AI MARKETING FOR REAL-TIME CUSTOMER SEGMENTATION: AUTOENCODER-ENHANCED TIME-EVOLVING CLUSTERING

Conventional customer segmentation methods often rely on static models that fail to capture dynamic consumer behavior. Consumer behavior is influenced by external factors such as economic shifts, cultural events, and seasonal trends, necessitating an adaptive approach. This study explores AI marketing segmentation as a solution to track and predict consumer state transitions. The research utilizes deep learning-based feature extraction and time-evolving clustering to analyze grocery store transaction data from April to June 2022, covering pre-Ramadan, Ramadan, and post-Ramadan periods in Indonesia. An autoencoder neural network is employed to reduce high-dimensional transaction data into a latent representation, which is then clustered using MiniBatch K-Means to identify evolving customer segments. The study also applies transition analysis to track segment shifts over time. Findings reveal that customer behavior is fluid, with distinct segment transitions occurring in response to Ramadan. The transition analysis highlights that static segmentation models fail to capture these changes. AI-driven marketing segmentation, enables faster tracking of shifting consumer behaviors, allowing businesses to proactively adjust their marketing strategies.