Machine learning technologies for identifying category purchases and generating digital product offers

1. A computer-implemented method of using machine learning to generate digital offers for products, the computer-implemented method comprising:

training, by at least one processor, a first machine learning model using a first set of training data identifying at least (i) a set of product catalogs, and (ii) a categorization of a purchased set of products within the set of product catalogs;

analyzing, by the at least one processor using the first machine learning model, product purchase data and a product catalog associated with an entity to determine a set of user affinity profiles respectively associated with a first set of individuals, wherein the product purchase data indicates a set of products purchased by the first set of individuals;

training, by at least one processor, a second machine learning model using a second set of training data identifying (i) an initial set of products purchased by a set of individuals, (ii) a set of offers provided to the set of individuals after the initial set of products is purchased by the set of individuals, and (iii) a subsequent set of products purchased by at least a portion of the set of individuals after purchase of the initial set of products and after the set of offers is provided to the set of individuals;

accessing, by at least one processor, a set of data identifying at least one product purchased by an individual of the first set of individuals, wherein the at least one product is associated with the entity;

analyzing, by the at least one processor using the second machine learning model, the set of data and a user affinity profile of the set of user affinity profiles associated with the individual to determine a digital offer for an additional product associated with the entity or an additional entity;

availing, by the at least one processor, the digital offer for review by the individual via an electronic device;

accessing, by at least one processor, an additional set of data indicating whether the additional product was purchased by the individual; 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 the at least one product purchased by the individual and the additional product are for the same type of product.

3. The computer-implemented method of claim 1 , wherein accessing the set of data identifying the at least one product purchased by the individual comprises:

receiving, by the at least one processor, a set of digital image data; and

analyzing, by the at least one processor, the set of digital image data to identify the at least one product purchased by the individual.

4. The computer-implemented method of claim 1 , wherein analyzing the set of data and the user affinity profile using the second machine learning model comprises:

analyzing, by the at least one processor, the set of data and the user affinity profile using the second machine learning model to determine a discount associated with purchase of the additional product.

5. The computer-implemented method of claim 1 , further comprising:

availing the set of user affinity profiles to the entity.

6. A system for using machine learning to generate digital offers for products, 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

one or more processors interfaced with the memory, and configured to execute the set of computer-readable instructions to cause the one or more processors to:

train the first machine learning model using a first set of training data identifying at least (i) a set of product catalogs, and (ii) a categorization of a purchased set of products within the set of product catalogs,

analyze, using the first machine learning model, product purchase data and a product catalog associated with an entity to determine a set of user affinity profiles respectively associated with a first set of individuals, wherein the product purchase data indicates a set of products purchased by the first set of individuals;

train the second machine learning model using a second set of training data identifying (i) an initial set of products purchased by a set of individuals, (ii) a set of offers provided to the set of individuals after the initial set of products is purchased by the set of individuals, and (iii) a subsequent set of products purchased by at least a portion of the set of individuals after purchase of the initial set of products and after the set of offers is provided to the set of individuals,

access a set of data identifying at least one product purchased by an individual of the first set of individuals, wherein the at least one product is associated with the entity,

analyze, using the second machine learning model, the set of data and a user affinity profile of the set of user affinity profiles associated with the individual to determine a digital offer for an additional product associated with the entity or an additional entity,

avail the digital offer for review by the individual via an electronic device,

access an additional set of data indicating whether the additional product was purchased by the individual, and

update the second machine learning model using the additional set of data to enable more accurate digital offer determinations in subsequent analyses.

7. The system of claim 6 , wherein the at least one product purchased by the individual and the additional product are for the same type of product.

8. The system of claim 6 , wherein to access the set of data identifying the at least one product purchased by the individual, the one or more processors is configured to:

receive a set of digital image data, and

analyze the set of digital image data to identify the at least one product purchased by the individual.

9. The system of claim 6 , wherein to analyze the set of data and the user affinity profile using the second machine learning model, the one or more processors is configured to:

analyze the set of data and the user affinity profile using the second machine learning model to determine a discount associated with purchase of the additional product.

10. The system of claim 6 , wherein the one or more processors is further configured to:

avail the set of user affinity profiles to the entity.

11. A non-transitory computer-readable storage medium configured to store instructions executable by one or more processors, the instructions comprising:

training, by at least one processor, a first machine learning model using a first set of training data identifying at least (i) a set of product catalogs, and (ii) a categorization of a purchased set of products within the set of product catalogs;

analyzing, by the at least one processor using the first machine learning model, product purchase data and a product catalog associated with an entity to determine a set of user affinity profiles respectively associated with a first set of individuals, wherein the product purchase data indicates a set of products purchased by the first set of individuals;

instructions for training a second machine learning model using a second set of training data identifying (i) an initial set of products purchased by a set of individuals, (ii) a set of offers provided to the set of individuals after the initial set of products is purchased by the set of individuals, and (iii) a subsequent set of products purchased by at least a portion of the set of individuals after purchase of the initial set of products and after the set of offers is provided to the set of individuals;

instructions for accessing a set of data identifying at least one product purchased by an individual of the first set of individuals, wherein the at least one product is associated with the entity;

instructions for analyzing, using the second machine learning model, the set of data and a user affinity profile of the set of user affinity profiles associated with the individual to determine a digital offer for an additional product associated with the entity or an additional entity;

instructions for availing the digital offer for review by the individual via an electronic device;

instructions for accessing an additional set of data indicating whether the additional product was purchased by the individual; 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.

12. The non-transitory computer-readable storage medium of claim 11 , wherein the instructions for accessing the set of data identifying the at least one product purchased by the individual comprise:

instructions for receiving a set of digital image data; and

instructions for analyzing the set of digital image data to identify the at least one product purchased by the individual.

13. The non-transitory computer-readable storage medium of claim 11 , wherein the instructions analyzing the set of data and the user affinity profile using the second machine learning model comprises:

instructions for analyzing the set of data and the user affinity profile using the second machine learning model to determine a discount associated with purchase of the additional product.

14. The non-transitory computer-readable storage medium of claim 11 , wherein the instructions further comprise:

instructions for availing the set of user affinity profiles to the entity.