywords from the keyword identifier 108, and is further configured to (i) identify the opportunity based on the received keywords, (ii) determine whether the identified opportunity exists within the database 30a, and (iii) trigger creation of the opportunity if the opportunity does not already exist in the database 30a. Similarly, the updation module 110b is configured to receive the identified keywords from the keyword identifier 108, and is further configured to cooperate with the opportunity identifier and creator module 110a to (i) identify data fields associated with the identified opportunity to be updated in the database 30a based on the received keywords and (ii) trigger updating of the identified data fields associated with the identified opportunity.
In an embodiment, the opportunity health tracker 112 includes a repository 112a, an extractor unit 112b, a scoring engine 112c, and a health tracking unit 112d. The repository 112a is configured to store a pre-determined set of parameters and scores associated with a plurality of pre-determined values corresponding to each of the parameters. In an embodiment, the parameters and scores are stored in the form of a priority matrix that has been pre-defined to rate opportunities to their closure. The parameters can be selected from the group consisting of, but not limited to, “designation of client executive”, “frequency of meetings”, “time between consecutive opportunity levels”, “Customer Satisfaction (CSAT) score”, “Click through rate”, “Change in Total Contract Value (TCV) and Gross Margin (GM)”, and “Number of active competitors”. The extractor unit 112b is configured to extract the values associated with the pre-determined parameters based on the identified keywords from the received transcript. The scoring engine 112c is configured to cooperate with the repository 112a to generate the score for each of the parameters based on the extracted values. The health tracking unit 112d is configured to cooperate with the scoring engine 112c to perform summation of the scores to evaluate the health of the opportunity.
The parameters and scores corresponding to the associated values as stored in the repository 112a are summarized below -
Parameter 1 - Number of Meetings with the Client Executives
The system 100 is integrated with a meeting scheduler tool (such as outlook) and also has an internal meeting tracker. The tracker premise is based on the following two parameters –
Parameter 1.1 - Designation of the Client Executive – The client designation plays an important role in identifying the ability to decide on a particular opportunity. IT Decision makers therefore carry more weightage than others.
Corporate Executives (CxO) Meeting factor (hereinafter denoted as ‘A’) = 7
Other IT Decision Maker factor (hereinafter denoted as ‘B’) = 3
Parameter 1.2 - Frequency of meeting – This is the second important aspect that must be considered while factoring the Health. Continuous meetings indicate activeness in the deal and is directly proportional to a healthy deal. The number of meetings are again divided into two parts –
Frequency of CxO Meeting factor (hereinafter denoted as ‘A1’) –
0 to 2 meetings = 0.25
2 to 5 meetings = 0.5
5+ meetings = 1
Frequency of Other IT Decision Maker factor (hereinafter denoted as ‘B1’)
0 to 8 meetings = 0.25
8 to 14 meetings = 0.5
14+ meetings = 1
The score for this parameter is calculated basis the following formula -
Meeting Frequency Score (1) = (A*A1) + (B*B1)
Parameter 2 - Change in Opportunity Level
All opportunities are classified on the basis of levels between L1 to L5 depending on their stage in the decision cycle (with L5 being the first stage whereas L1 being the final selection). Depending on the deal size, the stages are generally covered over a particular time frame. In the priority matrix, this parameter is divided into two brackets –
Parameter 2.1 - Deals with less than $5M TCV – Typically, the entire sell cycle is covered in 6 months with change in levels happening every month. The factor calculation is taken as –
Change in 2 consecutive stages in less than 31 days (2) = 10
Change in 2 consecutive stages more than 31 days (2) = 5
Parameter 2.2 - Deals with more than $5M TCV – Typically, the entire sell cycle for such large deals is covered in 9 months with change in levels happening every two months or so due to the complexity of the deal. The factor calculation therefore is modified to be taken as –
Change in 2 consecutive stages more than 59 days (2) = 10
Change in 2 consecutive stages more than 59 days (2) = 5
This score shall get reset after change in Level.
This score (2) is hence either 5 or 10 and will directly depend upon the TCV of the deal.
