Description:FORM 2

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

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:

SYSTEMS AND METHODS GENERATING SALES OPPORTUNITY-HEALTH ASSESSMENT AND PREDICTABILITY BASED ON ASSOCIATED INTENTS

Applicant

Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

Preamble to the description:
The following specification particularly describes the invention and the manner in which it is to be performed.
TECHNICAL FIELD
[001] The disclosure herein generally relates to data analytics, and, more particularly, to systems and methods generating sales opportunity-health assessment and predictability based on associated intents.

BACKGROUND
[002] Selling any solutions (e.g., information technology (IT) solutions, business solutions, and the like) to customers has been a core part of business engagements across Industries. However, applying learning from the past or previous engagements for new opportunities and extracting sales insights has always been a challenge. Sales-Leaders always provide guidance based on historical opportunities of wins and losses but doing this at a large scale is very difficult because of which organizations faces various challenges and this continues to pose risk over competition in the market. Existing approaches are based on statistical machine learning (ML) models. Hence, such approaches become inaccurate when new scenarios are imposed on the ML models.

SUMMARY
[003] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems.
[004] For example, in one aspect, there is provided a processor implemented method for generating explainability for sales qualified opportunities based on associated intents. The method comprises obtaining, via one or more hardware processors, a first set of data and a second set of data in a first format and a second format respectively, wherein the first set of data and the second set of data are specific to one or more sales opportunities associated with an entity; processing, via the one or more hardware processors, the first set of data to obtain a processed first set of data, wherein the processed first set of data comprises an opportunity status, and one or more qualified sales opportunities relevant to the entity; processing, via the one or more hardware processors, the second set of data to obtain the processed second set of data and a plurality of intents associated therebetween; generating, by using a machine learning (ML) model via the one or more hardware processors, a feature value and a feature weight for each feature comprised in the processed first set of data, the processed second set of data, and the plurality of intents; determining, by using the ML model via the one or more hardware processors, one or more sales opportunity impacting parameters based on the feature value and the feature weight, and computing a score for each of the one or more sales opportunity impacting parameters; performing, via the one or more hardware processors, a comparison of the score of each of the one or more sales opportunity impacting parameters with a threshold; and generating, via the one or more hardware processors, an explainability text for the score associated with the one or more sales qualified opportunities based on the comparison, wherein the explainability text is indicative of (i) a probability status, and (ii) an opportunity behaviour associated with a specific sales qualified opportunity.
[005] In an embodiment, the processed second set of data comprises information pertaining to a time period associated with a start of one or more sales qualified opportunities, a current stage of the one or more sales qualified opportunities, one or more intents from the plurality of intents being mapped to the one or more sales qualified opportunities, and a count of intents associated with a current stage of the one or more sales qualified opportunities and a previous stage of the one or more sale qualified opportunities.
[006] In an embodiment, the first format and the second format are different from each other.
[007] In an embodiment, the opportunity status is an active status.
[008] In an embodiment, the ML model is trained using a historical data comprising a plurality of first set of data and a plurality of second set of data in the first format and the second format respectively.
[009] In an embodiment, the one or more sales opportunity impacting parameters comprise at least one of a customer interaction, customer feedback, and a stage transition associated with the one or more sales qualified opportunities.
[010] In another aspect, there is provided a processor implemented system for generating explainability for sales qualified opportunities based on associated intents. The system comprises: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to obtain a first set of data and a second set of data in a first format and a second format respectively, wherein the first set of data and the second set of data are specific to one or more sales opportunities associated with an entity; process the first set of data to obtain a processed first set of data, wherein the processed first set of data comprises an opportunity status, and one or more qualified sales opportunities relevant to the entity; process the second set of data to obtain the processed second set of data and a plurality of intents associated therebetween; generate, by using a machine learning (ML) model, a feature value and a feature weight for each feature comprised in the processed first set of data, the processed second set of data, and the plurality of intents; determine, by using the ML model, one or more sales opportunity impacting parameters based on the feature value and the feature weight, and computing a score for each of the one or more sales opportunity impacting parameters; perform a comparison of the score of each of the one or more sales opportunity impacting parameters with a threshold; and generate an explainability text for the score associated with the one or more sales qualified opportunities based on the comparison, wherein the explainability text is indicative of (i) a probability status, and (ii) an opportunity behaviour associated with a specific sales qualified opportunity.
[011] In an embodiment, the processed second set of data comprises information pertaining to a time period associated with a start of one or more sales qualified opportunities, a current stage of the one or more sales qualified opportunities, one or more intents from the plurality of intents being mapped to the one or more sales qualified opportunities, and a count of intents associated with a current stage of the one or more sales qualified opportunities and a previous stage of the one or more sale qualified opportunities.
[012] In an embodiment, the first format and the second format are different from each other.
[013] In an embodiment, the opportunity status is an active status.
[014] In an embodiment, the ML model is trained using a historical data comprising a plurality of first set of data and a plurality of second set of data in the first format and the second format respectively.
[015] In an embodiment, the one or more sales opportunity impacting parameters comprise at least one of a customer interaction, customer feedback, and a stage transition associated with the one or more sales qualified opportunities.
[016] In yet another aspect, there are provided one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause generating explainability for sales qualified opportunities based on associated intents by obtaining a first set of data and a second set of data in a first format and a second format respectively, wherein the first set of data and the second set of data are specific to one or more sales opportunities associated with an entity; processing the first set of data to obtain a processed first set of data, wherein the processed first set of data comprises an opportunity status, and one or more qualified sales opportunities relevant to the entity; processing the second set of data to obtain the processed second set of data and a plurality of intents associated therebetween; generating, by using a machine learning (ML) model, a feature value and a feature weight for each feature comprised in the processed first set of data, the processed second set of data, and the plurality of intents; determining, by using the ML model, one or more sales opportunity impacting parameters based on the feature value and the feature weight, and computing a score for each of the one or more sales opportunity impacting parameters; performing a comparison of the score of each of the one or more sales opportunity impacting parameters with a threshold; and generating an explainability text for the score associated with the one or more sales qualified opportunities based on the comparison, wherein the explainability text is indicative of (i) a probability status, and (ii) an opportunity behaviour associated with a specific sales qualified opportunity.
[017] In an embodiment, the processed second set of data comprises information pertaining to a time period associated with a start of one or more sales qualified opportunities, a current stage of the one or more sales qualified opportunities, one or more intents from the plurality of intents being mapped to the one or more sales qualified opportunities, and a count of intents associated with a current stage of the one or more sales qualified opportunities and a previous stage of the one or more sale qualified opportunities.
[018] In an embodiment, the first format and the second format are different from each other.
[019] In an embodiment, the opportunity status is an active status.
[020] In an embodiment, the ML model is trained using a historical data comprising a plurality of first set of data and a plurality of second set of data in the first format and the second format respectively.
[021] In an embodiment, the one or more sales opportunity impacting parameters comprise at least one of a customer interaction, a customer feedback, and a stage transition associated with the one or more sales qualified opportunities.

