CASE STUDY

Solution Implemented 

The e-commerce business adopted the Karolium “out-of-the-box” predictive solution readily fit for their business circumstances. The process involved: 

• Flexible Data Integration: Historical customer data (including demographics, purchase history, engagement metrics) was easily uploaded into the environment using direct file uploads. Options for schema selection or dynamic queries were also available for more complex integrations. 

• Guided Data Preparation & Analysis: Performed essential data treatment, including handling missing values (imputing with mean/mode) and normalization, ensuring data quality for modeling. Leveraged built-in Exploratory Data Analysis (EDA) tools to visualize data distributions (box plots) and understand relationships between features and churn likelihood (feature correlation graphs, pair plots) without manual scripting. 

• Zero Code Predictive Modeling: Using the intuitive Karolium interface, the business optimized ‘Churn Status’ as the target variable, relevant customer features identified during EDA as predictors, using machine learning model training and classification algorithms available within the platform and identified the best-performing based on F1 score, for both precision and recall in identifying churners. 

• Automated Prediction Execution: Once the optimal model was trained and selected within Karolium, the business could upload new customer data batches. The platform automatically applied the trained model to generate churn probability scores for these customers, highlighting those at highest risk enabling the customer team to run effective campaigns.