Claims:WE CLAIM:

1. A processor implemented method (200), the method comprising:
receiving (202), by one or more hardware processors, a data pertaining to a plurality of users, wherein the data comprises a demographic data, a transaction data, a product holding data and a weblog data;
segmenting (204), by the one or more hardware processors, the plurality of users based on the corresponding data using a non-hierarchical clustering technique;
identifying (206), by the one or more hardware processors, a plurality of significant event triggers associated with a plurality of financial services corresponding to each of the plurality of users based on the segmented data using a ranking based event identification technique, wherein each of the plurality of significant event triggers are associated with an event-based score;
simultaneously computing (208), by the one or more hardware processors, a purchase probability for each of the plurality of financial services corresponding to each of the plurality of users based on the segmented data using a propensity score modelling technique;
computing (210), by the one or more hardware processors, a dynamic need-based score for the plurality of financial services corresponding to each of the plurality of users based on the plurality of significant event triggers, the corresponding event-based scores and the product purchase probability using a need-based score computation technique;
simultaneously generating (212), by the one or more hardware processors, a name rotation index based on a plurality of parameters associated with the user, wherein the plurality of parameters associated with the user comprises a recency value, a frequency value and a total relationship balance; and
identifying (214), by the one or more hardware processors, a plurality of non-overlapping target users from the plurality of users corresponding to each of the plurality of financial services based on the dynamic need-based score, the name rotation index, a scaled profitability value and a business guided strategic prioritization score using a prediction technique, wherein the prediction technique outcome is continuously updated.
2. The method as claimed in claim 1, wherein the method of identifying the plurality of significant event triggers associated with a plurality of financial services corresponding to each of the plurality of users based on the segmented data using a ranking based event identification technique comprises:
computing a correlation value for each of a plurality of event triggers corresponding to each of the plurality of financial services based on an association between the corresponding event trigger and a sale value of the corresponding financial service, wherein the plurality of event triggers comprise a life event trigger, a financial event trigger and a transactional event trigger;
computing an average correlation value for each of the plurality of financial services based on a corresponding plurality of correlation values;
computing the event-based score corresponding to each of the plurality of events associated with each of the plurality of financial services based on the corresponding correlation score and the computed average;
ranking each of the plurality of event triggers associated with each of the plurality of financial services based on the corresponding event-based score; and
identifying the plurality of significant event triggers based on a corresponding rank value and a selection threshold, wherein the plurality of event triggers with the corresponding rank value greater than the selection threshold are identified as the plurality of significant event triggers.
3. The method as claimed in claim 1, wherein the transaction data comprises a transaction time stamp, an amount transacted repayment structure and an End Of Month (EOM) outstanding balance.
4. The method as claimed in claim 1, wherein the product holding data comprises a plurality of products held, a tenure corresponding to each of the plurality of products held and a usage data.
5. The method as claimed in claim 1, wherein the weblog data comprises a time spent on relevant section of a website, a plurality of queries raised by the corresponding user, a corresponding response to each of a plurality of advertisements and a plurality of social media posts.
6. A system (100) comprising:
at least one memory (104) storing programmed instructions; one or more Input /Output (I/O) interfaces (112); and one or more hardware processors (102) operatively coupled to the at least one memory (104), wherein the one or more hardware processors (102) are configured by the programmed instructions to:
receive a data pertaining to a plurality of users, wherein the data comprises a demographic data, a transaction data, a product holding data and a weblog data;
segment the plurality of users based on the corresponding data using a non-hierarchical clustering technique;
identify a plurality of significant event triggers associated with a plurality of financial services corresponding to each of the plurality of users based on the segmented data using a ranking based event identification technique, wherein each of the plurality of significant event triggers are associated with an event-based score;
simultaneously compute a purchase probability for each of the plurality of financial services corresponding to each of the plurality of users based on the segmented data using a propensity score modelling technique;
compute a dynamic need-based score for the plurality of financial services corresponding to each of the plurality of users based on the plurality of significant event triggers, the corresponding event-based scores and the product purchase probability using a need-based score computation technique;
simultaneously generate a name rotation index based on a plurality of parameters associated with the user, wherein the plurality of parameters associated with the user comprises a recency value, a frequency value and a total relationship balance; and
identify a plurality of non-overlapping target users from the plurality of users corresponding to each of the plurality of financial services based on the dynamic need-based score, the name rotation index, a scaled profitability value and a business guided strategic prioritization score using a prediction technique, wherein the prediction technique outcome is continuously updated.
