Author Date	Authors Name	Methods: Study, types	Major Findings	Limitations Gaps	Implications for future research & practice
2021 [1]	Lalwani, Mishra, Chadha, & Sethi	The method used in this work is the gravitational search algorithm, used after preprocessing the data set.	It tackles the customer churn problem in the telecommunication sector, using machine learning algorithms.	Different fractions can be applied to the data set, and improvement in accuracy can be obtained.	Can be used for
2021 [2]	Gopal, & MohdNawi	Extended Support Vector Machine, KNN, PSO were discussed.	Customer identification was focused in this recent work according to customer churn.	The analytics were works on the unstructured data only.	Feature extraction and choosing require more attention.
2019 [3]	Çelik, & Osmanoglu	Different machine learning and regression models were explained.	Calculating effective price analysis keeping in view about customer churn.	General research on cost effective model is conducted.	This can be conducted specifically for a price-effective market benefiting the customers.
2019 [4]	Ahmad, Jafar, & Aljoumaa	Random Forest, Decision Tree, Gradient Boosted Machine and XGBOOST	Telecommunication sector requires a prediction about their customers. Based on social network analysis	The deficiencies were observed using feature-based analysis.	More betterment can be observed using it in an applied science area.
2020 [5]	Baker, Baugh, & Sammon	Statistical methods are used to find customer churn facts.	The features that are used were so proper if these features were selected to obtain any firm customer churn the growth will take place.	Artificial intelligence and machine learning are not used.	This can be implemented using artificial intelligence predictive models.