A lot of the advertising game is won in the data.

In other words:

Google Ads is no exception.

If anything, it's probably the platform with the most data you'll work with.

You've got multiple campaign types. Search, Shopping, Demand Gen, PMAX, Display.

And every single one of them is generating its own stream of data. Ad groups, keywords, assets, etc.

On top of that…

There’s a unique set of challenges specific to this platform.

For example:

Google often gets the last click on a purchase, when people search for the brand name.

But branded traffic depends heavily on what your prospecting campaigns are doing.

Demand Gen, Discover, Shopping, other channels. 

When prospecting slows down, fewer people discover your brand. Branded search drops with it.

If you're only looking inside Google's dashboard, you won't see that chain reaction.

You'll just watch numbers fall and start pulling the wrong levers.

This is why the conversion numbers on attribution tools like TripleWhale look different from what Google shows.

That's because TripleWhale sees the full journey.

If a customer touches 5 channels before buying, it splits credit across them, depending on your attribution model.

In those situations, which number do you trust?

Or more importantly…

How do you make sense of those numbers to make smart decisions and optimizations?

Well…

The answer is that it depends on what decision you're making.

First: daily Google Ads optimization

Google's machine learning learns from Google's conversion data.

When you set targets based on tools like TripleWhale...

You're letting the algorithm optimize toward one number while judging it against a different one.

Keep Google Ads decisions inside Google Ads. 

Bid adjustments, keywords, ad copy, Smart Bidding targets, all of it should be based on in-platform data

Second: Comparing channels & understanding the full customer journey

For this, use 3rd-party tools like TripleWhale or Northbeam.

I've seen brands with PMax campaigns running at 6+ ROAS inside Google.

But TripleWhale only shows 2x (sometimes even worse).

Turns out the campaigns are mostly retargeting people who already knew the brand

And only a small portion of those purchases are new customers.

Attribution tools reveal those issues and trace them back to the root cause.

Third: retention and LTV decisions

This is where TripleWhale's retention tab comes in.

For many brands, their unit economics let them operate at a lower ROAS and still be profitable long-term.

If the person managing the account doesn't account for that…

They'll shut down campaigns that are working, or refuse to scale ones that have room to grow.

Fourth: validating if a channel works

For this, you need incrementality testing or Marketing Mix Modeling.

The concept is simple:

You show ads to Audience A, don't show them to Audience B, then measure the difference.

Whatever the gap is, that's your real impact.

This is especially useful for channels that start the customer journey but rarely get credit for the sale.

One of our clients saw 1.7x ROAS on their YouTube campaigns in the Google dashboard.

With incrementality testing, the real number was 5.25x.

A 3 times difference, which is pretty insane if you think about it.

Now, I’ll end this email by saying this:

It’s easy to say “follow the data” or “scale what works.”

But in reality, there’s a lot of details behind that, things that many brands (and agencies) overlook.

In my experience…

This is where you see the clearest gap between A+ operators and average ones.

It’s crazy how a top brand obsesses over this.

Whereas for other people, it’s something they gloss over, sometimes don't even have proper tracking set up.

But if you’re serious about scaling Google Ads, this is one area you can't afford to be lazy about.

Jackson

Founder and CEO of Echelonn

P.S.

I made a YouTube video going through all of this in a lot more detail

Watch it here.

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