Claims:CLAIMS:
We claim:
1. A system for generating contextualized competitive insights for a B2B sales deal by using a competitive intelligence, comprising:
a first computing device comprises a competitive intelligence engine configured to collect an unstructured sales information at a deal level to provide competitive intelligence on one or more open deals, whereby the competitive intelligence engine comprises a speech recognition module configured to request one or more questions to end users; and

a cloud server configured to store the sales information of the end user and establish a communication channel with the first computing device by the competitive intelligence engine, whereby the competitive intelligence engine comprises a natural language processing module configured to extract the one or more structured sales tactics from the unstructured sales information obtained from the speech recognition module.

2. The system as claimed in claim 1, wherein the speech recognition module is configured to enable the end user to record the answers to the finite set of questions.

3. The system as claimed in claim 1, wherein the competitive intelligence engine comprises a conversational artificial intelligence module configured to predict and generate one or more questions to the end users.

4. The system as claimed in claim 1, wherein the natural language processing module is configured to categorize the sales actions performed by the end users.

5. The system as claimed in claim 1, wherein the competitive intelligence engine comprises a risk score calculating module configured to calculate the percentage of risk in losing the live deals.

6. The system as claimed in claim 1, wherein the competitive intelligence engine comprises a similarity identifying module configured to segment sales information and performs regression analysis.

7. The system as claimed in claim 1, wherein the competitive intelligence engine comprises a sales tactics recommendation module configured to recommend one or more sales tactics to the end user for the deal.

8. A method for generating contextualized competitive insights for a B2B sales deal by using a competitive intelligence, comprising:

collecting unstructured sales information from one or more end users at a deal level by a speech recognition module;

processing the unstructured sales information into structured sales information by a natural language processing module;

extracting one or more structured sales tactics from the sales information by the natural language processing module at the deal level; and

reducing the risk score and recommending one or more sales tactics to the end users by a sales tactics recommendation module.
, Description:
FORM 2

The Patent Act 1970 (39 of 1970)
&
The Patent Rules, 2005

COMPLETE SPECIFICATION
(SEE SECTION 10 AND RULE 13)

SYSTEM AND METHOD FOR GENERATING CONTEXTUALIZED COMPETITIVE INSIGHTS FOR A B2B SALES DEAL BY USING COMPETITIVE INTELLIGENCE

Applicant Name: M/s. Bridgei2i Analytics Solutions Private Limited

Nationality: INDIAN Company

Address: Umiya Business Bay, Tower 2,2nd Floor, Cessna Business Park, Kadubeesanahalli, Outer Ring Road, Bengaluru, Karnataka-560103.

The following specification particularly describes and ascertains the nature of this invention and the manner in which it is to be performed:-
SYSTEM AND METHOD FOR GENERATING CONTEXTUALIZED COMPETITIVE INSIGHTS FOR A B2B SALES DEAL BY USING COMPETITIVE INTELLIGENCE
TECHNICAL FIELD
[001] The disclosed subject matter relates generally to computing. More particularly, the present disclosure relates to a system and method for generating contextualized competitive insights for a b2b sales deal by using competitive intelligence on sales information on a deal level and providing competitive intelligence on open deals in order to reduce the risk of losing the deal.

BACKGROUND

[002] Sales teams of complex B2B sales organizations need to enhance their customer engagement during deal sales cycles. Customer engagement can be enhanced by surfacing winning and losing strategies for recently closed deals existing sales customer relationship management (CRM) systems or CRM based applications do not capture the win-loss information on a deal level. Organizations have realized the importance of win-loss information that could be captured at a deal level. Hence, several organizations have embarked on a program journey to capture the win-loss information, structure it and use it to improve their probability of winning every deal. However, these programs are unable to scale given the gap in infrastructure and the high degree of changemanagement that is required to achieve the program end state that organizations are looking for.

[003] In the light of aforementioned discussion, there exists a need for a system and method that would overcome or ameliorate the above-mentioned limitations.

SUMMARY

[004] The following presents a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure and it does not identify key/critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

[005] An objective of the present disclosure is directed towards capturing the unstructured information by mimicking a depth interview methodology and speech recognition technique.

[006] Another objective of the present disclosure is directed towards providing the ability to synthesize sales information regarding deal intelligence from the collected unstructured sales information.

[007] Another objective of the present disclosure is directed towards providing the ability to contextualize recommendations for every deal based on the synthesized sales information.

[008] Another objective of the present disclosure is directed towards providing theability to use other available information sources (like external data or unstructured CRM data) to extract sales insightson a deal level.

