Enterprise Precision Marketing Effectiveness Model Based on Data Mining Technology

Abstract

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.

The Importance of Consumer Insights for Precision Marketing in the Era of Big Data

“…With the data, users can be tagged with various attributes, preferences, behavioral trajectories, and values to form a user profile. Based on various labels and customer insights, brands can then operate on consumers by tiers and groups, for example, tiering them according to user value and life cycle and tiering them according to tags such as geography, preference, behavioral trajectory, and source and establish marketing mechanisms such as innovative marketing, event marketing, holiday marketing, social marketing, and fission marketing to interact with consumers and cultivate consumer stickiness and loyalty continuously [23]. The core of precision marketing lies in user operations, and it is vital to have relevant tools to help carry customers and operate customer data.…”