Predicting Customer Retention and Online Shopping Perception: Highly Recommended Machine Learning Approach

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.

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Publication Details

Journal
Recent Advances in Computer Science and Communications
Published
2024-08-23
DOI
https://doi.org/10.2174/0126662558309335240809064620
Citations
1
Primary Topic
Customer churn and segmentation
Type
article
Field-Weighted Citation Impact
0.44
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article

Predicting Customer Retention and Online Shopping Perception: Highly Recommended Machine Learning Approach

Madan Lal Saini, Purushottam Sharma, Ajay Kumar, Shilpi Kulshrestha
1 citations
Recent Advances in Computer Science and Communications
Customer churn and segmentation
0.44
article

Predicting Customer Retention and Online Shopping Perception: Highly Recommended Machine Learning Approach

Madan Lal Saini, Purushottam Sharma, Ajay Kumar, Shilpi Kulshrestha
article en
1 citations

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.

Recent Advances in Computer Science and CommunicationsVol. 18(7)
Chandigarh University (IN), Jagannath University (IN), Amity University (IN), Mohanlal Sukhadia University (IN)
Openalex Percentile: Top 28%
Customer churn and segmentation
0.44
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