METHOD AND SYSTEM FOR IDENTIFYING AT LEAST A PAIR OF ENTITIES FOR A MEETING

Disclosed herein is a system and method for identifying entities from a service provider side and a client side for a meeting. The system identifies a client service having a highest similarity score with the service type required by the client. It then identifies, a first set of service provider and client entities. It further identifies a second set of service provider and client entities based on a plurality of service provider and client parameters respectively such that the second set of service provider and client entities have a highest similarity score vis-à-vis the first set of service provider and client entities respectively. Further, it generates, based on a set of predicted time dependent win-ratios, one or more combinations comprising at least a pair of entities. Each combination is assigned with a success score and at least one combination is selected based on the success score.

TECHNICAL FIELD

The present invention relates to data analysis, and more particularly to analyzing data of an organization to provide an optimize match of entities for attending a meeting.

BACKGROUND OF THE INVENTION

In this competitive world, it becomes utmost important to make best use of the available resources to successfully attend any business opportunity with a client. The first step towards successfully attending the business opportunity is to find a suitable contact, within an organization, who can take the discussion ahead by establishing a successful meeting with the contacts associated with the client as randomly selecting a contact from the organization without considering the needs, likes and dislikes of the client may not yield positive results. It is, therefore, important to understand the needs of the client and thereafter, select best possible contacts from the organization based on—the client needs and the ability of the contacts to successfully attend a meeting.

The challenge is how to identify those contacts internal and external to the organization who would be most suitable for successfully attending the business meetings. Organization stores and manages huge amount data related to client details, employee details, past deals, and the like in a form of structured and unstructured format. However the technical problem is how to analyze such huge data to arrive at some meaningful insight. Specially, in the huge organizations like MNCs, corporates where lot of people leaves and joins the organization, analyzing such huge amount of data flow becomes another challenge.

SUMMARY OF THE INVENTION

The present disclosure overcomes one or more shortcomings of the prior art and provides additional advantages discussed throughout the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.

In one embodiment of the present disclosure, a method for identifying at least a pair of entities for a meeting is disclosed. The method comprises receiving a meeting information between a client and a service provider. The method further comprises determining, based on the meeting information, a service type to be provided to the client by the service provider. The method further comprises identifying a client service, from a plurality of client services provided over a period of time by the service provider and stored in a deal database, having a highest similarity score with the service type to be provided to the client. The method further comprises identifying, from the deal database, a first set of service provider entities and a first set of client entities based on the client service identified to be similar to the service type. The first set of service provider entities and the first set of client entities were involved in executing the client service between the service provider and the client respectively. The method further comprises identifying a second set of service provider entities and a second set of client entities based on a plurality of service provider parameters and a plurality of client parameters respectively such that the second set of service provider entities and the second set of client entities have a highest similarity score vis-à-vis the first set of service provider entities and the first set of client entities respectively. The method further comprises predicting a set of time dependent win-ratios, corresponding to the second set of service provider entities in such a manner that each time dependent win-ratio indicates a capability of a service provider entity of the second set of service provider entities to successfully execute the meeting with the client. Further, the method comprises generating, based on the set of time dependent win-ratios, one or more combinations comprising at least a pair of entities taken from the second set of service provider entities and the second set of client entities. Each combination is assigned with a success score predicting a probability for successfully attending the meeting, and at least one combination is selected based on the success score.

In one embodiment of the present disclosure, a system for identifying at least a pair of entities for a meeting is disclosed. The system comprises a receiving unit configured to receive a meeting information between a client and a service provider. The system further comprises a determination unit configured to determine, based on the meeting information, a service type to be provided to the client by the service provider. The system further comprises a client service identification unit configured to identify a client service, from a plurality of client services provided over a period of time by the service provider and stored in a deal database, having a highest similarity score with the service type to be provided to the client. The system further comprises an entity identification unit is configured to identify, from the deal database, a first set of service provider entities and a first set of client entities based on the client service identified to be similar to the service type. The first set of service provider entities and the first set of client entities were involved in executing the client service between the service provider and the client respectively. The entity identification unit is further configured to identify a second set of service provider entities and a second set of client entities based on a plurality of service provider parameters and a plurality of client parameters respectively such that the second set of service provider entities and the second set of client entities have a highest similarity score vis-à-vis the first set of service provider entities and the first set of client entities respectively. The system further comprises a prediction unit configured to predict a set of time dependent win-ratios, corresponding to the second set of service provider entities in such a manner that each time dependent win-ratio indicates a capability of a service provider entity of the second set of service provider entities to successfully execute the meeting with the client. Further, the system comprises a generation unit configured to generate, based on the set of time dependent win-ratios, one or more combinations comprising at least a pair of entities taken from the second set of service provider entities and the second set of client entities. Each combination is assigned with a success score predicting a probability for successfully attending the meeting, and at least one combination is selected based on the success score.

