How AI Attribution Works in a CDP and Improves Conversions

AI-driven attribution in Sitefinity Insight analyzes all customer touchpoints to determine how each one contributes to a conversion. Unlike traditional models that credit only the first or last interaction, this approach uses machine learning to evaluate the full journey and assign value accordingly. This helps marketers understand which channels and actions truly influence outcomes and make more informed decisions.

What algorithms are used in the AI-driven attribution model?

The AI-driven attribution model in Sitefinity Insight uses machine learning algorithms to analyze large sets of customer interaction data. These algorithms detect trends, surface opportunities and risks and assign value across multiple touchpoints based on real user behavior.

How does the model assign value to touchpoints across the customer journey?

The model evaluates user interactions throughout the journey and calculates the contribution of each touchpoint to eventual conversions. It assigns value using algorithms that assess the strategic importance of each interaction, enabling more accurate measurement of marketing performance.

How does AI attribution differ from traditional first-touch and last-touch models?

Unlike first-touch and last-touch models that assign credit to only one interaction, AI attribution in Sitefinity Insight considers the entire sequence of touchpoints. It offers a broader, data-driven view of how each touchpoint supports conversion, resulting in more balanced and accurate attribution insights.

What visualizations are available to explain attribution model outcomes?

Sitefinity Insight provides a tabular report that compares touchpoints using different attribution models. This helps marketers evaluate performance across multiple frameworks and better understand the data behind attribution results.

How does the platform improve attribution accuracy over time with new data?

The platform continuously learns from new user interactions. As data grows, the machine learning models refine their calculations and improve accuracy, allowing for more precise attribution and better marketing insights.