E-commerce big data processing based on an improved RBF model
In the dynamic landscape of China’s booming economy, the surge in e-commerce customer volume presents both opportunities and challenges, notably in managing customer churn (CC). Addressing this critical issue, this study introduces an innovative approach employing a radial basis function neural netw...
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| Format: | Article |
| Language: | English |
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De Gruyter
2024-12-01
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| Series: | Journal of Intelligent Systems |
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| Online Access: | https://doi.org/10.1515/jisys-2023-0131 |
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| author | Lu Qiuping |
| author_facet | Lu Qiuping |
| author_sort | Lu Qiuping |
| collection | DOAJ |
| description | In the dynamic landscape of China’s booming economy, the surge in e-commerce customer volume presents both opportunities and challenges, notably in managing customer churn (CC). Addressing this critical issue, this study introduces an innovative approach employing a radial basis function neural network for predicting CC within the e-commerce sector. To enhance the model’s performance in handling the vast and complex data inherent to e-commerce, the least absolute shrinkage and selection operator regression algorithm is employed, optimizing the model’s predictive accuracy. By meticulously analyzing the customer lifecycle, this refined model adeptly predicts churn at various stages, enabling the identification of features most correlated with churn. Empirical results underscore the model’s exceptional capability, achieving a prediction accuracy of 95% and a remarkably low loss rate of 3%. Furthermore, during the excavation, advanced, stable, and decline stages of the customer lifecycle, accuracy levels of 97.6, 93.1, 92.7, and 91.8% are attained, respectively, facilitating the precise selection of highly correlated customer features. Thus, the advanced churn prediction model proposed herein significantly contributes to the e-commerce domain, offering a robust tool for strategizing customer retention and mitigating churn. |
| format | Article |
| id | doaj-art-4a61583d4b7648d2a3e7d672ad719e2b |
| institution | Kabale University |
| issn | 2191-026X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj-art-4a61583d4b7648d2a3e7d672ad719e2b2025-01-20T11:08:53ZengDe GruyterJournal of Intelligent Systems2191-026X2024-12-01331849610.1515/jisys-2023-0131E-commerce big data processing based on an improved RBF modelLu Qiuping0Faculty of Economics and Trade, Henan Polytechnic Institute, Nanyang, 473000, ChinaIn the dynamic landscape of China’s booming economy, the surge in e-commerce customer volume presents both opportunities and challenges, notably in managing customer churn (CC). Addressing this critical issue, this study introduces an innovative approach employing a radial basis function neural network for predicting CC within the e-commerce sector. To enhance the model’s performance in handling the vast and complex data inherent to e-commerce, the least absolute shrinkage and selection operator regression algorithm is employed, optimizing the model’s predictive accuracy. By meticulously analyzing the customer lifecycle, this refined model adeptly predicts churn at various stages, enabling the identification of features most correlated with churn. Empirical results underscore the model’s exceptional capability, achieving a prediction accuracy of 95% and a remarkably low loss rate of 3%. Furthermore, during the excavation, advanced, stable, and decline stages of the customer lifecycle, accuracy levels of 97.6, 93.1, 92.7, and 91.8% are attained, respectively, facilitating the precise selection of highly correlated customer features. Thus, the advanced churn prediction model proposed herein significantly contributes to the e-commerce domain, offering a robust tool for strategizing customer retention and mitigating churn.https://doi.org/10.1515/jisys-2023-0131rbf modelcustomer churnlasso algorithmbig datalifecycle |
| spellingShingle | Lu Qiuping E-commerce big data processing based on an improved RBF model Journal of Intelligent Systems rbf model customer churn lasso algorithm big data lifecycle |
| title | E-commerce big data processing based on an improved RBF model |
| title_full | E-commerce big data processing based on an improved RBF model |
| title_fullStr | E-commerce big data processing based on an improved RBF model |
| title_full_unstemmed | E-commerce big data processing based on an improved RBF model |
| title_short | E-commerce big data processing based on an improved RBF model |
| title_sort | e commerce big data processing based on an improved rbf model |
| topic | rbf model customer churn lasso algorithm big data lifecycle |
| url | https://doi.org/10.1515/jisys-2023-0131 |
| work_keys_str_mv | AT luqiuping ecommercebigdataprocessingbasedonanimprovedrbfmodel |