Analysis of Insurance Marketing Planning Based on BD-Guided Decision Tree Classification Algorithm

• Published in: Security and communication networks Vol. 2022; pp. 1 - 9
• Main Author: Long, Juan
• Format: Journal Article
• Language: English
• Published: London Hindawi 15.06.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial intelligence; Big Data; Classification; Competition; Customer services; Customers; Data processing; Decision analysis; Decision making; Decision trees; Distribution channels; Insurance; Insurance companies; Insurance industry; Integrated marketing; Life insurance; Machine learning; Market strategy; Marketing; Planning; Sales; Value chain; China
• ISSN: 1939-0114; 1939-0122; 1939-0122
• DOI: 10.1155/2022/5418332

The emergence and development of Chinese insurance companies are affected by their own unique national conditions. The modern marketing concept lags behind, lacks the practical experience of scientifically formulating marketing strategies, and insurance practitioners lack marketing knowledge and the ability to absorb modern marketing achievements to guide practice. Therefore, China’s insurance industry inevitably has many problems in insurance marketing. In recent years, with the rapid development of big data (BD) technology, artificial intelligence, and machine learning in engineering and academia, relevant data models have been well developed. The advantages of the decision tree are its good robustness, full sample mining, high precision, fast implementation, fast running speed, and low implementation cost. This paper studies the application of the decision tree classification algorithm under the guidance of BD in insurance marketing planning. The running results of the decision tree classification algorithm model show what factors will affect the accuracy and recall rate of customer churn decision-making. The predicted value and scoring value of users are extracted to test the model, and the results are within a reasonable range. The running time of this model is 2,320.36 s, which is more efficient than the 34 min 25 s of traditional SAS. Therefore, the model can be put into use, and it is necessary to establish a long-term and stable relationship with customers.