Blog Comparison guide vs Google Enhanced Conversions vs
Google Enhanced Conversions and Google Enhanced Conversions are services that advertisers can use to improve online advertising performance.

Both services train Google how to make better bidding decisions based on the value. Google Enhanced Conversions
Value of the
conversion event
High High
Frequiency of the
conversion event
High Low
New audiences
to target
Google Enhanced Conversions
are most useful when you
  • Optimize your ads for a sale, rather than clicks and leads
  • Enjoy a large marketing budgets
  • Have a sales cycle that is less than 30 days
  • Configure data transfers to your CRM system
  • Experience more than 50 conversions per month
  • Are set up to deliver relevant data separately to other advertising platforms, such as Meta and LinkedIn
Fear of Missing Out on Conversions
Google Enhanced Conversions uses fewer datapoints compared to
  1. Google only sends actual conversions, while also sends synthetic conversions with a value representing the probability of a future conversion.
  2. Google only recognizes conversions that were made from a users signed in into their Google accounts.
The Limitations of Google Enhanced Conversions
  • You need a consistent budget to reach at least 50 optimization goals each month
  • The user data recycles every 30 days, causing you to lose valuable data if your sales cycle is typically longer.
  • There is limited data for optimization, reducing the training quality and speed for advertising campaigns.
on ad campaign learning When to use? offers predictive optimization services for leadgen,
product-led and subscription-based businesses
You want to use smart bidding, adjusting the cost of advertising for various audiences depending on the estimated user value
Your marketing budget isn’t enough for 50+ targeted actions every month
You have a difficult or long target to optimize (infrequent sales/subscription/LTV)
Sending conversions with value for
businesses with long transaction cycles
is no longer a problem
Traditional Digital
Marketing Bidding
Traditional Digital
Marketing Bidding

To use smart bidding, you assign the amount you want to spend for each conversion.

In businesses with long sales cycles, marketers calculate the cost per lead by dividing the customer acquisition cost (CAC) for a sale by the number of leads needed to make a sale.

The use of one-value-fits goes counter to the essence of smart bidding. Ideally, you would spend more on higher potential leads, but this isn’t possible with traditional ad platforms.

Smart Bidding With Predictive Conversions
Smart Bidding With Predictive Conversions analyzes user behavior on your site and correlates it to the value of their purchases, subscription times, LTV and other metrics you want to use to target your marketing. The sales cycle is not important – we store all user data from the moment the script is installed.

Based on the data, sends synthetic conversions with probability weighted purchase values to ad platforms for any user, even anonymous (unregistered) ones. Based on predictive value, the advertising platform learns to set a unique bid for each user – the higher the predictive value, the higher the bid.

 How Does Predict the Value?

By analyzing user behavior and comparing it to previous customer behavior, uses machine learning (ML) to find hudreds of patterns indicating the likelihood of a purchase and its size. This data is sent to the advertising platform, so ads can be served to high-intent buyers and similar users.

Compare and
Google Enhanced Conversions Google Enhanced Conversions
Performance Create synthetic, predictive value events based on behavioral data and send additional data to ad platforms to accelerate their learning and optimize for end of the funnelTransfer offline conversions from CRM and sending them to Google to optimize for end of the funnel
Accelerate experiments 
and reduce costs
Upload and create audiences
Improve analysis and 
measurement attribution
Conversions with value
*Event values are not sent every time
Accelerate optimization 
algorithms learning
Feedback loops in real time
Improve training quality
Optimization conversion quality

Transition to value-based bidding
*Not suitable for long sales cycle businesses
Sufficient conversion frequency
*Event values are not sent every time
Data transfer to various ad platforms at once Facebook, Google, LinkedinGoogle
Personalized customer support
Industry fit