[022] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
[023] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
[024] FIG. 1 depicts an exemplary system for generating explainability for sales qualified opportunities based on associated intents, in accordance with an embodiment of the present disclosure.
[025] FIG. 2 depicts an exemplary flow chart illustrating a method for generating explainability for sales qualified opportunities based on associated intents, using the system of FIG. 1, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[026] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments.
[027] As mentioned earlier, selling any solutions (e.g., information technology (IT) solutions, business solutions, and the like) to customers has been a core part of business engagements across industries. Applying learning from the past or previous engagements for new opportunities and extracting sales insights has always been a challenge. Sales-Leaders always provide guidance based on historical opportunities of wins and losses but doing this at a large scale is very difficult because of which organizations faces various challenges and this continues to pose risk over competition in the market. Further, expertise captured from sales-leaders or representatives in predominantly based on their intuition which is not easily recordable for information dissemination, and thus such information is not accessible at an organization level. Therefore, organizations ask their relevant teams to extract most critical factors that enable driving sales success from their proprietary customer relationship management (CRM) system. Considering that there is a lack of visibility into predicting sales outcome propensity, the challenges to overcome failure to strike a deal with customers continue to persist. For instance, few challenges include, but are not limited to, how to codify winning recipe at scale to accelerate organization growth? How to check quality of opportunity pipeline in an unbiased way? How to continuously evaluate opportunity-health and accelerate the course correction? How to build continual learning-culture within the teams?
[028] Embodiments of the present disclosure provide system and method for generating explainability for sales qualified opportunities based on associated intents. More specifically, the system and method of the present disclosure perform evaluation of win-loss analysis wherein it became evident that certain pursuits would have been won if some course correction was done in advance. More specifically, the analysis is done to provide insights such as where is/are an organization(s) with respect to conversion probability (e.g., risky, moderate, or healthy zone)? Why is/are the organization(s) in this position? (e.g., this is determined comparing sales opportunity against earlier Won-Lost opportunities), What can the organization(s) do to strengthen its position in Opportunity? and the like. The system and method provide insights to above questions by predicting opportunities that are sales qualified, wherein opportunities are analysed on various parameters such as, but are not limited to, competition, geography, opportunity stage, opportunity value, etc. on qualitative and quantitative (business) parameters and predict probability of winning an opportunity. Further, the insights provided by the system and method enable organization(s)/stakeholder to take informed decisions. This may be achieved based on a justification/rational provided to the predicted outcome for a specific opportunity wherein it can help various stakeholders identify shortcomings and improvisation accordingly. With the insights provided by the system, the system further determines how to strengthen the opportunity position by providing early course corrective actions for the opportunities which are in moderate and risky mode. This helps to translate sales opportunity in the healthy zone. The insights and strengthening of opportunity position are achieved by implementing one or more Natural Language Processing (NLP) technique(s) to process received unstructured data into structured and easily retrievable format. The unstructured data is obtained (e.g., from a Customer Relationship Manager) and converted it into a structured format by using NLP (Sentiment analysis/ intent extraction) and further combined with existing structured data. The system and method of the present disclosure further obtain input data associated with one or more sales opportunities (e.g., from CRM) and convert into structured data that is used for predictions by extracting intents from the processed data. Every status associated with opportunities is processed and extracted based on intents and then aligned to specific intents with respective features. These features are combined to predict a score that is compared with a threshold to generate explainability and opportunity behaviour for a given sales qualified opportunity.
