# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 2 Related Research
### New Customer Churn Model Variables
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Dataset** & **Algorithm** & **Results/Future Work** \\ \hline
[41] & Customer data from SyriaTel telecom company. & Decision tree, random forest, gradient boosted machine tree and extreme gradient boosting (XGBOOST). & The best results were obtained by applying the XGBOOST algorithm with 93.3\% the area under the curve (AUC) value. \\ \multicolumn{4}{l}{The unstructured data included:} \\
[7] & and feedback. & RFM technique & They recommended the integration of the structure data framework with real-time analytics to target customers in real time on the bases of location, time, etc. \\ \multicolumn{4}{l}{Available historical records extracted from the telecom industry.} & Logistic regression and decision trees in R. & The data mining techniques could be a promising solution for customer churn management. \\ \multicolumn{4}{l}{Two telecom industry datasets were considered. Type-1 contained 3333 records, and Type-2 contained 20,468 records.} & Axiomatic fuzzy set theory and parallel density-based spatial clustering on the Hadoop MapReduce framework. & The proposed model is more efficient than the existing system in terms of time and performance. \\ \multicolumn{4}{l}{Online available customers dataset at} \\
[2] & Kaggle [[https://www.kaggle.com/](https://www.kaggle.com/)]([https://www.kaggle.com/](https://www.kaggle.com/)) (accessed on 23 June 2021). & Used different classifiers implemented in WEKA, & Summed up their findings with a conclusion that bagging, and the SMO algorithm outperform with an accuracy of 99.8\% using 14 attributes. \\ \multicolumn{4}{l}{A total of 153,651 distinct tweets for the Twitter handles of five popular telecom brands in India.} & Semantic analysis. & Pruned that sentiment analysis can manage the higher growth rate of new subscribers who were added to the brand in the study period. \\ \multicolumn{4}{l}{Tweets related to Telkom’s broadband internet service and customer churn rate data history from the company’s data warehouse.} & Applied sentiment analysis using recurrent neural network LSTM. & Results indicated that the accuracy of the churn rate predictions (based on the previous three months) are correlated with negative moods. \\ \multicolumn{4}{l}{Used the Pokec social network data and generated synthetic call log details of 25,000 users} & Used influence maximisation & Future analysis should factor in both location and language to avoid geographical and cultural sampling errors. \\ \hline \hline \end{tabular}
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Table 1: Synthesis of the included studies related to customer churn and social media mining. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Num.** & **Customer Churn Variables** & **Description** & **Type of Variable** & **Range** \\ \hline  & & & 18–24, '1’ \\  & & & 25–34, 2’ \\
1 & Age & Age group has been identified & Ordinal variable & 35–44, 3’ \\  & & & 45–54, 4’ \\  & & & 55–64, 5’ \\
2 & Gender & Male or Female & Binary variable & Male, 0’ \\
3 & Has a relation at the same telecom company & Does the customer have a family member who used the same telecom provider as he/she did? & Binary variable & Female, 1’ \\
4 & Overdue bill & Does the customer have an unpaid bill? & Binary variable & Yes, 1’ \\  & & & No, 0’ \\  & & & \(\geq\)1, 1’ \\
5 & Long period & Contract length in month from start day of contract until June 2017 until June 2017 & Ordinal variable & 1 \(\geq\) 5, 2’ \\  & & & 5’ \(\geq\) 10, 3’ \\  & & & 10+, 4’ \\ \hline \hline \end{tabular}
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Table 2: Details of the customer churn variables. They combined call details from a social network with the information about the customers. Moreover, reference [38] used a relational learner to increase the performance of the churn prediction model. They analysed calling behaviour and network interaction. Different studies used the contract length as a churn predictor [6; 33; 36; 39; 45] concluded that customers with contract lengths between twenty-five and thirty months are more likely to churn.