#30 Barbara Galiza: Gaining the attribution edge

Is the search for the perfect measurement leading to imperfect decisions? It turns out that many Marketing teams may be fighting the wrong attribution battle.

They're running the risk of obsessing over building the perfect model, while their actual marketing performance gets worse.

We recently spoke to Barbara Galiza, a Growth and Marketing Analytics Consultant with experience at Dentsu and WeTransfer, about why attribution has become a bit of dangerous obsession — and what to do about it.

Here are the three biggest takeaways that could change how you think about marketing measurement:

Takeaway #1: The "One source of truth" is a dangerous myth

One of the most common attribution mistakes that Barbara outlines is companies spending months building comprehensive models, and then trusting them completely for budgetary decisions. Big mistake.

Here’s why: A company spends a great deal of time implementing pixels from every ad platform, building a multi-touch attribution model, and feeling confident they finally understand their marketing performance.

Then, they start shifting their budget around based on the model’s recommendations — often resulting in cuts to brand marketing and video campaigns, in order to invest more in search. The rest is that sales, most often, go down.

The issue is that the model missed the unmeasurable stuff that actually drives conversions. YouTube ads, out-of-home campaigns, and influencer marketing don't show up in most attribution models because they don't start with trackable clicks.

The desire for perfect attribution often leads to imperfect marketing decisions. Companies end up optimizing for what's measurable rather than what's necessarily profitable. It makes an unclear situation merely seem clear.

Takeaway #2: Every channel needs its own measurement approach

Instead of building one massive attribution model, smart companies measure each marketing channel differently based on how it actually works:

* For magazine ads, you might use voucher codes.

* For YouTube campaigns, you could run incremental tests in specific geographic markets.

* For influencer marketing, self-reported attribution ("How did you hear about us?") often works better than trying to track clicks.

Issues crop up when teams try to everything the same way for the sake of consistency. After all, paid search is perfectly measurable because it starts with a trackable click. Most other marketing activities don't work that way, and that's okay.

This approach requires accepting that some marketing activities will never be as measurable as something like paid search. But cutting them because they're hard to track often kills the activities that make your measurable channels work.

Ultimately, the goal is not perfect measurement. It’s smart budget allocation. Sometimes that means accepting imperfect data around high-impact activities.

Takeaway #3: Start with business questions, not attribution models

During our conversation, Barbara noted that most companies approach attribution backwards. They build sophisticated models first, then try to use them for decision-making. Barbara recommends flipping this approach.

Start with specific business questions: Which channel should get more budget? Is our brand marketing working? How do we improve lead quality? Then figure out the minimum viable measurement needed to answer those questions.

For companies just starting out, Barbara's advice is simple: "If you're only running one channel, then just have the ad platform pixel on it. Don't overcomplicate it."

The measurement complexity should match your marketing complexity. Running multiple channels across different customer touchpoints? You need more sophisticated measurement.

Running primarily search and LinkedIn ads? Keep it simple.

Barbara also emphasizes the difference between data for reporting and data for optimization. Attribution events for ad platforms need to fire quickly to help algorithms optimize. Attribution for budget allocation can be more complex and take longer to calculate.

Don’t build attribution models for the sake of having them. Build them to answer specific business questions that will change how you allocate resources.

The bigger picture

If we take a big-picture view, the obsession around attribution comes from a very understandable place: the pressure to prove marketing value.

But that same pressure to show that you deliver and actually delivering aren't the same thing. Sometimes they can even be the opposite.

Barbara suggests that companies often overcomplicate attribution because they're trying to justify every marketing dollar. But measurement is supposed to be a competitive advantage, not a reporting exercise.

The goal shouldn’t be to have perfect attribution.

It's whether you have the ability to make better marketing decisions.

Want to hear the full conversation with Barbara Galiza? Listen to the complete podcast episode for deeper insights on lead scoring, stakeholder management during attribution battles, and why AI won't solve your measurement problems. Check it out here.

Episode highlights 👇

00:00 – Barbara's story / Entering the world of attribution

05:50 – Discovering attribution gaps

08:05 – Breaking myths around crafting the perfect attribution model

12:02 – Stakeholder management during the battle with attribution

13:49 – Building an attribution model from scratch

20:36 – Measuring the quality of the traffic

23:28 – Lead scoring and attribution

36:19 – Aligning sales and marketing

40:06 – Fixed Q&A

45:00 – Kudos and Facepalms

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