Research on Telecom User Portrait Based on Integrated Learning and Customer Stratification

The continuous advancement of computer network technology has led to an increasing flow of information for enterprises and customers. Currently, telecom companies only categorize customers based on their value, neglecting other important customer characteristics. To better understand and identify customers, this paper proposes the integration of user portraits into telecom enterprises in order to mitigate the risk of pipeline processing. The construction of user portraits is essential for comprehending customer needs and implementing targeted marketing strategies. By enhancing the RFMN model to more accurately assess telecom customer value and utilizing an integrated model for customer stratification, we can optimize clustering effects. Subsequently, customer feature extraction is conducted to accumulate numerous attribute labels and construct user portraits for precision marketing purposes. This approach not only enables companies to gain a deeper understanding of customer needs but also helps reduce costs while facilitating faster decision-making for customers, ultimately leading to improved satisfaction and loyalty.