Real-time dayparting management
A method including obtaining real-time observed orders per minute (OPM) data. The method also can include training a prediction model to make a real-time OPM prediction for a current time period, based on the real-time observed OPM data over past time steps based on lagged time steps in a moving average. The method additionally can include determining, in real-time, whether a demand surge exists based on the real-time observed OPM data and the real-time OPM prediction, to generate a first surge modifier. The method further can include when the demand surge exists, generating, in real-time, a sub-hour revenue per click (RPC) prediction for a first sub-hour time interval. The method additionally can include determining, in real-time, the first surge modifier for the first sub-hour time interval. The method further can include uploading, in real-time, the first surge modifier to a dayparting system of a search engine to bypass existing time intervals and modifiers. Other embodiments are described.
1 . A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising:
obtaining real-time observed orders per minute (OPM) data;
training a prediction model to make a real-time OPM prediction for a current time period, based on the real-time observed OPM data over past time steps based on lagged time steps in a moving average;
determining, in real-time, that a demand surge exists based on the real-time observed OPM data and the real-time OPM prediction, wherein the determining comprises:
comparing the real-time observed OPM data to the real-time OPM prediction for the current time period; and
responsive to determining that the real-time observed OPM data exceeds the real-time OPM prediction, determining that the real-time observed OPM data is a statistical outlier for the real-time OPM prediction and that the demand surge exists;
responsive to the determination that the demand surge exists, generating, in real-time, a sub-hour revenue per click (RPC) prediction for a first sub-hour time interval;
determining, in real-time, a first surge modifier for the first sub-hour time interval; and
uploading, in real-time, the first surge modifier to a dayparting system of a search engine to bypass existing time intervals and modifiers.
2 . The system of claim 1 , wherein the determining that the real-time observed OPM data is the statistical outlier for the real-time OPM prediction comprises:
calculating a P value for the real-time observed OPM data based on the real-time OPM prediction.
3 . The system of claim 2 , wherein the determining that the real-time observed OPM data is the statistical outlier for the real-time OPM prediction further comprises:
determining that the P value is less than a threshold.
4 . The system of claim 1 , wherein the prediction model is a time-series prediction machine-learning model.
5 . The system of claim 4 , wherein the time-series prediction machine-learning model is an autoregressive integrated moving average (ARIMA) model.
6 . The system of claim 1 , wherein the operations further comprise:
while the demand surge exists, determining a second surge modifier for a second sub-hour time interval, wherein the first sub-hour time interval and the second sub-hour time interval are within a single hour.
7 . The system of claim 6 , wherein the operations further comprise:
while the demand surge exists, uploading the second surge modifier to the dayparting system of the search engine to bypass the first surge modifier.
8 . A method implemented via execution of computing instructions configured to run at one or more processors, the method comprising:
obtaining real-time observed orders per minute (OPM) data;
training a prediction model to make a real-time OPM prediction for a current time period, based on the real-time observed OPM data over past time steps based on lagged time steps in a moving average;
determining, in real-time, that a demand surge exists based on the real-time observed OPM data and the real-time OPM prediction, wherein the determining comprises:
comparing the real-time observed OPM data to the real-time OPM prediction for the current time period; and
responsive to determining that the real-time observed OPM data exceeds the real-time OPM prediction, determining that the real-time observed OPM data is a statistical outlier for the real-time OPM prediction and that the demand surge exists;
responsive to the determination that the demand surge exists, generating, in real-time, a sub-hour revenue per click (RPC) prediction for a first sub-hour time interval;
determining, in real-time, a first surge modifier for the first sub-hour time interval; and
uploading, in real-time, the first surge modifier to a dayparting system of a search engine to bypass existing time intervals and modifiers.
9 . The method of claim 8 , wherein the determining that the real-time observed OPM data is the statistical outlier for the real-time OPM prediction comprises:
calculating a P value for the real-time observed OPM data based on the real-time OPM prediction.
10 . The method of claim 9 , wherein the determining that the real-time observed OPM data is the statistical outlier for the real-time OPM prediction further comprises:
determining that the P value is less than a threshold.
11 . The method of claim 8 , wherein the prediction model is a time-series prediction machine-learning model.
12 . The method of claim 11 , wherein the time-series prediction machine-learning model is an autoregressive integrated moving average (ARIMA) model.
13 . The method of claim 8 , further comprising:
while the demand surge exists, determining a second surge modifier for a second sub-hour time interval, wherein the first sub-hour time interval and the second sub-hour time interval are within a single hour.
14 . The method of claim 13 , further comprising:
while the demand surge exists, uploading the second surge modifier to the dayparting system of the search engine to bypass the first surge modifier.