Research On Insurance Cross-selling Prediction Based On Data Mining Technology

With the booming national economy,the insurance industry has entered a period of high growth and high profits,but the competition in the insurance market is also intensifying,and the usual wide net marketing by insurance salesmen appears to be extremely inefficient.As a result,insurance companies have begun to focus more on the development of their existing customers,and how to increase the value of their existing customers and how to tap into their purchasing potential has become the focus of their marketing activities.At the same time,the advent of the big data era has made the marketing approach of the insurance industry bound to undergo digital transformation,and data mining-related technologies have brought new vitality to the insurance industry.This thesis is both a study and an exploration of the application of data mining technology to cross-selling in the insurance industry.Since cross-selling in the domestic insurance industry is still in the development stage,this thesis first comprehensively expounds the definition and concept of cross-selling and its related theoretical foundation,outlines and analyzes the current situation and characteristics of cross-selling in the insurance industry,and presents the problems that arise in it.At the same time,the theory and characteristics of data mining techniques are combined with cluster analysis and random forest to design the insurance cross-selling model based on cluster analysis and the insurance cross-selling prediction model based on random forest,respectively.In terms of empirical evidence,this thesis uses desensitization customer data provided by a company to validate the model proposed above.Through cluster analysis,customer characteristic groups interested in cross-selling products are derived,and combined with precision marketing theory,practical strategies for cross-selling insurance products are provided.The cross-selling prediction model was constructed through the random forest algorithm,and the model was evaluated to be able to identify potential customers for the insurance company and verify the value of the model.