# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 3 Methodology  
The two types of known customer churning are voluntary and involuntary [72]. The decision of a customer to move to another telecom company on their own is called voluntary, while a customer ceasing telecom company services for reasons outside their influence, such as death or change of the customer's job, is called involuntary [73]. Usually, the literature is interested in voluntary customer churning, because it describes the relationship between a customer and a company. There are two types of customer payment schemes: post-paid and pre-paid [74]. Post-paid customers receive a monthly bill for company services, while a pre-paid customer is charged in advance for company services. \begin{table}
\begin{tabular}{l l} \hline \hline
**Techniques** & **References** \\ \hline Decision tree & [6; 25; 27; 35; 36; 41; 42; 54; 55; 56; 62] \\ Logistic regression & [1; 6; 30; 34; 42; 54; 55; 57; 58; 60; 62; 63; 64] \\ Neural network & [28; 34; 35; 36; 54; 57; 58; 60; 62; 65; 66; 67] \\ Support vector machine & [2; 35; 57; 65; 68; 69] \\ J48 & [2; 23] \\ CART & [2; 32] \\ Naive Bayes & [46; 57; 58; 60; 65] \\ Fuzzy classification & [32] \\ Rule-based classification & [31] \\ k-means algorithm & [35; 59] \\ Random forest & [41; 70] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Most Common Techniques Used for Customer Churn Prediction. In this study, a churner is defined as a post-paid customer who voluntarily leaves the company and stops telecom services within our time window. By contrast, a non-churner in our study is a post-paid customer who remains with the company within our time window. Data mining refers to knowledge discovery from a large database [75]. The three most common data mining methodologies used to develop data mining models are knowledge discovery databases (KDD) [76], cross-industry standard process for data mining (CRISP-DM) [77; 78], and sample, explore, modify, model, assess (SEMMA), which was created by the SAS Institute (Inc. SI. SAS version 9.1., 2005, Wake County, NC, USA). The literature review indicated that KDD and CRISP-DM are more widely used than SEMMA [79; 80]. Although KDD includes nine phases and CRISP-DM has six phases, their phases are equivalent [79]. We adopted some of the steps of CRISP-DM [78] that suit our task to develop our churn prediction model (SentiChurn model, Figure 1) because CRISP-DM is appropriate for a business domain [81]. The six phases of CRISP-DM are shown in Figure 2.  
As you can see in Figure 1, the first phase, data set construction, includes variables defined and collected. Defining the variables entails collecting the variables from the sources (Figure 3). To collect the variables that can differentiate between churners and non-churners and use our model as input (Table 2), we collected them from three sources. First, we collected variables from the literature review. Some variables found in the review were disregarded because of the difficulty of obtaining them from telecom companies due to privacy concerns, such as name, phone number and code, call details and billing information. This is the case with many prediction model systems in other countries [46]. Next, we conducted a survey via questionnaire with the telecom customers. The questionnaire aimed to test the relationship between the collected variables and churning behavior from a customer's point of view. Afterwards, we conducted an informal interview with a Saudi telecom expert (a telecom business consultant) to show here the collected variables and question him about other variables from the company's point of view. Figure 1: Our SentiChurn model approach. Figure 2: The CRISP-DM approach (based on [78]). The telecom company divides its customers into segments based on their own selected set of variables and calculates the churn rate for each segment quarterly, half-yearly and annually. They propose that the variables for one segment have higher churn rates half-yearly because a higher churn rate must be obtained to train the prediction model.