Characterization and Prediction of Customer (Dis)satisfaction of a Mobile Internet Provider

Customer satisfaction is crucial for the success of mobile phone companies. The capacity to anticipate and comprehend the reasons behind dissatisfaction enables these companies to implement efficient customer retention strategies. Satisfaction is commonly measured using the Net Promoter Score (NPS), which classifies the customer as a detractor, neutral, or promoter based on the score given to the service. This study uses datasets provided by a large mobile phone provider and conducts an exploratory data analysis with predictive algorithms to identify patterns and feature correlations that lead to customer satisfaction or dissatisfaction. The dataset contains information such as contracted plan, brand and model of cell phone, region of origin, mobility, and volume of data downloaded by the device. We demonstrate that with users with fancy devices and expensive plans are willing to complaining more about the service. As a result, the findings contribute to prove insights and recommendations for understanding, anticipating, and improving customer satisfaction.