In one embodiment of the present invention, a non-transitory computer-readable storage medium is disclosed. The medium stored instructions that when processed by a processor cause the system to perform operations. The operations comprise receiving a meeting information between a client and a service provider. The operations further comprise determining, based on the meeting information, a service type to be provided to the client by the service provider. The operations further comprise identifying a client service, from a plurality of client services provided over a period of time by the service provider and stored in a deal database, having a highest similarity score with the service type to be provided to the client. The operations further comprise identifying, from the deal database, a first set of service provider entities and a first set of client entities based on the client service identified to be similar to the service type, such that the first set of service provider entities and the first set of client entities were involved in executing the client service between the service provider and the client respectively. The operations further comprise identifying a second set of service provider entities and a second set of client entities based on a plurality of service provider parameters and a plurality of client parameters respectively such that the second set of service provider entities and the second set of client entities have a highest similarity score vis-à-vis the first set of service provider entities and the first set of client entities respectively. The operations further comprise predicting a set of time dependent win-ratios, corresponding to the second set of service provider entities in such a manner that each time dependent win-ratio indicates a capability of a service provider entity of the second set of service provider entities to successfully execute the meeting with the client. The operations further comprise generating, based on the set of time dependent win-ratios, one or more combinations comprising at least a pair of entities taken from the second set of service provider entities and the second set of client entities, where each combination is assigned with a success score predicting a probability for successfully attending the meeting, and at least one combination is selected based on the success score.

DETAILED DESCRIPTION OF THE INVENTION

The foregoing has broadly outlined the features and technical advantages of the present disclosure in order that the detailed description of the disclosure that follows may be better understood. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure.

The novel features which are believed to be characteristic of the disclosure, both as to its organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present disclosure.

Disclosed herein is a system and method for identifying entities associated with a service provider and a client for a meeting between the service provider and the client. In the highly competitive world of today, it is very important for business organizations/service providers to make the best possible use of the available resources in order to score maximum business opportunities and in turn maximize their profits. Business organizations/service providers often engage in meetings with clients to either pitch their product, or offer solutions to clients based on their needs, etc. However, it may so happen that the entities (or people or connects or person) from the business organization/the service provider that were engaged in a meeting with a client do not understand the likes, dislikes and the needs of the client and were therefore, not able to successfully execute the meeting. This could therefore incur huge losses to the business organization/the service provider and would also hamper the professional growth of the entities engaged in the meeting. It may, however, be understood that an entity from the business organization/the service provider may not be capable of handling different clients. He/she may have been very successful with a certain client but not so much with some other client. For instance, a certain entity under the sales division of the business organization/the service provider might have been successful in handling national clients but not so successfully in handling international clients, or an entity from the legal division of the business organization/the service provider would not have appropriate skills to make a sales pitch to a client. It is, therefore, very crucial to select the best possible entities from the business organization/the service provider based on the needs, likes and dislikes of the client and the ability of various entities within the organization to successfully execute a meeting with a client.

The present disclosure understands this need and provides a system that first understands the kind of service required by a client by matching it with the services provided by the business organization/the service provider in the past and based on the understanding determines entities from the business organization/the service provider that are best suited to execute the meeting with the client. The system also determines entities from the client side that should be contacted for the meeting such that when the chosen entities from the business organization/the service provider side engage in a meeting with the chosen entities from the client, the likelihood of the meeting being successful is very high. For such identification, the system disclosed in the present disclosure analyses data from different perspectives, for example, service provider's perspective, client's perspective, past deal's perspective to find an optimal combination of people from service provider side as well as client side to attend the meeting, which has been explained in upcoming paragraphs of the specification.

FIG. 1shows an exemplary environment100of a system for identifying at least a pair of entities for a meeting, in accordance with an embodiment of the present disclosure. It must be understood to a person skilled in art that the system may also be implemented in various environments, other than as shown inFIG. 1.

