USING COGNITIVE COMPUTING TO PROVIDE TARGETED OFFERS FOR PREFERRED PRODUCTS TO A USER VIA A MOBILE DEVICE

1 . A computer-implemented method, comprising:

training, via one or more processors and based on historical financial data associated with a plurality of users, a machine learning system to identify shopping habits associated with the plurality of users;

generating, via the one or more processors, using the trained machine learning system, and based on spending data associated with a user, a shopping profile of the user, wherein the shopping profile indicates shopping habits of the user at a plurality of vendors;

receiving, via the one or more processors, an indication of a location of a mobile device associated with the user;

determining, via the one or more processors, that the location of the mobile device is within a threshold distance of a physical vendor location associated with a vendor;

determining, via the one or more processors, that the vendor is a preferred vendor of the user, within the plurality of vendors, based upon the shopping profile;

determining, via the one or more processors, a preferred product of the user, based upon the shopping profile;

identifying, via the one or more processors and within a set of commercial communications for products being offered by the preferred vendor at discounted prices, a communication associated with the preferred product; and

causing, via the one or more processors, transmission of the communication to the mobile device.

2 . (canceled)

3 . The computer-implemented method of claim 1 , wherein the shopping profile indicates at least one of: (i) names of the plurality of vendors; (ii) a frequency of how often the user shops at each of the plurality of vendors; (iii) an average of how much the user spends at each of the plurality of vendors; (iv) types of products the user has purchased at each of the plurality of vendors; or (v) brand names of products the user has purchased at each of the plurality of vendors.

4 . The computer-implemented method of claim 1 , wherein the shopping profile is customizable by the user.

5 . The computer-implemented method of claim 1 , wherein the communication includes an offer of reward points for completing a purchase of the preferred product within a given amount of time.

6 . The computer-implemented method of claim 1 , wherein the communication includes an offer of reward points for completing a purchase of the preferred product using a designated payment type.

7 . The computer-implemented method of claim 1 , wherein the communication indicates bundled pricing for a bundle of products offered by the preferred vendor.

8 . The computer-implemented method of claim 1 , wherein the communication includes an offer of reward points for completing a purchase of the preferred product via a loan product.

9 . The computer-implemented method of claim 1 , wherein the shopping profile indicates a predicted life event associated with the user, and the communication is associated with the predicted life event.

10 . The computer-implemented method of claim 9 , wherein the predicted life event includes at least one of: an educational event, an age-related event, a birth of a child, ora marriage.

11 . A computer system comprising one or more processors configured to:

train, based on historical financial data associated with a plurality of users, a machine learning system to identify shopping habits associated with the plurality of users;

generate, using the trained machine learning system, and based on spending data associated with a user, a shopping profile of the user, wherein the shopping profile includes a dataset indicative of shopping habits of the user at a plurality of vendors;

receive an indication of a location of a mobile device associated with the user;

determine that the location of the mobile device is within a threshold distance of a physical vendor location associated with a vendor;

determine that the vendor is a preferred vendor of the user, within the plurality of vendors, based upon the shopping profile;

determine a preferred product of the user, based upon the shopping profile;

identify, within a set of commercial communications for products being offered by the preferred vendor at discounted prices, an offer associated with the preferred product; and

cause transmission of the offer to the mobile device.

12 . (canceled)

13 . The computer system of claim 11 , wherein the shopping profile indicates at least one of names of the plurality of vendors a frequency of how often the user shops at each of the plurality of vendors, an average of how much the user spends at each of the plurality of vendors, types of products purchased at each of the plurality of vendors vendor by the user, or brand names of products purchased at each of the plurality of vendors by the user.

14 . (canceled)

15 . The computer system of claim 11 , wherein the offer is associated with reward points for completing a purchase of the preferred product within a given amount of time.

16 . The computer system of claim 11 , wherein the offer is associated with reward points for completing a purchase of the preferred product using a designated payment type.

17 . The computer system of claim 11 , wherein the offer indicates bundled pricing for a bundle of products offered by the preferred vendor.

18 . The computer system of claim 11 , wherein the offer is associated with reward points for completing a purchase of the preferred product via a loan product.

19 . The computer system of claim 11 , wherein the shopping profile indicates a predicted life event associated with the user, and the offer is associated with the predicted life event.

20 . The computer system of claim 19 , wherein the predicted life event includes at least one of an educational event, an age-related event, a birth of a child, or a marriage.

21 . The computer-implemented method of claim 1 , wherein:

a geofence indicates a range of geographical coordinates that correspond to a boundary associated with the physical vendor location, and

determining that the location of the mobile device is within the threshold distance of the physical vendor location comprises determining that the location of the mobile device is within the boundary indicated by the geofence.

22 . The computer-implemented method of claim 21 , wherein causing transmission of the communication is performed in response to determining that the location of the mobile device is within the boundary indicated by the geofence.

23 . The computer-implemented method of claim 21 , wherein generating the shopping profile is performed in response to determining that the location of the mobile device is within the boundary indicated by the geofence.