التفاصيل البيبلوغرافية
| العنوان: |
Predicting customer churn: A case study in the software industry |
| المؤلفون: |
Dias, João Pedro Rolim |
| المساهمون: |
António, Nuno Miguel da Conceição, RUN |
| سنة النشر: |
2023 |
| مصطلحات موضوعية: |
Data Mining, Customer Churn, Churn Prediction, Machine Learning, Supervised Learning, SaaS, Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação |
| الوصف: |
Project Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and Analytics |
| Description (Translated): |
Customer churn can be defined as the phenomenon of customers that discontinue their relationship with a company. This problem is transversal to many industries, including the software industry. This study uses Machine Learning to build a predictive model to identify potential churners in a Portuguese software house. Six popular Machine Learning models: Random Forest, AdaBoost, Gradient Boosting Machine, Multilayer Perceptron Classifier, XGBoost, and Logistic Regression, were developed to assess which one would have a better performance. The experimental results show that boosting techniques such as XGBoost present the best predictive performance. The XGBoost model presents a Recall of 0.85 and a ROC AUC of 0.86. Additionally to the model performance, the study of the model features’ importance revealed that some factors, such as the time to solve a support ticket, the type of application, the license age, and the number of incidents, significantly influence customer churn. These insights can help the software industry key drivers of churn and prioritize retention efforts accordingly. |
| Contents Note: |
TID:203385721 |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| الاتاحة: |
http://hdl.handle.net/10362/159898 |
| Rights: |
embargoed access |
| رقم الانضمام: |
rcaap.com.unl.run.unl.pt.10362.159898 |
| قاعدة البيانات: |
RCAAP |