[009] Another objective of the present disclosure is directed towards encouraging the end users to provide sales information.

[0010] An exemplary aspect of the present disclosure is directed towards a system for capturing sales information and providing competitive intelligence, comprising a first computing device comprises a competitive intelligence engine configured to collect an unstructured sales information at a deal level to provide competitive intelligence on one or more open deals.

[0011] Anotherexemplary aspect of the present disclosure is directed towardsthe competitive intelligence engine comprises a speech recognition module configured to request one or more questions to end users.

[0012] Anotherexemplary aspect of the present disclosure is directed towardsa cloud server configured to store the sales information of the end user and establish a communication channel with the first computing device by the competitive intelligence engine.

[0013] Anotherexemplary aspect of the present disclosure is directed towardsthe competitive intelligence engine comprises a natural language processing module configured to extract the one or more structured sales tactics from the unstructured sales information obtained from the speech recognition module.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In the following, numerous specific details are set forth to provide a thorough description of various embodiments. Certain embodiments may be practiced without these specific details or with some variations in detail. In some instances, certain features are described in less detail so as not to obscure other aspects. The level of detail associated with each of the elements or features should not be construed to qualify the novelty or importance of one feature over the others.

[0015] FIG. 1 is a diagram depicting a schematic representation of a system for generating contextualized competitive insights for a b2b sales deal by using competitive intelligence to enhance trades, in accordance with one or more exemplary embodiments

[0016] FIG. 2 is a block diagram depicting a schematic representation of the competitive intelligence engine 104 shown in FIG. 1, in accordance with one or more exemplary embodiments.

[0017] FIG. 3A is an example screen depicting a closure interview screen of sales information, in accordance with one or more exemplary embodiments.

[0018] FIG. 3B is an example screen depicting a response screen, in accordance with one or more exemplary embodiments.

[0019] FIG. 3C-3D are example screens depicting recommendations screens, in accordance with one or more exemplary embodiments.

[0020] FIG. 4 is a flowchart depicting an exemplary method for reducing risk score in live deals, in accordance with one or more exemplary embodiments.

[0021] FIG. 5 is a flowchart depicting an exemplary method for a historic deal, in accordance with one or more exemplary embodiments.

[0022] FIG. 6 is a flowchart depicting an exemplary method for a historic deal, in accordance with one or more exemplary embodiments.

[0023] FIG. 7 is a block diagram illustrating the details of a digital processing system in which various aspects of the present disclosure are operative by execution of appropriate software instructions.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0024] It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

[0025] The use of “including”, “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item. Further, the use of terms “first”, “second”, and “third”, and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

[0026] Referring to FIG. 1 is a diagram 100 depicting a schematic representation of a system for generating contextualized competitive insights for a b2b sales deal by using competitive intelligence to enhance trades, in accordance with one or more exemplary embodiments. The environment 100 may include, first computing device 102, a network 106 and a cloud server 108.The first computing device 102 may include acompetitive intelligence engine104. The first computing device 102 may include, but not limited to, a computer workstation, an interactive kiosk, and a personal mobile computing device such as a digital assistant, a mobile phone, a laptop, and storage devices, backend servers hosting database and other software and the like. The first computing device 102 may be operated by the end users. The end users may include, but not limited to, sales representativesof the organization, sales persons, business persons,competitors and so forth.

[0027] The first computing device 102 may include an competitive intelligence engine 104 which is accessed as mobile applications, web applications, software that offers the functionality of accessing mobile applications, and viewing/processing of interactive pages, for example, are implemented in the first computing device 102 as will be apparent to one skilled in the relevant arts by reading the disclosure provided herein.

[0028] The competitive intelligence engine 104 may be downloaded from the cloud server 108. For example, the modulemay be any suitable applications downloaded from, GOOGLE PLAY® (for Google Android devices), Apple Inc.'s APP STORE® (for Apple devices, or any other suitable database. In some embodiments, the modulemay be software, firmware, or hardware that is integrated into the first computing devices 102.

[0029] The network 106 may include but not limited to, an Internet of things (IoT network devices), an Ethernet, a wireless local area network (WLAN), or a wide area network (WAN), a Bluetooth low energy network, a ZigBee network, a WIFI communication network e.g., the wireless high speed internet, or a combination of networks, a cellular service such as a 4G (e.g., LTE, mobile WiMAX) or 5G cellular data service, a RFID module, a NFC module, wired cables, such as the world-wide-web based Internet, or other types of networks may include Transport Control Protocol/Internet Protocol (TCP/IP) or device addresses (e.g. network-based MAC addresses, or those provided in a proprietary networking protocol, such as Modbus TCP, or by using appropriate data feeds to obtain data from various web services, including retrieving XML data from an HTTP address, then traversing the XML for a particular node) and so forth without limiting the scope of the present disclosure.

