Customer Management Decision Model and Algorithm Based on Enterprise Sales Forecast

With the advancement of scientific concepts and technology and the need for the steady development of the corporate market, customer assets, as an important intangible asset of a company, have received widespread attention and become one of the key elements for measuring the market value of a company. Therefore, customer analysis has been paid more and more attention by enterprises. The purpose of this article is to study customer management decision-making models and algorithms based on enterprise sales forecasts. This article summarizes customer behavior analysis and enterprise sales forecasting, analyzes and compares the main mathematical models and algorithms used in the two on the market, and determines a relatively suitable model and algorithm on this basis. This paper improves the selected models and algorithms. This paper uses genetic algorithm theory to re-optimize the weights of the state transition probability matrix of the established prediction model; then combine the actual business requirements and related modeling software to make the proposed new model and algorithm are tested. This article improves the traditional customer behavior analysis and analyzes it together with the company’s sales forecast. Through the dialectical analysis of the two, analyze the dynamic relationship between the customer behavior status and the expected performance of the company, and adjust the customer behavior status by clarifying the relationship, so that the decision-making based on customer behavior can be more accurate to meet the best interests of the company. Experimental research shows that the accuracy of the prediction model proposed in this paper is 85.6%. The result proves that this forecasting system can reduce the cost of the enterprise to a certain extent, extend the life cycle of the enterprise, and stabilize the normal operation and rapid development of IT enterprises.