AI-Powered Product Purchase Prediction: A Multi-Modal Deep Learning Approach

Résumé fourni par la source

AI and digital platforms have changed how practical analysis has become in sectors like cosmetics. Consumer perception towards cosmetic brands is crucial in making purchase decisions in Bangladesh, but traditional methods of measuring lack the reality. However, no studies have been found on modeling trust in such a domain in the context of cosmetic buying behavior in Bangladesh. Modern methods commonly focus either on structured data or free text, where there is a lack of the combination, and often, they do not provide interpretability that poses an obstacle to business utilization. The purpose of this study is to propose a method for predicting customer trust in cosmetics using an AI-based approach. It combines structured demographic and behavioral data with unstructured textual reviews; uses CatBoost and DNN for classification learning of models by SHAP and LIME to increase interpretability, and checks deployment readiness. The approach is a mixture of a structured response survey (Sample size: 1000) and unstructured review text analyzed through BERT embeddings. CatBoost and DNN models are adopted to predict trust toward consumers, as well as purchase intention. Model effectiveness is evaluated by measures such as accuracy, Precision, Recall, F1-score, MAE, MSE, RMSE, and R2. SHAP and LIME are used to increase the transparency and interpretability. We have implemented the DL models that outperform the baseline models with accuracy (93%, 90%), Precision (93%, 90%), Recall (92%, 87%) F1 -Score (93%, 89%), MAE (0.047, 0.036), MSE (0.004, 0.002), RMSE (0.059, 0.045), and R2score (98%, 92%) for CatBoost, and DNN, respectively for the purchase intention. SHAP identified essential factors related to trust, such as brand trust and review sentiments. The statistical analysis (ANOVA and Friedman test) supported the validity of the model. This AI-based methodology provides valuable results to cosmetic marketers operating in Bangladesh, presenting personalized strategies and better consumer engagement plans. Structured and unstructured data combined allow artificial intelligence in cosmetics to predict customer trust for scalable decision-making.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.