Precision Marketing Prediction Model Based on Artificial Intelligence Recommendation Technology

Abstract

Artificial intelligence technology is widely used in data processing of industrial Internet and intelligent
manufacturing. Aiming at the problem of insufficient accuracy of marketing prediction model, this paper studies an
algorithm model based on combined artificial intelligence technology which is suitable for sales prediction. In this
paper, the application example of sales is compared with BP network prediction model. This paper describes the
content-based recommendation algorithm, model-based recommendation algorithm, rule-based recommendation
algorithm and collaborative filtering algorithm. After optimizing the algorithm, this paper constructs the retail
value customer churn model and product recommendation model. The model achieves the goal of digging
customer characteristics, continuously optimizing marketing strategy and improving product and service
innovation ability. The experimental results show that the combined model can make up for the defect that BP
network converges to the local optimal solution. Its advanced solution mechanism and thought can inspire the
application of artificial intelligence in other market forecasting.