Customer relationship management (CRM) is the tool to enhance customer relationship in any business. Due to the exponential growth of data volume, in any field, it is significant to develop new techniques to discover the customer knowledge, automation of the system and moreover customer satisfaction to win customer lifetime value. CRM with machine learning could bring a catalytic change in business. Several supervised and unsupervised machine learning techniques are utilized to improve the customer experience and profitability of business. This paper reviews the available literature on the CRM with machine learning techniques for customer identification, customer attraction, and customer retention and customer development. This study reveals that supervised learning techniques are 48.48% utilized, unsupervised learning techniques are utilized 15.15%, and 9.09% utilized other techniques in CRM. Paradigm is also shifted toward the deep learning from machine learning as 28.28% text has been reported to deep learning. Decision tree-based algorithm and support vector machine algorithms are most utilized algorithm of supervised learning. E-commerce and telecommunication sectors are the most important areas identified with the exponential growth of the users and hence need a suitable machine learning techniques for customer satisfaction and business profitability.

客户关系管理（CRM）是在任何业务中增强客户关系的工具。由于数据量在任何领域都呈指数级增长，因此开发新技术以发现客户知识，系统自动化以及客户满意度以赢得客户生命周期价值至关重要。带有机器学习的CRM可以带来业务的催化变化。几种有监督和无监督的机器学习技术被用来改善客户体验和业务盈利能力。本文回顾了有关CRM的可用文献，其中包括用于识别客户，吸引客户以及保留和发展客户的机器学习技术。这项研究表明，监督学习技术的使用率为48.48％，非监督学习技术的使用率为15。15％和9.09％的人在CRM中使用了其他技术。范式也从机器学习转向了深度学习，因为据报道有28.28％的文本用于深度学习。基于决策树的算法和支持向量机算法是监督学习最常用的算法。电子商务和电信行业是用户数量呈指数增长的最重要领域，因此需要一种合适的机器学习技术来提高客户满意度和业务获利能力。