Customer churn and scoring analysis with Azure


Customer churn and scoring analysis with Azure

Don’t let your most valuable customers go: advanced analytics for customer segmentation, churn prediction and prevention

How to use analytics to determine the probability of customer churn? And how to combine it with scoring and estimation of customer value?

Customer churn is one of the crucial metrics for a growing number of companies in a variety of industries, as the traditional consumer behavior patterns disappear, along with the decrease of customer loyalty and retention. To face this challenge, companies must make use of the data to analyze not only the probability of churning but also combine this with the evaluation of customer worth to make their churn prevention campaigns precisely targeted and more cost-effective.

Predictive analytics and machine learning models allow us to extract patterns and insights from numerous sources of data. The result of such analysis are customer segments with churn probability calculated for every one of them. This combination provides valuable information for businesses.

For instance, it can be used for an improved elaboration of customer retention strategies focused exactly on those segments of clients that are most vulnerable to churn and at the same time most profitable for the company. This way, it’s possible to invest the maximum effort in retaining the key customers. The analysis also makes it possible to find the factors and reasons behind customer attrition, and therefore use this information to improve customer experience.

The final result of churn and scoring analysis is the visualization of obtained data so it can be an insightful source of information for business units such as marketing, sales, and customer service and therefore allow smarter, data-driven decision-making in the day-to-day work with the customers at risk of churning.

Download our ebook to find out more about customer churn and scoring analysis with Machine Learning and Azure cloud!


Actionable insights To prevent the loss of the most valuable customers
Customer loyalty and satisfaction monitoring Detecting negative trends and variations in time and location
Customer segmentation For precise targeting and more cost-effective campaigns
Data visualization Supporting the work of business units with accessible information

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