Customer research is one of the important aspects of understanding customer behavior patterns with business enterprises and predicate how consumer satisfaction is achieved. Customer analysis brings out various underlying information about the customer patterns with enterprises and analysis decision helps to make better marketing strategies to improve the customer lifetime and also enhance the business profit. To perform effective customer analysis in this research Naive Bayes an ML algorithm is applied. The efficiency of NB comes from its conditional independence assumption and the violation of NB assumption results in poor prediction. But in most real-time customer datasets, the NB assumption is violated due to the presence of correlated, irrelevant, and noisy variables. To improve NB prediction with these customer customers, in this research Multi-Stage Variable Selection(MSVS) is proposed to select the relevant variables from the customer dataset which helps to predicate the customer patterns wisely. The proposed approach consists of two stages in selecting the relevant variable subset from the customer datasets. Further variable subset obtained from the proposed MSVS approach is experimented with using the NB algorithm and the results obtained are compared using the wrapper and filter approaches. From the results, it clearly shows the proposed MSVS approach performs better in selecting the variable subset and improves the NB prediction in customer analysis efficiency compare to wrapper and filter approaches. Further, the proposed approach works efficiently in time and less computational compare to wrapper and filter approaches.

客户研究是了解企业客户行为模式和预测如何实现消费者满意度的重要方面之一。客户分析揭示了有关企业客户模式的各种潜在信息，分析决策有助于制定更好的营销策略，以提高客户寿命并提高业务利润。为了在这项研究中执行有效的客户分析，朴素贝叶斯应用了机器学习算法。NB的效率来自于它的条件独立假设，违反NB假设会导致预测不佳。但在大多数实时客户数据集中，由于存在相关、不相关和噪声变量，违反了 NB 假设。为了改善对这些客户客户的 NB 预测，在这项研究中，提出了多阶段变量选择（MSVS）从客户数据集中选择相关变量，这有助于明智地预测客户模式。所提出的方法包括从客户数据集中选择相关变量子集的两个阶段。使用 NB 算法对从所提出的 MSVS 方法获得的进一步变量子集进行实验，并使用包装器和过滤器方法比较获得的结果。结果清楚地表明，与包装器和过滤器方法相比，所提出的 MSVS 方法在选择变量子集方面表现更好，并提高了客户分析效率中的 NB 预测。此外，与包装器和过滤器方法相比，所提出的方法在时间上有效并且计算量更少。