Predict Oracle's Predictive Models Explained

Welcome to Predict Oracle! This guide will help you understand the different predictive models available on our platform and how to choose the right one for your specific needs.

Available Predictive Models

1. Outcome Prediction - What it does: Predicts the likelihood of specific outcomes based on historical data - Best for: Customer conversion prediction, risk assessment, opportunity scoring - Example use case: Predicting which leads are most likely to convert to customers

2. Smart Segmentation - What it does: Automatically groups data into meaningful segments based on patterns - Best for: Customer segmentation, behavior clustering, product categorization - Example use case: Identifying distinct customer groups for targeted marketing

3. Time Series Forecasting - What it does: Projects future values based on historical time-based data patterns - Best for: Sales forecasting, demand prediction, resource planning - Example use case: Predicting monthly sales for the next 12 months

Choosing the Right Model

Consider these questions when selecting a model:

1. What type of prediction do you need?
2. Specific outcome (yes/no, high/medium/low) → Outcome Prediction
3. Group identification → Smart Segmentation

4. Future values over time → Forecasting

5. What data do you have available?

6. Historical outcomes with labeled data → Outcome Prediction
7. Multi-dimensional data without specific labels → Smart Segmentation

8. Time-stamped historical data → Forecasting

9. What is your business objective?

10. Identifying high-value opportunities → Outcome Prediction
11. Understanding customer behavior → Smart Segmentation
12. Planning for future demand → Forecasting

Getting Help

If you're unsure which model to choose, our recommendation engine can analyze your data and suggest the best approach. Simply upload a sample of your dataset, describe your objective, and we'll recommend the optimal model configuration.

Next steps: Check out our "Data Requirements" guide to understand what data you'll need to prepare for your chosen model.