Customer Churn and Real-time Analytics

By for March 4, 2017

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In today's fast-paced world, mobile phone customers have many choices and can easily switch between service providers. Many industries, including mobile providers, use Churn Models to predict which customers are most likely to leave, and to understand which factors cause customers to stop using their service. This solution shows you how to build a real-life churn model with Azure Machine Learning, make it enterprise-ready with Azure Data Factory, and deliver data insights with Power BI. You will also see how to collect real-time Call Details Records (CDRs), and use that to deliver analytical results with Power BI
> **Note:** If you have already deployed this solution, click [here]( to view your deployment. ### Estimated Provisioning Time: 20 Minutes ## Overview Businesses need an effective strategy for managing customer churn. Customer churn includes customers stopping the use of a service, switching to a competitor service, switching to a lower-tier experience in the service or reducing engagement with the service. Improving customer attrition rates and enhancing a customer's experience are valuable ways to reduce customer acquisition costs and maintain a high-quality service. In this solution, we look at how a mobile phone carrier company can leverage Azure to proactively identify customers more likely to churn in the near term in order to improve the service and create custom outreach campaigns that help retain the customers. Mobile phone carriers face an extremely competitive market. Many mobile carriers lose revenue from postpaid customers due to churn. Hence the ability to proactively and accurately identify customer churn at scale can be a huge competitive advantage. Some of the factors contributing to mobile phone customer churn includes: Perceived frequent service disruptions, poor customer service experiences in online/retail stores, offers from other competing carriers (better family plan, data plan, etc.). A mobile carrier needs to build a 360-degree view of their user base to enable the company to stay ahead of the competition. The Cortana Intelligence Suite provides advanced analytics tools through Microsoft Azure — data ingestion, data storage, data processing and advanced analytics components — all of the essential elements for building a customer churn model and real-time analytics for the telco industry. With this solution, organizations will be able to: * Build a real-life churn model with Azure Machine Learning, make it enterprise-ready with Azure Data Factory, and deliver data insights with Power BI. * Understand how to build a web application that simulates phone calls and collects Call Details Records (CDRs) using Event Hub. * Learn how a Stream Analytics job is used for transforming the raw CDR data into analytical results, and how the results are delivered with Power BI. The *Deploy* button will launch a workflow that will deploy an instance of the solution within a Resource Group in the Azure subscription you specify. The solution includes multiple Azure services (described below). ## Solution Diagrams This solution sets up the infrastructure in the diagram below. This image shows the overall architecture of the Solution Template for telco customer churn and real time analytics models: [![Telco](]( This diagram shows the churn model in greater detail: [![Telco](]( ## Additional Reference You can also watch [this video]( to learn how to build the customer churn solution end to end. ## Pricing Your Azure subscription used for the deployment will incur consumption charges on the services used in this solution. For pricing details, visit the [Azure Pricing Page]( See the 'Services Used' section on the right pane for the list of the services used in this solution. You should delete the solution after you stop using it. ## Disclaimer ©2017 Microsoft Corporation. All rights reserved. This information is provided "as-is" and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here. Third party data was used to generate the solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licenses in order to create similar datasets. ![ ](