# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 3 Hypotheses and Proposed Model  
Customer consumption tags distinguish and characterize customers by expense-related information, such as by monthly fee, package type, or mobile terminal price [29]. Precision marketing can be performed using telecom data to classify and identify customers. Using such information will allow telecom operators to concentrate on the target customers and convert them into potential customers. This could significantly optimize marketing expenses and avoid customer churn [29]. Expense-related data could be applied to understand the reasons for customer loss. Customers with similar consumption-expense behaviors have similar reasons for churn. Users with similar expense-related characteristics could be segmented into groups to conduct an analysis [30]. Thus, we propose the following hypotheses:  
**Hypothesis 1 (H1). **: _The total fee receivable for the month positively impacts customer loss;_
**Hypothesis 2 (H2). **: _The fixed monthly cost has a positive impact on customer loss;_
**Hypothesis 3 (H3). **: _The local fee has a positive impact on customer loss;_
**Hypothesis 4 (H4). **: _The roaming fee has a positive impact on customer loss;_
**Hypothesis 5 (H5). **: _China Unicom's network fee has a positive impact on customer loss;_
**Hypothesis 6 (H6). **: _The fee with China Mobile positively impacts customer loss;_
**Hypothesis 7 (H7). **: _The fixed-line fee positively impacts customer loss._Taiwan's telecommunication industry has experienced fierce competition since it removed the restriction of wireless telecom services, and customer churn management has become the operators' focus in order to retain telecom customers by satisfying their needs. One of the main challenges is to predict customer churn [31]. Using empirical analysis, different data mining methods that can be used to allocate 'propensity-to-churn' scores were evaluated from customer and operator perspectives. The results showed that call data along with neural network and DT methods could be applied for accurate customer churn prediction models. Furthermore, the customers' recent six-month transactions can be applied to predict customer churn for the coming month. The call data can also be included in the transaction data. Thus, we proposed the following hypotheses:  
**Hypothesis 8 (H8). **: _The total monthly caller MOUI positively impacts customer loss;_  
**Hypothesis 9 (H9). **: _The total monthly called MOUI has a positive impact on customer loss;_  
**Hypothesis 10 (H10). **: _The total local called MOUI positively impacts customer loss._  
The Data Warehouse system, which accumulates telecom data, such as for SMS, was used to increase the customer retention rate for SyriaTel. Generally, all SMS and MMS data that indicate customer behavior should be used, as it is unknown which features will be valuable in predicting churn. The SMS and MMS data for daily, weekly, and monthly users in the past nine months were aggregated for the research to identify related variables and see how they relate to each other. Three charts were built using three kinds of weights: (1) the standardized SMS and MMS quantities; (2) the standardized customer calling times; (3) the mean of the first two standardized weights. Two features for each chart were produced by applying the SenderRank and PageRank algorithms according to the directed charts [23]. The Indian liberalization and globalization process has influenced the telecom industry. The marked leader Airtel was selected to conduct a case study through its value proposition approach by concentrating on new value-added services such as the new SMS Pack plan. Consequently, the following hypotheses were also assessed:  
**Hypothesis 11 (H11). **: _China Unicom's SMS quantity positively impacts customer loss;_  
**Hypothesis 12 (H12). **: _China Mobile's SMS quantity positively impacts customer loss;_  
**Hypothesis 13 (H13). **: _China Telecom's SMS quantity positively impacts customer loss._  
Our hypotheses are listed in Table 1.