Heatmaps
Heatmaps provide a visual representation of viewer attention, highlighting the areas of an advertisement that capture the most focus. Unlike traditional eye-tracking methods, which rely on web cameras and calibration, this technology leverages a neural network trained on extensive human attention data to predict focus points without requiring specialized hardware or audience samples.
How it works
Our heatmap model is powered by a neural network trained on 2.5 million data points gathered from human interactions with video and image ads. The model analyzes visual elements such as colors, faces, and text to predict where viewers are likely to focus their attention.
Key features
- AI-Powered Prediction: Uses a trained neural network instead of traditional eye-tracking.
- Scalable & Efficient: No calibration, audience sample, or specialized equipment required.
- Enhanced Creative Optimization: Identifies high-attention areas to guide content adjustments.
Implementation & Use Cases
The attention heatmap is generated by overlaying predicted focus areas on an advertisement. By synchronizing viewer reactions (e.g., facial expressions) with ad content, advertisers gain insights into both attention patterns and emotional engagement. This enables:
- Scene optimization through cropping or edits.
- Identification of key visual elements that drive attention.
- Performance comparison across different ad creatives.
Accuracy & Validation
Our heatmap technology demonstrated 80% accuracy in a pilot study with a global consumer goods company, aligning closely with human-tested attention results. This validation confirms its effectiveness in predicting viewer focus without requiring direct gaze tracking.
Why use this technology?
Since attention is a prerequisite for ad effectiveness, integrating AI-driven heatmaps into creative workflows helps maximize engagement and brand impact. By understanding which elements drive attention and emotional response, advertisers can refine their content strategy for better performance.