Method, system, and apparatus for programmatically determining and adjusting electronic bid values for a digital content object

1. A computer-implemented method, comprising:

receiving one or more transaction signals associated with a digital content object;

generating a first set of cumulative transaction values, each cumulative transaction value of the first set of cumulative transaction values associated with a different one of one or more network time period segments within a first network time period, wherein each cumulative transaction value of the first set of cumulative transaction values is programmatically generated based on a total number of the one or more transaction signals received during each of the one or more network time period segments within the first network time period;

generating a second set of cumulative transaction values, each cumulative transaction value of the second set of cumulative transaction values associated with a different one of the one or more network time period segments within a second network time period, wherein each cumulative transaction value of the second set of cumulative transaction values is programmatically generated based on a total number of the one or more transaction signals received during each of the one or more network time period segments within the second network time period;

generating an accrued cumulative transaction value for each of the one or more network time period segments based on cumulating the first set of cumulative transaction values for each of the one or more network time period segments within the first network time period and the second set of cumulative transaction values for each of the one or more network time period segments within the second network time period;

generating, using a machine learning model, a slope value based on the accrued cumulative transaction value for each of the one or more network time period segments, wherein the slope value is associated with a particular network time period segment and indicates a transaction trend of the digital content object during the particular network time period segment; and

adjusting an electronic bid value of the digital content object during the particular network time period segment based at least on the slope value.

2. The computer-implemented method of claim 1 , wherein the machine learning model is a linear model.

3. The computer-implemented method of claim 2 , wherein the linear model is a linear regression model associated with an r-squared value and a p-value.

4. The computer-implemented method of claim 3 , further comprising:

responsive to determining that the slope value is larger than a first threshold slope value, the r-squared value is larger than a threshold r-squared value, or the p-value is smaller than a threshold p-value:

increasing the electronic bid value for the digital content object during the particular network time period segment associated with the slope value.

5. The computer-implemented method of claim 4 , wherein the first threshold slope value is 1, the threshold r-squared value is 0.75, and the threshold p-value is 10 −5 .

6. The computer-implemented method of claim 3 , further comprising:

responsive to determining that the slope value is smaller than a second threshold slope value, the r-squared value is not larger than the threshold r-squared value, or the p-value is not smaller than a threshold p-value:

decreasing the electronic bid value for the digital content object during the particular network time period segment associated with the slope value.

7. The computer-implemented method of claim 6 , wherein the second threshold slope value is −1, a first threshold slope value is 1, the threshold r-squared value is 0.75, and the threshold p-value is 10 −5 .

8. The computer-implemented method of claim 1 , wherein each of the one or more network time periods is associated with a start timestamp and a finish timestamp.

9. The computer-implemented method of claim 8 , wherein a time period between the start timestamp and the finish timestamp is 24-hours.

10. The computer-implemented method of claim 1 , wherein each of the one or more network time period segments dividing the one or more network time periods is a 1-hour period.

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

generating one or more multiplier values associated with each of the one or more network time period segments for determining one or more new electronic bid values for the digital content object during each of the one or more network time period segments, wherein each of the one or more multiplier values is associated with a respective slope value associated with the particular network time period segment indicating the transaction trend of the digital content object during the particular network time period segment.

12. The computer-implemented method of claim 11 , further comprising:

responsive to determining that the respective slope value is in a range of 1 to 10, generating a multiplier value associated with the particular network time period segment based on linearly scaling the respective slope value to a range of 1 to 1.5.

13. The computer-implemented method of claim 11 , further comprising:

responsive to determining that the respective slope value is in a range of −10 to −1, generating a multiplier value associated with the particular network time period segment based on linearly scaling the respective slope value to a range of 0.5 to 1.

14. The computer-implemented method of claim 11 , further comprising:

responsive to determining that the respective slope value is in a range of −1 to 1, generating a multiplier value associated with the particular network time period segment to be 1.

15. The computer-implemented method of claim 11 , further comprising:

determining the one or more new electronic bid values for the digital content object during each of the one or more network time period segments by multiplying an existing electronic bid value for the digital content object by the one or more multiplier values associated with each of the one or more network time period segments.

16. The computer-implemented method of claim 1 , wherein the digital content object is a keyword.

17. An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to:

receive one or more transaction signals associated with a digital content object;

generate a first set of cumulative transaction values, each cumulative transaction value of the first set of cumulative transaction values associated with a different one of one or more network time period segments within a first network time period, wherein each cumulative transaction value of the first set of cumulative transaction values is programmatically generated based on a total number of the one or more transaction signals received during each of the one or more network time period segments within the first network time period;

generate a second set of cumulative transaction values, each cumulative transaction value of the second set of cumulative transaction values associated with a different one of the one or more network time period segments within a second network time period, wherein each cumulative transaction value of the second set of cumulative transaction values is programmatically generated based on a total number of the one or more transaction signals received during each of the one or more network time period segments within the second network time period;

generate an accrued cumulative transaction value for each of the one or more network time period segments based on cumulating the first set of cumulative transaction values for each of the one or more network time period segments within the first network time period and the second set of cumulative transaction values for each of the one or more network time period segments within the second network time period;

generate, using a machine learning model, a slope value based on the accrued cumulative transaction value for each of the one or more network time period segments, wherein the slope value is associated with a particular network time period segment and indicates a transaction trend of the digital content object during the particular network time period segment; and

adjust an electronic bid value of the digital content object during the particular network time period segment based at least on the slope value.

18. A non-transitory computer-readable storage medium having instructions stored thereon that, with one or more processors of an apparatus, cause the apparatus to:

receive one or more transaction signals associated with a digital content object;

generate a first set of cumulative transaction values, each cumulative transaction value of the first set of cumulative transaction values associated with a different one of one or more network time period segments within a first network time period, wherein each cumulative transaction value of the first set of cumulative transaction values is programmatically generated based on a total number of the one or more transaction signals received during each of the one or more network time period segments within the first network time period;

generate a second set of cumulative transaction values, each cumulative transaction value of the second set of cumulative transaction values associated with a different one of the one or more network time period segments within a second network time period, wherein each cumulative transaction value of the second set of cumulative transaction values is programmatically generated based on a total number of the one or more transaction signals received during each of the one or more network time period segments within the second network time period;

generate an accrued cumulative transaction value for each of the one or more network time period segments based on cumulating the first set of cumulative transaction values for each of the one or more network time period segments within the first network time period and the second set of cumulative transaction values for each of the one or more network time period segments within the second network time period;

generate, using a machine learning model, a slope value based on the accrued cumulative transaction value for each of the one or more network time period segments, wherein the slope value is associated with a particular network time period segment and indicates a transaction trend of the digital content object during the particular network time period segment; and

adjust an electronic bid value of the digital content object during the particular network time period segment based at least on the slope value.