Segmenting and Predicting Consumer behavior in cosmetic industry: A Machine learning approach using Latent class and Random Forest techniques

The proposed study examines consumer purchasing behavior using the Latent Class Choice Models (LCCM) to determine the significant elements that impact purchase decisions. An online survey, encompassing the areas of awareness, attitude, perceived value, subjective norms, buying experience, and perceived risk was conducted and based on the data, consumers were divided into latent classes. The analysis identified different consumer groups with different reactions to these latent factors. These results provide insightful information to marketers and help them create focused strategies that appeal to various customer categories. Businesses can increase the efficacy of their marketing strategies by focusing on the most important elements. Combining LCCM with feature importance offers a thorough method for comprehending customer behavior and improving marketing strategy decision-making. According to a Random Forest model, that was used to evaluate and feature the importance of perceived effort, the most important factor impacting purchase intention, followed by subjective norms and attitude is perceived risk wherein awareness had less impact.