Customer churn prediction in telecommunication industry is a very essential factor to be achieved and it makes direct impact to customer retention and its revenues. Developing a good and effective churn prediction model is very important however it is a time-consuming process. This study presents a very good review of customer churn, its effects, identification of its causes, business needs, methods, and all the techniques used for churn prediction. On the other hand, this study provides the best understanding of the telecom dataset, datasets used by past researches and features used in different researches. Also, this study shows the best techniques used for the churn prediction and describes all performance measures used in the churn prediction models. In this study a wide range of researches are added from the year 2005 to 2020. It includes variety of methods proposed by past researches and technologies used in these researches. At the end, a state of art comparison is added that gives a very good and meaningful comparison of past researches. The study indicates that machine learning techniques are mostly used and feature extraction is a very important task for developing an effective churn prediction model. Deep learning algorithm CNN itself has the capability of feature extraction and establish itself as a powerful technique for churn model, in particular for large datasets. For performance ‘Accuracy’ is a good measure however measuring performance only with ‘Accuracy’ is not sufficient because on small datasets accuracy is more predictable and will be the same. With Accuracy, researchers also need to look at other performance measures such as confusion matrix, ROC, precision. F-measure etc. This study assures that new researchers can find everything regarding their churn prediction model requirements at one place. This study provides a comprehensive view by extensively detailing work which has happened in this area and will act as a rich repositorory of all knowledge regarding churn prediction in telecom sector.

电信行业的客户流失预测是要实现的一个非常重要的因素，它直接影响客户保留率及其收入。开发良好且有效的客户流失预测模型非常重要，但这是一个耗时的过程。这项研究很好地回顾了客户流失，其影响，确定其原因，业务需求，方法以及用于流失预测的所有技术。另一方面，本研究对电信数据集，过去研究使用的数据集以及不同研究中使用的功能提供了最佳理解。此外，本研究还显示了用于流失预测的最佳技术，并描述了流失预测模型中使用的所有性能指标。在这项研究中，从2005年到2020年进行了广泛的研究。它包括过去研究提出的各种方法以及这些研究中使用的技术。最后，添加了最先进的比较，可以对过去的研究进行很好的有意义的比较。研究表明，机器学习技术被广泛使用，特征提取对于开发有效的客户流失预测模型是一项非常重要的任务。深度学习算法CNN本身具有特征提取能力，并将其确立为搅动模型（尤其是大型数据集）的强大技术。对于性能，“准确性”是一个很好的度量，但是仅使用“准确性”来衡量性能是不够的，因为在小型数据集上，准确性更可预测，并且将是相同的。有了Accuracy，研究人员还需要研究其他性能指标，例如混淆矩阵，ROC，精确。F度量等。这项研究确保了新的研究人员可以在一个地方找到有关其流失预测模型要求的所有信息。这项研究通过广泛详细介绍该领域中发生的工作，提供了一个全面的观点，并将作为有关电信行业客户流失预测的所有知识的丰富资料库。