Decoding Consumer Behavior in the Used Car Market: A Machine Learning Approach to Key Decision Factors
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 platfo...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04033.pdf |
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| Summary: | 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. |
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| ISSN: | 2271-2097 |