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IJONIS
Enterprise AI

Predictive Analytics

Predictive Analytics uses statistical models and machine learning to derive predictions about future events from historical business data. From sales forecasts through customer churn to predictive maintenance — Predictive Analytics transforms data into forward-looking decision bases instead of retrospective reports.

Why does this matter?

Companies using Predictive Analytics don't react to problems — they anticipate them. Inventory is optimized before shortages occur. Customers are retained before they churn. Machines are maintained before they fail. This reduces costs and improves planning reliability across the entire organization.

How IJONIS uses this

We build Predictive Analytics solutions on your existing business data: time series forecasting with Prophet and ARIMA, classification models with XGBoost, and churn models with scikit-learn. Results are visualized in dashboards or integrated as automatic alerts into existing workflows.

Frequently Asked Questions

What data do I need for Predictive Analytics?
Historical data on the event you want to predict — typically 12-24 months. For sales forecasts you need sales data, for churn models customer behavior data. The cleaner and more complete the data, the more accurate the predictions. We analyze upfront whether your data base is sufficient.
How accurate are Predictive Analytics predictions?
Accuracy varies by use case: sales forecasts typically reach 85-95% accuracy, churn prediction 75-90%. The key insight is that even imperfect predictions enable better decisions than no forecast at all — the competitive advantage lies in systematic foresight.
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