If your stakeholders keep asking what will happen next, this guide shows how to move beyond historical reporting and start predicting churn, LTV, demand, and much more. Learn why analysts are best positioned to lead predictive modeling and how an AI co-pilot helps you go from a business question to ready-to-use predictions in days.
Inside you’ll find:
- Why analysts should own predictive modeling and how to close the skills gap with an AI co-pilot.
- A simple 4-step workflow: connect data, define the use case with a co-pilot, evaluate model performance with business metrics, and deploy predictions to your tools.
- Clear answers to FAQs about low-code predictive modeling, plus how explainability keeps you in control of inputs, features, and assumptions.
- Examples for how predictive analytics drives better decisions, including recent adoption and accuracy trends.
- Cross-industry examples that show practical impact in retail, D2C, insurance, and telecom.
This guide is perfect for:
- BI and data analysts who need a faster path to predictions.
- Analytics leaders who want measurable ROI without adding headcount.
- Ops teams in marketing, product, or CRM who want predictions delivered into daily workflows.
- Data engineers who support analysts and prefer governed, explainable pipelines.
- Any team ready to turn business questions into deployed predictive models in days.
