Cross-Screen Advertising Placement With Multi-Device Data Deduplication
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Solution Overview
Problem
Existing advertising strategies are inefficient and inflexible, failing to integrate disparate consumer data across multiple devices and platforms, leading to suboptimal advertising placement and increased costs due to manual processes and lack of real-time adaptability.
Innovation Solution
A system and method for optimizing advertising campaigns by analyzing consumer data from various devices, creating look-alike models, and integrating actual content consumption behavior to predict future consumption patterns, allowing for targeted advertising across multiple platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual processes are used for advertising placement, then flexibility in strategy adjustment is maintained, but efficiency and productivity deteriorate due to increased costs and time consumption
Solution Approach 1:
The system implements dynamic advertising strategies that automatically adjust in real-time based on consumer behavior data. The optimization engine continuously modifies placement decisions without manual intervention, enabling both high efficiency through automation and flexibility through adaptive response to changing conditions.
Solution Approach 2:
The system incorporates feedback loops where consumer behavior data from multiple devices is continuously collected and fed back into the optimization engine. This enables automatic strategy adjustment based on actual performance metrics, maintaining flexibility while eliminating manual processes.
2Measurement precision
If consumer data from multiple devices is integrated, then measurement precision and targeting accuracy improve, but device complexity and data processing requirements increase
Solution Approach 1:
The system introduces data integration layers and processing intermediaries that aggregate and harmonize consumer data from multiple devices. These intermediaries translate disparate device data into unified consumer profiles, improving targeting accuracy while managing complexity through structured data transformation.
Solution Approach 2:
The system segments consumer data by device type and behavior pattern, processing each segment through specialized algorithms. This segmentation approach improves measurement precision for specific device categories while managing overall system complexity through modular data handling.
3Productivity
If real-time campaign optimization is implemented, then advertising yield and ROI improve, but computational power and processing speed requirements increase
Solution Approach 1:
The system implements periodic optimization cycles that balance real-time responsiveness with computational efficiency. By structuring optimization as periodic batches rather than continuous processing, the system achieves high advertising yield through frequent adjustments while managing computational power requirements through time-sliced processing.
Solution Approach 2:
The system applies optimization to the most valuable advertising placements first, focusing computational power on high-impact decisions. By prioritizing partial optimization of critical placements over complete optimization of all placements, the system achieves significant yield improvement with reduced computational requirements.
4Adaptability or versatility
If look-alike models are created for target audience prediction, then adaptability and targeting precision improve, but data processing time and computational complexity increase
Solution Approach 1:
The system pre-processes consumer data and pre-generates look-alike model segments before actual advertising campaigns begin. By performing data integration and model creation in advance, the system improves adaptability and targeting precision during campaign execution while minimizing real-time data processing time.
Data Source
AI summary
A method may include obtaining an advertising campaign, determining one or more devices associated with a consumer, and associating the one or more devices with a consumer identifier. The method also includes obtaining data associated with the one or more devices associated and corresponding the data with the consumer identifier. The method also includes deduplicating the data related to the consumer identifier across one or more viewing methods. The method also includes displaying the deduplicated data. The method also includes adjusting the advertising campaign based on user input.