[029] Referring now to the drawings, and more particularly to FIGS. 1 through 2, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[030] FIG. 1 depicts an exemplary system 100 for generating explainability for sales qualified opportunities based on associated intents, in accordance with an embodiment of the present disclosure. In an embodiment, the system 100 includes one or more hardware processors 104, communication interface device(s) or input/output (I/O) interface(s) 106 (also referred as interface(s)), and one or more data storage devices or memory 102 operatively coupled to the one or more hardware processors 104. The one or more processors 104 may be one or more software processing components and/or hardware processors. In an embodiment, the hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is/are configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices (e.g., smartphones, tablet phones, mobile communication devices, and the like), workstations, mainframe computers, servers, a network cloud, and the like.
[031] The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.
[032] The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic-random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, a database 108 is comprised in the memory 102, wherein the database 108 comprises information pertaining to sales opportunities associated with an entity (e.g., such as organization, customers, and the like), in various formats (e.g., structured, unstructured, and the like). The database 108 further comprises preprocessed data of the sales opportunities associated with the entity, intents associated with sales opportunities, feature value(s) and feature weight(s) comprised in the preprocessed data, various opportunity impacting parameters, explainability and opportunity behaviour(s) for each qualified sales opportunity and the like. The memory comprises one or more machine learning model(s), one or more deep learning (DL) models, or combinations thereof. The memory 102 further comprises (or may further comprise) information pertaining to input(s)/output(s) of each step performed by the systems and methods of the present disclosure. In other words, input(s) fed at each step and output(s) generated at each step are comprised in the memory 102 and can be utilized in further processing and analysis.
[033] FIG. 2, with reference to FIG. 1, depicts an exemplary flow chart illustrating a method for generating explainability for sales qualified opportunities based on associated intents, using the system 100 of FIG. 1, in accordance with an embodiment of the present disclosure. In an embodiment, the system(s) 100 comprises one or more data storage devices or the memory 102 operatively coupled to the one or more hardware processors 104 and is configured to store instructions for execution of steps of the method by the one or more processors 104. The steps of the method of the present disclosure will now be explained with reference to components of the system 100 of FIG. 1, and the flow diagram as depicted in FIG. 2.
[034] At step 202 of the method of the present disclosure, the one or more hardware processors 104 obtain a first set of data and a second set of data in a first format and a second format respectively. The first set of data and the second set of data are specific to one or more sales opportunities associated with an entity. In an embodiment of the present disclosure, the first format and the second format are different from each other. For instance, the first format comprises a structured data format, and the second format comprises an unstructured data format. Therefore, the first set of data is in the structured data format and the second set of data is in the unstructured data format. In few scenarios, the first set of data may be in the unstructured data format and the second set of data may be in the structured data format. For instance, assuming the first set of data is structured data, and examples of the first set of data comprises but are not limited to, opportunity maturity period, number of weeks from start of an opportunity, one or more opportunities associated with an opportunity, a buying department, a country, a region, a competitor name, a service practice name, and an account name. Assuming the second set of data is unstructured data, and examples of the second set of data comprises but are not limited to, one or more status update posted by various stakeholders involved in engagement of one or more opportunities specific to the entity (e.g., a sales team engagement/interaction during the opportunity life cycle). It is to be understood by a person having ordinary skill in the art that the above structured data format and unstructured data format are exemplary data formats, and such data formats shall not be construed as limiting the scope of the present disclosure. In the present disclosure, the system and method described herein have obtained inputs such as opportunities that are (i) more than ‘x’ weeks old (e.g., say 10 weeks old), and (ii) in qualified stage (stage-03 Expression of Interest (EOI) and/or Request for information (RFI) submitted till stage-06 shortlisted). Various stages for a specific engagement/opportunity may include, say, stage 00 – initiate meeting, stage 01, stage 02, stage 03 – RFI submitted, stage 04 – RFP in progress, stage 05 – RFP submitted, stage-06 – shortlisted, stage 07 – selected, and the like. It is to be understood by a person having ordinary skill in the art or person skilled in the art that such stages are exemplary stages and shall not be construed as limiting the scope of the present disclosure.
