Community Forum
What’s the best way to build a customer churn model in Power BI?
To build a customer churn model in Power BI, you will review the data from past customers and identify the previous behavior patterns that will look like the future customers that will churn from your product or service. You can utilize past customer historical data that confirms customer behavior patterns including: frequency of purchase, frequency of service usage, customer complaints, demographic information of the customers, etc. Using calculated columns and DAX measures, you can set churn indicators (e.g. no activity for 90 days), and then train a logistic regression or decision tree models using integrated Python Scripts or R Scripts in Power BI. Useful visualizations for identifying actionable content within the segments of customers that are at high risk for churn include: scatter plots, KPI cards, and heatmap charts.
Taking a Power BI Course in Pune will provide the necessary skills for preparing, transforming, analyzing large datasets in order to produce the predictive model for churn. The course will prepare the students with best practices for integrating advanced analytics in Power BI using built-in AI visuals and external scripting.
Taking a Power BI Training in Pune provides the benefit of having students navigate a hands-on project that utilizes churn prediction in real life. It will allow the students to experience model accuracy, utilizing the filter and slicer purposes for reporting, how to present deployable team dashboards, in addition to the benefit of adding value to an SCI as well as significantly influence decision making in marketing and customer service teams on best path forward.