Customer Churn and Scoring Analysis 1-month PoC
Put your data to work and prevent customer churn. Thanks to our Proof of Concept find out how to use machine learning and predictive analytics to get insights from your data and take informed action.
In order to get ahead of customer churn, organizations need to analyze the probability of it. Thanks to our PoC (Proof of Concept) you can check how churn looks at your company. During the PoC (Proof of Concept) we use predictive analytics and Azure Machine Learning models to extract patterns and insights from many sources of customer data. The result is a set of calculated churn probability metrics for any given customer segment shown through data visualization.
Benefits of customer churn and scoring analysis with Azure: dashboards with actionable insights; supporting the work of business units with accessible information; customer loyalty and satisfaction monitoring; detecting negative trends and variations in time and location; customer segmentation; for precise targeting and more cost-effective campaigns.
The PoC (Proof of Concept) includes: a planning & requirements collection session; building a fully operational solution for a selected, most vital sub-area of your company; a Microsoft Azure cloud-based solution allowing for easy management and scalability; creating a single machine learning model for churn prediction; performing customer profiling based on scoring mechanisms; Power BI dashboards for a comprehensive overview of scoring & modeling results.
The PoC (Proof of Concept) stages:
1. Envision: gathering specific requirements, setting the criteria of success;
2. Plan: specify quick wins; indicate relevant business areas;
3. Build: implement churn modeling and customer scoring, prepare a set of managerial Power BI Dashboards;
4. Test: specify essential test cases; carry out the acceptance process;
5. Deploy: implement the finished solution, establish further production development plans.
Factors that might affect the estimated pricing: number of requirements, number of data sources, design dashboards, implementation of data, travel&requirement for on-site work, the complexity of the process.