Cross-Device Ad Decision Framework for Automated Media Buying
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Solution Overview
Problem
Existing ad buying decision systems are suboptimal for mobile devices due to differences in data and ad hoc nature, lack a formal decision framework, and rely heavily on manual processes, leading to inefficient and suboptimal distribution of ad inventory across devices and channels.
Innovation Solution
A decision framework that automates ad transactions across networked devices, incorporating rules for ad delivery on both desktop and mobile devices, optimizing and automating buying and selling decisions based on measurable goals, considering various factors and constraints, and using utility functions to measure ad placement effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for ad buying decisions, then flexibility and control are maintained, but decision-making efficiency and optimization are reduced
Solution Approach 1:
The system enables self-service through automated decision frameworks that independently analyze data, evaluate ad opportunities, and execute buying decisions without manual intervention. The framework uses predefined rules and algorithms to autonomously optimize ad transactions across multiple devices and channels, significantly improving decision-making efficiency while maintaining strategic control through configurable parameters.
Solution Approach 2:
Manual mechanical processes for ad buying decisions are replaced with an automated computational system. The decision framework uses data processing algorithms and utility function calculations to substitute human manual analysis and decision-making, transforming the mechanical process into an automated electronic system that operates at scale across networked devices.
2Productivity
If ad transactions are optimized for each device separately, then device-specific performance is improved, but overall system efficiency and revenue are reduced
Solution Approach 1:
The system merges ad transaction optimization across multiple devices into a unified decision framework. Instead of treating each device separately, the framework consolidates data from all devices and executes coordinated buying decisions that optimize overall system efficiency and revenue while managing complexity through centralized control and standardized processing protocols.
Solution Approach 2:
The decision framework is designed with universal applicability across different device types and platforms. It performs multiple functions including data aggregation, opportunity evaluation, bid generation, and performance optimization in a single unified system that works across desktop, mobile, and other networked devices without requiring device-specific complex systems.
3Measurement precision
If comprehensive data analysis is performed for ad decisions, then decision accuracy and optimization are improved, but processing time and computational resources are increased
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing data, establishing decision rules and utility functions in advance. Historical data is analyzed beforehand to create predictive models and evaluation criteria, so that when real-time ad opportunities arise, the framework can quickly apply pre-established criteria to make accurate decisions without extensive real-time processing.
Solution Approach 2:
The decision framework applies partial action by focusing computational resources on the most critical data points and decision factors for each ad opportunity. Rather than analyzing every possible variable equally, the system identifies and processes the key parameters that have the greatest impact on decision accuracy, reducing processing time while maintaining high decision quality.
Data Source
AI summary
A method relates to receiving, by a processing device executing a decision framework, a request comprising a plurality of attribute values associated with an opportunity to place an ad on one or more devices associated with a user, generating a state dynamic model comprising a plurality of state variables based on the plurality of attribute values associated with the opportunity, determining a plurality of utility functions, wherein each one of the plurality of utility function is determined based on at least one of the plurality of state variables, calculating a decision based on the plurality of utility functions, providing an ad to a selection of devices, tracking performance data for interactions with the ad, and dynamically improving performance of the system using the tracked performance data and a feedback loop.