As user level precision shifts to prediction, we'll see
a greater investment in measurement modeling and machine learning.
This helps you maximize your own data and do more with less.
It does this by augmenting what consented, user-provided data
is available and layering on other available data points.
How does it do this?
Machine learning can understand massive and diverse data inputs quickly,
continuously learn, and recognize patterns to make predictions,
all without needing any personally identifiable data.
In the context of conversions and your campaign measurement, machine
learning can analyze current observable inputs, such as device, date
and time, and conversion type, and model across active campaigns.
A BCG study found that businesses that deploy ML-based
technologies can boost campaign performance by 35%.
We see that the synergy of strong first-party data
and machine learning yields the best results for your business.
Let's peek under the hood
even further and talk about some core product principles that allow Google
to achieve sophisticated conversion modeling for your campaigns.
While conversion modeling happens across various teams at Google,
there are certain commonalities that apply across all conversion
models and can be broadly grouped into one of three categories.
The first is our high quality standards, which refers
to our techniques to ensure strong model accuracy.
Our data scientists continuously make algorithm
improvements to increase the accuracy and scale of modeling.
We also introduce new measurement products that unlock new sources
of observable data, which helps fine tune our models.
We only include model data in our reporting when we have high confidence
that conversions actually occurred as a result of ad interactions.
This rigor ensures that we avoid systematically over
reporting and in cases where we don't observe enough data
to be able to confidently model, we do not provide conversion modeling.
We proactively test and validate models using techniques
like holdback validation to check the accuracy of our modeling.
This allows us to regularly measure
inaccuracies and biases and continuously tune our models.
The next principle is that our models are customized to your business,
meaning they are unique to your specific customer behavior.
This means that once a modeling algorithm is determined to address a specific gap,
we apply that algorithm
to each advertiser's data separately and arrive at unique results
that reflect unique user behavior and conversion rates for that advertiser.
For example, if your users have a very high tendency
to start their journey on one device and convert on another device,
there will be higher-than-average cross-device conversions reported.
This is important because no two advertisers
are the same, based on their differing user needs and business outcomes,
so our models cannot be one-size-fits-all.
We know that it can be hard to stay on
top of measurement changes and industry complexities, which is why,
where possible, we integrate modeling directly in your account
for the most accurate reporting and optimization data, seamlessly.
The last is that our models are privacy-safe at their core.
Our goal is to always provide you with the most accurate measurement
solutions that prioritize user privacy.
This approach is in direct contrast with non privacy-safe
tactics like fingerprinting, which relies on heuristics,
such as IP addresses, and attempts to identify and track individual users.
Google has a strict policy against using fingerprinting, as
it doesn't allow reasonable user control and transparency, nor
do we let others bring fingerprinting data into our advertising products.
Instead, we use the behavior of observed groups
to predict the behavior of other groups using signals like first party
data, platform APIs and similar data sets.
Our North Star is to put the user first,
which is why we've developed our approach in the interest of preserving the user
experience, upholding their privacy, and protecting their data.
But what makes Google different from others in this space?
For one, our solutions operate across a breadth of advertisers,
which enables our conversion models to be tested and validated across
a large set of interactions and conversion actions.
Next our, signed-in base of users allows our models to continue
functioning independent of cookies and other identifiers.
It also provides a rich base of consented data across
a representative set of users.
Additionally, Google's model data is surfaced in campaign
reporting, but also automatically powers optimization and bidding.
This renders the data actionable as it works towards your business goals.
Lastly, baked into everything we do, our conversion modeling
practices are privacy-safe as they do not identify the individual user.
Google has a strict policy against fingerprinting, as mentioned.
And while not shown, we also have years of technical expertise
in machine learning, having mastered this across measurement products
that have employed modeling for years.
This includes the advanced measurement products discussed earlier,
like Google Ads Autobidding, as well as products
beyond measurement, like YouTube Recommendations.
To learn more about Conversion Modeling,
visit our Help Center articles or talk to your Google Account team.