Automated generation of personalized mail

1. A method comprising:

receiving, by a computing device, input information related to an offered product or service, two or more layouts of a print advertisement for the offered product or service, demographics of potential buyers of the offered product or service, and online behavior of the potential buyers, wherein the online behavior of the potential buyers includes actions taken by the potential buyers when presented with online advertisements;

normalizing, by the computing device, the input information into a predefined schema for a machine-learning-based recommendation engine operated by the computing device;

determining, by the machine-learning-based recommendation engine, respective selections of the two or more layouts for the potential buyers, wherein a particular layout is selected for a particular potential buyer based on the offered product or service, content and organization of the particular layout, demographics of the particular potential buyer, and online behavior of the particular potential buyer; and

transmitting, to a printing system, one or more output files representing the offered product or service, the particular layout, and the particular potential buyer, wherein reception of the one or more output files by the printing system causes the printing system to print a mailable document conforming to the particular layout, and wherein the mailable document includes a representation of the offered product or service.

2. The method of claim 1 , wherein the input information also includes respective historical transactions or orders made by the potential buyers, and wherein the particular layout selected for the particular potential buyer is also based on historical transactions or orders made by the particular potential buyer.

3. The method of claim 1 , wherein the input information also includes respective proposed transactions for the potential buyers, wherein the particular layout selected for the particular potential buyer is also based on a particular proposed transaction for the particular potential buyer, and wherein the particular proposed transaction involves the offered product or service.

4. The method of claim 1 , wherein the demographics of the particular potential buyer comprise age, gender, and geographic location of the particular potential buyer.

5. The method of claim 1 , wherein the one or more output files representing the offered product or service comprise a buyer mail ready file, recommendation file, and products file, wherein the buyer mail ready file comprises a buyer identification number, a name, and a mailing address of the particular potential buyer, wherein the recommendation file comprises a recommendation for the offered product or service, and wherein the products file comprises a product identification number and image associated with the offered product or service.

6. The method of claim 5 , wherein the one or more output files further comprise a proposed transaction file, wherein the recommendation file further comprises a recommendation for a particular proposed transaction for the particular potential buyer, and wherein the proposed transaction file comprises a proposed transaction identification number and amount associated with the particular proposed transaction.

7. The method of claim 5 , wherein the one or more output files further comprise a message file, wherein the recommendation file further comprises a recommendation for a particular message for the particular potential buyer, and wherein the message file comprises a message identification number and advertising text associated with the particular message.

8. The method of claim 1 , further comprising:

receiving feedback from the printing system relating to the one or more output files, wherein the feedback comprises a file receipt report and an open jobs file, wherein the file receipt report comprises information relating to a total amount of output files received by the printing system, a status of the output files received by the printing system, and a total amount of output files rejected by the printing system, and wherein the open jobs file comprises a report of a total number of print jobs that are still pending.

9. The method of claim 1 , wherein the determining, by the machine-learning-based recommendation engine, respective selections comprises applying one or more machine learning algorithms to the input information as normalized, wherein the machine learning algorithms use k-means clustering, alternating least squares, or regression.

10. The method of claim 1 , wherein the particular layout comprises non-customizable and customizable fields, wherein the printing system populates the customizable fields with a representation of the offered product or service, a particular proposed transaction, or a particular message relating to the offered product or service, and wherein the particular message is based on the demographics of the particular potential buyer.

11. A non-transitory computer-readable medium having stored therein instructions executable by a processor to cause a control device to perform operations comprising:

receiving input information related to an offered product or service, two or more layouts of a print advertisement for the offered product or service, demographics of potential buyers of the offered product or service, and online behavior of the potential buyers, wherein the online behavior of the potential buyers includes actions taken by the potential buyers when presented with online advertisements;

normalizing the input information into a predefined schema for a machine-learning-based recommendation engine operated by the control device;

determining, by the machine-learning-based recommendation engine, respective selections of the two or more layouts for the potential buyers, wherein a particular layout is selected for a particular potential buyer based on the offered product or service, content and organization of the particular layout, demographics of the particular potential buyer, and online behavior of the particular potential buyer; and

transmitting, to a printing system, one or more output files representing the offered product or service, the particular layout, and the particular potential buyer, wherein reception of the one or more output files by the printing system causes the printing system to print a mailable document conforming to the particular layout, and wherein the mailable document includes a representation of the offered product or service.

12. The non-transitory computer-readable medium of claim 11 , wherein the input information also includes respective historical transactions or orders made by the potential buyers, and wherein the particular layout selected for the particular potential buyer is also based on historical transactions or orders made by the particular potential buyer.

13. The non-transitory computer-readable medium of claim 11 , wherein the input information also includes respective proposed transactions for the potential buyers, wherein the particular layout selected for the particular potential buyer is also based on a particular proposed transaction for the particular potential buyer, and wherein the particular proposed transaction involves the offered product or service.

14. The non-transitory computer-readable medium of claim 11 , wherein the demographics of the particular potential buyer comprise age, gender, and geographic location of the particular potential buyer.

15. The non-transitory computer-readable medium of claim 11 , wherein the one or more output files representing the offered product or service comprise a buyer mail ready file, recommendation file, and products file, wherein the buyer mail ready file comprises a buyer identification number, a name, and a mailing address of the particular potential buyer, wherein the recommendation file comprises a recommendation for the offered product or service, and wherein the products file comprises a product identification number and image associated with the offered product or service.

16. The non-transitory computer-readable medium of claim 15 , wherein the one or more output files further comprise a proposed transaction file, wherein the recommendation file further comprises a recommendation for a particular proposed transaction for the particular potential buyer, and wherein the proposed transaction file comprises a proposed transaction identification number and amount associated with the particular proposed transaction.

17. The non-transitory computer-readable medium of claim 11 , wherein the one or more output files further comprise a message file, wherein the recommendation file further comprises a recommendation for a particular message for the particular potential buyer, and wherein the message file comprises a message identification number and advertising text associated with the particular message.

18. The non-transitory computer-readable medium of claim 11 , further comprising:

receiving feedback from the printing system relating to the one or more output files, wherein the feedback comprises a file receipt report and an open jobs file, wherein the file receipt report comprises information relating to a total amount of output files received by the printing system, a status of the output files received by the printing system, and a total amount of output files rejected by the printing system, and wherein the open jobs file comprises a report of a total number of print jobs that are still pending.

19. The non-transitory computer-readable medium of claim 11 , wherein the determining, by the machine-learning-based recommendation engine, respective selections comprises applying one or more machine learning algorithms to the input information as normalized, wherein the machine learning algorithms use k-means clustering, alternating least squares, or regression.

20. A computing device comprising:

a processor;

memory; and

program instructions, stored in the memory, that upon execution by the at least one processor cause the computing device to perform operations comprising:

receiving input information related to an offered product or service, two or more layouts of a print advertisement for the offered product or service, demographics of potential buyers of the offered product or service, and online behavior of the potential buyers, wherein the online behavior of the potential buyers includes actions taken by the potential buyers when presented with online advertisements;

normalizing the input information into a predefined schema for a machine-learning-based recommendation engine operated by the control device;

determining, by the machine-learning-based recommendation engine, respective selections of the two or more layouts for the potential buyers, wherein a particular layout is selected for a particular potential buyer based on the offered product or service, content and organization of the particular layout, demographics of the particular potential buyer, and online behavior of the particular potential buyer; and

transmitting, to a printing system, one or more output files representing the offered product or service, the particular layout, and the particular potential buyer, wherein reception of the one or more output files by the printing system causes the printing system to print a mailable document conforming to the particular layout, and wherein the mailable document includes a representation of the offered product or service.