Personalized product service

1. A computer-implemented method, comprising:

parsing a purchase history of a consumer to verify a sufficient depth to provide a recommendation;

evaluating the purchase history of the consumer based on a hierarchy of a universal product code provided by a retailer;

filtering out a product when a score of a product identified by the universal product code is less than a threshold, to form a product list;

scoring the product in the product list based at least on one of a purchase probability of the product by the consumer, a value of the product, and a stock availability of the product identified by the universal product code at the retailer;

padding the product list with a default universal product code when the purchase history is exhausted before completing a pre-selected quota;

providing the product list including the universal product codes sorted according to a score to a remote server for assembling a digital payload for a consumer;

assembling the digital payload including media files associated with each of the products in the product list;

providing the digital payload to the consumer;

receiving a tracking pixel indicative that the consumer has interacted with the digital payload; and

notifying, in response to the tracking pixel, a retailer that the consumer has interacted with the digital payload in response to a tracking pixel triggered by a client device with the consumer upon downloading the digital payload.

2. The computer-implemented method of claim 1 , further comprising providing a standard list of default universal product codes when the purchase history is not deep enough.

3. The computer-implemented method of claim 1 , further comprising selecting the threshold as a percentage number of retailer stores that have the product identified by the universal product code in stock.

4. The computer-implemented method of claim 1 , further comprising selecting the threshold as a percentage number of times the product identified by the universal product code appears in the purchase history of the consumer.

5. The computer-implemented method of claim 1 , wherein scoring the universal product code comprises weighting the score positively when a geolocation of the consumer overlaps with a geolocation of a retailer store having the product identified by the universal product code in stock.

6. The computer-implemented method of claim 1 , further comprising receiving, from the remote server, a request from a service provider, the request including an identification code associated with the consumer.

7. The computer-implemented method of claim 1 , wherein to form a product list comprises selecting a personalized list of universal product codes based on a consumer identification code and a purchase history log in a database.

8. The computer-implemented method of claim 1 , further comprising receiving, from the remote server, a tracking pixel indicative that the consumer has interacted with the digital payload.

9. The computer-implemented method of claim 1 , wherein filtering out a product to form a list comprises filtering out the product based on a time interval cutoff from the purchase history of the consumer in a database.

10. The computer-implemented method of claim 1 , wherein to form a list comprises selecting universal product codes associated with products that are for sale at a retail store serviced by the remote server.

11. A system, comprising:

one or more processors; and

a memory storing instructions which, when executed by the one or more processors, cause the system to:

parse a purchase history of a consumer to verify a sufficient depth to provide a recommendation;

evaluate the purchase history of the consumer based on a hierarchy of a universal product code provided by a retailer;

filter out a product when a score of a product identified by the universal product code is less than a threshold, to form a list;

score the product in the list based at least on one of a purchase probability of the product by the consumer, a value of the product, and a stock availability of the product identified by the universal product code at the retailer;

provide the list including the universal product codes sorted according to a score to a remote server for assembling a digital payload for a consumer;

assemble the digital payload including media files associated with each of the products in the list;

provide the digital payload to the consumer;

receive a tracking pixel indicative that the consumer has interacted with the digital payload; and

notify a retailer that the consumer has interacted with the digital payload in response to a tracking pixel triggered by a client device with the consumer upon downloading the digital payload.

12. The system of claim 11 , wherein the one or more processors further execute instructions to provide a standard list of default universal product codes when the purchase history is not deep enough.

13. The system of claim 11 , wherein the one or more processors further execute instructions to select the threshold as a percentage number of retailer stores that have the product identified by the universal product code in stock.

14. The system of claim 11 , wherein to form the list, the one or more processors execute instructions to select multiple products based on a time interval cutoff from the purchase history of the consumer in a database.

15. The system of claim 11 , wherein to score the universal product code the one or more processors execute instructions to weight the score positively when a geolocation of the consumer overlaps with a geolocation of a retailer store having the product identified by the universal product code in stock.

16. The system of claim 11 , wherein to form the list, the one or more processors execute instructions to select a universal product code associated with a products that is for sale at a retail store serviced by the remote server.

17. The system of claim 11 , wherein to form the list, the one or more processors execute instructions to train a non-linear algorithm for classifying a consumer identification code based on the purchase history of the consumer, and to identify a likelihood that a consumer associated with the consumer identification code, will purchase a product in the list.

18. The system of claim 11 , wherein to form the list, the one or more processors execute instructions to integrate the list with an application programming interface hosted by the remote server and to provide a product picture, a product description, or a product pricing with the list.

19. The system of claim 11 , wherein the one or more processors further execute instructions to request, to the remote server, a data element associated with at least one product in the list, and to edit an advertisement for the consumer based on the data element.

20. The system of claim 11 , wherein the one or more processors further execute instructions to provide a measurement data to the remote server based on a consumer interaction with the digital payload.