Predictive modeling is a set of processes that follow mathematical methods to forecast future events. It analyzes data patterns from given input and performs the prediction process. It is widely used in various aspects such as predicting TV ratings, customer purchases, corporate earnings, and more. With the help of ML-based statistical techniques, this model enhances prediction accuracy.

Predictive models utilize data analysis, AI, ML development, and statistical models to identify accurate data patterns for improved predictions. In forecasting the future of a company, this model employs historical and current customer demand data to predict upcoming events. Data scientists use this model to find correlations between elements through statistical formulation.

The churn prediction model is a type of predictive model designed to identify the likelihood of when an individual customer will cease engagement with a business. As the modern business landscape becomes increasingly competitive, this model plays a crucial role in predicting the risk of losing individual customers.

Customer segmentation is an effective strategy employed by companies to identify and understand their target audience. This process enables brands to group customers based on behavior and engagement modes, ultimately improving revenue and increasing client retention rates by facilitating more personalized services or products.

Demand forecasting is the process of anticipating customer demands and predicting the quantity of goods and services needed in the future. By analyzing real-time data on current and past customer demands, it accurately predicts future needs. For modern businesses, demand forecasting is essential for optimizing the supply chain process and increasing profitability.