[035] At step 204 of the method of the present disclosure, the one or more hardware processors 104 process the first set of data to obtain a processed first set of data. The processed first set of data comprises an opportunity status, one or more qualified sales opportunities relevant to the entity. More specifically, processing (or pre-processing) the first set of data includes, merging data from different sources, checking if an opportunity status, selecting one or more qualified opportunities, and the like. For instance, data from different sources may include information related to sales opportunities, business engagements, interactions associated with various stakeholders (e.g., vendors, customers/clients, potential leads, and the like). Examples of static data include offering, geography, competitor, buying department, unit name, and the like. Examples of run-time data includes but is not limited to, opportunity status description, opportunity life time, previous states, and the like. The static data and the run-time data are merged which arrive from various sources as mentioned above (e.g., from sales team, stakeholders, and the like). In the present disclosure, the opportunity status is an active status (also referred as ‘active opportunity status’ and interchangeably used herein). Example of qualified sales opportunities includes, but is not limited to, 1) opportunity_id - 14728, week – 91, curr_stage – 705 (e.g., say final stage). Since the opportunity is more than 10 weeks old and is in qualified sales stage (705), this opportunity is selected by the system for prediction. Example of non-qualified sales opportunities, includes but is not limited to, 1) opportunity_id - 12345, week – 5, curr_stage – 702, etc. Since the opportunity is less than 10 weeks old and is not in qualified sales stage (702), this opportunity will not be selected the system 100 for prediction. It is to be understood by a person having ordinary skill in the art or person skilled in the art that such exemplary qualified sales opportunities and non-qualified sales opportunities shall not be construed as limiting the scope of the present disclosure. In an embodiment of the present disclosure, opportunities having the active status or qualified sales opportunities are processed further for prediction via the steps 206 through 214 described below. Non-qualified sales opportunities may not be processed for prediction.
[036] At step 206 of the method of the present disclosure, the one or more hardware processors 104 process the second set of data to obtain the processed second set of data and a plurality of intents associated therebetween. The system 100 implements one or more Natural Language Processing (NLP) techniques are known in the art for classifying (or categorizing) the second set of data (e.g., comments, observations, remarks made by various stakeholders) into respective intents which are then used for further processing.
[037] At step 208 of the method of the present disclosure, the one or more hardware processors 104 generating, via a machine learning (ML) model (e.g., XGBoost), a feature value and a feature weight for each feature comprised in the processed first set of data, the processed second set of data, and the plurality of intents. In an embodiment of the present disclosure, the ML model is trained using a historical data comprising a plurality of first set of data and a plurality of second set of data in the first format and the second format respectively. In other words, the ML model is trained with historical data comprising structured and unstructured data pertaining to various opportunities that are specific to various entities (e.g., opportunities within an organization, say company A, opportunities external to an organization, or outside of the organization, and the like). The steps of 206 and 204 are better understood by way of following description:
[038] The processed second set of data comprises information pertaining to a time period associated with a start of one or more sales qualified opportunities, a current stage of the one or more sales qualified opportunities, one or more intents from the plurality of intents being mapped to the one or more sales qualified opportunities, and a count of intents associated with a current stage of the one or more sales qualified opportunities and a previous stage of the one or more sale qualified opportunities. Example of an opportunity with count details associated with various intents (also referred as mapping), include, opportunity_id - xxxxx, week – 91, curr_stage – 705, prev_Awaiting – 3, prev_Basic_Info – 3, prev_customer_meeting_planned (CMP) – 5, prev_Delayed – 2, prev_extension_renewal (ER) – 2, prev_Negative_Outlook (NO) – 1, prev_ Positive_Outlook (PO) – 0, prev_Proactive_proposal (PP) – 0, prev_Submission – 5, prev_Update_from_customer_meeting (UCM)-NO – 0, prev_ Update_from_customer_meeting_planned (UCMP) – 0, prev_work_in_progress (WIP) – 1, curr_Awaiting – 0, curr_Basic_Info – 1, curr_CMP – 0, curr_Delayed – 0, curr_ER – 1, curr_NO – 0, curr_PO – 0, curr_PP – 0, curr_Submission – 0, curr_UCM-NO – 0, curr_UCMP – 1, curr_WIP – 0. It is to be understood by a person having ordinary skill in the art or person skilled in the art that such exemplary qualified counts of various stages in an opportunity/engagement shall not be construed as limiting the scope of the present disclosure. In other words, the system 100 processes the second set of data (e.g., the unstructured data) through feature engineering technique as known in the art and obtains (i) number of weeks from