Businesses often suffer investment losses when customers begin to churn, such as when they unsubscribe, buy from a competitor or cancel a contract. It is therefore crucial that companies figure this out early on, as acquiring new customers often costs more than retaining existing ones.

With our expertise in machine learning projects, our experts can develop or optimize an existing predictive churn model. For example, our Data Scientist recently helped one of our clients by converting their existing model into a smart and fully automated model running entirely on Amazon Web Services (AWS). With an accurate forecasting tool, our client could take immediate action and prevent churn from happening. For a better and clear overview, all prediction insights are visualized in a Power BI dashboard that shows the key factors that could contribute to churn, what the churn risk is per customer and which one should be prioritized. During this project, we assisted our client with:

With the current market turbulence, our client wanted to have a clear picture of the potential business risks. The most ideal scenario would be, of course, to prevent churn from occuring by identifying the key factors that can help stakeholders take immediate action. This means they need to gain insights into the key factors that can contribute to churn, what the likelihood of customer churn is and which key factor should be prioritized. Furthermore, to ensure our client gets a better and clear overview of the predictions, all insights had to be visualized in a user-friendly Power BI dashboard.