Boost Campaign Performance with AI Analysis During the Prelaunch Phase

Until now, the acceptance and performance of campaigns had to be analyzed through time-consuming and costly focus groups, online surveys, or A/B tests. The results of these methods took a considerable amount of time to obtain and, depending on the available budget, often failed to yield representative results.

Here at dmcgroup, we draw on decades of experience in designing creative and effective campaigns. However, only by analyzing and comparing different concepts can we gain a high degree of certainty regarding their potential performance.

As an agency offering a wide range of AI services, we use artificial intelligence to test the performance of campaign creatives early in the creative process—in addition to any conventional testing methods.

Thanks to artificial intelligence that combines neuroscience and modern machine learning architectures, campaigns can be analyzed based on data and optimized for performance even before they launch.

How AI Analysis of Campaigns Works

We use the AI analytics platform Neurons as a standard tool in the design process. The platform is built on an extensive database of eye-tracking studies and EEG/brain scans. This includes data from over 300,000 participants gathered over approximately 20 years of research, combined with more than one billion behavioral data points. This data has been used to train highly precise models—in particular, attention prediction models with over 95% accuracy.

In this way, when conducting automated campaign tests with Neurons, we benefit from the wealth of experience provided by real test data gathered over decades of research.

In just a few steps, we can accurately assess and improve campaign drafts based on the standardized evaluation provided by AI analysis.

This guarantees excellence in design combined with high performance within a cost-effective framework.

Key Metrics and Scores for Evaluation

How exactly is the predicted performance of our campaign creatives evaluated?

The platform generates specific scoring metrics such as:

• Focus Score – measures the concentration of attention on specific areas of an image; calculated from heatmap data.
• Cognitive Demand Score – measures the amount and complexity of visual information that must be processed.
• Engagement Score – measures the intensity of positive emotions, such as interest, engagement, and joy, that the image or video evokes.
• Memory Score – a metric that measures the likelihood that viewers will remember an image or video. It measures the asset’s ability to leave a lasting impression.
• These scores are combined to calculate the Neurons Impact Score (NIS) —a condensed metric (on a scale of 1–10) that integrates attention, emotion, and recall, thereby enabling quick, well-informed decisions.