[0030] The competitive intelligence engine 104 may be configured to collectand harness the sales information at a deal level.The deal level may include, but not limited to, open deal, closed deal, historic deal, live deal, and so forth. An open or live deal may refer to an ongoing deal. An historic or closed deal may refer to a completed deal. The sales information may include, but not limited to, win deal, loss deal, past and present sales, past and present marketing efforts, customer-related information and/or marketing-related information, won opportunities information of the sales, lossopportunities information of the sales and so forth. The competitive intelligence engine 104 may be configured to structure thesales information and federate the sales information as competitive intelligence on every open deal. Thecompetitive intelligence engine104 may also be configured to contextualize sales information for the open deals. The competitive intelligence engine 104 may be configured to provide an optimal set of sales tactics for every deal to reduce the risk of losing the live deals.The cloudserver108 may be configured to supply data in response access from the competitive intelligence engine 104.

[0031] Referring to FIG. 2 is a block diagram 200 depicting a schematic representation of the competitive intelligence engine 104 shown in FIG. 1, in accordance with one or more exemplary embodiments. The competitive intelligence engine 104 includes a speech recognition module 202, a conversational artificial intelligence module 204, a natural language processing module 206, anda risk score calculating module 208, asimilarity identifying module 210, a sales tactics recommendation module 212, and a central database 214.

[0032] The bus 201 may include a path that permits communication among the modules of the competitive intelligence engine 104. The speech recognition module 202 may be configured to capture the sales information by mimicking a depth interview methodology at a deal level. The speech recognition module 202 may provide a high level of accuracy during different usage scenarios. The usage scenarios may include, but not limited to, providing speech in an office space, providing speech in a noisy environment, or providing speech while driving, and so forth. The speech recognition module 202 may be configured to able to capture sales terminologies that are specific to a company or industry context (For example, recognizing incorrectly transcribed product names). The speech recognition module 202 may be configured to enrichunstructured texts with the right sales jargons and terminologies that are specific to a company or industry context (For example – recognizing incorrectly transcribed product names).The speech recognition module 202 may also be configured identify incorrectly transcribed words by the standard voice to text application programming interface. The speech recognition module 202 may also be configured to maximize the sales information on the deal by dynamically designing a depth interview. The speech recognition module 202 may be configured togenerate a finite set of questions to the end users to obtain the maximum sales information.The speech recognition module 202 may also be configured toenable the end user to record the answers to the finite set of questions. The finite set of questions may include, but not limited to, What was your strategy on this deal ?, What was competitors strategy on this deal, What were reasons why you won this deal ?, What was your strategy on this deal ?, What was competitors strategy on this deal?, What were reasons why we lost this deal ? What collaterals did you use on this tactic ?, What people did you engage on this tactics ?, What teams did you engage on this tactic ?, What references did you use on this tactic ? Can you provide the specific number on this tactic?.Based on the user’s sales information, the conversational artificial intelligence module 204 may be configured to predict and generatethe finite set of questions to the end users to provide information whether a deal would be successful or not.

[0033] The speech recognition module 202 may be configured to obtain thesales informationin the form of unstructured textsat a deal level.The natural language processing module 206 may be configured to extract the structured sales tactics from the unstructured sales information obtained from the speech recognition module 202. The natural language processing module 206 may be configured to extract sales tactics at multiple levels (for example, three levels). For example, the first level may be a category of sales tactics, the second level may be a specific sales actions within a category and the third level may be subjects and objects that provide specificity to this action. The natural language processing module 206 may be configured to categorize the sales actions performed by the end users. Therisk score calculating module 208 may be configured to calculate the percentage of risk in losing the live deals. The similarity identifying module 210 may be programmed to segment sales information and perform various regression analysis. The methodology takes into consideration various aspects of the deal like recency of a deal, deal frequency, monetary value, firmographics, commercial construct, and so forth.

