Predictive models for customer attrition

客户流失或流失/背叛领域的研究和开发是非常新的，尚未得到充分的探索。为了使企业能够充分利用人工智能/机器学习（ML）功能，对潜在的客户流失进行定性的因果分析，需要进行更多的调查。了解客户流失背后的原因可以使企业大幅增加客户保留率，从而提高投资回报。我们的目标是建立能够预测可能的客户保留努力对客户行为的影响的模型。

Research and development in the area of customer attrition or churn/defection is very new and not fully explored yet. More investigation is needed in order to enable businesses to take full advantage of artificial intelligence/machine learning (ML) capabilities to produce a qualitative causal analysis of the potential customer churn. Knowing the causal factors behind the churn can enable businesses to dramatically increase the customer retention efforts, thus improving return on investments. Our goal is to build models capable of predicting the effect of possible customer retention efforts on customer behaviour. Our industry partner, Predikat Inc., focuses on Software as a Service (SaaS) businesses for which the churn rate is among the main indicators of the health of their business. This research project will be determining what techniques to apply to this specific area. We will explore further research opportunity in developing extensions and optimizations of current ML techniques to better solve problems in this domain. In particular, we will design and test a research prototype that will include the theoretical framework and uplift modelling for causal analysis of the customer attrition, as well as effective churn mitigation techniques.