>> ERIN: Hi, I’m Erin Clement, and I’m a Performance Media Manager and the Attribution
Lead for Media Lab within Google Marketing.
>> TOM: Hi, I’m Tom Garvey, and I’m also
part of the Media Lab, and I’m the Performance team’s Incrementality Lead.
If this is one of your first Google Marketing Live sessions, welcome.
We hope this will be fun. And if this is your tenth session in a row?
There’s a pause button. Take a walk. Go outside. We’ll be here when you get back, I promise.
As much as I’d love to be seeing you all in person today, I’ve found that’s one of
the best parts of these virtual events.
That, and the fact that my dog and cat have probably heard the word “incrementality”
more often than even the most measurement-minded marketer out there.
They think I’m doing great work. And, I think they are very cute and very smart.
Before we get to the fun parts of this presentation, it would
probably help some of you if we defined what incrementality and attribution actually are.
I’m sure most of you won’t need to hear this, but we won’t take long, so thanks for humoring us.
Incrementality: It is the answer to the question, “How many sales, leads, sign-ups,
etcetera would have happened even if I hadn’t spent a penny on media?” Or, in other words,
“What benefit can I definitively prove that my marketing budget drove that I
wouldn’t have gotten organically without it?” These questions are extremely difficult to answer,
even once you understand what incrementality is, and what it isn’t. And to help with some
of that confusion, Erin is, ironically (I think I’m using that word right), going to talk
about something that is not incrementality.
>> ERIN: Attribution is, simply put, how you
assign credit for sales and conversions across the different touchpoints in a conversion path.
There are a number of rules-based models: last click, first click, time decay, for example.
These models are fixed and assign credit to different touchpoints the same way every time.
Data-driven models use machine learning to assign fractional credit to various touchpoints.
Google’s data-driven attribution models are trained on and validated
against incrementality experiments.
Attribution is great for day-to-day, always-on measurement, and it’s effective for setting
ad budgets and informing bid strategies at a campaign or channel level.
If appropriate for your business, incrementality experiments can unlock
causal insights that can inform channel-level budgeting and validate attribution results.
>> TOM: There you have it. Attribution and incrementality. 2 separate marketing
buzzwords describing 2 different problems.
Then why are they in the same presentation? Well, before we get to answering that very
smart question and thereby solving all of your performance measurement problems,
first we want to make sure that that point is just incredibly clear. Because if you
aren’t asking the right questions for your business, you’ll never get the right answers.
So please repeat after me, unless you’re sitting next to someone that, for some reason, doesn’t
seem interested in attribution and incrementality. Here it is: incrementality and attribution
are different problems that require different solutions, but they are both integral parts of
an effective performance media plan.
Now, your turn.
Great job. Now let’s get to it.
>> ERIN: Most marketers today are relying on outdated metrics to make decisions.
For the longest time, the default attribution model in most media platforms has been last click.
Meaning the last interaction a user had with an ad is the one that gets 100% of the credit.
Even if they saw a bunch of banners, YouTube videos, and PR announcements before converting
off a search ad, those earlier touchpoints don’t get any credit for assisting the conversion.
While there is value in knowing what that final touchpoint was in your customer’s journey,
a last click model will do nothing to help you better understand the true contribution
of all channels on your media plan and will ultimately devalue your upper funnel tactics.
>> TOM: Before I get into the analogy that you will eventually be begging us to move on from
(preview, not gonna happen), I should mention where it came from, because as much
as I wish I could, I can’t take credit for it. I’ve tried to explain these measurement problems
many, many times with extremely varying degrees of success.
During one of my less successful attempts, someone was nice enough to explain my ramblings
back to me to see if they were following rather than just saying, “You just said a whole lot of
words back to back that barely seem related to each other.” I’ve gotten that feedback.
And in their explanation, they used a fantastic 4x100 relay analogy,
and it clicked so well for everyone, I’ve been using it in nearly every presentation since,
including today’s. So, Garrett, thank you.
Now for the race.
The 4x100 relay is an event that most of us watch once about every 4 years or so,
sometimes 5, but just as a reminder: It’s a team of four, with each member running one-quarter of
the way around the track, passing a baton between them as they make their way through the race.
And the first team to have their anchor, final runner, cross the line
with that baton is declared the winner. I don’t think I needed to explain that,
but it’s too late. I did.
So, now, let’s think back to team Jamaica’s relay dominance that was certainly helped by
one of the best athletes of all time, Usain Bolt.
No matter how much Jamaica would win by, he always only ran one-quarter of the race.
He just happened to be the last one holding the baton. Well, not happened to be.
