Analysis of Demographic and Consumer Behavior Factors on Satisfaction with AI Technology Usage in Digital Retail Using the Random Forest Algorithm

Abstrak

The rapid integration of artificial intelligence (AI) into digital retail has reshaped consumer interactions, enabling personalized services and operational enhancements. This study investigates the demographic and behavioral factors influencing consumer satisfaction with AI technologies in digital retail, using the Random Forest classification algorithm for predictive modeling. After comprehensive preprocessing and hyperparameter tuning through grid search cross-validation, the Random Forest model achieved an overall accuracy of 83%. While the model showed strong performance for predicting satisfied consumers yielding a precision of 0.84, recall of 0.97, and F1-score of 0.90, it performed poorly in identifying dissatisfied users, with a recall of only 0.27 and F1-score of 0.39, highlighting a class imbalance issue. Feature importance analysis revealed that experiential factors, particularly enhanced AI experience and preference for online services, significantly influenced satisfaction levels, whereas demographic variables such as age and gender had limited predictive value. These findings emphasize the need for digital retailers to focus on user-centric design and service personalization, rather than demographic segmentation alone, to enhance customer satisfaction and loyalty. Furthermore, the study contributes methodologically by demonstrating the effectiveness of Random Forest in handling complex consumer datasets and theoretically by validating TAM and Customer Satisfaction Theory in the context of AI adoption. Despite limitations related to class imbalance and sector-specific data, this research offers actionable insights for retailers, marketers, and system developers aiming to improve AI-driven service quality and consumer engagement. Future studies are encouraged to address these limitations through the inclusion of emotional and contextual variables and by expanding the analysis to other industries for broader applicability.