Abstract
As a low-cost marketing model, telemarketing has always been the most important channel for banks to promote wealth management products. Traditional telemarketing has not only brought intrusiveness to many telephone access customers, but also a waste of resources for the bank itself. In order to improve the success rate of bank telemarketing, it is necessary to predict in advance which customers are most likely to purchase the wealth management product, so as to achieve precision marketing. Aiming at the complex high-dimensional nonlinear characteristics of the factors affecting the success rate of telemarketing, a t-SNE (t-distributed stochastic neighbor embedding) feature extraction method, and then take the extracted low-dimensional features as input, use nonlinear support vector machine (SVM) for training and prediction. The empirical results show that the bank phone based on t-SNE-SVM proposed in this paper. The marketing prediction model has good learning ability and generalization ability, which can provide certain decision-making reference for banks and other industries to achieve precision marketing.
Highlights
As a typical strategy to promote business development, marketing activities can generally be divided into mass marketing and direct marketing
T-distributed stochastic neighbor embedding (t-SNE) is a nonlinear dimensionality reduction and visualization algorithm proposed by Maaten et al (2008), which maps multi-dimensional data to two or three dimensions suitable for human observation for visualization research (Maaten et al, 2008). t-Distributed Stochastic Neighbor Embedding (t-SNE) is derived from SNE (Stochastic Neighbor Embedding)
Considering the cost of manpower and other costs, it is assumed that the bank manager can only select half of its customers for telemarketing. If it is not randomly selected using the classification model, it is expected to cover 50% of the target customers, and use the t-SNE-Support Vector Machine (SVM) model in this paper, we can get 85.1% of the customer response, and the bank benefit increased by 35.1% under the same cost (better than the 29% improvement obtained in the literature (Moro et al, 2014)
Summary
As a typical strategy to promote business development, marketing activities can generally be divided into mass marketing and direct marketing. Many commercial banks have not implemented effective classified marketing strategies for their customers and often sell the same wealth management product to many customers, which is not effective Use information to analyze customer needs, wasting a lot of manpower and other resources (Liu & Zhang, 2008) It is necessary for companies implementing telemarketing to analyze customer data in advance using predictive models in order to select those customers who are most likely to respond to targeted marketing (Sing’oei et al, 2013), which can improve the marketing efficiency of bank managers, and maximize To reduce intrusiveness to non-target customers. This paper proposes a t-SNE-SVM-based bank telephone marketing prediction method This method first uses the t-SNE algorithm to visually reduce the number of input attributes that may affect the success rate of telemarketing, while reducing complexity while maximizing information of original input features is retained. Use the reduced-dimensional low-dimensional data as input to the SVM algorithm to learn and train to predict which customers will buy the wealth management products
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