Claims:WE CLAIM:
1. A method of providing one or more purchase recommendations (108) to a user (101), the method comprising:
extracting, by a purchase prediction system (107), purchase details (104) corresponding to purchase of one or more products by the user (101) from one or more digital receipts (103);
collecting, by the purchase prediction system (107), user information (106) from one or more data sources (105) associated with the user (101);
determining, by the purchase prediction system (107), a plurality of optimal purchase parameters (211) for the user (101) by analyzing the purchase details (104) based on the user information (106), wherein the plurality of optimal purchase parameters (211) comprises age of the user (101), location details of the user (101) and current trends in one or more retail stores; and
providing, by the purchase prediction system (107), one or more purchase recommendations (108) to the user (101) based on the plurality of optimal purchase parameters (211).

2. The method as claimed in claim 1, wherein the purchase details (104) comprises at least one of name of the user (101), name of the one or more products purchased by the user (101), purchase value of the one or more products and details of the one or more retail stores comprising the one or more products purchased by the user (101).

3. The method as claimed in claim 1, wherein the user information (106) comprises at least one of name of the user (101), age of the user (101), location details of the user (101), details of one or more previous purchases by the user (101), number of visits by the user (101) to the one or more retail stores, weekly average values of the number of visits and yearly average values of the number of visits.

4. The method as claimed in claim 1 and further comprising classifying the purchase details (104) prior to determining the plurality of optimal purchase parameters (211).

5. The method as claimed in claim 1, wherein providing the one or more purchase recommendations (108) comprises identifying one or more procurement factors based on at least one of the plurality of optimal purchase parameters (211).
6. The method as claimed in claim 5, wherein the one or more procurement factors are identified based on:
significance of purchase to the user (101) if the age of the user (101) is higher than a predetermined threshold value; or
frequency of purchase by the user (101) if the age of the user (101) is less than or equal to the predetermined threshold value.

7. The method as claimed in claim 1, wherein the one or more purchase recommendations (108) comprises details of one or more retail stores for purchasing the one or more products in an optimal savings rate.

8. A purchase prediction system (107) for providing one or more purchase recommendations (108) to a user (101), the purchase prediction system (107) comprises:
a processor (203); and
a memory (205), communicatively coupled to the processor (203), wherein the memory (205) stores processor-executable instructions, which, on execution, causes the processor (203) to:
extract purchase details (104) corresponding to purchase of one or more products by the user (101) from one or more digital receipts (103);
collect user information (106) from one or more data sources (105) associated with the user (101);
determine a plurality of optimal purchase parameters (211) for the user (101) by analyzing the purchase details (104) based on the user information (106), wherein the plurality of optimal purchase parameters (211) comprises age of the user (101), location details of the user (101) and current trends in one or more retail stores; and
provide one or more purchase recommendations (108) to the user (101) based on the plurality of optimal purchase parameters (211).

9. The purchase prediction system (107) as claimed in claim 8, wherein the purchase details (104) comprises at least one of name of the user (101), name of the one or more products purchased by the user (101), purchase value of the one or more products and details of the one or more retail stores comprising the one or more products purchased by the user (101).

10. The purchase prediction system (107) as claimed in claim 8, wherein the user information (106) comprises at least one of name of the user (101), age of the user (101), location details of the user (101), details of one or more previous purchases by the user (101), number of visits by the user (101) to the one or more retail stores and weekly average values of the number of visits and yearly average values of the number of visits.

11. The purchase prediction system (107) as claimed in claim 8, wherein the instructions further cause the processor (203) to classify the purchase details (104) prior to determining the plurality of optimal purchase parameters (211).

12. The purchase prediction system (107) as claimed in claim 8, wherein the processor (203) identifies one or more procurement factors based on at least one of the plurality of optimal purchase parameters (211) to provide the one or more purchase recommendations (108).

13. The purchase prediction system (107) as claimed in claim 12, wherein the processor (203) identifies the one or more procurement factors based on:
significance of purchase to the user (101) if the age of the user (101) is higher than a predetermined threshold value; or
frequency of purchase by the user (101) if the age of the user (101) is less than or equal to the predetermined threshold value.

14. The purchase prediction system as claimed in claim 8, wherein the one or more purchase recommendations (108) comprises details of one or more retail stores to purchase the one or more products in an optimal savings rate.

Dated this 8th day of March, 2017

SWETHA S. N
OF K & S PARTNERS
ATTORNEY FOR THE APPLICANT
, Description:TECHNICAL FIELD
The present subject matter is related, in general to retail environment, and more particularly, but not exclusively to a method and a system for providing one or more purchase recommendations to a user.