7. The system of claim 6, wherein the method of identifying the plurality of significant event triggers associated with a plurality of financial services corresponding to each of the plurality of users based on the segmented data using a ranking based event identification technique comprises:
computing a correlation value for each of a plurality of event triggers corresponding to each of the plurality of financial services based on an association between the corresponding event trigger and a sale value of the corresponding financial service, wherein the plurality of event triggers comprise a life event trigger, a financial event trigger and a transactional event trigger;
computing an average correlation value for each of the plurality of financial services based on a corresponding plurality of correlation values;
computing the event-based score corresponding to each of the plurality of events associated with each of the plurality of financial services based on the corresponding correlation score and the computed average;
ranking each of the plurality of event triggers associated with each of the plurality of financial services based on the corresponding event-based score; and
identifying the plurality of significant event triggers based on a corresponding rank value and a selection threshold, wherein the plurality of event triggers with the corresponding rank value greater than the selection threshold are identified as the plurality of significant event triggers.
8. The system of claim 6, wherein the transaction data comprises a transaction time stamp, an amount transacted repayment structure and an End Of Month (EOM) outstanding balance.
9. The system of claim 6, wherein the product holding data comprises a plurality of products held, a tenure corresponding to each of the plurality of products held and a usage data.
10. The system of claim 6, wherein the weblog data comprises a time spent on relevant section of a website, a plurality of queries raised by the corresponding user, a corresponding response to each of a plurality of advertisements and a plurality of social media posts.

Dated this 24th Day of March 2022
Tata Consultancy Services Limited
By their Agent & Attorney

(Adheesh Nargolkar)
of Khaitan & Co
Reg No IN-PA-1086 , 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:
METHOD AND SYSTEM FOR DYNAMIC NEED-BASED PRODUCT RECOMMENDATIONS FOR NON-OVERLAPPING TARGET USERS

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 the field of adaptive marketing and, more particularly, to a method and system for dynamic need-based product recommendation for non-overlapping target users.
BACKGROUND
[002] Targeting right consumer or ideal consumer is the main objective of any financial service organizations like banks and insurance companies to increase their sales and revenue. Mainly, the financial service organizations capture their consumers through respective portfolio campaigns. However, right consumers should be identified to make such portfolio campaigns successful. Nowadays, the financial service organizations are facing tough competition in identifying right consumers to increase their sales due to non-availability of data about consumers. This leads to many misdirected campaigns and redundant communication which results in targeting same set of consumers or users by multiple product managers for their respective portfolio campaigns.
[003] Conventional methods are mainly target users based on their revenue and buying behaviors. Some conventional methods focus only on life event-based targeting of users and fails to consider buying behavior of the users. Moreover, most of the conventional methods are targeting unintended users and there is repetitive targeting of users. This leads to higher marketing cost, lower product penetration and declining customer satisfaction. Overall, these are translating into lower return on marketing investment and higher customer attrition. Moreover, targeting mechanisms are not always transparent and open to measurement and control. Hence there is a challenge in developing a holistic system to identify non redundant intended consumers for product purchases.

SUMMARY
[004] 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. For example, in one embodiment, a method for dynamic need-based product recommendation for non-overlapping target users is provided. The method includes receiving, by one or more hardware processors,
a data pertaining to a plurality of users, wherein the data comprises a demographic data, a transaction data, a product holding data and a weblog data. Further, the method includes segmenting, by the one or more hardware processors, the plurality of users based on the corresponding data using a non-hierarchical clustering technique. Furthermore, the method includes identifying, by the one or more hardware processors, a plurality of significant event triggers associated with a plurality of financial services corresponding to each of the plurality of users based on the segmented data using a ranking based event identification technique, wherein each of the plurality of significant event triggers are associated with an event-based score. Furthermore, the method includes simultaneously computing, by the one or more hardware processors, a purchase probability for each of the plurality of financial services corresponding to each of the plurality of users based on the segmented data using a propensity score modelling technique. Furthermore, the method includes computing, by the one or more hardware processors, a dynamic need-based score for the plurality of financial services corresponding to each of the plurality of users based on the plurality of significant event triggers, the corresponding event-based scores and the product purchase probability using a need-based score computation technique. Furthermore, the method includes simultaneously generating, by the one or more hardware processors, a name rotation index based on a plurality of parameters associated with the user, wherein the plurality of parameters associated with the user comprises a recency value, a frequency value and a total relationship balance. Finally, the method includes identifying, by the one or more hardware processors, a plurality of non-overlapping target users from the plurality of users corresponding to each of the plurality of financial services based on the dynamic need-based score, the name rotation index, a scaled profitability value and a business guided strategic prioritization score using a prediction technique, wherein the prediction technique outcome is continuously updated.