start of the opportunities, (ii) a current stage of the opportunity, (iii) one or more intents from various sentences comprised in the unstructured data wherein the sentences are passed to an intent model (e.g., Random Forest classifier) comprised in the memory 102 to generate the one or more intents, (iv) assignment information of intents to respective opportunity(ies), (v) conversion of the intents and various stages of opportunities into a current intent count, and a previous intent count. The second set of data is processed (or pre-processed) for all stages of the opportunities for all status updates. In other words, the system repeats the processing of the second set of data for all stages and for all the status updates until intent(s) are obtained and mapped to each (qualified) opportunity. In an embodiment of the present disclosure, the one or more intents comprise but are not limited to, (i) submission of one or more proposals, business documents, engagement documents, and the like, (ii) delay in one or more processes, service(s), activity, work item, action item, and the like, (iii) awaiting status, (iv) basic information relating to one or more opportunities, (v) work in progress, (vi) one or more customer feedbacks, (vi) a positive outlook, (vii) a negative outlook, (viii) a customer meeting planned, (ix) an extension (e.g., extension may be related to timeline, service, activity, work item, action item, contract, and the like), (x) proactiveness, and (xi) one or more presentations being or to be provided to various stakeholders. The one or more customer feedbacks comprise positive feedback, negative feedback, neutral feedback, or combinations thereof. Examples of features and associated weight for various stages in the processed first set and second set of data, include but are not limited to, curr_stage - 0.735010426, weeks - 0.592122667, curr_CMP - 0.252507551, curr_Basic Info - 0.048487868, prev_Awaiting - 0.032657631, curr_ER - 0.029342947, curr_Submission - 0.015256974, prev_ER - 0.01360513, curr_WIP - 0.012606994, prev_PO - 0.011317545, prev_Delayed - 0.006336336, prev_NO - 0.004248017, curr_UCM-NO - 0.004111673, prev_CMP - 0.003708833, prev_UCM-NO - 0.003481637, prev_UCMP - 0.001797614, prev_Basic Info - 0.000863511, curr_UCMP - 0.00150099, prev_Submission - 0.002023073, prev_WIP - 0.008957677, curr_NO - 0.023473677, curr_PO - 0.039344618, curr_Awaiting - 0.076953566, curr_Delayed - 0.089517333, static_score - 0.5, and the like. It is to be understood by a person having ordinary skill in the art or person skilled in the art that the above feature and associated weight(s) shall not be construed as limiting the scope of the present disclosure. It is to be understood by a person having ordinary skill in the art or person skilled in the art that the above example of the ML model (e.g., XGBoost) and its implementation as described herein by the system and method shall not be construed as limiting the scope of the present disclosure.
[039] At step 210 of the method of the present disclosure, the one or more hardware processors 104 determine, by using the ML model, one or more sales opportunity impacting parameters based on the feature value and the feature weight and compute a score for each of the one or more sales opportunity impacting parameters. The one or more sales opportunity impacting parameters comprise at least one of a customer interaction, customer feedback, and a stage transition associated with the one or more sales qualified opportunities. It is to be understood by a person having ordinary skill in the art or person skilled in the art that the above examples of the one or more sales opportunity impacting parameters shall not be construed as limiting the scope of the present disclosure. The score may be computed using one or more score computing techniques known in the art. The system 100 computes the score as follows, in one example embodiment. For instance, the category wise scores for opportunity, may include, for opportunity impacting parameter such as Customer Feedback score - 0.1 (wherein Ideal score - 0.135), for opportunity impacting parameter such as Customer Interaction score - 0.05 (wherein Ideal score - 0.106), and for opportunity impacting parameter such as Stage transition score - 0.8 (Ideal score - 1.7), and the like.
[040] At step 212 of the method of the present disclosure, the one or more hardware processors 104 perform a comparison of the score of each of the one or more sales opportunity impacting parameters with a threshold (e.g., threshold for Customer Feedback is 0.135, threshold for interaction is 0.106, and threshold for stage transition is 1.7). 0.35 may be the overall score computed by the system 100 (which could be summation of scores and/or weights associated with the one or more sales opportunity impacting parameters, in one example embodiment). In the above case, 0.1 is compared with 0.135, 0.05 is compared with 0.106 and 0.8 is compared with 1.7 respectively. The threshold may be a pre-defined threshold by the system 100 or an empirically determined threshold based on the implementation and application of the method described herein, in one example embodiment.
