Imagine you're working on a complex puzzle.
But as you put it together, you realize there are a few key pieces missing.
Unfortunately, this means there are going to be unwanted
holes in your final picture, unless you can fill in those blanks.
The good news, you have a lot of context clues
to help you understand what those pieces should look like.
The colors and shapes of the surrounding pieces, the complete reference
image on the box, your past experience with puzzle building.
All of these are example inputs that can help you confidently estimate
what those missing pieces would've looked like if they hadn't been lost.
Conversion modeling works similarly.
Conversion Modeling helps connect those missing puzzle pieces
as a result of shifting customer privacy expectations,
browser restrictions and regulatory updates.
It helps us assign linkages between ad interactions
and conversions, where otherwise that linkage is unobservable
and it helps you respect your user's privacy needs.
Conversion modeling is about the linkage between ads and conversions.
In other words, we are not creating new conversions;
but rather we are assigning connections
between ad interactions and conversions that may have been unobserved.
Conversion modeling allows you
to unlock a more accurate picture of your advertising outcomes,
as well as a more complete picture of the path to conversion,
meaning, get better reporting data automatically.
Secondly, beyond measurement alone, it also enables more effective
campaign optimization to help you achieve better business results.
Privacy regulations and technology limitations mean
that marketers may lose the ability to observe certain cohorts of users.
For example, users using particular device types or browsers.
In this scenario, our automated bidding algorithms
would need to make optimization decisions based on incomplete data,
resulting in biased learning.
As a result, automated bidding could deprioritize those cohorts, since they have
a lower reported performance, leading to overall poorer performance.
However, modeling solves for these biases and corrects them in
reporting to ensure automated bidding has access to the most representative
performance data.
Finally, conversion modeling ensures your measurement remains privacy-safe.
Conversion modeling uses the behavior of observed groups to predict
the behavior of unobserved groups without identifying any one individual.
We use non-sensitive inputs to our conversion models.
These observable inputs are used to create reference points
for the unobservable groups and help us effectively model conversion rates.
In our conversion models we are not using inputs like age, gender
or other demographics that could be viewed as sensitive.
Whether we use a given input and how much we weigh it's dependent
on the model.
For example, for cross-device modeling, we may weigh "device type" more,
while for browser based modeling, we may use "browser type" more.
We know that modeling is critical for business success,
and we've looked at some of the inputs.
But how does it all work?
First, Google receives data in the form of ad interactions
like clicks or views and conversions, like a purchase or download.
Sometimes we can see the linkage between the two using cookies
and can understand which conversions were caused by which ad interactions.
Other times however, Google receives an ad interaction
and a conversion, but there is no linkage between the two.
This means that we understand an ad interaction and conversion occurred,
but we are unable to determine which ad interactions caused which conversions.
This can happen in the case of cookie restrictions.
We then take the observed group,
a.k.a the group where we can see the linkage between the ad interaction
and the conversion, and we divide it into smaller subgroups.
Remember the non-sensitive inputs we mentioned?
We now use those to determine how to create these subdivisions
within the observed group.
Then within each of these subgroups, we calculate conversion rates.
You can imagine for a large campaign,
we may have dozens of subgroups, each with their own conversion rate.
For example, subgroup one may all be in country A
use browser B, and are on Device C.
We see in the unobserved group similar characteristics
in the ad interaction and conversion data,
except for one of these dimensions, which is the one
we are trying to predict against,
for example browser type.
So, we align these groups appropriately based on their similarities.
This separates the "unobserved" population into subgroups as well,
aligned with the subgroups created in the previous step.
This brings us to the final step.
Now that we have aligned subgroups among both the observed and unobserved data,
we are able to create a machine learning model using
the known conversion rates from the observed subgroups
to predict conversion rates for the unobserved subgroups.
This looks at the unobserved data and assign linkages,
where appropriate, between ad interactions and conversions.
Based on the model output, not every ad interaction will link to a conversion.
But this is okay,
as we don't expect every ad interaction to lead to a conversion,
just as we don't expect every conversion to be caused
by an interaction.
Once all of the ad interactions and conversions have the appropriate
linkages between them, we combine them and surface them in your reporting.
Now we'll go over steps you can take today to improve your modeling.
First, make sure you're using robust data
collection solutions to build a strong measurement infrastructure.
By investing in a robust infrastructure today, you can stay on top of ongoing
industry changes and power conversion modeling with the most accurate data.
On the web this means implementing a tagging solution
like Google Tag Manager or the Global site tag.
For your App campaigns, this means investing in the Google
Analytics 4 Firebase SDK.
Next, adopt privacy-safe solutions
to strengthen the first party data that you provide to Google,
and as a result, the modeling that Google can provide to you.
One example is Enhanced Conversions, which helps
recover gaps like cross-device or browser restrictions.
Another privacy-safe product built atop a robust tagging
foundation is Consent Mode, which allows you to adjust
how your Google tags behave based on your users' cookie consent choices.
This brings us to our third and final step,
which is to stay plugged in to our best practices in this area.
We continue to update our Help Center with the latest guidance
around future-proofing your measurement approach.
By activating modeling best practices, including those we've just mentioned,
you can unlock a more accurate foundation of conversion data.
Getting modeling right isn't just a nice-to-have,
it leads to more efficient reporting, bidding,
and ultimately revenue for your business.
To learn more about Conversion Modeling,
visit our Help Center articles or talk to your Google Account team.