[005] In another aspect, a system for dynamic need-based product recommendation for non-overlapping target users is provided. The system includes at least one memory storing programmed instructions, one or more Input /Output (I/O) interfaces, and one or more hardware processors operatively coupled to the at least one memory, wherein the one or more hardware processors are configured by the programmed instructions to receive a data pertaining to a plurality of users, wherein the data comprises a demographic data, a transaction data, a product holding data and a weblog data. Further, the one or more hardware processors are configured by the programmed instructions to segment the plurality of users based on the corresponding data using a non-hierarchical clustering technique. Furthermore, the one or more hardware processors are configured by the programmed instructions to identify a plurality of significant event triggers associated with a plurality of financial services corresponding to each of the plurality of users based on the segmented data using a ranking based event identification technique, wherein each of the plurality of significant event triggers are associated with an event-based score. Furthermore, one or more hardware processors are configured by the programmed instructions to simultaneously compute a purchase probability for each of the plurality of financial services corresponding to each of the plurality of users based on the segmented data using a propensity score modelling technique. Furthermore, the one or more hardware processors are configured by the programmed instructions to compute a dynamic need-based score for the plurality of financial services corresponding to each of the plurality of users based on the plurality of significant event triggers, the corresponding event-based scores and the product purchase probability using a need-based score computation technique. Furthermore, the one or more hardware processors are configured by the programmed instructions to simultaneously generate a name rotation index based on a plurality of parameters associated with the user, wherein the plurality of parameters associated with the user comprises a recency value, a frequency value and a total relationship balance. Finally, the one or more hardware processors are configured by the programmed instructions to identify a plurality of non-overlapping target users from the plurality of users corresponding to each of the plurality of financial services based on the dynamic need-based score, the name rotation index, a scaled profitability value and a business guided strategic prioritization score using a prediction technique, wherein the prediction technique outcome is continuously updated.
[006] In yet another aspect, a computer program product including a non-transitory computer-readable medium having embodied therein a computer program for dynamic need-based product recommendation for non-overlapping target users is provided. The computer readable program, when executed on a computing device, causes the computing device to receive a data pertaining to a plurality of users, wherein the data comprises a demographic data, a transaction data, a product holding data and a weblog data. Further, the computer readable program, when executed on a computing device, causes the computing device to segment the plurality of users based on the corresponding data using a non-hierarchical clustering technique. Furthermore, the computer readable program, when executed on a computing device, causes the computing device to identify a plurality of significant event triggers associated with a plurality of financial services corresponding to each of the plurality of users based on the segmented data using a ranking based event identification technique, wherein each of the plurality of significant event triggers are associated with an event-based score. Furthermore, the computer readable program, when executed on a computing device, causes the computing device to simultaneously compute a purchase probability for each of the plurality of financial services corresponding to each of the plurality of users based on the segmented data using a propensity score modelling technique. Furthermore, the computer readable program, when executed on a computing device, causes the computing device to compute a dynamic need-based score for the plurality of financial services corresponding to each of the plurality of users based on the plurality of significant event triggers, the corresponding event-based scores and the product purchase probability using a need-based score computation technique. Furthermore, the computer readable program, when executed on a computing device, causes the computing device to simultaneously generate a name rotation index based on a plurality of parameters associated with the user, wherein the plurality of parameters associated with the user comprises a recency value, a frequency value and a total relationship balance. Finally, the computer readable program, when executed on a computing device, causes the computing device to identify a plurality of non-overlapping target users from the plurality of users corresponding to each of the plurality of financial services based on the dynamic need-based score, the name rotation index, a scaled profitability value and a business guided strategic prioritization score using a prediction technique, wherein the prediction technique outcome is continuously updated.
[007] 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

[008] 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:
[009] FIG. 1 is a functional block diagram of a system for dynamic need-based product recommendation for non-overlapping target users, in accordance with some embodiments of the present disclosure.
