Predictions for Consumer Behaviour of E-Commerce Sales Data 2023-2024 Based on the LightGBM Model

Jiayi Du

2024

Abstract

With the outbreak of the pandemic, online consumption has gradually become the main consumption mode, promoting the rapid development of e-commerce. In this context, consumer behaviour and feedback are particularly important for companies to develop strategies. This study implements a LightGBM model to explore the potential links between customer information, behaviour, and feedback scores in the "2023-24 E-commerce Sales Data". Based on the analysis, age, geographical location, and frequency of purchases are the key factors that affect online customer ratings. The study found that there is a significant correlation between these factors and customer ratings, suggesting that customers of different ages, regions, and frequent purchasers may rate goods and services differently on e-commerce platforms. This provides valuable insights for e-commerce businesses, especially small and medium-sized businesses, that help them better understand customer behaviour and optimize service quality and customer experience. The significance of this study is that by using a relatively simple but effective model to predict the likelihood of customer ratings, it provides a practical idea for small and medium-sized enterprises to develop a customer scoring system in the fierce market competition, to help these enterprises better attract and retain customers and improve overall operational efficiency. These results provide novel methods and tools for personalized service and customer relationship management in the field of e-commerce and has important practical application value.

Paper Citation

in Harvard Style

Du J. (2024). Predictions for Consumer Behaviour of E-Commerce Sales Data 2023-2024 Based on the LightGBM Model. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 109-116. DOI: 10.5220/0013207400004568

in Bibtex Style

@conference{ecai24,
author={Jiayi Du},
title={Predictions for Consumer Behaviour of E-Commerce Sales Data 2023-2024 Based on the LightGBM Model},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={109-116},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013207400004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Predictions for Consumer Behaviour of E-Commerce Sales Data 2023-2024 Based on the LightGBM Model
SN - 978-989-758-726-9
AU - Du J.
PY - 2024
SP - 109
EP - 116
DO - 10.5220/0013207400004568
PB - SciTePress