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客户流失预测

机器学习

解决方案构想 Solution Idea

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客户流失预测使用 Cortana Intelligence Suite 组件来预测流失概率,并帮助查找与预测流失率相关的现有数据中的模式。Customer Churn Prediction uses Cortana Intelligence Suite components to predict churn probability and helps find patterns in existing data associated with the predicted churn rate.

体系结构Architecture

体系结构关系图 下载此体系结构的SVGArchitecture Diagram Download an SVG of this architecture.

说明Description

有关如何构建此解决方案的更多详细信息,请访问 GitHub中的解决方案指南。For more details on how this solution is built, visit the solution guide in GitHub.

保持现有客户的成本要低于获得新客户的成本的五倍。Keeping existing customers is five times cheaper than the cost of attaining new ones. 出于此原因,市场主管经常会发现他们试图估算客户流失的可能性,并寻找必要的措施来最大程度地降低流失率。For this reason, marketing executives often find themselves trying to estimate the likelihood of customer churn and finding the necessary actions to minimize the churn rate.

客户流失预测使用 Azure 机器学习预测流失概率,并帮助查找与预测流失率相关的现有数据中的模式。Customer Churn Prediction uses Azure Machine Learning to predict churn probability and helps find patterns in existing data associated with the predicted churn rate. 此信息为企业提供切实可行的智能,以改善客户的保留期和利润率。This information empowers businesses with actionable intelligence to improve customer retention and profit margins.

本指南的目的是演示零售商用于预测客户流失的预测数据管道。The objective of this guide is to demonstrate predictive data pipelines for retailers to predict customer churn. 零售商可以使用这些预测来防止客户流失,方法是使用其域知识和适当的营销策略来解决风险的客户。Retailers can use these predictions to prevent customer churn by using their domain knowledge and proper marketing strategies to address at-risk customers. 本指南还介绍了如何重新训练客户流失模型,以便在数据可用时利用其他数据。The guide also shows how customer churn models can be retrained to leverage additional data as it becomes available.

后台内容What's Under the Hood

使用 Microsoft Azure 在云中实现端到端解决方案。The end-to-end solution is implemented in the cloud, using Microsoft Azure. 该解决方案由多个 Azure 组件组成,其中包括数据引入、数据存储、数据移动、高级分析和可视化。The solution is composed of several Azure components, including data ingest, data storage, data movement, advanced analytics and visualization. 高级分析是在 Azure 机器学习中实现的,其中一个可使用 Python 或 R 语言构建数据科学模型 (或重复使用现有的内部或第三方库) 。The advanced analytics are implemented in Azure Machine Learning, where one can use Python or R language to build data science models (or reuse existing in-house or third-party libraries). 使用数据引入,解决方案可以根据从本地环境传输到 Azure 的数据进行预测。With data ingest, the solution can make predictions based on data that being transferred to Azure from an on-premises environment.

解决方案仪表板Solution Dashboard

下面的快照显示了一个示例 PowerBI 仪表板,可深入了解整个客户群的预测流失率。The snapshot below shows an example PowerBI dashboard that gives insights into the the predicted churn rates across the customer base.

洞察力