[0010] FIG. 2 is an exemplary flow diagram illustrating a processor implemented method for dynamic need-based product recommendation for non-overlapping target users, implemented by the system of FIG. 1, in accordance with some embodiments of the present disclosure.
[0011] FIG. 3 is an overall functional architecture for the processor implemented method for dynamic need-based product recommendation for non-overlapping target users implemented by the system of FIG. 1, in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[0012] 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 spirit and scope of the disclosed embodiments.
[0013] Conventional methods are not enabling the optimization of multiple objectives like 1) dynamically assessing user’s changing needs and preferences 2) factoring incremental value of a new product conversion 3) preventing communication fatigue that could arise out of repeatable and duplicate contacts 4) continuously assimilating feedback post recommendations 5) ensuring solution transparency and control.
[0014] Embodiments herein provide a recommendation engine driven by the principle of multiple objective evolutionary algorithms. The present disclosure can recommend a non-duplicate intended user list and a corresponding product based on dynamic needs of users using statistical approach. Initially, the system receives a data pertaining to a plurality of users, wherein the data comprises a demographic data, a transaction data, a product holding data and a weblog data. Further, the system segments the plurality of users based on the corresponding data using a non-hierarchical clustering technique. Post clustering, a plurality of significant event triggers associated with a plurality of financial services corresponding to each of the plurality of users are identified based on the segmented data using a ranking based event identification technique, wherein each of the plurality of significant event triggers are associated with an event-based score. Simultaneously, a purchase probability is computed for each of the plurality of financial services corresponding to each of the plurality of users based on the segmented data using a propensity score modelling technique. Further, the system computes a dynamic need-based score for the plurality of financial services corresponding to each of the plurality of users based on the plurality of significant event triggers, the corresponding event-based scores and the product purchase probability using a need-based score computation technique. Simultaneously, a name rotation index is generated based on a plurality of parameters associated with the user, wherein the plurality of parameters associated with the user comprises a recency value, a frequency value and a total relationship balance. Finally, the system identifies a plurality of non-overlapping target users from the plurality of users corresponding to each of the plurality of financial services based on the dynamic need-based score, the name rotation index, a scaled profitability value and a business guided strategic prioritization score using a prediction technique. The prediction outcome is continuously updated. The need-based score is continuously updated based on a plurality of user feedbacks.
[0015] Referring now to the drawings, and more particularly to FIGS. 1 through 3, 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.
[0016] FIG. 1 is a functional block diagram for dynamic need-based product recommendation for non-overlapping target users, in accordance with some embodiments of the present disclosure. The system 100 includes or is otherwise in communication with hardware processors 102, at least one memory such as a memory 104, an I/O interface 112. The hardware processors 102, memory 104, and the Input /Output (I/O) interface 112 may be coupled by a system bus such as a system bus 108 or a similar mechanism. In an embodiment, the hardware processors 102 can be one or more hardware processors.
[0017] The I/O interface 112 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 112 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a printer and the like. Further, the I/O interface 112 may enable the system 100 to communicate with other devices, such as web servers, and external databases. For example, other devices comprises a plurality of sensors and a plurality of camera
[0018] The I/O interface 112 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the I/O interface 112 may include one or more ports for connecting several computing systems with one another or to another server computer. The I/O interface 112 may include one or more ports for connecting several devices to one another or to another server.
[0019] The one or more hardware processors 102 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, node machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more hardware processors 102 is configured to fetch and execute computer-readable instructions stored in the memory 104.
[0020] The memory 104 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, the memory 104 includes a plurality of modules 106. The memory 104 also includes a data repository (or repository) 110 for storing data processed, received, and generated by the plurality of modules 106.
[0021] The plurality of modules 106 include programs or coded instructions that supplement applications or functions performed by the system 100 for dynamic need-based product recommendation for non-overlapping target users. The plurality of modules 106, amongst other things, can include routines, programs, objects, components, and data structures, which performs tasks or implement particular abstract data types. The plurality of modules 106 may also be used as, signal processor(s), node machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions. Further, the plurality of modules 106 can be used by hardware, by computer-readable instructions executed by the one or more hardware processors 102, or by a combination thereof. The plurality of modules 106 can include various sub-modules (not shown). The plurality of modules 106 may include computer-readable instructions that supplement applications or functions performed by the system 100 for the context based authentication factor recommendation. In an embodiment, the plurality of modules 106 includes a segmentation module (shown in FIG. 3), a significant event trigger identification module (shown in FIG. 3), a propensity score computation module (shown in FIG. 3), a dynamic need-based score computation module (shown in FIG. 3), a name rotation index generation module (shown in FIG. 3) and a non-overlapping target user identification module (shown in FIG. 3). FIG. 3 is an overall functional architecture for the processor implemented method for dynamic need-based product recommendation for non-overlapping target users implemented by the system of FIG. 1, in accordance with some embodiments of the present disclosure.