Parameter 3 - Client Sentiment
Client sentiment relates to the image that a particular client has about the company. Naturally, the better the image, the better are the chances of getting a deal. This parameter is broken into 4 aspects –
Parameter 3.1 - External CSAT score – Every company does an external CSAT score which helps them to gauge the quality of delivery as well as the relation with the client. The score is typically drawn on a range of 0 to 10. The following table shows the methodology of rating used –
Range Score (A.3)
0 – 2 0
2 – 4 0.5
4 – 6 1.0
6 – 8 1.5
8 – 10 2.5
Parameter 3.2 - Internal CSAT Score – Similar to the external CSAT, companies on a more frequent level, have internal surveys that help them gauge the quality of the relationship and delivery. Again, the score is typically drawn on a range of 0 to 10. The following table shows the rating methodology used –
Range Score (B.3)
0 – 2 0
2 – 4 0.5
4 – 6 1.0
6 – 8 1.5
8 – 10 2.5

Parameter 3.3 - Project Health Status – All internal quality teams maintain a status of the ongoing projects and typically classify them into three categories – Red (standing for disconnect with client and deliverables remaining incomplete), amber (which typically represents those projects which are behind schedule but still will be completed within time) and green (on track projects which the client is happy with).
The following formula is used for determining the Project Health Status – Project Health Status (C.3) =
([(2.5*No.of green projects)+(1.0*No.of amber projects)+(0*No.of red projects)] )/([Total number of Projects])

Parameter 3.4 - Client Click Through Rate – Client Click through rate tries to capture the number of internal connects within a client which open communication from a particular service provider. Higher click through rate reflects a larger acceptance as well as recognition of the service provider and is therefore a good indicator of the interest within a target company. The following table helps with the scoring for Client Click through rate within a company -
Click Through Rate Score (Denoted by ‘D.3’)
50% and above 2.5
35% to 50% 1.5
15% to 35% 0.5
Less than 15% 0

The total client Sentiment value is based on the following formula –
Client Sentiment Score (3) – A.3+B.3+C.3+D.3
Parameter 4 - TCV and Gross Margin of the Deal
After the L3 stage, continuous reduction in Deal Size and Gross Margin (GM) reflect strong competition which is in turn a detriment to the likelihood of winning the deal. We look at change in GM also from the angle of fulfilment wherein a lower GM is likely to result in fulfilment challenges and ramp up in the project. The defined scoring pattern is therefore tilted more towards GM with Gross Margin Percentage being half of it.
Change in TCV & GM% -
No of Changes Score (A.4)
5 iterations or below 5
5 to 10 iterations 3
More than 10 iterations 2

B. Gross Margin Percentage of the deal (pre loading) –
GM% Score (B.4)
45% & above 5
35% to 45% 3
Less than 35% 2
Final Score (4) – A.4+B.4

Parameter 5 - No of active competitors
The higher the competition, the lesser likelihood of winning the deal is a given. As the deal progresses, the competition is reduced and finally two service providers remain in the bidding. To factor this in, the following scoring is used -
No of Competitors Score (5)
10 and above 2
5 to 10 companies 3
to 5 companies 5
Final 2 7
Final Score - To arrive at the final score, scores related to all the above parameters are summed up. Hence the final score is given as –
Opportunity Health (OH) = 1+2+3+4+5
In an embodiment, the processing engine 106 may be configured to display the computed OH score on a display screen of the user interface 20. In another embodiment, the processing engine 106 may be configured to display a color indicating opportunity healthy (OH) on the basis of a pre-determined color coding. For example, green color may indicate an OH of 30 and above. Amber color may indicate an OH of 18 to 30. Red color may indicate an OH of less than 18.
In an embodiment, the processing engine includes a trigger reminder module configured to cooperate with an email scheduler tool such as Microsoft outlook, which includes a schedule of upcoming events and activities. The trigger reminder module is configured to generate at least one trigger based on the schedule to remind the user 10 about the upcoming events and activities.
