Author(s)

Wei Wu ¹

Affiliation(s)

¹ AWS EKS Team, Amazon, Seattle, Washington, 98121, United States

Corresponding Author

Wei Wu

ABSTRACT

In today's data-driven business environment, user behavior analysis and customer traffic value evaluation have become an important basis for enterprises to formulate marketing strategies. The customer traffic data of friends contains potential market insight. How to effectively identify and evaluate the traffic value of friends customers has become the key for enterprises to gain advantages in the fierce market competition. This paper presents a customer traffic value recognition model based on improved random forest algorithm. Through the optimization of the algorithm, we not only improve the prediction accuracy of the model, but also achieve remarkable results in analyzing the value characteristics of customer traffic. The experimental results show that the improved random forest model is superior to the traditional method in accuracy, recall rate, F1 score and AUC, and has higher recognition accuracy and application potential. The model proposed in this paper provides a reliable technical support for enterprises in the fierce market competition to mine the value of business traffic, which is helpful to improve the scientific and accurate degree of marketing decision.

KEYWORDS

Customer traffic, value recognition, random forest, machine learning, algorithm optimization

CITE THIS PAPER

Wei Wu, Research on Customer Traffic Value Recognition Model Based on Improved Random Forest Algorithm. Accounting and Corporate Management (2024) Vol. 6: 30-35. DOI: http://dx.doi.org/10.23977/acccm.2024.060505.

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