[0022] The data repository (or repository) 110 may include a plurality of abstracted piece of code for refinement and data that is processed, received, or generated as a result of the execution of the plurality of modules in the module(s) 106.
[0023] Although the data repository 110 is shown internal to the system 100, it will be noted that, in alternate embodiments, the data repository 110 can also be implemented external to the system 100, where the data repository 110 may be stored within a database (repository 110) communicatively coupled to the system 100. The data contained within such external database may be periodically updated. For example, new data may be added into the database (not shown in FIG. 1) and/or existing data may be modified and/or non-useful data may be deleted from the database. In one example, the data may be stored in an external system, such as a Lightweight Directory Access Protocol (LDAP) directory and a Relational Database Management System (RDBMS).
[0024] FIG. 2 is an exemplary flow diagram illustrating a method 200 for dynamic need-based product recommendation for non-overlapping target users implemented by the system of FIG. 1 according to some embodiments of the present disclosure. In an embodiment, the system 100 includes one or more data storage devices or the memory 104 operatively coupled to the one or more hardware processor(s) 102 and is configured to store instructions for execution of steps of the method 200 by the one or more hardware processors 102. The steps of the method 200 of the present disclosure will now be explained with reference to the components or blocks of the system 100 as depicted in FIG. 1 and FIG. 3 and the steps of flow diagram as depicted in FIG. 2. The method 200 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 200 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. The order in which the method 200 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 200, or an alternative method. Furthermore, the method 200 can be implemented in any suitable hardware, software, firmware, or combination thereof.
[0025] At step 202 of the method 200, the one or more hardware processors 102 are configured by the programmed instructions to receive the data corresponding to the plurality of users. The data includes the demographic data, the transaction data, the product holding data and the weblog data. The demographic data includes an age, a gender, an occupation, and a plurality of live events. The transaction data includes a transaction time stamp, an amount transacted repayment structure and an End Of Month (EOM) outstanding balance. The product holding data includes a plurality of products held, a tenure corresponding to each of the plurality of products held and a usage data. The weblog data includes a time spent on relevant section of a website, a plurality of queries raised by the corresponding user, a corresponding response to each of a plurality of advertisements and a plurality of social media posts.
[0026] At step 204 of the method 200, the segmentation module (302) executed by the one or more hardware processors 102 is configured by the programmed instructions to segment the plurality of users based on the corresponding data into a plurality of segments or groups using a non-hierarchical clustering technique. For example, the clustering technique used here is a K-means clustering technique. The plurality of users are segmented based on the data pertaining to each of the plurality of users. For example, the segmentation is performed based on the transaction data, the age, the income and the like. The plurality of segments represents subsets of the active customer base that are very similar within and dissimilar across. The plurality of segments and associated profile helps the system to understand user persona and drive personalization.
[0027] At step 206, the significant event trigger identification module (304) executed by the one or more hardware processors 102 is configured by the programmed instructions to identify a plurality of significant event triggers associated with a plurality of financial services corresponding to each of the plurality of users based on the segmented data using a ranking based event identification technique. Each of the plurality of significant event triggers are associated with an event-based score. For example, the ranking based event identification technique used here is spearman rank correlation technique. The spearman rank correlation technique analyzes the association between event and sales and a corresponding response pattern and provides a ranking based on that. Further, the plurality of events is identified based on a corresponding ranking threshold
[0028] In an embodiment, the method of identifying the plurality of significant event triggers associated with the plurality of financial services corresponding to each of the plurality of users based on the segmented data using the ranking based event identification technique is explained below. Initially, the method computes a correlation value for each of a plurality of event triggers corresponding to each of the plurality of financial services (for example, mortgage, personal loan, education loan, house loan and the like) based on an association between the corresponding event trigger and a sale value of the corresponding financial service. The plurality of event triggers comprises a life event trigger, a financial event trigger and a transactional event trigger. The method further computes an average correlation value for each of the plurality of financial services based on a corresponding plurality of correlation values. Post computing the average correlations core, the method computes the event-based score corresponding to each of the plurality of events associated with each of the plurality of financial services based on the corresponding correlation score and the computed average. The method further ranks each of the plurality of event triggers associated with each of the plurality of financial services based on the corresponding event-based score. Finally, the method identifies the plurality of significant event triggers based on a corresponding rank value and a selection threshold. In an embodiment, the plurality of event triggers with the corresponding rank value greater than the selection threshold are identified as the plurality of significant event triggers. Now referring to Table I, the Table I includes the plurality of events including salary increment (event 1), becoming parent (event 2), marriage (event 3), and job change (event 4) and the corresponding purchase values of the products P1, P2, P3 and P4. Table II illustrates the correlation score and the corresponding event-based score for the product 1 and Table III illustrates the correlation score and the corresponding event-based score for the product 2. Now referring to Table II, the significant events for product 1 are event 1 and event 4 since the event-based score is more. Similarly, referring to Table III, the significant events for product 2 is event 3 since the event-based score is more.
