[MUSIC PLAYING]
MATT WALTER: Hi, my name is Matt Walter,
and I'm a product manager on Google Analytics.
And today, I'm here to talk about modeling
in Google Analytics 4.
This video is part of a larger training series
to give you an idea of how to use Google Analytics
4 in a little bit more detail.
For today's agenda, I will first be giving an introduction
to modeling in Google Analytics 4.
Then I will be doing a deep dive on two modeling features
in GA4, behavior modeling and conversion modeling,
followed by a recap to finish the session.
The measurement landscape has changed dramatically
in recent years with the rise of privacy-focused browsers
and the decline of cookies.
This has made it more difficult to track conversions
across different devices and websites
and to get a complete view of the behavior of users
on your website or app.
Modeling is a way to fill in these gaps in data
and provide a more accurate picture of your performance.
Today, we'll be focusing on conversion modeling
and behavior modeling in GA4.
The goal is for you to better understand
how we build the models, how you can benefit from them,
and how they are impacting your reports.
By using these models, you can get a better understanding
of how users are interacting with your website or app
and how they are converting while respecting
users' privacy.
Evolving user expectations and regulatory and platform changes
are challenging the ability to maintain accurate measurement.
People's expectations of ads' privacy have shifted.
People are increasingly concerned about how
their online information gets collected and used
for advertising.
Responding to these concerns, regulatory bodies
across the globe are drafting new privacy laws
and updating existing regulatory frameworks that are restricting
the digital ads ecosystem.
At the same time, platforms such as browsers
and mobile operating systems have announced or implemented
new policies to change the way user data is captured,
shared, and measured.
They are shifting away from mechanisms
that track users across sites and apps
by restricting third-party cookies and mobile ad
identifiers.
As the granularity and precision of event-level conversion data
becomes more limited, marketers will
need to take action on their measurement strategy
so that they can drive performance and ultimately
their bottom line.
We know that the changing digital ecosystem
and the effects on measurement are top of mind for marketers.
This is a problem that is affecting the entire industry.
As a result, we must pay more attention to measurement
as the ecosystem evolves.
According to an IAS 2021 Industry Pulse Report,
85% of digital media professionals
say cookie loss and accurate measurement
are two of their top three challenges.
The third challenge was cross-device attribution.
Previously, we lived in a world of fully observable data.
This new ecosystem is dynamic, and change is the new norm.
If the availability of identifiers
will degrade over time, the way we are using these identifiers
must evolve as well.
These changes are pushing the industry
to aggregated and anonymized measurement and data
collection.
As the ceiling of observable ad-to-conversion linkages
continually decreases, the reliance on conversion modeling
to provide comprehensive measurement reporting
will continue to increase.
Even with all the right infrastructure in place,
there are still gaps that no one will be able to fill.
The data we report in your conversions column
is a combination of observable data and modeled data.
Before most of these new regulations and changes
were in place, browsers began to restrict third-party cookies,
the industry was able to provide almost all observed data.
The new industry and browser changes
have caused a shift towards more modeled data.
The composition of the reported modeled conversions
will continue to grow as the industry faces
more gaps due to browser and regulation changes.
Please note this is an illustrative example.
This does not reflect the exact breakout
of modeled and observed data in a given account.
As a result of the changes in the ecosystem,
GA4 has been designed to embody Google's
industry-leading approach to privacy.
Our priority has always been to build
solutions to get accurate answers to your questions
quickly.
However, in a privacy-centric world,
we have to answer these questions
in new, innovative ways, which is why we are now
innovating for a future that is consented, first-party,
and modeled.
First, consented.
GA4 allows you to collect and enrich first-party data
in a privacy-safe way.
GA4's infrastructure is scalable for your business
and built for an evolving ecosystem.
You can measure a more unified holistic user journey
by connecting multiple data sources--
app and web, data import, and measurement protocol,
for example.
First party enables you to collect and use
first-party data to understand and engage your users.
By understanding users' behaviors
throughout their journey, you will
be able to tailor the right experience based
on their needs.
71% of consumers now expect to receive
personalized interactions.
And, lastly, modeled.
You can enhance your first-party data with modeling
to fill in the gaps from cookie loss and measurement
restrictions.
GA4 allows marketers to preserve behavioral measurement
while preserving user privacy.
Google's privacy vision includes privacy-preserving technologies
such as conversion modeling, which
is resilient to future ecosystem changes.
We believe privacy and performance are not
at odds with each other and can coexist.
These ecosystem changes are an opportunity
to build sustainably and to up-level the entire ecosystem
for everyone.
Investing in privacy-preserving technologies
will establish a foundation for long-term sustainability
of the ad-supported ecosystem.
There are several types of modeling
that are available in Google Analytics
to help give you the most accurate vision
of your business.
First, conversion modeling uses Google's AI
to help you measure the impact of your marketing efforts
when you can't directly attribute conversions
to a traffic source.
This will help you fill in the gaps in your data
due to changes in privacy regulation and deprecation
of cookies.
