Predicting customer churn using targeted proactive retention

Mishachandar, B; Kumar, Kaushal

doi:10.14419/ijet.v7i2.27.10180

Cited by 10 publications

(7 citation statements)

References 16 publications

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“…While SVM predict E-Commerce customer churn, recommendation strategy suggests personalized retention actions. Reference [25] come up with a customer churn model that predict the possibility and time of churn. The model used Naïve Bayes classification and Decision Tree algorithm.…”

Section: B Forecast Customer Churnmentioning

confidence: 99%

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

Seo

Yoo²

2023

IEEE Access

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With the development of big data and deep learning technology, big data and deep learning technology have also been applied to the marketing field, which was a part of business administration. Customer churn management is one of the most important areas of marketing. In this paper, we proposed a method to prevent customer churn and increase purchase conversion rate by issuing customized discount coupons to customers with high churn rate based on big data in real time. After segmenting customer segments with two-dimensional segment analysis, a real-time churn rate estimation model based on clickstream data was generated for each segment. After that, we issued customized coupons to our customers. Finally, we tested the conversion rate and sales growth. A two-dimensional cluster analysis-based churn rate estimation combined with a recommendation system was found to be significantly more useful than the respective simple models. Using this proposed model, it is possible to increase sales by automatically estimating the customer's churn probability and shopping propensity without the burden of marketing costs in the online shopping mall.

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Section: B Forecast Customer Churnmentioning

confidence: 99%

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

Seo

Yoo²

2023

IEEE Access

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“…Mishachandar and Kumar(2018) come up with a customer churn model that predict the possibility and time of churn. The model used Naïve Bayes classi cation and Decision Tree algorithm [25]. Mainak and Arnaud(2021) used LSTM model to predict customer churn prediction with clickstream data [26].…”

Section: Forecast Customer Churnmentioning

confidence: 99%

Prevention of Customer Churn Due To Issuance of Real-Time Coupons Based on Deep Learning

Seo

Choi

Yoo

2022

Preprint

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With the development of big data and deep learning technology, big data and deep learning technology have also been applied to the marketing field, which was a part of business administration. Customer churn management is one of the most important areas of marketing. In this paper, we proposed a method to prevent customer churn and increase purchase conversion rate by issuing customized discount coupons to customers with high churn rate based on big data in real time. In this paper, we proposed a method to prevent customer cancellation and increase purchase conversion rate by issuing customized discount coupons to customers with high cancellation rates in real time based on big data. After segmenting customer segments with two-dimensional segment analysis, a real-time churn rate estimation model based on clickstream data was generated for each segment. After that, we issued customized coupons to our customers. Finally, we tested the conversion rate and sales growth. A two-dimensional cluster analysis-based churn rate estimation combined with a recommendation system was found to be significantly more useful than the respective simple models. Using this proposed model, it is possible to increase sales by automatically estimating the customer’s cancellation probability and shopping propensity without the burden of marketing costs in the online shopping mall.

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“…Businesses must move beyond predicting churn to understand the reasons behind it, such as service quality, customer satisfaction, and feedback. This requires a deep dive into data to uncover the drivers of churn and devise proactive strategies to mitigate it [17].…”

Section: Introductionmentioning

confidence: 99%

Deciphering Digital Social Dynamics: A Comparative Study of Logistic Regression and Random Forest in Predicting E-Commerce Customer Behavior

2024

J. Appl. Data Sci.

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This study compares Logistic Regression and Random Forest in predicting e-commerce customer churn. Utilizing the E-commerce Customer dataset, it navigates the complexities of customer interactions and behaviors, offering a rich context for analysis. The methodology focuses on meticulous data preprocessing to ensure data integrity, setting the stage for applying and evaluating Logistic Regression and Random Forest. Both models were assessed using accuracy, precision, recall, F1-Score, and AUC-ROC. Logistic Regression showed an accuracy of 90%, precision of 91% for class 0 and 82% for class 1, recall of 98% for class 0 and 50% for class 1, F1-Score of 94% for class 0 and 62% for class 1, and AUC-ROC of 0.88. Random Forest, with its ability to handle complex patterns, demonstrated higher overall performance with an accuracy of 95%, precision of 95% for class 0 and 93% for class 1, recall of 99% for class 0 and 74% for class 1, F1-Score of 97% for class 0 and 82% for class 1, and an AUC-ROC of 0.97. This comparative analysis offers insights into each model's strengths and suitability for predicting customer churn. The findings contribute to a deeper understanding of machine learning applications in e-commerce, guiding stakeholders in enhancing customer retention strategies. This research provides a foundation for further exploration into the digital social dynamics that shape customer behavior in the evolving digital marketplace.

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Predicting customer churn using targeted proactive retention

Cited by 10 publications

References 16 publications

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

Prevention of Customer Churn Due To Issuance of Real-Time Coupons Based on Deep Learning

Deciphering Digital Social Dynamics: A Comparative Study of Logistic Regression and Random Forest in Predicting E-Commerce Customer Behavior

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