Provides an Attribution IQ feature for applying and comparing multiple models retroactively.
With the Attribution IQ engine, analysts can go beyond standard last-touch models to analyze marketing channel impacts. The platform allows users to apply various rule-based models and data-driven algorithmic models to any custom event or metric within Analysis Workspace. A major advantage is that this modeling is retroactive and non-destructive, enabling analysts to apply different attribution logic to historical data without altering the dataset. Teams can build comparison tables to see how different models value channels, offering flexibility in proving ROI, given correct campaign tracking.
Uses data-driven models to distribute conversion credit across marketing touchpoints.
Moving away from traditional rules-based models, this tool employs a proprietary Data-Driven Attribution model by default. The machine learning system analyzes historical conversion paths to assign fractional credit to all marketing channels influencing a user's decision. This offers a realistic view of how top-of-funnel campaigns assist bottom-funnel channels. Users can access a dedicated workspace to compare different models side-by-side. However, the DDA model operates as a 'black box,' preventing analysts from inspecting or tweaking the specific weighting algorithms used.
The built-in Multi-Channel Conversion Attribution feature allows users to evaluate campaign performance using various standard attribution models.
Multi-Channel Conversion Attribution is a native feature that helps marketers move beyond the default last-click analysis. Analysts can easily compare how different marketing channels (like organic search, paid ads, or email) contribute to a final goal using various standard models, such as First Interaction, Last Non-Direct, Linear, or Time Decay. This provides essential visibility into the entire customer journey, particularly for understanding how top-of-funnel campaigns drive later conversions. The system allows for clear, side-by-side model comparison within the interface. However, unlike advanced enterprise marketing suites, it does not offer proprietary, algorithmic data-driven attribution (machine learning models) that automatically weight channels based on historical success probabilities.
Multi-channel attribution reports allow marketers to apply various standard models (like First Click or Linear) to understand campaign performance.
To help marketers evaluate campaign ROI, the platform includes a native Multi-Channel Attribution tool. Rather than defaulting strictly to last-click measurement, analysts can apply various standard models—such as First Click, Last Non-Direct Click, Linear, Position-Based, and Time Decay—to any defined conversion goal. This is crucial for understanding how top-of-funnel awareness campaigns assist in driving final conversions. The interface allows for easy model comparison to see how credit shifts between channels. However, it does not currently feature proprietary, machine-learning-driven algorithmic attribution (Data-Driven Attribution) that automatically weights touchpoints based on historical conversion probabilities, relying instead on these fixed, rule-based models.
Basic attribution modeling focused on last-click.
Attribution modeling is rudimentary, focusing on simple last-click attribution. This provides a basic understanding of conversion paths but lacks depth and flexibility. Users seeking insights into complex customer journeys may find this feature insufficient. External tools or custom setups may be needed for detailed analysis. The feature serves basic attribution needs but is limited for detailed insights.
