Digital Signage Ad Evaluation Using Camera-Based Viewer Detection
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Solution Overview
Problem
The digital signage industry lacks a method to determine the effectiveness of ad content, making it difficult for operators to provide clear return on investment (ROI) data and target demographics effectively.
Innovation Solution
The implementation of a system that uses cameras and machine learning models to detect eye contact and gather impression data, such as view counts and dwell time, to refine and adapt ad content based on viewer demographics and behavior, enabling targeted advertising and improved ROI measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital signage displays advertising content in public venues, then advertising engagement and dynamism are improved, but the ability to measure ad effectiveness and provide ROI data deteriorates
Solution Approach 1:
The patent implements feedback by capturing viewer data through cameras and sensors, processing this data through machine learning models to generate demographic and engagement metrics, then using these metrics to measure ad effectiveness and provide ROI data. This closed-loop feedback system transforms the previously unmeasurable advertising impact into quantifiable performance indicators.
2Duration of action of moving object
If digital signage content is changed in real time based on promotions and events, then advertising dynamism is improved, but the ability to target specific demographics deteriorates
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict which demographic groups are most likely to be present and engaged at different times and locations. This allows the system to pre-select and schedule appropriate ad content for specific demographics before the actual viewing occurs, enabling proactive demographic targeting rather than reactive content changes.
Data Source
AI summary
Systems and techniques for displaying advertisements on a digital display and gathering impression and view data related to the advertisement that may be used to refine or score the advertisements for greater effectiveness. The impression and view data may be used to identify effective portions of advertisements and subsequently to train a machine learning model to predict impression data for advertisements that may be used to iteratively improve the advertisements.