The detailed explanation of the exemplary environment100is explained in conjunction withFIG. 2that shows a block diagram200of a system for identifying at least a pair of entities for a meeting, in accordance with an embodiment of the present disclosure. Although the present disclosure is explained considering that the system202is implemented on a server, it may be understood that the system202may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, a cloud-based computing environment. It may be understood that the system202may be accessed by multiple users through one or more user devices228or applications residing on the user devices. In one implementation, the system202may comprise the cloud-based computing environment in which a user may operate individual computing systems configured to execute remotely located applications. Examples of the user devices228may include, but are not limited to, a IoT device, IoT gateway, portable computer, a personal digital assistant, a handheld device, and a workstation. The user devices228are communicatively coupled to the system202through a network226.

In one implementation, the system202may comprise an I/O interface204, a processor206, a memory208and the units210. The memory208may be communicatively coupled to the processor206and the units210. Further, the memory208may store a deal database102and meeting information104. The significance and use of each of the stored quantities is explained in the upcoming paragraphs of the specification. The processor206may 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 processor206is configured to fetch and execute computer-readable instructions stored in the memory208. The I/O interface204may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface204may allow the system202to interact with the user directly or through the user devices228. Further, the I/O interface204may enable the system202to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface204can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface204may include one or more ports for connecting many devices to one another or to another server.

In one implementation, the units210may comprise a receiving unit212, a determination unit214, a client service identification unit216, an entity identification unit218, a prediction unit220, a generation unit222and a similarity score generation unit224. According to embodiments of present disclosure, these units212-224may comprise hardware components like processor, microprocessor, microcontrollers, application-specific integrated circuit for performing various operations of the system202. It must be understood to a person skilled in art that the processor206may perform all the functions of the units212-224according to various embodiments of the present disclosure.

Now, referring toFIG. 1, the environment100shows meeting information104pertaining to a new meeting with a client received by the system202via the receiving unit212through a user device228connected to the system202when a new meeting is scheduled between a service provider and a client. The meeting information104comprises details such as client name (e.g., Pi Inc.), client's business type (e.g., Electronic Equipments), client's requirement (e.g., IC chips), meeting location (e.g., Delhi office) and meeting date and time (e.g., Dec. 10, 2020; 10:00 AM). The meeting information104is stored in the memory208. Based on the meeting information104, the determination unit214determines the service type required by the client (Pi Inc.) in the exemplary environment100. According to the exemplary environment100, the service type may be determined as requirement of IC Chips by the electronic equipment manufacturing company, Pi. Inc.

Once, the service type required by the client has been determined based on the meeting information104, the client service identification unit216identifies a client service provided by the service provider in the past that best matches the service type. For this, client service identification unit216may access the deal database102in which data pertaining to past client services is stored. As shown inFIG. 1, the deal database102comprises details regarding service provider entities, client name and client entities corresponding to client services A, B and C. Apart from the data shown inFIG. 1, the deal database102may store additional data (numeric or text) associated with each client service. Examples of such additional data is presented in table 1.

TABLE 1Additional data associated with the client services.Numeric DataText DataNumber of people bidding for theRegionservice typePricingCompetitors - Service Providerand ClientCompetitor wallet sharePast relationship with serviceproviderCustomer growth trajectoryCustomer relationsBilling rates of Different regionSales regions

Further, apart from the above listed data, revenue, net income and client assets as extracted from the annual reports of a client by using a Named Entity Recognition (NER) technique may also be stored in the deal database102. The numeric data stored in the deal database is used as it is, however, the text data is converted into a numeric (machine-readable) form by employing Label Encoding technique. Further, to match the service type with each of the client services A, B and C stored in the deal database102, cosine similarity technique may be employed. For applying the cosine similarity technique, each of the client services A, B and C stored in the deal database102, and the determined service type is converted into a vector representation by the similarity score generation unit224. The similarity score generation unit224generates client service vector representations, {right arrow over (A)}, {right arrow over (B)} and {right arrow over (C)} corresponding to client services A, B and C. It further generates a service type vector representation (say {right arrow over (S)}) corresponding to the determined service type. The similarity score generation unit224then applies the cosine similarity technique on each of the client service vector representation {right arrow over (A)}, {right arrow over (B)} and {right arrow over (C)} vis-à-vis the service type vector representation {right arrow over (S)}) to generate a plurality of similarity scores.

For instance, to determine the similarity score between the client service vector representation A and service type vector representation {right arrow over (S)}, cosine similarity is applied as—

Similar implementation of cosine similarity technique may be applied to determine the similarity score between the client service vector representation {right arrow over (B)} and service type vector representation {right arrow over (S)} and between the client service vector representation {right arrow over (C)} and service type vector representation {right arrow over (S)}. The similarity scores generated by this technique are usually in the form of decimals between 0 and 1, where ‘1’ denotes the highest match and ‘0’ denotes the lowest match. However, the similarity score can be converted in other forms such as a percentage based on the type of application.