Table I
Decile Salary increment Becoming a parent Marriage Job Change Product Purchase(P1) Product Purchase(P2) Product Purchase(P3) Product Purchase(P4)
1 25 15 20 20 30 25 30 24
2 20 20 25 22 25 20 20 30
3 22 25 21 19 20 25 22 15
4 25 10 13 21 15 10 11 12
5 4 17 15 11 5 15 11 11
6 4 13 6 7 5 5 6 8

Table II
Correlation for Prod 1 Event-based Score
Event 1 0.849395302 123
Event 2 0.303526832 44
Event 3 0.795851914 115
Event 4 0.844426242 122

Table III
Correlation for Prod 2 Score
Event 1 0.584184642 83
Event 2 0.706439384 101
Event 3 0.88009315 126
Event 4 0.63025956 90

[0029] For example, the financial events include a plurality of financial events like Bonus payout, increment, turnover trends overtime by tracking the credit and debit inflows. Further, the financial event analysis includes analyzing job shift pattern. In an embodiment, full and final settlement can be depicted as Job Change of the user.
[0030] For example, the life event trigger includes Childbirth registration, childcare benefits, joint accounts, New dependent account, resident address change and the like. For example, if a user is becoming a parent, then there is a chance for taking insurance plan for child protection. If a user is marrying means, there is a possibility for personal loan, house loan and the like. If a user is starting a job or changing jobs, then there is a possibility for car loan, credit card, personal loan and the like. If a user is approaching retirement, then there is a chance for leisure product, health protection plan and the like. If a user crosses 40 years of age, then there is a possibility of acquiring retirement products. If a user is liable for a geographical migration, then there is chance for looking for geographic specific offers and the like.
[0031] For example, the transactional event trigger includes number of logons to a webpage, a repeated visit to a webpage, time spent on webpage section visit, a transaction on an app, an online purchase, a travel related purchase and the like.
[0032] At step 208 of the method 200, the propensity score computation module 306 executed by the one or more hardware processors 102 is configured by the programmed instructions to simultaneously compute a purchase probability for each of the plurality of financial services corresponding to each of the plurality of users based on the segmented data using a propensity score modelling technique.
[0033] At step 210 of the method 200, the dynamic need-based score computation module 308 executed by the one or more hardware processors 102 is configured by the programmed instructions to compute the dynamic need-based score for the plurality of financial services corresponding to each of the plurality of users based on the plurality of significant event triggers, the corresponding event-based scores and the product purchase probability using the need-based score computation technique. Table IV illustrates the event-based score, the propensity score and the corresponding need-based score for example products pertaining to some users and the Identification number of each user is represented in App ID column. The terms “product” and the “financial service” is interchangeably used throughout the document. In the present disclosure, event-based is given the higher weightage. Wherever event-based score is not populated propensity score is used.
Table IV
App ID Event-based score Propensity score Need-based score
P1 P2 P3 P4 P1 P2 P3 P4 P1 P2 P3 P4
44477 123 101 56 98 94 113 123 101 94 113
77399 122 113 113 113 122
33292 115 126 56 150 115 126 150
25560 90 115 169 38 90 38 115

[0034] In an embodiment, a pseudocode for computing the need-based score is given below. Now referring to the following pseudocode for computing the need-based score, if the event-based score is more than zero, then the event-based score is assigned to the need-based score. If the event-based score is equal to zero and the propensity score is greater than zero, then propensity score is assigned as need-based score.
