US-12620004-B2 - Real-time dayparting management

US12620004B2US 12620004 B2US12620004 B2US 12620004B2US-12620004-B2

Abstract

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.

Inventors

ChangZheng Liu
Boning Zhang
Changfu Li

Assignees

WALMART APOLLO, LLC

Dates

Publication Date: 20260505
Application Date: 20230810

Claims (14)

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.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. patent application Ser. No. 17/587,054, filed Jan. 28, 2022. U.S. patent application Ser. No. 17/587,054 is incorporated herein by reference in its entirety. TECHNICAL FIELD This disclosure relates generally to real-time dayparting management. BACKGROUND Search engine marketing (SEM) is a form of Internet marketing that involves promotion of websites by increasing their visibility in search engine results pages (SERPs), primarily through paid advertising, often through bidding for advertisements (ads). Some search engines, such as Google and Bing, offer SEM dayparting. Dayparting is a way to split a day into multiple intervals (e.g., six different time intervals per day) and use a respective modifier of a base bid for search engine marketing (SEM) advertisement bids during each of those time intervals. Those who use dayparting generally set the time intervals and modifiers through manual operations and/or do not adjust in real-time to changing behavior of users of the search engines. BRIEF DESCRIPTION OF THE DRAWINGS To facilitate further description of the embodiments, the following drawings are provided in which: FIG. 1 illustrates a front elevational view of a computer system that is suitable for implementing an embodiment of the system disclosed in FIG. 3; FIG. 2 illustrates a representative block diagram of an example of the elements included in the circuit boards inside a chassis of the computer system of FIG. 1; FIG. 3 illustrates a block diagram of a dayparting management system that can be employed for providing real-time dayparting management, according to an embodiment; FIG. 4 illustrates a graph showing hourly RPC on Google on a website across an eight day non-holiday period; FIG. 5 illustrates a graph showing OPM on a website on a day during the holiday shopping season; FIG. 6 illustrates a flow chart for a method of providing real-time dayparting management, according to an embodiment; FIG. 7 illustrates a flow chart for a method of determining time intervals using a decision tree, according to an embodiment; FIG. 8 illustrates an exemplary decision tree; FIG. 9 illustrates a timeline showing a rolling horizon-approach to train and predict; FIG. 10 illustrates a flow chart for a method of performing real-time demand surge detection, according to an embodiment; FIG. 11 illustrates a flow chart for a method of performing a demand surge check, according to an embodiment; FIG. 12 illustrates a graph 1200 showing a normal distribution of the predicted OPM data; FIG. 13 illustrates a flow chart for a method of providing real-time dayparting management, according to an embodiment; and FIG. 14 illustrates a flow chart for an activity of FIG. 13 of determining, in real-time, whether a demand surge exists based on the real-time observed OPM data. For simplicity and clarity of illustration, the drawing figures illustrate the general manner of construction, and descriptions and details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the present disclosure. Additionally, elements in the drawing figures are not necessarily drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help improve understanding of embodiments of the present disclosure. The same reference numerals in different figures denote the same elements. The terms “first,” “second,” “third,” “fourth,” and the like in the description and in the claims, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms “include,” and “have,” and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, device, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, system, article, device, or apparatus. The terms “left,” “right,” “front,” “back,” “top,” “bottom,” “over,” “under,” and the like in the description and in the claims, if any, are used for descriptive purposes and not necessarily for describing permanent relative positions. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the apparatus, methods, and/or articles of manufacture described herein are, for example, capable of operation in other orientations than those illustrated or otherwise described herein. The terms “couple,” “coupled,” “couples,” “coupling,” and the like should be bro