Precise Marketing Of Bank Calls Based On Data Mining

With the advent of the data era and the widespread application of data mining technology,bank financial products are no longer a single,extensive Internet sales,but rely on intelligent analysis of big data and accurate judgment of algorithms to diversify precision marketing.As a traditional marketing method in the banking industry,telemarketing is effective in acquiring customers.The traditional bank telephone marketing method is difficult to meet the needs of the times due to randomness and low hit rate.How to make good use of various data in the database of the bank to carry out scientific and effective telephone marketing is the key to realizing the digitalization of banks and the intelligence of outlets.The research object of this paper is to predict the results of bank telephone marketing.Due to the characteristics of the industry,bank customer data sets are unbalanced.Most of the current bank telephone marketing research focuses on improving the effectiveness of the model.Although the model prediction results are good,because the data set selected for model training is mostly a balanced data set,the original distribution of the data set is changed.The optimal model is selected based on only one or two indicators,which is not comprehensive enough and deviates from the actual application.Therefore,this topic starts from the data level,combines the distribution of data sets,uses data mining tools,comprehensively considers multiple evaluation indicators,studies the impact of various sampling strategies of unbalanced data sets on model effects,and compares to obtain the best sampling strategy.Find the best model under the best sampling strategy.Therefore,this topic starts from the data level,combines the distribution of data sets,uses data mining tools,comprehensively considers multiple evaluation indicators,studies the impact of various sampling strategies of unbalanced data sets on model effects,and compares to obtain the best sampling strategy.Looking for the best model under the best sampling strategy,in order to improve the model prediction effect and marketing success rate,to achieve precision marketing.Finally,a statistical analysis was made on the sample of prediction errors,and the customer group was classified according to the prediction results,and the characteristics of potential customers were tapped to provide practical and effective suggestions for bank telephone marketing from the aspects of increasing bank profits and reducing customer acquisition costs.The experimental data set in this paper is selected from the Portuguese bank data set on the UCI website,with a total of 41188 items,with a positive and negative ratioof 1:7.8.According to the ratio of 8:2,the data set is divided into training set and test set,and the training set is sampled by means of ENN,Borderline-SMOTE,SMOTE + ENN,and TS sampling proposed in this paper,and then using logistic regression,decision tree,XGBoost,Light GBM and other classification models to train the sampled data.Through comprehensive analysis of F1 value,KS value,AUC value and other evaluation indicators,it is found that ENN sampling has the best comprehensive effect on each model,and the Light GBM model under ENN sampling has the best prediction effect.The TS sampling effect proposed in this paper is not obvious.After analysis,the main reason is that the information is reused during the sampling process,which is easy to overfit.In addition,through the classification of ENN customer groups,the characteristics of potential customers are very similar to those of successful customers,mainly in the following aspects: young and middle-aged people between the ages of 31 and 50;have high school education and above;work is relatively stable,such as technicians,administrators and other occupations;married persons with stable marital status;no bad records,such as default loans,mortgages;due to the characteristics of cellular contact.