What Is Conversion Modeling?

Conversion modeling is a computational framework that estimates the probability of user conversions when direct tracking mechanisms are unavailable or incomplete. This approach addresses the data loss inherent in traditional tracking methods such as cookies, particularly for users who do not consent to data collection. The primary function of conversion modeling is to fill these data gaps by leveraging machine learning algorithms to infer unobserved conversion events.

The methodology involves:

• Evaluating the incremental impact of individual website visits using visitor behavioral data.
• Analyzing observed conversions from consented users and historical trends to predict conversions among non-consented users.
• Estimating user behavior patterns along website interaction paths, thereby reconstructing probable conversion outcomes.

In scenarios where consent mode is activated—resulting in Google tags operating without cookie data—conversion modeling becomes critical. Consent Mode modifies tag behavior to respect user privacy, but this creates discontinuities in ad measurement data. Conversion modeling compensates for these gaps by applying Google AI to observable signals and historical conversion patterns, thus bridging the connection between ad interactions and actual conversions (Google, 2023).

Implementation of this framework typically requires integration with a Google-certified Consent Management Platform (CMP) to ensure compliance and maximize data utility.

Key Findings:

• Conversion modeling significantly improves conversion attribution accuracy under privacy constraints by estimating conversions that traditional tracking misses.
• Machine learning models trained on consented user data provide robust predictions for unconsented user behavior.
• The method enables advertisers to maintain measurement fidelity despite increased regulatory restrictions on cookie usage and tracking technologies.