Predictive Analytics

Coming Soon - Enterprise Plan

clariBI's predictive analytics will help you identify trends, forecast future performance, and make data-driven predictions based on your historical data.

Time series forecasting
Trend analysis
Anomaly detection

Overview

Predictive analytics in clariBI uses machine learning algorithms to analyze historical patterns and generate forecasts. This feature helps you anticipate future trends, identify potential issues, and make proactive business decisions.

Available Predictions

  • Time Series Forecasting: Predict future values based on historical trends
  • Anomaly Detection: Identify unusual patterns or outliers in your data
  • Trend Analysis: Understand the direction and velocity of change
  • Correlation Analysis: Discover relationships between different metrics

Prerequisites

  • Enterprise Plan Required

    Predictive analytics is exclusively available on the Enterprise plan

  • Historical Data

    Connected data source with at least 3 months of historical data (6+ months recommended)

  • Time-Series Data

    Data with regular time intervals (daily, weekly, monthly)

Prediction Types

Time Series Forecasting

Predict future values based on historical trends, seasonality, and patterns. Best for metrics like sales, revenue, and user growth.

Example: Forecast next quarter's revenue based on the last 2 years of sales data.

Anomaly Detection

Identify unusual patterns or outliers that deviate from normal behavior. Useful for monitoring system health and detecting issues.

Example: Detect unusual spikes in customer churn or system errors.

Trend Analysis

Understand the direction and velocity of change in your metrics. Helps identify whether trends are accelerating or decelerating.

Example: Analyze whether customer acquisition is growing linearly or exponentially.

Interpreting Results

Predictive analytics results include:

Forecast Values

The predicted future values for your selected metrics

Confidence Intervals

The range of uncertainty around predictions

Accuracy Metrics

How well the model fits historical data

Trend Indicators

Direction and strength of trends

Best Practices

  • Use sufficient historical data: At least 3-6 months for meaningful predictions
  • Consider seasonality: Account for recurring patterns (holidays, seasons, etc.)
  • Validate predictions: Compare forecasts with actual results to improve accuracy
  • Update regularly: Refresh predictions as new data becomes available
  • Monitor confidence levels: Pay attention to prediction confidence intervals

Important Note

Predictive analytics provides forecasts based on historical patterns. External factors, market changes, or unprecedented events can affect accuracy. Always use predictions as one input in your decision-making process.

Limitations

  • - Requires Enterprise plan
  • - Minimum 3 months of historical data recommended
  • - Predictions consume AI credits
  • - Accuracy depends on data quality and consistency
  • - Cannot predict unprecedented events or market disruptions

Troubleshooting

"Not enough historical data"

Ensure you have at least 3 months of consistent historical data. Consider using a different time granularity (weekly instead of daily) if needed.

"Low prediction accuracy"

Check for data quality issues, missing values, or significant changes in your business that might affect patterns.

"Predictions seem unrealistic"

Review your data for outliers or errors. Consider adjusting the forecast horizon or including additional context about business changes.

Next Steps