Next, attribution modeling, data-driven attribution,
determines how credit for conversions
is assigned to advertising touchpoints.
Third, behavioral modeling uses machine
learning to estimate behavioral metrics for users who do not
consent to Analytics cookies.
And, lastly, predictive metrics help
you anticipate user behavior, such as purchase or churn,
and we surface actionable insights in GA4.
Later in this presentation, we will
explore conversion modeling and behavior modeling
in more detail for you to understand how these two
models can help you get a more accurate picture
of your business.
Modeling poses several benefits to your business,
including a more accurate view of your performance.
Modeling can help you get a more complete picture
of your website traffic and conversions
when you lose data due to cookies consent, browser
updates, and other factors.
It does this by filling in the gaps in your data
with estimates based on historical data and machine
learning algorithms.
Better decision-making.
Modeling can help you to identify trends and patterns
that you might not have otherwise seen
and to understand how your website is performing.
It helps you to make better decisions about your marketing
and advertising campaigns and to allocate your resources
more effectively.
And, lastly, accurate, privacy-safe measurement.
Google Analytics modeling is helping advertisers preserve
measurement while respecting user consent
choice by enhancing your understanding of the customer
journey when observed behavioral and conversion data is not
available.
For example, with behavioral modeling,
if users don't consent to Analytics cookies,
you are still able to generate important customer insights
while respecting your users' privacy preferences.
Now let's go ahead and do a deep dive on behavioral modeling.
Behavioral modeling for Consent Mode
is only available in Google Analytics 4,
and it is not available in Universal Analytics.
Consequently, if you use a cookie consent banner, consent
widget, or another consent measurement solution,
you'll miss data from users who opt out in Universal Analytics.
Without modeling, you have a partial understanding
of user behavior on your site with only glimpses
into it based on that observed data you have available.
Behavioral modeling in GA4 allows
you to gain useful insights from your Analytics reports
while respecting your user privacy,
using machine learning to model the behavior of users who
decline Analytics cookies based on the behavior
of similar users who accept Analytics cookies.
For example, behavioral modeling estimates
data based on user and session metrics,
such as daily active users and conversion rate,
that may be unobservable when identifiers
like cookies or user IDs are not fully available.
It helps you answer common, important questions such as,
how many daily active users do I have, how many new users did I
acquire from my last campaign, or, what is the user
journey from landing on my website to actually making
a purchase?
Behavioral modeling for Consent Mode
uses machine learning to model the behavior of users
who decline Analytics cookies based
on the behavior of similar users who accept Analytics cookies.
This allows you to get valuable insights from your Analytics
reports without collecting any personal information from users
who decline consent.
When you implement a consent banner,
Analytics will not drop cookies for users
who decline Analytics cookies.
However, if you enable a Consent Mode
and meet the eligibility requirements
of behavioral modeling, Analytics
will use machine learning to model
the behavior of these users.
The training data comes from the property
where Consent Mode is enabled.
Consequently, when modeling is turned on,
Analytics seamlessly integrates modeled data and observed data
in your reports.
For example, behavioral modeling estimates
data based on user and session metrics,
such as daily active users and conversion rate,
that may be unobservable when identifiers
like cookies or user IDs are not fully available.
Google uses a variety of signals to power behavioral modeling
in GA4.
To create an accurate aggregate and modeled
view of customer behavior, machine learning
can analyze different observable signals
such as event type, country, region, city, date,
time, or other non-identifiable dimensions
associated with the device.
We are committed to protecting the privacy of our users,
and Google's behavioral modeling approach
applies the following machine learning practices
to accomplish that.
We use the highest quality of observable data to power
and test our models.
This data is collected from users
who have consented to it being collected.
Second, we only include modeled data in our reporting
when we have high confidence in the data.
This means that we have tested the model
and are confident that it is accurate when users have access
to modeled data.
And third, we do not use fingerprinting,
nor do we allow it on our platforms.
Fingerprinting is a technique that
uses the unique characteristics of a device to identify it.
Because the model is trained on the observed
data for your Google Analytics 4 property,
your property must have enough data to train the model.
To be eligible for behavioral modeling,
your property must meet the following criteria.
First, Consent Mode needs to be enabled across all pages
of your site and/or your apps.
Second, Consent Mode for web pages
must be implemented so that tags are loaded before the consent
dialog appears.
And Google tags load in all cases, not only if the user
consents.
Third, the property needs to collect at least 1,000 events
per day with analytics_storage set to 'denied' for at least
seven days.
And then, inversely, you also need
to have 1,000 daily users sending events
with analytics_storage set to 'granted' for at least 7
of the previous 28 days.
Just as a side note, behavioral modeling
starts from the date a given property first
becomes eligible.
Next, let's talk about, what is Consent Mode?
Consent Mode lets you communicate
your user's cookie or app identifier consent status
to Google.
Tags adjust their behavior and respect the user's choices.
Consent Mode interacts with your consent management platform
or custom implementation for obtaining
visitor consent, such as a cookie consent banner.
