Customer relationship management using machine learning and statistical approaches

客户关系管理（CRM）是企业通过与客户建立和谐的关系来创造长期利润的重要业务功能。技术的进步使新的方法-特别是机器学习-被应用于寻找最佳的CRM策略。然而，在真实的生活应用中，收集客户信息进行分析 ** 是一个非标准化的过程，收集的数据质量无法得到保证。这个项目的目的是 ** 找到一个CRM模型，能够解决这些问题的融合，同时仍然有一个合理的 ** 程度的普遍性，在整个人口。为达致此目的，本集团将采用不同的统计及机器学习技术及方法，包括逻辑回归、决策树、神经网络、贝氏分类、随机森林、集成方法、支持向量机及 ** 等，以预测客户流失倾向。在应用学习算法之前，将对数据进行预处理，包括缺失数据 ** 处理、特征选择和归一化，以提高 ** 预测精度。最佳策略和不同的国家的最先进的方法 ** 的比较将被提出。预计所提出的研究将为 ** 预测理论和真实的生活应用带来重大贡献。加拿大的企业主可以利用研究结果来精确预测客户行为，以增加企业的利润。

It is widely recognized nowadays that Customer Relationship Management (CRM) represents one vital**business function that generates long term profit by developing harmonious relationship with customers. The**technological advancement has enabled new approaches - notably machine learning - to be applied for finding**the best CRM strategies. However, in real life applications, the gathering of customer information for analysis**is a non-standardized process and the quality of the data collected cannot be guaranteed. This project aims at**finding a CRM model capable of addressing an amalgamation of these issues while still having a reasonable**degree of generality across the entire population. In order to achieve this objective, different statistical and**machine learning technologies and methodologies will be implemented including logistic regression, decision**trees, neural networks, Bayesian classification, random forest, ensemble approach, support vector machine and**etc. to predict the propensity of customer defection (churn). Data will be pre-processed including missing data**handling, feature selection, and normalization before the learning algorithms are applied in order to improve**the prediction accuracy. The optimal strategy and the comparison of different state-of-the-art methodologies**will be presented. It is expected that the proposed research will bring significant contributions to both**prediction theory and real life applications. Canadian business owners can make use of the research results to**precisely predict customer behaviors in order to increase businesses' profits.