Improving Market Segmentation via Customer Personality Prediction using Deep AI Analysis

Abstract

Market segmentation plays a pivotal role in designing effective marketing strategies tailored to diverse customer groups. This study proposes a novel approach to enhance market segmentation by leveraging customer personality prediction using Convolutional Neural Networks (CNNs) for deep learning analysis. Traditional market segmentation techniques often rely on demographic and behavioral data, which may overlook the underlying psychological characteristics of customers. In contrast, our approach integrates advanced deep learning techniques to predict customer personalities based on their digital footprints, such as social media activity and online behavior. By employing CNNs, we extract complex features from unstructured data sources and model intricate patterns inherent in customer personas. This enables us to uncover latent insights and segment customers more accurately, leading to personalized marketing campaigns and improved customer engagement. Through empirical evaluations on real-world datasets, we demonstrate the effectiveness of our proposed methodology in enhancing market segmentation accuracy and effectiveness. Our findings underscore the potential of integrating deep learning analysis, specifically CNNs, with market segmentation practices to achieve more nuanced and actionable insights into customer behavior and preferences.