# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 1 Introduction  
Global competition for telecommunication services drives companies to enhance their customers' satisfaction. Extensive research correlates customer satisfaction with customer loyalty and customer churn [1; 2; 3]. Customer churn is defined in the telecommunication field as transferring customers from one telecom company to another [4]. Recent research shows that the cost of having a new customer is more than the cost of keeping an existing customer [5]. Thus, companies are more concerned with keeping customers than ever before. Hence, as seen in the literature review section, many studies have been done in various industries in CRM (customer relationship management) to manage customer retention and develop an efficient model to predict the churners. This paper addresses the following problems related to customer churn prediction models:  
* The current churn prediction models have a relatively short life, as they rely on the customers' historical data. The data become less valuable over time for making predictions [6], which may not provide telecom companies with the best churn prediction experience. * There is a lack of research that integrates a structural data framework with real-time analytics to target customers in real-time [7]. * The current churn prediction models exclude location and language factors and that causes geographical and cultural sampling errors [8]. As you can see from the problems above, there is a need for a new approach for the churning prediction model. Social media is a key part of many people's lives today, and approximately 67% of all active internet users use social media platforms [9]. Social media is a type of communication tool that allows people to share their sentiments, thoughts, opinions and moods [10]. It is a more cost-effective marketing communication method than the usual methods [11]. Amongst social media, Twitter is a popular and widely used messaging service categorised as a microblogging website [12]. Twitter is chosen for this paper because it is one of the most influential sites among Saudi people [13], with 92% of Saudi tweets produced in the Arabic language [14]. Analysing social media can help create real-time analytics and mine information about public opinions of and emotions felt towards products, leadership, decisions, cultures and events [15]. However, current subjectivity and sentiment analysis tools are mainly designed for the English language. Few tools have been designed for the Arabic language, even though Arabic is used by a large segment of the world's population and is the fourth most widespread language in the world [16]. Arabic is a morphologically rich language [17; 18]. Several studies have analysed the specific challenges brought about by the nature of the Arabic language and demonstrated some solutions to such challenges [15; 17; 19; 20]. Sentiment analysis of the Arabic language is quite challenging [19] because of the various forms of the language, such as Modern Standard Arabic, the use of informal or colloquial Arabic [21] and the structure of the language, which is written from right to left. Therefore, this study proposes a new approach that depends on real-time Twitter mining methods and a data warehouse to develop a churn prediction model. The model also takes into consideration language, time and location factors. The present study intends to introduce a notion of customer interaction for Saudi telecommunication companies based on the 'lost customer' phenomenon (or customer churn). The contribution of this study is as follows:  
1. It is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies. 2. It identifies and evaluates the main gaps in the current churn prediction models. 3. It proposes and evaluates a novel design of a churn prediction model to address the gaps in current churn prediction models by providing a real-time method that suits the telecom data and the Arabic data set. 4.