Due to the explosive growth of massive consumption data, the traditional marketing model of enterprises has become stretched. The rapid advancement of data mining technology has given new impetus to the innovation of the marketing strategies of enterprises, promoting the progressive transformation of enterprises from traditional passive marketing to precise and refined marketing. Data mining technology’s basic task is to analyze data, acquire insight into cause and effect, and then predict the future. As a result, the technology is coupled with the enterprises’ marketing activities, and a precise marketing model is built using big data consumption. This will aid in the comprehensive and three-dimensional description of customers, as well as the analysis of potential customers’ attributes, to give a scientific basis for the formulation and implementation of precise data-driven decision-making for enterprises. As a result, our study enhances and optimizes the K-means algorithm in combination with the artificial bee colony algorithm aiming at fixing the issue that the K-means algorithm is sensitive to cluster center initialization and improving the enterprise precision marketing model’s clustering performance. In the precise marketing scenario of the telecom business, the improved K-means clustering model is utilized to realize the analysis and prediction of telecom customers, as well as to carry out precise marketing based on the predicted findings. Finally, the optimized K-means clustering algorithm can objectively and comprehensively reflect the characteristics of telecom customer value segmentation, efficiently mining future clients and preventing blind marketing by enterprises, based on the model's actual verification results. Simultaneously, it provides substantial data support for telecom enterprises’ resource planning, as well as pointing out the next step in increasing market share.