[041] At step 214 of the method of the present disclosure, the one or more hardware processors 104 generate an explainability text for the score associated with the one or more sales qualified opportunities based on the comparison, wherein the explainability text is indicative of (i) a probability status, and (ii) an opportunity behaviour associated with a specific sales qualified opportunity. For instance, the ML model (e.g., XGBoost) 100 uses the features comprised in the processed first set and processed second set of data to output explainability text that is indicative of probability text and the opportunity behaviour associated with a specific sales qualified opportunity. The above output serves as an early warning signal indicating a predicted win % of the opportunity. The score that is compared with the threshold indicates an overall probability of qualified sales opportunity to win. The expression ‘explainability text’ refers to rational behind winning (or losing) probability in terms of ‘customer feedback’, ‘customer interaction’, ‘stage transition’, and the like. If the score is 1 for a given opportunity, the explainability text may indicate probability status from customer feedback point of view. If the score is 2 the explainability text may indicate probability status from customer interaction point of view. If the score is 3 the explainability text may indicate probability status from stage transition point of view, and the like. Additionally, the system 100 provides probable actions that can be taken by various stakeholders involved in the qualified sales opportunity. For instance, the system 100 (or the ML model) generates suggestions on what actions or activities can be performed by a specific stakeholder(s) (e.g., say sales team, sales lead, customer relationship manager, business relationship manager, a client partner) to strengthen their position to win the opportunity in hand. For instance, the system 100 may generate RED flags with explainability text and opportunity behaviour which indicate the following: 1) There may be misalignment between sales team and customer stakeholders. If possible, please revisit team composition and ensure that a proper diverse A team is positioned. 2) Revisit and improve relationship mapping, customer interactions and touchpoints and showcase the capabilities and experiences better, 3) Ensure regular engagement with appropriate set of influencers/ coaches / decision makers in the customer organization, 4) Make meetings more insightful and valuable than seeking more information without giving any direction on next steps, 5) Bring in more Relationship Comfort and Confidence on Solution Approach yet to achieve, and the like. Other flags such as GREEN flags with explainability text and opportunity behaviour may include 1) Ensure to keep the winning momentum throughout the pursuit phase, and the like. The steps 202 through 214 can be referred as generating sales opportunity-health assessment and predictability based on associated intents wherein win-loss predicted is generated by the system 100 (e.g., refer suggestions, and various flags generated by the system with explainability text and opportunity behaviour – which indicates scope of improvement and no change to be made in the process of the one or more opportunities.
[042] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[043] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g., hardware means like e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g., using a plurality of CPUs.
[044] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[045] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[046] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[047] It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.
, Claims:We Claim:
1. A processor implemented method, comprising:
obtaining, via one or more hardware processors, a first set of data and a second set of data in a first format and a second format respectively (202), wherein the first set of data and the second set of data are specific to one or more sales opportunities associated with an entity;
processing, via the one or more hardware processors, the first set of data to obtain a processed first set of data (204), wherein the processed first set of data comprises an opportunity status, and one or more qualified sales opportunities relevant to the entity;
processing, via the one or more hardware processors, the second set of data to obtain the processed second set of data and a plurality of intents associated therebetween (206);
generating, via a machine learning (ML) model via the one or more hardware processors, a feature value and a feature weight for each feature comprised in the processed first set of data, the processed second set of data, and the plurality of intents (208);
determining, by using the ML model via the one or more hardware processors, one or more sales opportunity impacting parameters based on the feature value and the feature weight, and computing a score for each of the one or more sales opportunity impacting parameters (210);
performing, via the one or more hardware processors, a comparison of the score for each of the one or more sales opportunity impacting parameters with a threshold (212); and
generating, via the one or more hardware processors, an explainability text for the score associated with the one or more sales qualified opportunities based on the comparison, wherein the explainability text is indicative of (i) a probability status, and (ii) an opportunity behaviour associated with a specific sales qualified opportunity (214).