[0034] The sales tactics recommendation module 212 may be configured to recommend a finite set of sales tactics for the deal under consideration. The sales tactics may include the overall strategy that may include reference to collaterals, teams or people, communicating advantages in terms of product features or operational efficiency, etc. The sales tactics recommendation module 212 may be configured to simulate scenarios of usage of a single or combination of sales tactics. The sales tactics recommendation module 212 may also be configured to calculate the risk score for each of these scenarios. The scenario with the highest reduction in risk score is provided as a recommendation.The sales tactics recommendation module 212 may be able to adjust to data sparsity and density. The sales tactics recommendation module 212 mayre-tuned based on availability of newer transcripts information as well as newer sales tactics that may have been identified.A central database 214 may be configured to store the sales information, usage scenarios, risk score, deal level, sales tactics and so forth.

[0035] Referring to FIG. 3Ais an example screen 300a, depicting a closure interview screen of sales information, in accordance with one or more exemplary embodiments. The screen 300a includes a question 302a, a record option 304, a previous question option 306, a next question option 308, a review and submit option 310, updates option 312, my deals option 314, and my competitor’s option 316.

[0036] The question 302a may be generated by the conversational artificial intelligence module 204. The record option 304 may be configured to allow the end user to record the answers to the questionsgenerated by the conversational artificial intelligence module 204. The previous question option 306 may be configured to enable the end user to view the previous question. The next question option 308 may be configured to enable the end user to view the next question. The review and submit option 310 may be configured to enable the end users to review and edit the answers for the questions before submitting. The updates option 312 may be configured to enable the end user to view the deal updates. The deal updates may include, but not limited to, won the deal opportunity, lost the deal opportunity, and so forth. My deals option 314 may be configured to enable the end user to view the end user deals. My competitor’s option 316 may be configured to enable the end user to view the competitors of the end users.

[0037] Referring to FIG. 3Bis an example screen 300b, depicting a response screen, in accordance with one or more exemplary embodiments. The screen 300b includes anedit option 318, a submit option 320, and questions 302b-302c. The edit option 318 may be configured to enable the end users to edit the answers for the questions. The submit option 320 may be configured to enable the end users to submit the answers to the questions. The questions 302b-302c are may be the set of questions queried by the competitive intelligence engine 104.

[0038] Referring to FIG. 3C-3Dare example screens 300c-300d, depicting recommendations screen, in accordance with one or more exemplary embodiments. The screen 300cincludes a risk score 322, and a recommendations screen 324.The risk score 318 may be configured to display the percentage of losingthe deal.The recommendations screen 320 may be configured to display the sales tactics of the end users. The screen 300c includes a similar won deals screen 326, a similarity score 328. The similar won deals screen 326 may be configured to depict the similarity score 328and the similar won deals of the end users.

[0039] Referring to FIG. 4 is a flowchart 400 depicting an exemplary method for capturing sales information and providing competitive intelligence, in accordance with one or more exemplary embodiments. As an option, the method 400 is carried out in the context of the details of FIG. 1, FIG. 2, and FIG. 3. However, the method 400 is carried out in any desired environment. Further, the aforementioned definitions are equally applied to the description below.

[0040] The exemplary method 400 commences at step 402, collecting the unstructured sales information from the end users at a deal level by the speech recognition module. Thereafter at step 404, processing the unstructured sales information into structured sales information by thenatural language processing module.Thereafter at step 406, extracting the structured sales tactics from the sales information by the natural language processing module at the deal level. Thereafter at step 408, reducing the risk score and recommending the sales tactics to the end users by the sales tactics recommendation module.

[0041] Referring to FIG. 5is a flowchart 500 depicting an exemplary method for a historic deal, in accordance with one or more exemplary embodiments. As an option, the method 400 is carried out in the context of the details of FIG. 1, FIG. 2, FIG. 3 and FIG. 4. However, the method 500 is carried out in any desired environment. Further, the aforementioned definitions are equally applied to the description below.

[0042] The exemplary method 500 commences at step 502, identifying the deals that are closed in the first computing device by the competitive intelligence engine. Thereafter at step 504, capturing the sales information by the conversational artificial intelligence module through a series of questions that mimic a depth interview. Thereafter at step 506, extracting the sales tactics from a finite pool of pre-identified sales tactics by the natural language processing module. Thereafter at step 508, generating the sales information against historical closed deals in the central database.

[0043] Referring to FIG. 6is a flowchart 600 depicting an exemplary method for reducing risk score in live deals, in accordance with one or more exemplary embodiments. As an option, the method 600 is carried out in the context of the details of FIG. 1, FIG. 2, FIG.3, FIG.4 and FIG. 5. However, the method 600 is carried out in any desired environment. Further, the aforementioned definitions are equally applied to the description below.

