Statistical Modeling And Analysis On The Marketing Data Of A Non-profit Organization

With the increasingly fierce competition in the advertising market and the rapid development of information technology,database marketing methods come into being in order to overcome the low efficiency of traditional marketing methods.This approach is based on customer data,quantitative analysis and marketing demand.It takes different marketing strategies to different consumers.Therefore,marketing costs are reduced.The most important part of this approach is to establish a marketing decision model based on customer data so as to provide a basis for marketing decision.The paper introduces the development of database marketing and summarizes the basic process of customer response model.The three kinds of classification prediction models,such as decision tree,neural network and random forest,are introduced from the aspects of basic concepts,algorithm ideas and so on.Based on the marketing data of a nonprofit organization in foreign countries,the CART decision tree model,BP neural network model and random forest model are built.In conclusion,the random forest model has the highest accuracy.First of all,the paper briefly introduces the theory of customer response model and modeling process.Also,common methods for model checking are introduced.Then,the paper introduces the data preprocessing method from the aspects of business understanding,data understanding,data cleaning,variable selecting and imbalanced data processing.As the data cleaning,variable selecting and imbalanced data processing will have an important impact on the results of the model,the paper details the specific work in these three aspects and solves the problems of abnormal data,missing values and imbalanced data.Data preprocessing provides the basic data for modeling work.Finally,the paper uses the CART decision tree model,the BP neural network model and the random forest model to carry out the modeling and analysis work on the marketing data of a foreign nonprofit organization.The process of parameter selection is explained in detail,and the modeling results of each model are summarized.The prediction results of the three models are analyzed and compared.It is shown that the accuracy of the random forest model is the highest,which indicates that the predictive effect of the model has significant commercial significance.It can help the organization to identify the high-response customers and improve the revenue.