CLIAMS:We Claim:
1. A method of enabling real time location based personalized offer management to a customer, said method comprising:
identifying, by a processor of a personalized offer management system, a plurality of potential customers likely visiting an establishment;
creating, by the processor, a segregated customer data (SCM) associated with the plurality of potential customers, wherein the SCM comprises historical data of one or more buying patterns (BP) and the one or more areas of interests associated with the plurality of potential customers;
mapping, by the processor, a plurality of personalized offers applicable on one or more products with the SCM;
determining, by the processor, a plurality of relevant personalized offers (RPO) based on mapping of the plurality of personalized offers with the SCM;
receiving dynamically, by the processor, information associated with presence of the plurality of potential customers within the establishment from an external device, based on current location (CL) of the plurality of potential customers; and
generating, by the processor, one or more real time recommendations of offer to the plurality of potential customers present within the establishment, based on one or more buying patterns and offer acceptance to the plurality of relevant personalized offers made to the plurality of potential customers.

2. The method as claimed in claim 1, wherein identifying the plurality of potential customers likely visiting the establishment comprises the steps of:
identifying the plurality of potential customers and determining one or more buying patterns (BP), real time customer activity (CA) and product interest (PI) of the plurality of identified potential customers;
estimating a customer convenience (CC) score based on the one or more buying patterns (BP), customer activity (CA), current location (CL), and product interest (PI) score of the plurality of potential customers thus determined along with past buying patterns and past customer activity;
determining a possibility of store visit (PSV) score for the plurality of potential customers based on the estimated CC score; and
comparing the determined possibility of store visit (PSV) score with a predetermined possibility of store visit threshold (PSVT) value stored in the customer data repository; and
identifying the plurality of potential customers likely visiting the establishment based on the comparison.

3. The method as claimed in claim 2, wherein identifying the plurality of potential customers comprising the steps of:
creating one or more customer records (CR) for one or more visitors to the establishment, wherein the CR comprises a plurality of responses corresponding to a plurality of predefined questions related to interest areas of the visitor;
calculating a relationship index (RI) associated with the one or more customer records based on the plurality of responses made by the one or more visitors;
comparing the calculated relationship index with a predetermined threshold relationship index stored in the customer data repository; and
identifying the one or more visitors as the plurality of potential customers based on the comparison.

4. The method as claimed in claim 1, wherein upon determining dynamically the presence of the plurality of potential customers within the establishment, the method comprising the steps of:
determining location of one or more offered products associated with the plurality of relevant personalized offers;
generating a navigation path (NP) to reach the one or more offered products based on store layout, determined location of the one or more offered products, and current location of the plurality of potential customers; and
displaying the generated NP on one or more devices associated the plurality of potential customers to navigate along the generated NP.

5. The method as claimed in claim 1, further comprising:
estimating customer activity (CA) in response to the plurality of relevant personalized offers by the plurality of potential customers present within the establishment;
determining whether the plurality of potential customers have used the relevant personalized offer based on the determined customer activity (CA) and offer acceptance to the plurality of relevant personalized offers; and
deriving offer usage by the plurality of potential customers based on the determination.

6. The method as claimed in claims 1 and 5, wherein generating one or more real time offer recommendations to the plurality of potential customers comprising the steps of:
determining real time product interest score associated with one or more areas of interest and real time buying patterns of the plurality of potential customers based on customer activity (CA) and past product interest score;
determining one or more alternate offers (AO) based on the real time product interest score and real time buying patterns thus determined; and
providing the one or more alternate offers (AO) to the plurality of potential customers.

7. The method as claimed in claim 1, further comprising:
determining real time campaign effectiveness index based on the real time offer usage, CA, SVI, AO, RI, PI and CC scores each SCM associated with the plurality of customers;
comparing the real time campaign effectiveness index thus determined with a predetermined threshold campaign effectiveness index;
modifying, based on the comparison, the customer convenience (CC), one or more customer records (CR), the SCM and determining a new product interest (PI) score based on store visit information, past buying patterns and past customer activity;
comparing the new product interest (PI) score with the past product interest (PI) score; and
determining one or more relevant personalized offers and one or more alternate offers based on the comparison.

8. A personalized offer management system for enabling real time location based personalized offer management to customer, said system comprising:
a processor;
a customer data repository, coupled with the processor, for storing segregated customer data (SCM) comprising historical data of one or more buying patterns (BP) and the one or more areas of interests of the plurality of potential customers; and
a memory disposed in communication with the processor and storing processor-executable instructions, the instructions comprising instructions to:
identify a plurality of potential customers likely visiting an establishment;
create the segregated customer data (SCM) associated with the plurality of identified potential customers;
map a plurality of personalized offers applicable on one or more products with the SCM;
determine a plurality of relevant personalized offers (RPO) based on mapping of the plurality of personalized offers with the SCM;
receive dynamically information associated with presence of the plurality of potential customers within the establishment from an external device, based on current location (CL) of the plurality of potential customers; and
generate one or more real time recommendations of offer to the plurality of potential customers present within the establishment, based on one or more buying patterns and offer acceptance to the plurality of relevant personalized offers made to the plurality of potential customers.