[0044] The exemplary method 600 commences at step 602, capturing the deals that are live in the first computing device. Thereafter at step 604, Determining whether any change in the parameters on the live deals are identified by the competitive intelligence engine. If answer to the step 604 is yes, processing the sales information and generating the sales information featuresby the competitive intelligence engine. If answer to the step 604 is no, the method redirects to the step 602. Thereafter at step 608, identifying the historic deals that are similar to the live deals by the competitive intelligence engine. Thereafter at step 610, generating a similarity score by the competitive intelligence engine. Thereafter at step 612, calculating the risk score associated with the live deals and factors effecting risk by the competitive intelligence engine. Thereafter at step 614, identifying the set of sales tactics to reduce risk of losing thelive deals by the competitive intelligence engine.

[0045] Referring to FIG. 7 is a block diagram 700 illustrating the details of a digital processing system 700 in which various aspects of the present disclosure are operative by execution of appropriate software instructions. The Digital processing system 700 may correspond to the computing device 102 (or any other system in which the various features disclosed above can be implemented).

[0046] Digital processing system 700 may contain one or more processors such as a central processing unit (CPU) 710, random access memory (RAM) 520, secondary memory 727, graphics controller 760, display unit 770, network interface 780, and input interface 790. All the components except display unit 770 may communicate with each other over communication path 750, which may contain several buses as is well known in the relevant arts. The components of Figure 7 are described below in further detail.

[0047] CPU 710 may execute instructions stored in RAM 720 to provide several features of the present disclosure. CPU 710 may contain multiple processing units, with each processing unit potentially being designed for a specific task. Alternatively, CPU 710 may contain only a single general-purpose processing unit.

[0048] RAM 720 may receive instructions from secondary memory 730 using communication path 750. RAM 720 is shown currently containing software instructions, such as those used in threads and stacks, constituting shared environment 725 and/or user programs 726. Shared environment 725 includes operating systems, device drivers, virtual machines, etc., which provide a (common) run time environment for execution of user programs 726.

[0049] Graphics controller 760 generates display signals (e.g., in RGB format) to display unit 770 based on data/instructions received from CPU 710. Display unit 770 contains a display screen to display the images defined by the display signals. Input interface 790 may correspond to a keyboard and a pointing device (e.g., touch-pad, mouse) and may be used to provide inputs. Network interface 780 provides connectivity to a network (e.g., using Internet Protocol), and may be used to communicate with other systems (such as those shown in Figure 1) connected to the network 106.

[0050] Secondary memory 730 may contain hard drive 735, flash memory 736, and removable storage drive 737. Secondary memory 730 may store the data software instructions (e.g., for performing the actions noted above with respect to the Figures), which enable digital processing system 700 to provide several features in accordance with the present disclosure.

[0051] Some or all of the data and instructions may be provided on removable storage unit 740, and the data and instructions may be read and provided by removable storage drive 737 to CPU 710. Floppy drive, magnetic tape drive, CD-ROM drive, DVD Drive, Flash memory, removable memory chip (PCMCIA Card, EEPROM) are examples of such removable storage drive 737.

[0052] Removable storage unit 740 may be implemented using medium and storage format compatible with removable storage drive 737 such that removable storage drive 737 can read the data and instructions. Thus, removable storage unit 740 includes a computer readable (storage) medium having stored therein computer software and/or data. However, the computer (or machine, in general) readable medium can be in other forms (e.g., non-removable, random access, etc.).

[0053] In this document, the term "computer program product" is used to generally refer to removable storage unit 740 or hard disk installed in hard drive 735. These computer program products are means for providing software to digital processing system 700. CPU 710 may retrieve the software instructions, and execute the instructions to provide various features of the present disclosure described above.

[0054] The term “storage media/medium” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical disks, magnetic disks, or solid-state drives, such as storage memory 730. Volatile media includes dynamic memory, such as RAM 720. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.

[0055] Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus (communication path)750. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

[0056] Reference throughout this specification to “one embodiment”, “an embodiment”, or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases “in one embodiment”, “in an embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0057] Furthermore, the described features, structures, or characteristics of the disclosure may be combined in any suitable manner in one or more embodiments. In the above description, numerous specific details are provided such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of embodiments of the disclosure.

[0058] Although the present disclosure has been described in terms of certain preferred embodiments and illustrations thereof, other embodiments and modifications to preferred embodiments may be possible that are within the principles and spirit of the invention. The above descriptions and figures are therefore to be regarded as illustrative and not restrictive.

[0059] Thus the scope of the present disclosure is defined by the appended claims and includes both combinations and sub-combinations of the various features described hereinabove as well as variations and modifications thereof, which would occur to persons skilled in the art upon reading the foregoing description.