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Title: :  Integrated Churn Prediction and Segmentation
PaperId: :  20376
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 3 2023
DUI:    16.0415/IJARIIE-20376
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Rohit RanjanBangalore Institute of Technology
Ram Kumar PandeyBangalore Institute of Technology
Rahul Kumar SainiBangalore Institute of Technology
Mrityunjay PrakashBangalore Institute of Technology
Nikitha K. S.Bangalore Institute of Technology

Abstract

AI/ML
SMOTE, EDA, K-Means, K-Fold Cross Validation, Normalization, Bagging Tree, Extra Tree, Random Forest, Segmentation
Customer churn prediction and segmentation are crucial aspects of data-driven marketing. This paper presents a comprehensive approach that encompasses various modules to tackle these challenges effectively. The methodology proposed in this study includes data processing, churn prediction using machine learning algorithms, customer segmentation through K-means clustering, and result analysis. To ensure the quality and consistency of the dataset, the data processing stage performs essential tasks such as data transformation, cleaning, and normalization. Additionally, the Synthetic Minority Over-sampling Technique (SMOTE) is employed to address any issues of data imbalance, enhancing the reliability of the results. The churn prediction module plays a pivotal role in identifying potential churners accurately. By employing bagging tree, extra trees, and random forest algorithms, this module achieves high prediction accuracy. Furthermore, k-fold cross-validation and feature selection techniques are utilized for robust model evaluation and determination of variable importance. The customer segmentation module utilizes Exploratory Data Analysis (EDA) techniques to extract meaningful insights from the data. By employing K-means clustering, customers are grouped based on their similarities and behaviors, enabling businesses to tailor their marketing strategies to different customer segments effectively. Finally, the results obtained from the churn prediction and segmentation models are thoroughly analyzed to assess their effectiveness. This analysis provides valuable insights for businesses seeking to proactively manage customer churn and implement targeted marketing strategies, ultimately leading to improved business performance and enhanced customer satisfaction. This paper offers a comprehensive and practical approach for customer churn prediction and segmentation in data-driven marketing. The proposed methodology and its associated modules provide a structured framework for businesses to effectively manage customer churn and implement targeted marketing strategies, resulting in improved business performance and customer satisfaction.

Citations

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IJARIIE Rohit Ranjan, Ram Kumar Pandey, Rahul Kumar Saini, Mrityunjay Prakash, and Nikitha K. S.. "Integrated Churn Prediction and Segmentation" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 3 2023 Page 1948-1958
MLA Rohit Ranjan, Ram Kumar Pandey, Rahul Kumar Saini, Mrityunjay Prakash, and Nikitha K. S.. "Integrated Churn Prediction and Segmentation." International Journal Of Advance Research And Innovative Ideas In Education 9.3(2023) : 1948-1958.
APA Rohit Ranjan, Ram Kumar Pandey, Rahul Kumar Saini, Mrityunjay Prakash, & Nikitha K. S.. (2023). Integrated Churn Prediction and Segmentation. International Journal Of Advance Research And Innovative Ideas In Education, 9(3), 1948-1958.
Chicago Rohit Ranjan, Ram Kumar Pandey, Rahul Kumar Saini, Mrityunjay Prakash, and Nikitha K. S.. "Integrated Churn Prediction and Segmentation." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 3 (2023) : 1948-1958.
Oxford Rohit Ranjan, Ram Kumar Pandey, Rahul Kumar Saini, Mrityunjay Prakash, and Nikitha K. S.. 'Integrated Churn Prediction and Segmentation', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, 2023, p. 1948-1958. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Integrated_Churn_Prediction_and_Segmentation_ijariie20376.pdf (Accessed : ).
Harvard Rohit Ranjan, Ram Kumar Pandey, Rahul Kumar Saini, Mrityunjay Prakash, and Nikitha K. S.. (2023) 'Integrated Churn Prediction and Segmentation', International Journal Of Advance Research And Innovative Ideas In Education, 9(3), pp. 1948-1958IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Integrated_Churn_Prediction_and_Segmentation_ijariie20376.pdf (Accessed : )
IEEE Rohit Ranjan, Ram Kumar Pandey, Rahul Kumar Saini, Mrityunjay Prakash, and Nikitha K. S., "Integrated Churn Prediction and Segmentation," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, pp. 1948-1958, May-Jun 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/Integrated_Churn_Prediction_and_Segmentation_ijariie20376.pdf [Accessed : ].
Turabian Rohit Ranjan, Ram Kumar Pandey, Rahul Kumar Saini, Mrityunjay Prakash, and Nikitha K. S.. "Integrated Churn Prediction and Segmentation." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 3 ().
Vancouver Rohit Ranjan, Ram Kumar Pandey, Rahul Kumar Saini, Mrityunjay Prakash, and Nikitha K. S.. Integrated Churn Prediction and Segmentation. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : ]; 9(3) : 1948-1958. Available from: https://ijariie.com/AdminUploadPdf/Integrated_Churn_Prediction_and_Segmentation_ijariie20376.pdf
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