Customer Satisfaction Prediction Model Based on Big Data and XGBoost

Customer satisfaction prediction is a very important and complex task for the long-term development of the enterprise. Predicting customer satisfaction can provide accurate insight into customer behavior and turn post-event retention into pre-event attraction, thereby increasing brand loyalty and facilitating business conversion. This paper introduces the construction process of customer satisfaction prediction model, through the collection of B-domain, O-domain, and C-domain data, which is conducive to big data analysis to clarify the benchmark values of key driving factors. Furthermore, this paper classifies users according to four dimensions, and forms a complete preliminary model of customer satisfaction prediction. The XGBoost algorithm was introduced to iteratively upgrade the model carrying out comprehensive analysis, realizing the role of service evaluation and supervision for telecom operators. The practical application of this model is of great significance for improving the brand awareness and word-of-mouth communication of telecom operators.