Machine learning technologies for identifying category purchases and generating digital product offers
Systems and methods for using machine learning to determine product offers for individuals are disclosed. According to certain aspects, the systems and methods may determine, using a machine learning model and based on a set of products purchased by a user, a set of digital offers associated with an additional set of products to provide to the user. The user may redeem any of the digital offers, and the systems and methods may use any additional product purchase information to update the machine learning model for use in subsequent analyses.
1. A computer-implemented method of training multiple machine learning models, the computer-implemented method comprising:
training, by at least one processor, a first machine learning model using a first set of training data indicating how a purchased set of products or services is categorized within a set of data structures;
receiving, by the at least one processor from a plurality of electronic devices associated with a plurality of users, a plurality of digital images captured by the plurality of electronic devices;
analyzing, by the at least one processor using an image recognition technique, the plurality of digital images to identify, as included in the plurality of digital images, a plurality of identifiers corresponding to a plurality of products or services purchased by the plurality of users;
analyzing, by the at least one processor using the first machine learning model, (i) data indicating the plurality of products or services purchased by the plurality of users, and (ii) a data structure associated with an entity to determine a plurality of user affinity profiles respectively associated with the plurality of users;
training, by the at least one processor, a second machine learning model using a second set of training data identifying (i) a training set of products or services purchased by a set of individuals, and (ii) a set of offers provided to the set of individuals in association with the purchase of the training set of products or services by the set of individuals;
identifying, by the at least one processor from the data indicating the plurality of products or services purchased by the plurality of users, at least one product or service associated with the entity and purchased by a user of the plurality of users;
analyzing, by the at least one processor using the second machine learning model, data identifying the at least one product or service and a user affinity profile of the plurality of user affinity profiles associated with the user of the plurality of users to determine a digital offer for an additional product or service associated with the entity or an additional entity;
availing, by the at least one processor, the digital offer for review by the user via an electronic device, of the plurality of electronic devices, associated with the user;
accessing, by at least one processor from a digital image captured by the electronic device associated with the user, an additional set of data indicating whether the additional product or service was purchased by the user; and
updating, by the at least one processor, the second machine learning model using the additional set of data to enable more accurate digital offer determinations in subsequent analyses.
2. The computer-implemented method of claim 1 , wherein training the first machine learning model comprises:
training, by at least one processor, the first machine learning model using the first set of training data indicating how the purchased set of products or services is categorized within the set of data structures, wherein the purchased set of products or services is categorized within the set of data structures in a manner that is mutually exclusive and cumulatively exhaustive.
3. The computer-implemented method of claim 1 , wherein training the second machine learning model comprises:
training, by the at least one processor, the second machine learning model using the second set of training data identifying (i) an initial training set of products or services purchased by the set of individuals, (ii) the set of offers provided to the set of individuals after the initial training set of products or services is purchased by the set of individuals, and (iii) a subsequent training set of products or services purchased by at least a portion of the set of individuals after purchase of the initial training set of products or services and after the set of offers is provided to the set of individuals.
4. The computer-implemented method of claim 1 , wherein the at least one product or service purchased by the user and the additional product or service are for the same type of product or service.
5. The computer-implemented method of claim 1 , wherein analyzing the data identifying the at least one product or service and the user affinity profile comprises:
analyzing, by the at least one processor using the second machine learning model, the data identifying the at least one product or service and the user affinity profile to determine a discount associated with purchase of the additional product or service.
6. The computer-implemented method of claim 1 , further comprising:
availing the plurality of user affinity profiles to the entity.
7. A system for training multiple machine learning models, comprising:
a memory storing a set of computer-readable instructions and data associated with a first machine learning model and a second machine learning model; and
at least one processor interfaced with the memory, and configured to execute the set of computer-readable instructions to cause the at least one processor to:
train the first machine learning model using a first set of training data indicating how a purchased set of products or services is categorized within a set of data structures,
receive, from a plurality of electronic devices associated with a plurality of users, a plurality of digital images captured by the plurality of electronic devices,
analyze, using an image recognition technique, the plurality of digital images to identify, as included in the plurality of digital images, a plurality of identifiers corresponding to a plurality of products or services purchased by the plurality of users,
analyze, using the first machine learning model, (i) data indicating the plurality of products or services purchased by the plurality of users, and (ii) a data structure associated with an entity to determine a plurality of user affinity profiles respectively associated with the plurality of users,
train the second machine learning model using a second set of training data identifying (i) a training set of products or services purchased by a set of individuals, and (ii) a set of offers provided to the set of individuals in association with the purchase of the training set of products or services by the set of individuals,
identify, from the data indicating the plurality of products or services purchased by the plurality of users, at least one product or service associated with the entity and purchased by a user of the plurality of users,
analyze, using the second machine learning model, data identifying the at least one product or service and a user affinity profile of the plurality of user affinity profiles associated with the user of the plurality of users to determine a digital offer for an additional product or service associated with the entity or an additional entity,
avail the digital offer for review by the user via an electronic device, of the plurality of electronic devices, associated with the user,
access, from a digital image captured by the electronic device associated with the user, an additional set of data indicating whether the additional product or service was purchased by the user, and
update the second machine learning model using the additional set of data to enable more accurate digital offer determinations in subsequent analyses.
