Analyzing the Factors Affecting Customers’ Satisfaction, Based on Neural Network (Case Study: SAIPA Group Automotives)

Effort is made in the present research to study and analyze the factors affecting customers’ satisfaction. This research is of descriptive-surveying type and its case study includes the buyers of SAIPA Group. To collect data in the present research, database of the Iranian Standard & Quality Inspection Co. (ISQI) that makes monthly polls in the automotive section was used and 1,200 data records of 5 main products of SAIPA Group were made available to the researcher with the collaboration of the said company. The main goal of the researcher is to study the factors affecting on satisfaction and influence level of these factors on the automotives of SAIPA Company. For this purpose, the main model of research was considered as follows: c = f (sale, after sale services, product quality, demographic characteristic) Where, factors on the right hand are independent variables and c, consumer’s behavior (satisfaction) is the same dependent variable of the research. Artificial neural network approach has been used in parallel with analysis of information and establishment of relations between research variables. Use of this technique has increased the accuracy of calculations and has provided a more complete model. In the neural network model of this research, data is divided with a certain proportion into learning and test samples in order to test model fitness. A 3 to 7 ratio was applied to the samples, i.e., 70% of data was allocated to learning sample and 30% was allocated to test sample. SPSS software was used for data analysis which is highly capable in neural networks calculations.