Real-Time Dayparting for Demand Surge Bid Adjustment

Solution Overview

Problem

Existing search engine marketing (SEM) dayparting systems rely heavily on manual operations and fail to adjust in real-time to changing user behavior, particularly during non-holiday time-of-day effects and demand surges, leading to inefficiencies in ad bidding strategies.

Innovation Solution

Implementing an automated dayparting management system that utilizes machine learning algorithms and time-series forecasting to dynamically adjust SEM bids based on real-time data, including normal and peak-time operation modes, to optimize revenue per click (RPC) and handle demand surges.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual operations are used to set time intervals and modifiers, then ease of operation is maintained, but productivity and adaptability deteriorate due to inability to adjust in real-time

Engineering Contradiction:

ImproveSEM bid optimization efficiencyVSAvoidautomation system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system performs self-service by automatically detecting demand surges and adjusting bid modifiers without human intervention. The machine learning model continuously monitors performance data and autonomously optimizes bidding strategies, eliminating the need for manual reconfiguration while maintaining high adaptability to changing conditions.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring SEM campaign performance data and using this information to adjust bid modifiers in real-time. The machine learning model processes performance metrics and automatically updates bidding strategies, creating a closed-loop system that adapts to changing demand patterns without manual intervention.

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If manual dayparting management is used, then device complexity is low, but adaptability to changing demand patterns deteriorates

Engineering Contradiction:

Improvereal-time adaptation to demand changesVSAvoidautomation system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs self-service by automatically detecting demand surges and adjusting bid modifiers without human intervention. The machine learning model continuously monitors performance data and autonomously optimizes bidding strategies, eliminating the need for manual reconfiguration while maintaining high adaptability to changing conditions.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system transitions from static manual bid settings to dynamic automated adjustments. The machine learning model continuously adapts bid modifiers based on real-time performance data and detected demand patterns, enabling the system to respond flexibly to changing conditions without requiring manual reconfiguration.

Inventive Principle:

Principle #15Dynamics

3Productivity

If real-time automated adjustment is implemented, then productivity and adaptability improve, but loss of time for manual operations increases

Engineering Contradiction:

ImproveSEM bid optimization efficiencyVSAvoidtime for manual monitoring and adjustment

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs self-service by automatically detecting demand surges and adjusting bid modifiers without human intervention. The machine learning model continuously monitors performance data and autonomously optimizes bidding strategies, eliminating the need for manual reconfiguration while maintaining high adaptability to changing conditions.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system maintains continuous useful action by operating 24/7 without interruption. The machine learning model continuously monitors performance data and adjusts bid modifiers in real-time, ensuring optimal bidding strategies are maintained at all times without requiring manual intervention or breaking operational continuity.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS12620004B2Real-time dayparting management

Publication Date: 2026.05.05 WALMART APOLLO LLC

AI summary

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.