Data Mining is considered as most important techniques while analyzing the consumer behaviours and used efficiently. There are several types of techniques under the data mining which can be selected according to the requirement.
For getting the actual result and implementing new technologies the researchers goes through all the database of consumer behaviour.
Collected data are simulated to get the optimum result to analyze the consumer behaviour that depends on different factors such as purchasing, availability and price.
Statistical analysis or predictive analysis is done using different strategies to find out the consumer behavior such as regression model, discrete choice model, survival or duration analysis and conjoint approach.
AMOS 6.0 can be used for the statistical analyses confirmatory factor analysis (CFA) and structural equation modeling (SEM). The main advantage of CFA and SEM is that it is possible to explicitly account for measurement error when a latent variable of interest is represented by multiple manifest variables. Measures of how well the implied variance-covariance matrix, based on the parameter estimates, reflects the observed sample variance-covariance matrix can be used to determine whether the hypothesized model gives an acceptable representation of the analyzed data.(Thøgersen; 2009).
数据挖掘是视为最重要的技术在分析消费者行为和有效的使用。有几种类型的技术，在数据挖掘，可以根据需要选择。
得到的结果与实际实施新技术的研究人员通过所有数据库中的消费者行为。
收集的数据进行模拟，得到最优的结果分析消费者行为，取决于不同的因素，如采购，供应和价格。
统计分析和预测分析是使用不同的策略，找出消费者的行为，如回归模型，离散选择模型，生存或时间分析和结合的方法。
阿摩司6可用于统计分析验证性因素分析（非洲法郎）和结构方程模型（扫描电镜）。主要利用终审法院和扫描电镜是它可能是明确考虑到测量误差时，潜变量的利息是由多个变量。措施以及如何隐含的方差协方差矩阵，基于参数估计，反映了观察样本方差协方差矩阵可以用来确定是否假设模型给出了一个可以接受的表示和分析数据。（次øgersen；2009）。