8. The system of claim 7 , wherein the purchased set of products or services is categorized within the set of data structures in a manner that is mutually exclusive and cumulatively exhaustive.
9. The system of claim 7 , wherein the second set of training data identifies (i) an initial training set of products or services purchased by the set of individuals, (ii) the set of offers provided to the set of individuals after the initial training set of products or services is purchased by the set of individuals, and (iii) a subsequent training set of products or services purchased by at least a portion of the set of individuals after purchase of the initial training set of products or services and after the set of offers is provided to the set of individuals.
10. The system of claim 7 , wherein the at least one product or service purchased by the user and the additional product or service are for the same type of product or service.
11. The system of claim 7 , wherein the at least one processor analyzes, using the second machine learning model, the data identifying the at least one product or service and the user affinity profile to determine a discount associated with purchase of the additional product or service.
12. The system of claim 7 , wherein the at least one processor is further configured to:
avail the plurality of user affinity profiles to the entity.
13. A non-transitory computer-readable storage medium configured to store instructions executable by one or more processors, the instructions comprising:
instructions for training a first machine learning model using a first set of training data indicating how a purchased set of products or services is categorized within a set of data structures;
instructions for receiving, from a plurality of electronic devices associated with a plurality of users, a plurality of digital images captured by the plurality of electronic devices;
instructions for analyzing, using an image recognition technique, the plurality of digital images to identify, as included in the plurality of digital images, a plurality of identifiers corresponding to a plurality of products or services purchased by the plurality of users;
instructions for analyzing, using the first machine learning model, (i) data indicating the plurality of products or services purchased by the plurality of users, and (ii) a data structure associated with an entity to determine a plurality of user affinity profiles respectively associated with the plurality of users;
instructions for training a second machine learning model using a second set of training data identifying (i) a training set of products or services purchased by a set of individuals, and (ii) a set of offers provided to the set of individuals in association with the purchase of the training set of products or services by the set of individuals;
instructions for identifying, from the data indicating the plurality of products or services purchased by the plurality of users, at least one product or service associated with the entity andpurchased by a user of the plurality of users;
instructions for analyzing, using the second machine learning model, data identifying the at least one product or service and a user affinity profile of the plurality of user affinity profiles associated with the user of the plurality of users to determine a digital offer for an additional product or service associated with the entity or an additional entity;
instructions for availing the digital offer for review by the user via an electronic device, of the plurality of electronic devices, associated with the user;
instructions for accessing, from a digital image captured by the electronic device associated with the user, an additional set of data indicating whether the additional product or service was purchased by the user; and
instructions for updating the second machine learning model using the additional set of data to enable more accurate digital offer determinations in subsequent analyses.
14. The non-transitory computer-readable storage medium of claim 13 , wherein the instructions for training the first machine learning model comprise:
instructions for training the first machine learning model using the first set of training data indicating how the purchased set of products or services is categorized within the set of data structures, wherein the purchased set of products or services is categorized within the set of data structures in a manner that is mutually exclusive and cumulatively exhaustive.
15. The non-transitory computer-readable storage medium of claim 13 , wherein the instructions for training the second machine learning model comprise:
instructions for training the second machine learning model using the second set of training data identifying (i) an initial training set of products or services purchased by the set of individuals, (ii) the set of offers provided to the set of individuals after the initial training set of products or services is purchased by the set of individuals, and (iii) a subsequent training set of products or services purchased by at least a portion of the set of individuals after purchase of the initial training set of products or services and after the set of offers is provided to the set of individuals.
16. The non-transitory computer-readable storage medium of claim 13 , wherein the at least one product or service purchased by the user and the additional product or service are for the same type of product or service.
17. The non-transitory computer-readable storage medium of claim 13 , wherein the instructions for analyzing the set of data and the user affinity profile comprise:
instructions for analyzing, using the second machine learning model, the data identifying the at least one product or service and the user affinity profile to determine a discount associated with purchase of the additional product or service.