Forecasting . Based on the analyzed data,

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models are built that can predict future events. For example, a model can predict when a customer will return to the site to make a purchase or which product will be most in demand next season.

Interpretation of results . Forecasts must be easy to interpret so that effective decisions can be made based on them. This is important for businesses that not only want to predict, but also to correctly apply the information obtained.
Examples from real business

Marketing and sales : In marketing, predictive analytics helps to forecast consumer behavior, such iceland phone number list as when they are likely to unsubscribe or stop buying a certain product. For example, Amazon uses such models to personalize offers and product recommendations based on user behavior.
Inventory management : In retail, predicting inventory needs helps reduce excess inventory costs while minimizing stockout risks.
Finance . Banks and financial institutions use predictive analytics to assess customer creditworthiness and prevent fraud. Forecasts help identify high risks and take action in advance.