Using cognitive computing to provide targeted offers for preferred products to a user via a mobile device
Techniques are disclosed utilizing cognitive computing to improve commercial communications from vendors to users. A user's financial account(s) and location may be monitored to determine when a user is within a threshold distance of a vendor. If the user is within the threshold distance the methods and systems disclosed may determine which targeted commercial communications to transmit to the user based upon a shopping profile for the user. The shopping profile may include a dataset indicative of the shopping habits of the user.
1. A computer-implemented method, comprising:
receiving, via one or more processors and from one or more financial institutions, financial transaction information associated with transactions between a user and a plurality of merchants;
generating, via a machine learning system and using the one or more processors, a shopping profile of the user based upon the financial transaction information, wherein the shopping profile indicates:
a preferred merchant of the user, within the plurality of merchants;
a preferred product of the user; and
a predicted life event associated with the user;
receiving, via the one or more processors, an indication of a physical location of a mobile device of the user;
determining, via the one or more processors, that the physical location of the mobile device is within a threshold distance of a physical merchant location associated with the preferred merchant;
identifying, via the one or more processors, a set of commercial communications for products that are currently being offered by the preferred merchant at discounted prices;
identifying, via the one or more processors, one or more preferred product communications, from the set of commercial communications, that are associated with the preferred product and the predicted life event;
causing, via the one or more processors, transmission of the one or more preferred product communications to the mobile device; and
directing, via the one or more processors, the mobile device to present the one or more preferred product communications to the user, based on determining that the physical location of the mobile device is within the threshold distance of the physical merchant location.
2. The computer-implemented method of claim 1 , wherein the machine learning system is trained using supervised or unsupervised machine learning to generate the shopping profile for the user based upon the financial transaction information.
3. The computer-implemented method of claim 1 , wherein the shopping profile of the user includes at least one or more of: (i) names of merchants at which the user shops; (ii) a frequency that the user shops at each merchant; (iii) an average of how much the user spends at each merchant; (iv) types of products purchased at each merchant; or (v) brand names of products purchased at each merchant by the user.
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 one or more preferred product communications include an offer of reward points if a purchase of the preferred product is completed within a given amount of time.
6. The computer-implemented method of claim 1 , wherein the one or more preferred product communications include an offer of reward points if a purchase of the preferred product is completed using a designated payment type.
7. The computer-implemented method of claim 1 , wherein the one or more preferred product communications include bundled pricing for a bundle of products offered by the preferred merchant.
8. The computer-implemented method of claim 1 , wherein the one or more preferred product communications include an offer of reward points if a purchase of the preferred product is completed via a loan product.
9. The computer-implemented method of claim 1 , 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.
10. 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 merchant location, and
determining that the physical location of the mobile device is within the threshold distance of the physical merchant location comprises determining that the physical location of the mobile device is a location within the boundary indicated by the geofence.
11. A computer system comprising one or more processors, the computer system being configured to:
receive, from one or more financial institutions, financial transaction information associated with transactions between a user and a plurality of merchants;
generate, via a machine learning system, a shopping profile of the user based upon the financial transaction information, wherein the shopping profile indicates:
a preferred merchant of the user, within the plurality of merchants;
a preferred product of the user; and
a predicted life event associated with the user;
receive an indication of a physical location of a mobile device of the user;
determine that the physical location of the mobile device is within a threshold distance of a physical merchant location associated with the preferred merchant;
identify a set of commercial communications for products that are currently being offered by the preferred merchant at discounted prices;
identify one or more preferred product communications, from the set of commercial communications, that are associated with the preferred product and the predicted life event;
transmit the one or more preferred product communications to the mobile device; and
direct the mobile device to present the one or more preferred product communications to the user, based on determining that the physical location of the mobile device is within the threshold distance of the physical merchant location.
12. The computer system of claim 11 , wherein the machine learning system is trained using supervised or unsupervised machine learning to generate the shopping profile for the user based upon the financial transaction information.
13. The computer system of claim 11 , wherein the shopping profile for the user includes at least one of: names of merchants at which the user shops, a frequency that the user shops at each merchant, an average of how much the user spends at each merchant, types of products purchased at each merchant by the user, or brand names of products purchased at each merchant by the user.
14. The computer system of claim 11 , wherein the shopping profile is customizable by the user.
15. The computer system of claim 11 , wherein the one or more preferred product communications includes an offer of reward points if a purchase of the preferred product is completed within a given amount of time.
16. The computer system of claim 11 , wherein the one or more preferred product communications includes an offer of reward points if a purchase of the preferred product is completed using a designated payment type.
17. The computer system of claim 11 , wherein the one or more preferred product communications includes bundled pricing for a bundle of products offered by the preferred merchant.
18. The computer system of claim 11 , wherein the one or more preferred product communications includes an offer of reward points if a purchase of the preferred product is completed via a loan product.
19. The computer system of claim 11 , 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.
20. The computer system of claim 11 , wherein:
a geofence indicates a range of geographical coordinates that correspond to a boundary associated with the physical merchant location, and
the computer system is configured to determine that the physical location of the mobile device is within the threshold distance of the physical merchant location by determining that the physical location of the mobile device is a location within the boundary indicated by the geofence.