Now, in the exemplary environment100as shown inFIG. 1, the client service A is assumed to have highest similarity with the service type. From the identified client service A, a first set of service provider entities and a first set of client entities are identified by the entity identification unit218as the entities that were involved in providing the client service A to a client X. For instance, John, Mary and Jacob are identified as the “first set of service provider entities” whereas Philip and Peter are identified as the “first set of client entities”.

Further, for the identified first set of service provider entities, the entity identification unit218identifies a plurality of service provider parameters. These parameters may comprise, but not limited to, at least one of designation, band-level, skills, experience, business unit and current availability. Similarly, for the identified first set of client entities, the entity identification unit218identifies a plurality of client parameters. These parameters may comprise, but not limited to, at least one of designation, band-level, skills, experience and business unit. This step is crucial for the system202to understand what kind of entities would be suitable for executing the meeting with the client Pi. Inc. in terms of the kind of skills required by the entities, the required experience, the business unit association of the entities etc. Further, it also helps in indicating the kind of entities that would be suitable to be approached from the client side for the meeting. For instance, in the exemplary environment100, the first set of service provider entities are identified as John, Mary and Jacob. For each of the identified service provider entities, the above-mentioned parameters are considered as shown as an example in table 2.

Based on the above-mentioned service provider parameters, the entity identification unit218understands that for executing the meeting for the determined service type, the service provider entities must include a Sales Manager, a Marketing Manager and a Manager from the Manufacturing Division. Now, the entity identification unit218identifies a “second set of service provider entities” that have a high similarity score against the “first set of service provider entities”—John, Mary and Jacob. For this, again cosine similarity technique is employed. For applying the cosine similarity technique, each of the first set of service provider entities—John, Mary and Jacob are converted into a first set of service provider vector representations by the similarity score generation unit224. Further, a remaining set of entities associated with the service provider, excluding the first set of service provider entities, are also converted into a second set of service provider vector representation. The similarity score generation unit224then applies the cosine similarity technique on each of the first set of service provider vector representations corresponding to John, Jacob and Mary vis-à-vis the second set of service provider vector representations corresponding to the remaining set of service provider entities.

Based on the similarity scores and the current availability status, a second set of service provider entities is identified. For instance, as shown in table 2, the current availability status of John and Mary is listed as “available” while that of Jacob is listed as “Unavailable”. Therefore, since John and Mary are available, the second set of service provider entities include John and Mary. However, since Jacob is not available due to any of the reasons, Julie is identified as a third member of the second set of service provider entities having highest similarity score in comparison to Jacob. Therefore, the second set of service provider entities now include John, Mary and Julie. In one embodiment, the second set of service entities can be same as the first set of service provider entities if each of the first set of service provider entities are listed as available. In another embodiment, the second set of service provider entities may be entirely different or a subset of the first set of service provider entities depending on the current availability status of each of the first set of service provider entities.

Further, similar processing is done by the similarity score generation unit224to identify a second set of client entities corresponding to the client (Pi Inc.) having a highest similarity to the first set of client entities corresponding to client X associated with client service A. This is achieved first generating a first set of client vector representations corresponding to the first set of client entities and generating a second set of client vector representations corresponding to a plurality of client entities corresponding to the client Pi Inc. Further, the cosine similarity technique is applied on the second set of client vector representations vis-à-vis the first set of client vector representations respectively to calculate a plurality of similarity scores in order to identify the second set of client entities. In the exemplary environment100as shown inFIG. 1, Richard and Jane are identified as the second set of client entities corresponding to the client Pi Inc. and having highest similarity scores in comparison to the first set of client entities—Philip and Peter corresponding to client X. Further, in the exemplary environment100, the client Pi Inc. is a new client and therefore, the second set of client entities can neither be same as the first set of client entities and nor be the subset of the first set of client entities. However, in another embodiment if the client Pi Inc. is not a new client, then the second set of client entities may either be same as the first set of client entities or a subset of the first set of client entities.

Once, the second set of service provider entities have been identified by the entity identification unit218, the prediction unit220predicts win-ratio for each of the second set of service provider entities for the current time frame by employing an Auto-Regressive Integrated Moving Average (ARIMA) model. The win-ratio helps in predicting the capability of an entity for successfully executing a meeting. The prediction of the win-ratio for the current time frame is based on the calculated win-ratios for the previous time frames. The time frame can be monthly, quarterly, half-yearly or yearly. Further, in one embodiment the win-ratio may be calculated as the weighted average of the number of meetings divided by number of wins for each of the second set of service provider entities. It may be understood by a skilled person that there may be other methods of calculating the win-ratio than the one described herein. As shown in the exemplary environment100, the win-ratios of the second set of service provider entities—John, Mary and Julie for the current time frame are predicted to be 0.7, 0.8 and 0.6 respectively. This implies that Mary has the highest probability of successfully executing a meeting in comparison to John and Julie.