2. The processor implemented method as claimed in claim 1, wherein the processed second set of data comprises information pertaining to a time period associated with a start of one or more sales qualified opportunities, a current stage of the one or more sales qualified opportunities, one or more intents from the plurality of intents being mapped to the one or more sales qualified opportunities, and a count of intents associated with a current stage of the one or more sales qualified opportunities and a previous stage of the one or more sale qualified opportunities.

3. The processor implemented method as claimed in claim 1, wherein the first format and the second format are different from each other.

4. The processor implemented method as claimed in claim 1, wherein the opportunity status is an active status.

5. The processor implemented method as claimed in claim 1, wherein the ML model is trained using a historical data comprising a plurality of first set of data and a plurality of second set of data in the first format and the second format respectively.

6. The processor implemented method as claimed in claim 1, wherein the one or more sales opportunity impacting parameters comprise at least one or more of a customer interaction, a customer feedback, and a stage transition associated with the one or more sales qualified opportunities.

7. A system (100), comprising:
a memory (102) storing instructions;
one or more communication interfaces (106); and
one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
obtain a first set of data and a second set of data in a first format and a second format respectively, wherein the first set of data and the second set of data are specific to one or more sales opportunities associated with an entity;
process the first set of data to obtain a processed first set of data, wherein the processed first set of data comprises an opportunity status, and one or more qualified sales opportunities relevant to the entity;
process the second set of data to obtain the processed second set of data and a plurality of intents associated therebetween;
generate, via a machine learning (ML) model, a feature value and a feature weight for each feature comprised in the processed first set of data, the processed second set of data, and the plurality of intents;
determine, by using the ML model, one or more sales opportunity impacting parameters based on the feature value and the feature weight, and computing a score for each of the one or more sales opportunity impacting parameters;
perform a comparison of the score of each of the one or more sales opportunity impacting parameters with a threshold; and
generate an explainability text for the score associated with the one or more sales qualified opportunities based on the comparison, wherein the explainability text is indicative of (i) a probability status, and (ii) an opportunity behaviour associated with a specific sales qualified opportunity.

8. The system as claimed in claim 7, wherein the processed second set of data comprises information pertaining to a time period associated with a start of one or more sales qualified opportunities, a current stage of the one or more sales qualified opportunities, one or more intents from the plurality of intents being mapped to the one or more sales qualified opportunities, and a count of intents associated with a current stage of the one or more sales qualified opportunities and a previous stage of the one or more sale qualified opportunities.

9. The system as claimed in claim 7, wherein the first format and the second format are different from each other.

10. The system as claimed in claim 7, wherein the opportunity status is an active status.

11. The system as claimed in claim 7, wherein the ML model is trained using a historical data comprising a plurality of first set of data and a plurality of second set of data in the first format and the second format respectively.

12. The system as claimed in claim 7, wherein the one or more sales opportunity impacting parameters comprise at least one or more of a customer interaction, a customer feedback, and a stage transition associated with the one or more sales qualified opportunities.

Dated this 17th Day of October 2022

Tata Consultancy Services Limited
By their Agent & Attorney

(Adheesh Nargolkar)
of Khaitan & Co
Reg No IN-PA-1086