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2000
Volume 18, Issue 7
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566

Abstract

Introduction

In today’s fast-paced world, online shopping has become a staple activity, especially for busy individuals seeking convenience. Online shopping has emerged as a predominant activity, particularly popular among the younger demographic. The hectic lifestyles of the working class have elevated online shopping to a convenient and often essential practice, providing a respite amidst their busy schedules. Simultaneously, businesses in the competitive E-commerce market recognize the importance of understanding customer behavior and perceptions to foster loyalty and ensure sustainability.

Methods

In response to the evolving landscape of online shopping, we have undertaken a comprehensive analysis using machine learning techniques. Our approach involves the utilization of machine learning algorithms to recognize patterns and make precise predictions. We divide the data set into quarters, assess sales income per quarter, and further partition the data into training and testing sets. The subsequent steps involve forecasting revenue for upcoming quarters and identifying top-performing commodities to devise a Python-based model for strategic customer retention. The methodology begins by segregating the sales data set into quarters, followed by the calculation of quarterly sales income. Using a machine learning system, our approach forecasts revenue for future quarters and identifies high-performing commodities based on quarterly sales rates.

Results

The culmination of these results leads to the development of a Python-based model for strategic customer retention. The outcome of our analysis not only facilitates precise predictions of future revenue but also contributes to the creation of a strategic model for customer retention.

Conclusion

By identifying high-performing commodities and leveraging machine learning algorithms, we establish a symbiotic loop of sales and purchasing between customers and the E-commerce company. This algorithmically-driven loop promises incremental profitability for both the customers and the E-commerce company, creating a symbiotic relationship.

© 2025 Bentham Science Publishers
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Predicting Customer Retention and Online Shopping Perception: Highly Recommended Machine Learning Approach
Recent Advances in Computer Science and Communications 18, E26662558309335 (2025); https://doi.org/10.2174/0126662558309335240809064620
/content/journals/rascs/10.2174/0126662558309335240809064620
/content/journals/rascs/10.2174/0126662558309335240809064620
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  • Article Type:     Research Article
Keyword(s): customer perception; electronic-commerce; profitability; retention strategy; sales prediction; sustainability; Time series analysis

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