Method, system, and apparatus for programmatically generating a channel incrementality ratio

1. An apparatus comprising at least one processor and at least one memory storing instructions that, with the at least one processor, configure the apparatus to:

generate a touchpoint timestamps subset based at least in part on a plurality of touchpoint timestamps associated with a first transaction timestamp of a plurality of transaction timestamps;

determine a latest channel landing-page touchpoint timestamp for each unique channel of a plurality of channels associated with at least one touchpoint timestamp of the touchpoint timestamps subset;

generate a sorted channel identifier list comprising channel identifiers of each unique channel of the plurality of channels in an order according to a latest channel landing-page touchpoint timestamp associated with each channel identifier; and

generate a channel incrementality ratio associated with each unique channel based at least in part on applying a machine learning model to the plurality of transaction timestamps, the touchpoint timestamps subset, and the sorted channel identifier list.

2. The apparatus of claim 1 , wherein the machine learning model is further applied to one or more of a weighting factor assigned to each unique channel based on a location of the channel identifier in the sorted channel identifier list or a plurality of touchpoint timestamps subsets.

3. The apparatus of claim 1 , wherein each touchpoint timestamp of the touchpoint timestamps subset was received within a period of network time prior to the first transaction timestamp.

4. The apparatus of claim 1 , wherein the at least one memory stores instructions that, with the at least one processor, further configure the apparatus to:

receive a plurality of touchpoint signals from a plurality of client devices.

5. The apparatus of claim 4 , wherein each touchpoint signal of the plurality of touchpoint signals is associated with a channel of a plurality of channels.

6. The apparatus of claim 1 , wherein each unique channel of the plurality of channels comprises a source of network traffic.

7. The apparatus of claim 1 , wherein a transaction timestamp represents a transaction signal receiving time and a touchpoint timestamp represents a touchpoint signal receiving time.

8. The apparatus of claim 1 , wherein each latest channel landing-page touchpoint timestamp represents a latest touchpoint signal receiving time associated with each unique channel that occurred immediately prior to a transaction signal receiving time associated with a particular touchpoint timestamps subset.

9. The apparatus of claim 1 , wherein the at least one memory stores instructions that, with the at least one processor, further configure the apparatus to:

determine or adjust a channel currency allocation value associated with each unique channel based at least on its associated channel incrementality ratio.

10. The apparatus of claim 3 , wherein the period of network time prior to the first transaction timestamp is three days.

11. A computer-implemented method, comprising:

generating a touchpoint timestamps subset based at least in part on a plurality of touchpoint timestamps associated with a first transaction timestamp of a plurality of transaction timestamps;

determining a latest channel landing-page touchpoint timestamp for each unique channel of a plurality of channels associated with at least one touchpoint timestamp of the touchpoint timestamps subset;

generating a sorted channel identifier list comprising channel identifiers of each unique channel of the plurality of channels in an order according to a latest channel landing-page touchpoint timestamp associated with each channel identifier; and

generating a channel incrementality ratio associated with each unique channel based at least in part on applying a machine learning model to the plurality of transaction timestamps, the touchpoint timestamps subset, and the sorted channel identifier list.

12. The method of claim 11 , wherein the machine learning model is further applied to one or more of a weighting factor assigned to each unique channel based on a location of the channel identifier in the sorted channel identifier list or a plurality of touchpoint timestamps subsets.

13. The method of claim 11 , wherein each touchpoint timestamp of the touchpoint timestamps subset was received within a period of network time prior to the first transaction timestamp.

14. The method of claim 11 , further comprising:

receiving a plurality of touchpoint signals from a plurality of client devices.

15. The method of claim 14 , wherein each touchpoint signal of the plurality of touchpoint signals is associated with a channel of a plurality of channels.

16. The method of claim 11 , wherein each unique channel of the plurality of channels comprises a source of network traffic.

17. The method of claim 11 , wherein a transaction timestamp represents a transaction signal receiving time and a touchpoint timestamp represents a touchpoint signal receiving time.

18. The method of claim 11 , wherein each latest channel landing-page touchpoint timestamp represents a latest touchpoint signal receiving time associated with each unique channel that occurred immediately prior to a transaction signal receiving time associated with a particular touchpoint timestamps subset.

19. The method of claim 11 , further comprising:

determining or adjusting a channel currency allocation value associated with each unique channel based at least on its associated channel incrementality ratio.

20. The method of claim 13 , wherein the period of network time prior to the first transaction timestamp is three days.