There is, of course, strategy involved in selecting the team’s closer, but either way,
is Usain Bolt the only one that deserves a medal?
>> ERIN: No, of course not. Every person on that relay team played an important role in
getting that baton over the finish line.
So why would you optimize your media like the last step is all that matters?
We’ll grapple with that question more in a minute. But before we do,
we should talk about that last word: “matters.”
>> TOM: At this point, I think you’ve decoded our very heavy-handed analogy.
The baton? That’s your sale, lead, install, or whatever is key to your business.
Usain Bolt? He’s the last interaction your media had with that customer before they converted.
And his teammates are all those channels that rarely get the credit they deserve for nurturing that sale.
Sure, Usain Bolt may have mattered more than
literally any other sprinter ever has during the final leg, but to prove it, Jamaica would have to
be willing to conduct a control race with Bolt as a holdout. I don’t think we’re going to see that.
And even if they did, I still wouldn’t be giving him 4 medals for his team’s race.
Fortunately, you can do a test like this with your media and not cost yourself the race, or the sale.
So what part of your media “matters”? That’s where incrementality comes in.
You probably know what that means at this point, but you might not actually know how
to measure it. And if you do, fantastic. Maybe there is a fast-forward button below my face.
But be warned, you’ll probably miss a few very funny and very scripted jokes that
I’ve rewritten 25 times and am trying my best to read like I’m making them up on the spot.
Measuring incrementality.
There are a few ways to do this,
starting off with measuring what we call channel silos. And this is a big step for most.
Google offers Conversion Lifts studies, or you could do these on your own with well-controlled
experiments. But, what the key is here is understanding the data you will get as a result,
leading to better in-flight optimizations and better spending decisions for your business.
Let’s say your investment appears to be 20% incremental.
You know what the positive side means.
But, does this mean 80% was wasted?
Very importantly, no. It simply means that you really don’t know much about the other 80%.
That 80% may have been the reason someone’s coworker told their mother-in-law about a great
product they saw in a video that they eventually went home 3 states away and bought 6 months later.
You just don’t know these things when you’re measuring in channel silos.
That’s where much more complex solutions come into play. And, this is when I would usually
talk at great length about the complexities, advantages, and shortcomings of most cross-stack
and complete portfolio incrementality tests like marketing mix modeling and GeoX studies.
But, when we rehearsed that, we came in well over 15 minutes. So, I’ll instead include
some great links along with this presentation that you can read on your own. You’re welcome.
So, if there’s one takeaway here, it’s simply that you need to understand the right questions to ask
when talking about incrementality, and that they should be related to
“How can I make sure that I’m putting more of my marketing budget behind media that
I can prove is driving impact that I would not have gotten otherwise?”
Alright. Incrementality solved, right? So let’s go back to Usain Bolt’s teammates.
Because we need to cover off on the very separate, but very related, problem of attribution before we
talk about how Google is going to solve them both.
>> ERIN: All of this incrementality work Tom talked about is a huge step in the right direction.
Now that we know which channels and tactics are driving incremental value,
we’re left with the challenge of translating that insight into action.
There are a number of tools available today that we use to help us get closer,
but no 1 solution checks all the boxes on our criteria for data coverage and actionability.
At a macro level, we use Ads Data Hub or Google Analytics to help us make decisions around how
we allocate budget across channels and tactics.
But these high-level insights are incredibly challenging to translate into real-time,
granular optimizations.
At a micro level, we’re leaning into data-driven attribution models within Google Ads or SA360,
which allow us to make smarter real-time bidding decisions
and move us a little bit further away from giving Mr. Bolt 100% of the credit.
Even so, current DDA solutions are siloed and bidding functionality doesn’t yet exist everywhere.
As we look to the future of attribution and incrementality in a postcookie, privacy-safe
world, we are using attribution reporting in the new Google Analytics Advertising Workspace.
In this new workspace, we’re able to compare attribution models and forecast cross-channel
or platform-specific media budgets and use real-time data to take action.
Google Analytics 4 will also bring our measurement workstreams closer together,
with attribution models that are trained on and validated against incrementality experiments.
At this point you might be wondering what you can do to get ready for Google Analytics 4.
We recommend getting started today with a dual setup.
This means setting up a new Google Analytics 4 property alongside your
existing Analytics implementation.
This approach will allow you to use your existing Analytics as your source of truth,
build data history in Google Analytics 4, and take advantage of new features
and functionality as they become available.
Eventually Google Analytics 4 will become your new source of truth.
>> TOM: I hope you aren’t tired of this analogy yet, because I can’t let it go
without a good one-liner.
Don’t forget who deserves the medals: everybody on the team, not just the one
who crosses the finish line.