Pseudocode for computing need-based score:
if (Event-based Score > 0) {
Need-based Score = Event-based score ;
} else if (Event-based Score = 0 && Propensity Score >0) {
Need-based Score = Propensity Score ;
} else {
Need-based Score = 99999 ;
}
[0035] At step 212 of the method 200, the name rotation index generation module 310 executed by the one or more hardware processors 102 is configured by the programmed instructions to generate the name rotation index based on the plurality of parameters associated with the user. For example, the plurality of parameters associated with the user comprises the recency value, the frequency value and the total relationship balance.
[0036] In an embodiment, the name rotation index is represented as a grid-based structure as given in Table V leveraging recency frequency and total relationship balance as components is used. The grid computes historic response rates across the identified cells and then estimates which all cells have response rates significantly higher or lower than average. On that basis it suggests prospect name rotation and suppression strategies wherein the plurality of parameters comprises the recency value, the monetary value, the frequency value and the Total Relationship Balance (TRB). In an embodiment, the recency value is the number of days since last user contact happened, the frequency value is the number of contacts made in a specific period of time (for example, during last month or during last 6 months). The cells of name rotation index is represented using various colours. For example, green cells indicate segments for aggressive targeting & red cells indicate segments where no targeting is required. These are derived as per response rate.
Table V
Recency TRB Freq1 Freq2 Freq3 Freq4 Freq5
Rec_1 TRB_1 0.62 0.5 0.21 0.12 0.02
Rec_1 TRB_2 0.74 0.74 0.44 0.66 0.35
Rec_1 TRB_3 0.12 0.67 0.27 0.76 0.56
Rec_1 TRB_4 0.84 0.39 0.36 0.53 0.58
Rec_1 TRB_5 0.7 0.23 0.24 0.08 0.37
Rec_2 TRB_1 0.18 0.79 0.36 0.51 0.16
Rec_2 TRB_2 0.31 0.69 0.36 0.63 0.06
Rec_2 TRB_3 0.31 0.69 0.63 0.09 0.5
Rec_2 TRB_4 0.01 0.6 0.6 0.73 0.25
Rec_2 TRB_5 0.33 0.05 0.23 0.33 0.35
[0037] At step 214 of the method 200, the non-overlapping target user identification module 312 executed by the one or more hardware processors 102 is configured by the programmed instructions to identify the plurality of non-overlapping target users from the plurality of users corresponding to each of the plurality of financial services based on the dynamic need-based score, the name rotation index, a scaled profitability value and a business guided strategic prioritization score using the prediction technique, wherein the prediction technique is continuously updated. For example, the prediction technique can be any classification technique like logistic regression and the like. The need-based score and profitability are based on data driven inputs and the strategic prioritization score is driven by business input. For example, the strategic prioritization score is a business input provided by the organization like, which product the organization want to prioritize for a given time.
[0038] The need-based score is continuously updated based on the plurality of user feedbacks. For example, the system can receive the plurality of user feedbacks from users through a Graphical User Interface (GUI) associated with the system 100. The plurality of user feedbacks are analysed using Natural Language based Processing (NLP) techniques and updated in the system 100 to increase the performance of the system.
[0039] In an embodiment, the prediction technique used in step 214 is a probability measure based on historical value. The present disclosure utilized a range of predictive techniques to estimate user product purchase probability. One of those techniques is logistic regression which provide us a score based methodologies to compute purchase probabilities. Other classification techniques like support vector machine and random forest are also used during experimentation.
[0040] In an embodiment, the present disclosure is experimented as follows: For example, the present disclosure is experimented in banking sector for recommending the plurality of non-overlapping intended users for personal loan segment and the present disclosure was capable of recommending 89% of intended users.
[0041] In another example, the present disclosure was experimented in telecom sector to increase the revenue and it is observed that 84 billion incremental revenue was identified.
[0042] 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.
[0043] The embodiments of present disclosure herein address the unresolved problem of recommending a non-overlapping intended target users to identify right users based on dynamic needs of the user. The method identifies non-overlapping target users based on the dynamic needs of the user, wherein the dynamic needs of the user are obtained based on the event-based score and the propensity score. Further, the method includes generating a name rotation index for the identified non-overlapping intended target users.
[0044] 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 modules 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, GPUs and edge computing devices.
[0045] 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 modules described herein may be implemented in other modules or combinations of other modules. 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. 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 and spirit 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. 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. 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.
[0046] 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.