9. The system as claimed in claim 8, wherein the processor is configured to identify the plurality of potential customers likely visiting the establishment by performing the steps of:
identifying the plurality of potential customers and determining one or more buying patterns (BP), real time customer activity (CA) and product interest (PI) score of the plurality of identified potential customers;
estimating a customer convenience (CC) score based on the one or more buying patterns (BP), customer activity (CA), current location (CL), and product interest (PI) score of the plurality of potential customers thus determined along with past buying patterns and past customer activity;
determining a possibility of store visit (PSV) score for the plurality of potential customers based on the estimated CC score; and
comparing the determined possibility of store visit (PSV) score with a predetermined possibility of store visit threshold (PSVT) value stored in the customer data repository; and
identifying the plurality of potential customers likely visiting the establishment based on the comparison.

10. The system as claimed in claim 9, wherein the processor is configured to identify the plurality of potential customers by the steps of:
creating one or more customer records (CR) for one or more visitors to the establishment, wherein the CR comprises a plurality of responses corresponding to a plurality of predefined questions related to interest areas of the visitor;
calculating a relationship index (RI) associated with the one or more customer records based on the plurality of responses made by the one or more visitors;
comparing the calculated relationship index with a predetermined threshold relationship index stored in the customer data repository; and
identifying the one or more visitors as the plurality of potential customers based on the comparison.

11. The system as claimed in claim 8, wherein upon determining dynamically the presence of the plurality of potential customers within the establishment, the processor is further configured to:
determine location of one or more offered products associated with the plurality of relevant personalized offers;
generate a navigation path (NP) to reach the one or more offered products based on store layout, determined location of the one or more offered products, and current location of the plurality of potential customers; and
display the generated NP on one or more devices associated the plurality of potential customers to navigate along the generated NP.

12. The system as claimed in claim 8, wherein the processor is further configured to:
estimate customer activity (CA) in response to the plurality of relevant personalized offers by the plurality of potential customers present within the establishment;
determine whether the plurality of potential customers have used the relevant personalized offer based on the determined customer activity (CA) and offer acceptance to the plurality of relevant personalized offers; and
derive offer usage by the plurality of potential customers based on the determination.

13. The system as claimed in claims 8 and 12, wherein the processor is configured to provide one or more real time offer recommendations to the plurality of potential customers by performing the steps of:
determining real time product interest score associated with one or more areas of interest and real time buying patterns of the plurality of potential customers based on customer activity (CA) and past product interest score;
determining one or more alternate offers (AO) based on the real time product interest score and real time buying patterns thus determined; and
providing the one or more alternate offers (AO) to the plurality of potential customers.

14. The system as claimed in claim 8, wherein the processor is further configured to:
determine real time campaign effectiveness index based on the real time offer usage, CA, SVI, AO, RI, PI and CC scores each SCM associated with the plurality of customers;
compare the real time campaign effectiveness index thus determined with a predetermined threshold campaign effectiveness index;
modify, based on the comparison, the customer convenience (CC), one or more customer records (CR), the SCM and determining a new product interest (PI) score based on store visit information, past buying patterns and past customer activity;
compare the new product interest (PI) score with the past product interest (PI) score; and
determine one or more relevant personalized offers and one or more alternate offers based on the comparison.

15. A non-transitory computer readable medium including instructions stored thereon that when processed by at least one processor cause a system to perform acts of:
identifying a plurality of potential customers likely visiting an establishment;
creating a segregated customer data (SCM) associated with the plurality of identified potential customers, wherein the SCM comprises historical data of one or more buying patterns (BP) and the one or more areas of interests of the plurality of potential customers;
mapping a plurality of personalized offers applicable on one or more products with the SCM;
determining a plurality of relevant personalized offers (RPO) based on mapping of the plurality of personalized offers with the SCM;
receiving dynamically information associated with presence of the plurality of potential customers within the establishment from an external device, based on current location (CL) of the plurality of potential customers; and
generating one or more real time recommendations of offer to the plurality of potential customers present within the establishment, based on one or more buying patterns and offer acceptance to the plurality of relevant personalized offers made to the plurality of potential customers.

Dated this 29th day of June, 2015

M.S. Devi
Of K&S Partners
Agent for the Applicant ,TagSPECI:FIELD OF THE DISCLOSURE
The present subject matter is related, in general to offer management system, and more particularly, but not exclusively to method and a system for enabling real time location based personalized offer management.