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 audio recording from the user 10. The front end layer may be implemented using an installable application. The back end layer includes the processing engine 106 and the core customer management engine 30. The processing engine 106 processes and analyses the received audio recording and updates the core customer management engine 30 based on the analysis. The processing engine 106 also evaluates health of the opportunity based on the received audio recording. 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 managing opportunities in a core back end customer management engine 30 and for determining health of the opportunities. The customer management engine 30 includes a database 30a comprising a list of opportunities and a plurality of data fields defining each of the opportunities. The method 200 comprises the following steps:
At step 202, communicatively coupling, a server 102 to at least one user interface 20;
At step 204: receiving, by the server 102, an audio recording of a client meeting associated with an opportunity from a user 10 via the user interface 20;
At step 206, generating, by a transcription module 104 of the server 102, a transcript of the received recording;
At step 208, receiving, by a processing engine 106 of the server 102, the transcript from the transcription module 104;
At step 210, identifying, by a keyword identifier 108 of the processing engine 106, a pre-determined set of keywords in the received transcript;
At step 212, identifying, by a data manager 110 of the processing engine 106, the opportunity in the database 30a, wherein the step of identifying the opportunity in the database comprises (i) receiving, by an opportunity identifier and creator module 110a, the identified keywords from the keyword identifier 108, (ii) identifying, by the opportunity identifier and creator module 110a, the opportunity based on the received keywords, (iii) determining, by the opportunity identifier and creator module 110a, whether the identified opportunity exists within the database 30a, and (iv) triggering, by the opportunity identifier and creator module 110a, creation of the opportunity if the opportunity does not already exist in the database 30a.
At step 214, managing, by the data manager 110, the data fields associated with the opportunity based on the identified keywords, wherein the step of managing the data fields associated with the opportunity comprises (i) receiving, by an updation module 110b, the identified keywords from the keyword identifier 108, (ii) identifying, by the updation module 110b, data fields to be updated in the database 30a based on the received keywords, and (iii) updating, by the updation module 110b, the identified data fields associated with the identified opportunity, and
At step 216, evaluating, by an opportunity health tracker 112, the health of the opportunity based on the identified keywords, wherein the step of evaluating the health of the opportunity comprises (i) storing, in a repository 112a, a pre-determined set of parameters and scores associated with a plurality of pre-determined values corresponding to each of the parameters, (ii) extracting, by an extractor unit 112b, the values associated with the pre-determined parameters based on the identified keywords from the received transcript, (iii) generating, by a scoring engine 112c, the score for each of the parameters based on the extracted values, and (iv) summing, by a health tracking unit 112d, the scores to evaluate the health of the opportunity.
Referring to the logic diagram of Figure 3, an exemplary embodiment depicting the working of the processing engine 106 is described below. After a meeting with an Enterprise ‘X’, a user ‘Y’ (Sales lead) decides to update the opportunity details in the back end customer management engine 30 so that the accounts team can quickly pick up the actions that are expected of them. The user ‘Y’ opens the front end application of the system 100 (step 302) and records (step 304) the following:
“Had a meeting with Enterprise ‘X’ today in their Atlanta office. The deal TCV now seems to be lesser than what we earlier anticipated as the scope of work has been narrowed. There is likely a reduction of around 10% there. Also, our key competitor is upping the game and is doing a deal even with a hit on his margin. We will also have to reduce our margins by 3% to stay competitive. There is also a likely a chance of 2 more companies being invited to bid in the process which will increase the competition further. The deal is also likely to hit us only in Q4 now as against Q3 and so Mr. ‘Z’ (delivery lead) will have to factor that in his planning. Our earlier project on Salesforce implementation has also been highlighted in our internal quality team as being behind schedule”
The system 100 transcribes (step 306) the received recording and analyses the generated transcript using one of NLP, AI, and ML techniques. During the analysis, the system 100 identifies the opportunity from the transcript and determines (step 308) if the identified opportunity already exists in the CRM database 30a. If the opportunity does not already exist, the system 100 triggers creation of the identified opportunity (step 310) in the CRM database 30a. The system 100 then checks if the pre-determined keywords for updating the CRM database 30a are present in the transcript (step 312) and accordingly triggers updation of data fields (step 314) relating to the opportunity in the CRM database 30a. The system 100 further checks if the pre-determined keywords for triggering the reminder system present in the transcript (step 316), and accordingly triggers the reminder system to generate reminders (step 318).
