Issue |
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
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Article Number | 04033 | |
Number of page(s) | 6 | |
Section | AI and Advanced Applications | |
DOI | https://doi.org/10.1051/itmconf/20257004033 | |
Published online | 23 January 2025 |
Decoding Consumer Behavior in the Used Car Market: A Machine Learning Approach to Key Decision Factors
Math, Santa Monica College, Los Angeles, California, 90001, United States of America
Corresponding author: zhang_minshun01@student.smc.edu
This paper analyzes the main factors influencing consumer decision-making in the used car market. With the growing importance of the second-hand vehicle industry, understanding buyer behavior has become crucial for optimizing market strategies. Additionally, the increasing reliance on digital platforms has shifted the dynamics of how consumers evaluate and purchase used cars. The focus is on how online and offline trading platforms affect purchasing behavior. Using machine learning techniques, the paper compares multiple models to predict key purchase factors and visualize the data through various graphs. The findings reveal that factors such as car price, mileage, brand reputation, and online reviews play critical roles in shaping buyer preferences. Specifically, car price and mileage were found to be the most influential factors, with buyers showing a clear preference for vehicles offering better value relative to these parameters. Brand reputation further adds to consumer confidence, often tipping the balance when similar cars are compared. Additionally, online reviews and ratings significantly impact consumer trust, with buyers relying on peer feedback to assess the credibility of the seller and the condition of the vehicle. These factors collectively highlight the interplay between economic considerations and trust-building in consumer decision-making. The analysis provides practical recommendations for both consumers and platforms to optimize decision-making processes.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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