Consent Mode receives your user's consent choices
from your cookie banner or widget
and dynamically adapts the behavior of analytics, ads,
and third-party tags that create or read cookies.
When users deny consent, instead of storing cookies,
tags send pings to Google.
If you are using Google Analytics 4,
Google fills the data collection gaps with conversion modeling
and behavioral modeling.
Next, let's talk about the implementation
steps to set up behavioral modeling.
When you enable Consent Mode, Google measurement products
ensure that a visitor's Consent Mode state is preserved
across the page they visit.
If consent is denied, tags that fire do not store cookies.
Instead, they communicate a minimum of information
about user activity.
Consent state and user activity are then
communicated by sending, for Google Analytics,
cookieless pings.
They are sent from each page of a website
where Google Analytics is implemented on load
and when events are logged.
To benefit from behavioral modeling,
you need first to implement Consent Mode
by configuring the default tag behavior
and update tag behavior.
You may need to follow the troubleshooting steps
if you don't see modeled data after implementing Consent
Mode.
First, ensure you are not blocking tags.
Unblocking tags allows you to fire cookieless pings
while respecting Analytics user cookie consent status.
We never set cookies when users decline.
Also, verify that the Blended reporting identity is
enabled in the user interface.
Please note that to stop seeing modeled data,
you can select the observed option as well.
Next, let's talk about what this actually looks
like in the product.
The in-product banner and reporting badge
help you understand when data is modeled in your property.
The banner will appear at the top of your properties reports,
and the badge will appear below the green mark
if any data is modeled.
You can click on the badge to learn more
about the modeling process and how it affects your reports.
Behavior modeling effects user and session-based metrics
and dimensions and conversions in
all their respective reports.
The following features don't support
using modeled behavioral data--
audiences, explorations except free-form tables,
retention reports, segments with a sequence,
and predictive metrics.
Modeled data also is not included
in the BigQuery export.
Modeled data, if behavioral modeling
is applied to the property, can be pulled from the API
and will provide the same data as the reporting
section of Google Analytics at an aggregated level.
Behavioral modeling is helping marketers like Nestle
to get more accurate insights for their business.
They said, behavioral modeling with Consent Mode
in Google Analytics 4 drove a 23% increase
in the observable traffic in analytics reporting
on European and UK websites.
Next, let's talk about conversion modeling in Google
Analytics 4.
GA4 introduced conversion modeling to recover as much
loss in observability as possible by training data
on the observed space to model for cases where conversion
events cannot be deterministically connected
with previous engagement events.
So first, what is conversion modeling in GA4?
Conversion modeling refers to the use
of machine learning to quantify the impact of each traffic
sources when a subset of conversions
can't be tied to initial interaction.
Conversion modeling comes into play
when we encounter data that is subject to browser
and operating system and regulatory restrictions.
It helps us assign linkages between traffic sources
and conversions where otherwise that linkage is unobservable.
Conversion modeling reattributes conversions more accurately.
Let's take an example.
100 users are visiting a website, and 20 of them
are making a purchase.
Without conversion modeling, some of the 20 conversions
can't directly be tied to previous interactions
and would be attributed by default to the direct channel
due to factors such as ITP, ETP, or third-party cookie
deprecation or other reasons as well.
With conversion modeling, GA4 uses
machine learning to reattribute conversions
to the different marketing channels, including
Google Ads, third-party ads, organic search, and email
in order to give customers a full picture of how
conversions on their website or app are driven.
With GA4 conversion modeling, the total number of conversions
will not change.
Only the attributed conversions for each channel will change.
Reports in Google Analytics will automatically
attribute conversion events across channels
based on a mix of observed data where possible and modeled
data where necessary.
So where does commercial modeling actually
surface in the Google Analytics 4 user interface?
You can access modeled conversion data in GA4's core
reports, such as the Events report or the Conversions
report, for example, and explorations that
use event scope dimensions.
These reports automatically attribute conversion events
across channels using a mix of observed and modeled data.
Now that we've talked about both behavioral modeling
and conversion modeling, you might
be wondering what is the difference between the two.
Behavioral modeling aims to restore measurement for session
and user metrics that are unobservable when users
deny Analytics cookie storage.
The models are trained on observable data from users who
have granted proper consent, whereas conversion modeling was
built to model for cases where conversion events cannot be
deterministically connected with previous engagement events.
Models are trained on observable data
to provide more accurate reporting
on how conversions should be attributed across channels.
Now for a few key takeaways.
The first is that the future is consented, it's first-partied,
and as we've discussed in this series, it's modeled.
Behavioral modeling can help you to gain useful insights
from your Analytics reports while respecting your user
privacy.
And conversion modeling can help you to recover as much loss
in observability as possible by training data on the observed
space to model for cases where conversion events cannot be
determined or connected with previous engagement events.
Thank you for tuning in today.
As I mentioned, this video is part of a larger training
series, and there will be more videos to come in the future.
In the meantime, if you have any questions,
feel free to go to the Google Analytics Help Center online.
Or if you're a 360 customer, feel
free to reach out to your account representative.
Thank you.