A sample of data fields updated based on the receive recording is shown in table below.
Earlier Post Recording
TCV -$100 M TCV -$90M
GM% -35% GM% -32%
Closure –Q3 Closure –Q4
Opportunity health Opportunity Health
(green color) (amber color)
The conventional methodology for managing back end customer management engine is heavily manual and depends on the data operator entry. Further, the data entry too is dictated by either entry into directly the Sales Platform or its mobile equivalent through typing. The system 100 and the method 200 of the present disclosure completely eliminate manual entry by uniquely automating entry and modification of Sales data in the core sales engine 30. This process is completely unique as all forms today are of manual type-written entry. The system 100 smartly also decides whether to modify certain entries depending on what words are spoken in meeting notes. It also contains a unique reminder system that can be configured to get triggered in order to remind pre-decided account owners/users 10 in a pre-entered hierarchy. The system 100 also determines opportunity health that helps define the conversion probability of an opportunity. This leads to:
a faster revenue realization, thereby reducing the ramp up time by at least in half in certain cases;
improved system efficiency, thereby saving a lot of manual data entry errors that are common;
ease of updating of details, leaving the Sales Lead to focus on more important tasks;
faster responsiveness from the entire accounts team;
more visibility to the entire team; and
efficient staffing.
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 managing opportunities and determining health of opportunities that:
can pick up inferences from qualitative inputs that a sales person records;
automatically updates fields in a core back end sales engine based on conversations being recorded in meeting notes without any manual interaction;
facilitates seamless data entry in the core back end sales engine;
saves data entry time;
can determine health of an opportunity on the basis of audio meeting notes during all the stages of the sales cycle;
improves supply side visibility;
allows sales executives to generate performance metrics for opportunities using data analytics techniques;
is error free;
results in faster revenue realization;
improves responsiveness of the entire account team; and
improves staffing for Talent Acquisition (TA)/Resource Management Group (RMG) functions due to increased dynamic visibility.
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.
Any discussion of documents, acts, materials, devices, articles or the like that has been included in this specification is solely for the purpose of providing a context for the disclosure. It is not to be taken as an admission that any or all of these matters form a part of the prior art base or were common general knowledge in the field relevant to the disclosure as it existed anywhere before the priority date of this application.
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.
While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.
,CLAIMS:WE CLAIM:
1. A system (100) for managing opportunities in a core back end customer management engine (30) and determining health of said opportunities, said customer management engine (30) having a database (30a) comprising a list of opportunities and a plurality of data fields defining each of said opportunities, said system (100) comprising:
a server (102) communicatively coupled to at least one user interface (20), said server (102) configured to receive an audio recording of a client meeting associated with an opportunity from a user (10) via said user interface (20), said server (102) comprising:
i. a transcription module (104) configured to generate a transcript of said received recording;
ii. a processing engine (106) configured to cooperate with said transcription module (104) to receive said transcript, said processing engine (106) comprising:
o a keyword identifier (108) configured to traverse through the received transcript to identify a pre-determined set of keywords in said transcript;
o a data manager (110) configured to identify said opportunity and manage data fields associated with said identified opportunity in said database (30a) based on the identified keywords; and
o an opportunity health tracker (112) configured to evaluate the health of said opportunity based on the identified keywords,
wherein the transcription module (106) and the processing engine (106) are implemented using one or more processor(s).
2. The system (100) as claimed in claim 1, wherein said data manager (110) includes:
i. an opportunity identifier and creator module (110a) configured to receive said identified keywords from said keyword identifier (108), and further configured to:
i. identify said opportunity based on said received keywords;
ii. determine whether said identified opportunity exists within said database (30a); and
iii. trigger creation of said opportunity if said opportunity does not already exist in said database (30a),
ii. an updation module (110b) configured to receive said identified keywords from said keyword identifier (108), and further configured to cooperate with said opportunity identifier and creator module (110a) to:
i. identify data fields associated with said identified opportunity to be updated in said database (30a) based on said received keywords; and
ii. trigger updating of said identified data fields associated with said identified opportunity.
