Modern Research in Decision Making

Presenting a Hybrid Model for Predicting Consumer Purchase Intent Based on Machine Learning and Social Media Feedback

Document Type : Original Article

Authors

Monireh Hosseini 1
Mohadese Karkaboodi 2

1 Associate Professor, Department of Information Technology, Faculty of Industrial Engineering, Khajeh Nasiruddin Toosi University of Technology, Tehran, Iran

2 Master's student, Department of Information Technology, Faculty of Industrial Engineering, Khajeh Nasiruddin Toosi University of Technology, Tehran, Iran

Abstract
With the rapid growth of online shopping and the significant role of social media in consumer decision-making, purchase intention prediction has become one of the key issues in digital marketing. Accurate prediction of this behavior can improve advertising targeting, increase customer conversion rates, and optimize sales strategies. The purpose of this study is to propose a hybrid machine learning model for predicting users’ purchase intentions based on their responses to advertisements. The dataset consists of 1,000 records and 10 relevant features (including age, gender, income, time spent on the website, daily internet usage, advertisement title, location and time of interaction, and click label), which were collected and cleaned from public sources (Kaggle). After preprocessing and feature selection, a stacking approach was employed, where the outputs of base models (logistic regression and support vector machine) were combined and used for final prediction. Model evaluation using accuracy, precision, recall, and F1-score demonstrated that the proposed model achieved strong performance (accuracy = 0.96, precision = 0.98). The findings indicate that combining demographic and behavioral features can improve purchase intention prediction and provide practical implications for targeted advertising in e-commerce platforms.

Keywords

Purchase intent
customer behavior
social media
machine learning
marketing
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