Data Analysis And Survey Of Customer Satisfaction For The Brand Product

With the arrival of new economic era, the brand is an effective weapon of competition between enterprises, and the brand share becomes an important indicator to measure the local economic. Customer satisfaction is an important component of brand selection, which is essential for enterprises, and is also the key factor for the long-term development of enterprises. At present, the analysis of the survey information and evaluation of customer satisfaction are required for perfecting the brand selection. Since 2001 the Chinese famous brand strategy promoting Committee begun brand selection, Hubei Province has got response to the call of the country for an annual brand selection and requirements to declare the Hubei famous brand enterprise evaluation.This thesis introduced the 2013 Hubei famous brand evaluation methods, test process and results. The information on evaluation results were effectively analyzed. Some methods were proposed to improve the service quality of the enterprises and customers satisfaction. In addition, quantitative relationship between indicators was constructed according to the structural equation model. Suggestions for improving brand selection was offered according to data features.To guarantee the validity of data information, the method of customer satisfaction measurement for brand selection was constructed with theoretical and empirical analysis in this thesis. And the results were used in the evaluation of customer satisfaction in selection of 2013 Hubei famous brand products. To improve the accuracy of data analysis, effective data entry system was used. For the analysis of data and information, the structural equation model of customer satisfaction index of production information model was constructed with PLS(Partial Least Squares Regression)method. Finally, data was imported into Smart PLS through SPSS. While, quantity relationship of latent variable and latent variable, latent variable and variable were got from model calculation. Suggestions for improving brand selection were proposed according to industry classification, including mechanical, electrical, chemical, biological, textile etc. According to comparison between different structural equation model parameters in different industry, some effective methods to improve the brand selection of various industries were proposed.