3. The system (100) as claimed in claim 1, wherein said opportunity health tracker (112) includes:
i. a repository (112a) configured to store a pre-determined set of parameters and scores associated with a plurality of pre-determined values corresponding to each of said parameters;
ii. an extractor unit (112b) configured to extract the values associated with said pre-determined parameters based on the identified keywords from said received transcript;
iii. a scoring engine (112c) configured to cooperate with said repository (112a) to generate said score for each of said parameters based on said extracted values; and
iv. a health tracking unit (112d) configured to cooperate with said scoring engine (112c) to perform summation of said scores to evaluate the health of said opportunity.
4. The system (100) as claimed in claim 1, wherein said processing engine includes a trigger reminder module configured to cooperate with an email scheduler tool, wherein said email scheduler tool includes a schedule of upcoming events and activities, said trigger reminder module configured to generate at least one trigger based on the schedule to remind said user about the upcoming events and activities.
5. The system (100) as claimed in claim 1, wherein said keywords are selected from the group consisting of “new opportunity”, “Total Contract Value (TCV)”, “Date”, “Closure”, “Quarter”, “Postpone”, “Reminder”, “Reduce”, “Increase”, “Margin value”, “Percentage”, and “Account team names”.
6. The system (100) as claimed in claim 4, wherein said parameters are selected from the group consisting of “designation of client executive”, “frequency of meetings”, “time between consecutive opportunity levels”, “Customer Satisfaction (CSAT) score”, “Click through rate”, “Change in Total Contract Value (TCV) and Gross Margin (GM)”, and “Number of active competitors”.
7. The system (100) as claimed in claim 1, wherein said processing engine (106) employs any of an artificial intelligence, a machine learning, or natural language processing technique for managing said data fields of opportunities in said customer management engine (30) and for determining the health of said opportunities.
8. A method for managing opportunities in a core back end customer management engine (30) and for determining health of said opportunities, said customer management engine (30) having a database (30a) comprising a list of opportunities and a plurality of data fields defining each of said opportunities, said method comprising the following steps:
i. communicatively coupling, a server (102) to at least one user interface (20);
ii. receiving, by said server (102), an audio recording of a client meeting associated with an opportunity from a user (10) via said user interface (20);
iii. generating, by a transcription module (104) of said server (102), a transcript of said received recording;
iv. receiving, by a processing engine (106) of said server (102), said transcript from said transcription module (104);
v. identifying, by a keyword identifier (108) of said processing engine (106), a pre-determined set of keywords in said received transcript;
vi. identifying, by a data manager (110) of said processing engine (106), said opportunity in said database (30a);
vii. managing, by said data manager (110), said data fields associated with said opportunity based on the identified keywords; and
viii. evaluating, by an opportunity health tracker (112), the health of said opportunity based on the identified keywords.
9. The method as claimed in claim 8, wherein said step of identifying said opportunity in said database (30a) comprises the following sub-steps:
i. receiving, by an opportunity identifier and creator module (110a), said identified keywords from said keyword identifier (108);
ii. identifying, by said opportunity identifier and creator module (110a), said opportunity based on said received keywords;
iii. determining, by said opportunity identifier and creator module (110a), whether said identified opportunity exists within said database (30a); and
iv. triggering, by said opportunity identifier and creator module (110a), creation of said opportunity if said opportunity does not already exist in said database (30a).
10. The method as claimed in claim 8, wherein said step of managing said data fields associated with said opportunity comprises the following sub-steps:
i. receiving, by an updation module (110b), said identified keywords from said keyword identifier (108);
ii. identifying, by said updation module (110b), data fields to be updated in said database (30a) based on the received keywords; and
iii. updating, by said updation module (110b), said identified data fields associated with said identified opportunity.
11. The method as claimed in claim 8, wherein said step of evaluating the health of said opportunity comprises the following sub-steps:
i. storing, in a repository (112a), a pre-determined set of parameters and scores associated with a plurality of pre-determined values corresponding to each of said parameters;
ii. extracting, by an extractor unit (112b), the values associated with said pre-determined parameters based on the identified keywords from said received transcript;
iii. generating, by a scoring engine (112c), said score for each of said parameters based on said extracted values; and
iv. summing, by a health tracking unit (112d), said scores to evaluate the health of said opportunity.