Bridging the Gap Between Data and Marketing Teams

What’s up everyone, today we have the pleasure of sitting down with Barbara Galiza, Growth and Marketing Analytics Consultant.

Summary: Attribution is a bit like navigating Amsterdam’s canals: mesmerizing but full of hidden turns that don’t always make sense. You don’t need to chart every twist—just focus on finding the direction that moves you forward. Instead of obsessing over every click, use attribution like a compass, not a GPS. Multi-touch attribution (MTA) gives you some of the story, but often misses those quiet yet powerful nudges that drive real decisions. Layering in rule-based or incrementality testing can fill the gaps, giving a clearer picture of what’s driving your wins. For startups, it’s even simpler: stick to what’s working and forget complex attribution—qualitative feedback is often the best guide in the early days. Data doesn’t need to be perfect, just practical, and sometimes trusting that a strategy is working is enough to keep pushing it.

About Barbara

• Barbara was an early employee at Her (YC), the biggest platform for LGBTQ women where she would eventually become Head of Growth
• She was also Head of Growth at different startups like Pariti and Homerun
• She worked at an agency where she led data and analytics for Microsoft EMEA
• Barbara then went out on her own as a GTM and Analytics consultant for various companies like Gitpod, WeTransfer, Sidekick and dbt Labs
• She has a newsletter on marketing data: 021 newsletter.
• She produces content for data brands (dbt, Mixpanel, Amplitude) like case studies and webinars

Building Data Literacy Through SQL

Data literacy is essential for modern marketers, but it doesn't have to be intimidating. Barbara’s advice is simple: learn SQL. While marketers today are surrounded by user-friendly tools and drag-and-drop interfaces, those who want to truly grasp their data should get comfortable with SQL. It’s not about becoming a data engineer but about understanding how the numbers you rely on every day are built. SQL helps you see how data connects, how it’s organized, and how you can group it to make sense of what’s happening in your campaigns.

What’s great is that you don’t need to dive into formal classes or certifications. Start where you are. Most companies are sitting on a goldmine of structured marketing data, whether it’s Google Analytics data in BigQuery or Amplitude events stored in a data warehouse. The next time you’re building a report, try using SQL for a small part of the process. It’s a skill that compounds over time. Once you get familiar with the basics, you’ll start to see data in a different way, and you’ll be able to spot insights faster.

Barbara also points out a crucial, often overlooked skill: understanding why your tools give credit to certain campaigns. Why does one Facebook ad outperform others in your reports? Why does Google Analytics attribute more conversions to certain sources? Getting to the bottom of these questions puts you in a much stronger position as a marketer. If you can explain how attribution models work and why certain data points appear, you're already ahead of most.

At the end of the day, it’s about making smarter decisions. Barbara believes that marketers who can confidently say, “I know why these numbers look the way they do,” are in the top 10% of data-driven marketers. It’s not just about collecting data; it’s about making sense of it and using it to steer your strategies.

Key takeaway: Learning SQL gives marketers the power to truly understand their data. Starting small, even with basic queries, can unlock a deeper understanding of how marketing data is structured and why campaigns perform the way they do. The key is to build practical skills that help you make more informed decisions.

Rethinking Attribution and Understanding Its Role in Measurement

Barbara brings clarity to two commonly conflated concepts: attribution and measurement. While many marketers default to thinking of attribution as purely click-based or multi-touch attribution (MTA), Barbara challenges this view. She argues that attribution goes beyond just tracking clicks and touches throughout a customer’s journey. It’s about understanding the overall impact of marketing efforts—whether through incrementality tests, media mix modeling (MMM), or holdout groups. Attribution is meant to explain how marketing drives results, but it’s not the only tool for assessing campaign success.

MTA, particularly click-based models, excels at measuring bottom-funnel actions like search marketing, where high-intent users click on an ad and then convert. This method works well for campaigns that rely on clicks to move the needle. However, Barbara notes that it has its limitations, especially when it comes to non-click-based channels like video or display. MTA often over-credits search campaigns because that’s where the conversion is tracked, but it misses the broader influence of awareness-building efforts. In essence, MTA can tell you what happened after the click, but not what inspired it in the first place—be it a podcast mention or an engaging piece of content seen days before.

On a broader level, Barbara explains that attribution is not the same as measurement. Attribution focuses specifically on tying marketing efforts to business results, such as leads or revenue. Measurement, on the other hand, casts a wider net. It includes performance across various metrics, not just conversions. For instance, measuring how well different messaging resonates with audiences is crucial, but it doesn’t always directly lead to immediate sales. Measurement can inform future strategies by offering insights into engagement, customer preferences, and channel effectiveness.

As Barbara sees it, attribution is a subset of measurement. It’s a tool for understanding what drives business outcomes, but it shouldn’t be the only tool marketers rely on. For example, MTA has its place but should be used alongside other models like MMM to paint a fuller picture. Measurement, meanwhile, helps marketers assess the effectiveness of everything from messaging to customer touchpoints, beyond just the end goal of conversion.

Key takeaway: Attribution is one piece of the measurement puzzle, focusing on business outcomes, while measurement encompasses a broader range of insights. Marketers should use a mix of attribution models to understand their campaigns and apply measurement tools to gain a holistic view of performance.

Limitations of Multi-Touch Attribution in Credit Distribution

Multi-touch attribution (MTA) is often seen as a way to distribute credit across different customer touchpoints, but Barbara questions its effectiveness in this role. She argues that MTA is inherently limited because it only attributes credit to interactions that involve a click. This creates a skewed view of the customer journey, where only click-driven strategies—like search ads—are recognized, leaving other key touchpoints, like connected TV (CTV) or social media, out of the equation. The result is a narrow perspective that doesn't capture the full influence of various channels.

Barbara points out that for marketers to make better decisions, MTA needs more than just click data. One alternative she suggests is pairing MTA with rule-based attribution models, where data from "How did you hear about us?" surveys are integrated into the analysis. This way, marketers can capture insights from channels that don’t typically generate clicks but still play a crucial role in driving awareness or consideration. By adding this type of first-party data, businesses get a broader understanding of what’s really influencing their customers.

Some data agencies are also experimenting with es...