A study on the logistic service satisfaction for internet marketing enterprise using data mining technology

In the related researches of internet marketing, it can be seen that four flows (for example, commercial flow, payment flow, logistic flow and information flow) emphasized by general scholars are all the most important business operation rings in internet shopping. The logistic flow mechanism is the most important key for the success of internet shopping seller. That is, whether the dealt merchandise can be sent to the hand of buyer safely and effectively is the most important thing. In this article, we take first the questionnaire survey result of quality and service satisfaction of seller's logistic flow of online auction to perform analysis, then we select the related principal component of analysis result to be used as independent variable, meanwhile, we use the entire satisfaction of auction seller's logistic service in the question items of the questionnaire survey as dependent variable to be combined into the sample data of this article; finally, this article uses newer Artificial Fish Swarm Algorithm to optimize the parameter of General Regression Neural Network, meanwhile, it is used together with Data Mining technique such as General Regression Neural Network and Multiple Regression to perform the construction of classification forecast model of logistic flow quality and service satisfaction of internet marketing enterprise. From the analytical result, this article has selected 5 principal components as the independent variables of the model; among three data mining techniques, we have found that the RMSE value of Artificial Fish Swarm Algorithm optimized General Regression Neural Network model has very good convergent result and very good classification forecast capability.