What is conversion modeling?

Conversion modeling is the practice of using machine learning to evaluate marketing performance when some conversions cannot be directly attributed to ad interactions.

• Conversion modeling refers to observed conversions and uses cookies to connect between ad interactions and conversions.
• Conversion modeling refers to the import of observable conversions into Google Ads that model only the best quality conversions.
• Conversion modeling refers to measuring marketing using machine learning when a subset of conversions can’t connect to ad interactions.
• Conversion modeling refers to the process of creating custom columns in Google Ads to model your conversion data and performance.

Explanation:

Conversion modeling is an essential method that uses machine learning to fill in gaps where conversion data cannot be fully observed. In today’s privacy-first environment, directly attributing every conversion to an ad interaction is often not possible due to limitations such as cookie restrictions, user consent settings (for example, through Consent Mode), and users switching across devices. When some conversions cannot be directly tracked, conversion modeling analyzes patterns from available conversion data and other relevant signals to estimate the missing conversions. This approach provides a more complete and reliable view of campaign performance, helping advertisers make better optimization and bidding decisions.