Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm

Compared with the past questionnaire survey, this paper applies the intelligent algorithm developed rapidly in recent years to identify the tendency of customers to buy financial products in the market. In addition, for the single state customer classification indicators based on the previous demographic information and action information, it is proposed to combine the action of market activities with demographic information; that is, the static integrated customer classification index is further combined with the improved neural network model to study the classification and preference of enterprise financial customers. Firstly, the enterprise financial customer classification model based on neural network algorithm is studied. Aiming at the shortcomings of easy falling into the local optimal solution of neural network algorithm, slow convergence speed of algorithm, and difficult setting of network structure, combined with the characteristics of genetic algorithm, the concept of adaptive genetic neural network algorithm is proposed. Then, the design of adaptive genetic neural network model is studied. Secondly, combined with the customer data of a financial enterprise and the characteristics of enterprise finance, this paper analyzes the risk influencing factors of enterprise financial customers, analyzes the customer data, evaluates the enterprise financial customers through the adaptive genetic neural network model, and realizes the classification of enterprise financial customers. Through an example, it is proved that the enterprise financial customer classification and preference model based on the adaptive genetic neural network algorithm discussed in this paper has better customer classification accuracy and can provide better method support for enterprise financial customer management.

A Relevant Customer Identification Algorithm Based on the Internet Financial Platform

In order to improve the intelligent search capabilities of Internet financial customers, this paper proposes a search algorithm for Internet financial data. The proposed algorithm calculates the customers corresponding to the two selected financial platforms based on the candidate customer set selected from the seed dataset and combined with the restored social relationship. Moreover, it also calculates the similarity of each field between the pairs. Furthermore, this article proposes an entity customer classification model based on logistic regression. Through the SNC model, threshold propagation, and random propagation, the model is transformed into an algorithm that identifies the associated customers, eliminates redundant customers, and realizes associated user identification. Experimental results verify that pruning increases the accuracy of identifying related customers by 8.44%. The average sampling accuracy of the entire customer association model is 79%, the lowest accuracy is 40%, and the highest is 1. From the sampling results, the overall recognition effect of the model reaches the expected goal.

Customer-Classified Intracity Fruit Distribution Path Optimization: A Case Study in Jining, China

With the development of technology and economy, customer satisfaction is becoming increasingly important to businesses. Customers expect more in regard to product quality, personalised service and delivery date. This study randomly selected customers from a small-to-medium sized company in China as case study and used a K-Means cluster analysis approach to present the available management in logistics. The mileage saving method was also used to contribute the distribution path planning. This resulted in a more scientific distribution route based on comparative analysis, which helped the target company save resources and improve efficiency. This study helped companies effectively identify customer value by combining customer classification with intracity distribution path optimization. Simultaneously, it provides possible empirical reference to service quality improvement, the distribution path optimization, the resource wasting reduction and the companies’ operation efficiency enhancement. It enriches the current literature about food distribution path optimization for small and medium sized food company.

A New K-Means Clustering Algorithm for Customer Classification in Precision Marketing

K-means is wildly used in data mining and clustering for its powerful data clustering ability, but its inherent limitations affect its application fields and accuracy. Theoriginal K-means algorithm is improved and applied in customer clustering in precision marketing. Firstly, integrates K-means algorithm with particle swarm optimization according to analyzing the source of the K-means calculation limitations; Secondly, improves the improved algorithm in its operation time, convergence speed, global solution exploration ability successively and redesigns the calculation procedures; Finally applies it in customer classification in precision marketing and the experiment results shows that the new algorithm can increasecustomer clustering effectiveness, validity, accuracy and has satisfactory results in practice.