Out-of-home Campaign Intelligence for Audience Targeting
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Solution Overview
Problem
Advertisers face challenges in effectively targeting and optimizing out-of-home (OOH) advertising campaigns due to limited control over billboard placements and lack of granular data integration, making it difficult to achieve high reach and sales-driven results compared to digital advertising.
Innovation Solution
A computerized methodology that combines probability of exposure estimates with segment-level data to provide intelligence for sales conversion and KPI determination, using a recommendation engine and optimization procedures tailored to each scenario, enabling better decision-making for OOH advertising campaigns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional OOH advertising methods are used, then advertisers can place physical advertisements in public spaces, but they lack granular control over audience targeting and placement optimization
Solution Approach 1:
The patent segments the OOH advertising system into multiple components: probability of exposure estimation models, segment-level data layers, and optimization procedures. Each component processes specific aspects of audience targeting independently, allowing granular control over different dimensions of ad placement and audience selection without requiring complex integration of all data simultaneously.
Solution Approach 2:
The patent introduces an intermediary recommendation engine that sits between the raw data sources (travel studies, geographic inventory data, viewability estimates) and the final advertising decisions. This intermediary processes and synthesizes multiple data sources into actionable intelligence, simplifying the complexity of data integration while providing sophisticated audience targeting capabilities.
2Measurement precision
If advertisers want granular audience estimates and placement control, then they can improve campaign effectiveness, but the process becomes time-consuming and complex
Solution Approach 1:
The patent performs preliminary actions by pre-calculating probability of exposure estimates for various geographic locations and audience segments before the actual advertising campaign. Travel studies and geographic inventory data are processed in advance to create pre-segmented audience profiles and placement recommendations, so that when campaign planning is needed, the analysis is already complete and ready for rapid deployment.
Solution Approach 2:
The patent changes parameters by transforming raw data into standardized probability estimates and segment-level metrics. By converting diverse data sources (travel patterns, geographic locations, viewability data) into unified probability parameters, the system enables precise audience estimation while maintaining computational efficiency and reducing the time required for campaign generation.
3Productivity
If advertisers blend intelligence from physical space placement with digital advertising capabilities, then they can achieve better reach and sales results, but the integration process is currently cumbersome
Solution Approach 1:
The patent merges physical OOH advertising intelligence with digital advertising capabilities by integrating probability of exposure estimates from physical space analysis with segment-level data and optimization algorithms. This combination creates a unified system that provides both the geographic precision of physical advertising and the audience targeting precision of digital advertising, enabling comprehensive campaign optimization across both domains.
Data Source
AI summary
Embodiments of the invention overcome the shortcomings of prior art by transforming the understanding of how different creative placements and location helps drive sales and other KPIs to a computerized methodology that may allow advertising planners and buyers to generate plans that meet these expectations on effectiveness of their advertising campaign. Aspects of the invention fusing the “probability of exposure” estimates with segment level data to provide layers of intelligence in determining the probability estimates for sales conversion or other KPIs. Moreover, aspects of the invention may predict based on various models the reach and frequency relationship tradeoff for different impression levels.