Further, the generation unit224generates one or more combinations of the second set of service provider entities and the second set of client entities. Each combination is simultaneously assigned a success score based on the predicted win-ratios of each of the second set of service provider entities. The assigned success score predicts a probability of successfully attending the meeting when executed by the entities present in the one or more combinations. For instance, as shown in the exemplary environment100, the generation unit224generates two combinations. The first combination comprises John and Mary from the second set of service provider entities and Richard from the second set of client entities. This combination is assigned a success score of 0.8. Whereas, the second combination comprises Mary and Julie from the second set of service provider entities and Jane from the second set of client entities. This combination is assigned a success score of 0.7. The first combination has a higher success score in comparison to the second combination as the cumulative win-ratio of service provider entities (John and Mary) of the first combination is greater than the cumulative win-ratio of service provider entities (Mary and Julie) of the second combination. Based on the determined success scores of each of the one or more combinations, the service provider can appropriately select the combination that would have the highest probability for successfully attending the meeting.

FIG. 3depicts a method300for identifying at least a pair of entities for a meeting, in accordance with an embodiment of the present disclosure.

As illustrated inFIG. 3, the method300includes one or more blocks illustrating a method for identifying at least a pair of entities for a meeting. The method300may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform specific functions or implement specific abstract data types.

At block302, the method300may include receiving a meeting information between a client and a service provider.

At block304, the method300may include determining, based on the meeting information, a service type to be provided to the client by the service provider.

At block306, the method300may include identifying a client service, from a plurality of client services provided over a period of time by the service provider and stored in a deal database, having a highest similarity score with the service type to be provided to the client.

At block308, the method300may include identifying, from the deal database, a first set of service provider entities and a first set of client entities based on the client service identified to be similar to the service type. The first set of service provider entities and the first set of client entities were involved in executing the client service between the service provider and the client respectively.

At blocks310and312, the method300may include identifying a second set of service provider entities and a second set of client entities based on a plurality of service provider parameters and a plurality of client parameters respectively such that the second set of service provider entities and the second set of client entities have a highest similarity score vis-à-vis the first set of service provider entities and the first set of client entities respectively.

At block314, the method300may include predicting a set of time dependent win-ratios, corresponding to the second set of service provider entities in such a manner that each time dependent win-ratio indicates a capability of a service provider entity of the second set of service provider entities to successfully execute the meeting with the client.

At block316, the method300may include generating, based on the set of time dependent win-ratios, one or more combinations comprising at least a pair of entities taken from the second set of service provider entities and the second set of client entities, and assigning a success score to each of the one or more combinations predicting a probability for successfully attending the meeting.

Computer System

FIG. 4illustrates a block diagram of an exemplary computer system400for implementing embodiments consistent with the present disclosure. It may be understood to a person skilled in art that the computer system400and its components is similar to the system202referred inFIG. 2. In an embodiment, the computer system400may be a peripheral device, which is used for facilitating systematic escalation of information related to an event in an organizational hierarchy. The computer system400may include a central processing unit (“CPU” or “processor”)402. The processor402may comprise at least one data processor for executing program components for executing user or system-generated business processes. The processor402may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

In some embodiments, the processor402may be disposed in communication with a communication network414via a network interface403. The network interface403may communicate with the communication network414. The communication unit may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc.

The communication network414can be implemented as one of the several types of networks, such as intranet or Local Area Network (LAN) and such within the organization. The communication network414may either be a dedicated network or a shared network, which represents an association of several types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the communication network414may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.

The memory405may store a collection of program or database components, including, without limitation, user/application, an operating system, a web browser, mail client, mail server, web server and the like. In some embodiments, computer system may store user/application data, such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle® or Sybase®.

REFERENCE NUMERALS

Reference NumeralDescription100Exemplary environment of a system for identifyingat least a pair of entities for a meeting102Deal database104Meeting Information200Block diagram of the system202System204I/O Interface206Processor208Memory210Units212Receiving Unit214Determination Unit216Client Service Identification Unit218Entity Identification Unit220Prediction Unit222Generation Unit224Similarity Score Generation Unit226Network228User devices300Method for identifying at least a pair of entitiesfor a meeting