# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 2 Related Research
### New Customer Churn Model Variables
Many studies are related to contract length and overdue bills as churn predictors. Reference [36] found that customers with contract lengths between twenty-five and thirty months and four overdue bills are more likely to churn. In agreement with this result, reference [33] concluded that churning happens more for customers with contract lengths between twenty-five and thirty months and who have more than four overdue payments within six months. Reference [32] chose five attributes to predict churning, one of which also includes unpaid balances. Most studies analysed use the customer call details as primary churn predictor [37]. Reference [30] assessed the categorical and continuous data transformation in the performance of the churn prediction model. Their dataset was from a European telecommunication company. Some of the variables they selected were the number of minutes for outgoing calls and the number of contacts with the call centre. In addition, reference [6] compared some techniques used in churn modelling. Their dataset was from a UK mobile telecommunication company. They included several variables, one of which was call usage detail. In 2016, reference [42] proposed a model for churn prediction for telecommunication companies. They used historical records related to the telecom company. The attributes included phone and call details. Reference [31] applied rule-based classification to predict whether a customer is likely to churn or not. Their dataset contained customer information such as call details (billing information and length of calls). Furthermore, reference [2] applied different data mining techniques to predict customer churning. They applied their methodology on the online dataset from Kaggle. They used fourteen attributes, including call details, customer service calls and phone number. Reference [25] built a churning prediction model for a mobile telecommunication company. They used two datasets: customer information and statistical data, which contained call length and complaint information. Reference [32] assessed many techniques to predict customer churning and used the dataset from an Indian telecommunication company. They chose five attributes to predict churning, such as customer dissatisfaction and satisfaction, switching costs, quality of services, service usage in terms of used minutes in calls, call details and unpaid balances. They also used customer-related variables, such as customer gender, customer status or whether a customer is an active user. Reference [46] concluded \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Num.** & **Customer Churn Variables** & **Description** & **Type of Variable** & **Range** \\ \hline
6 & New customer & Has the customer used a telecom provider recently? & Binary variable & Yes, \({}^{\prime}\) \\  & & & No, \({}^{\prime}\)0  \\
7 & Inactive & Is the customer active? & Binary variable & Yes, \({}^{\prime}\)1  \\  & & No, \({}^{\prime}\)0  \\
8 & Low data & Does the customer have low data usage? & Binary variable & Yes, \({}^{\prime}\)1  \\  & & No \({}^{\prime}\)0  \\
9 & Low talk & Does the customer make few phone calls? & Binary variable & Yes, \({}^{\prime}\)1  \\  & No Internet and talk & Does the customer not use the Internet, phone calls & Binary variable & Yes, \({}^{\prime}\)1  \\  & and SMS & and short message service? & & No, \({}^{\prime}\)0  \\
11 & No value-added service & Does the customer use any of the non-core services? & Binary variable & Yes, \({}^{\prime}\)1  \\  & & No, \({}^{\prime}\)0  \\
12 & Customer satisfaction & Percentage of customer satisfaction from & & No, \({}^{\prime}\)0  \\  & Twitter analysis [44] & Continuous variable & & \\
13 & Churn status & Does the customer churn? & Binary variable &
\begin{tabular}{l} Churner/Non-churner \\ Churner, \({}^{\prime}\)1  \\ Non-churner, \({}^{\prime}\)0  \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._that customers with no active plans and with no incoming and outgoing calls within six months are likely to churn.