Analyzing Consumer Ad Preferences: Utilizing Machine Learning for Exploratory Data Analysis and Feature Engineering based on Neurophysiological Measurements
In a groundbreaking development, a team of researchers led by Prof. Dr. Kai-Uwe Kühnberger at the University of Osnabrück has created an artificial intelligence (AI) system that can predict consumer preferences for advertisements with remarkable accuracy.
The AI system, based on machine learning techniques such as k-Nearest Neighbors, Support Vector Machine, and Random Forest, utilizes two physiological monitoring tools: electrodermal activity (EDA) and Facial Expression Analysis (FEA). These tools analyze data from videos, providing insights into a viewer's emotional responses and engagement levels.
The system incorporates an eXplainable AI module based on feature importance, allowing marketing specialists to better understand the prediction process. This transparency is invaluable, as it enables users to make informed decisions about the advertisements they create and distribute.
The study identified Attention, Engagement, Joy, and Disgust as the four most crucial features influencing consumer ad preference prediction. Surprisingly, emotions such as Joy, Disgust, and Surprise were also found to be relevant for consumer preference prediction.
The Random Forest technique within the AI system demonstrated the highest performance, achieving an 81% Accuracy, 84% Precision, 79% Recall, and an F1-score of 81% in predicting consumer preferences. This high level of accuracy makes the system an invaluable tool for marketing specialists, who can effectively utilize these computerized systems as supporting tools for their work.
One of the key advantages of this AI system is its versatility. It is not limited to specific types of advertisements, as it can be applied to video-based data for preference prediction. This means that marketing specialists can use the system to analyze a wide range of advertisements, ensuring they are creating content that resonates with their target audience.
This research marks a significant step forward in the field of AI and its applications in marketing. By combining physiological monitoring tools with machine learning techniques, the team has created an AI system that can accurately predict consumer ad preferences, paving the way for more effective marketing strategies in the future.
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