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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.
Jump to a Section
- Rethinking Attribution and Understanding Its Role in Measurement
- Limitations of Multi-Touch Attribution in Credit Distribution
- Navigating Attribution in a Multi-Channel World
- Is Incrementality the Golden Alternative to MTA?
- Balancing Experimentation, Measurement and Execution
- Comparing MTA, MMM, and Incrementality in Marketing Attribution
- The Value and Limitations of Self-Reported Attribution
- Building Strong Foundations for Effective Marketing Data
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 Dentsu where she led data and analytics for Microsoft EMEA
- Barbara then went out on her own as a Marketing Analytics consultant for various companies like WeTransfer and Veed.
- She writes a newsletter on the intersection of marketing and data: 021 Newsletter.
- She produces content for data brands (dbt, Mixpanel, Amplitude) like case studies and webinars.
- She’s currently organizing an attribution masterclass.
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.
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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.
Check out Barbara’s Attribution Masterclass if you want to get really serious about this stuff.
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 estimating clicks based on other types of data to fill in the gaps left by traditional MTA models. While these efforts are still evolving, Barbara remains skeptical of relying solely on click-based MTA, noting that it can give marketers a false sense of confidence in their understanding of the customer journey. It’s a tool, but not one that should be used in isolation.
In the end, Barbara emphasizes that without more comprehensive data sources, MTA is only useful for a slice of the customer journey—mainly those interactions where a click is involved. To get a clearer picture of how different channels contribute to conversions, marketers must look beyond clicks and explore other forms of attribution and data collection.
Key takeaway: Multi-touch attribution is limited by its reliance on click data, which means it often overlooks important touchpoints in the customer journey. To get a fuller understanding of channel impact, marketers should complement MTA with rule-based attribution models and other alternative data sources.
Navigating Attribution in a Multi-Channel World
Barbara offers a nuanced take on the complexity of modern attribution, especially in a landscape where marketing spans multiple channels. While multi-touch attribution (MTA) has historically relied on third-party cookies, the rise of privacy regulations and the decline of third-party data have exposed significant flaws in this approach. Vendors like Rockerbox claim to overcome these challenges by leveraging first-party data, probabilistic methods, and platform partnerships. However, Barbara remains cautious about the reliability of these methods, noting that validation is still a major hurdle.
She points out that methodologies like media mix modeling (MMM) provide an alternative by estimating the impact of a channel on outcomes, such as sales or clicks, through causal inference. For example, an MMM model might examine daily impressions from a channel and then correlate them with daily sales, using statistical models to determine if there’s an actual cause-effect relationship. This type of modeling can be adapted to estimate synthetic clicks, feeding into an MTA model for further analysis. However, even this is not a perfect solution.
Barbara emphasizes that the marketing ecosystem has changed drastically. In the past, when companies primarily relied on paid search, MTA was more effective because it focused on a singular channel. Today, marketers use a mix of strategies—everything from paid search to podcast sponsorships—and MTA struggles to provide a clear picture across these diverse touchpoints. Even with cookies intact and no privacy regulations, MTA would still leave gaps in understanding the full user journey.
Her approach to attribution is pragmatic. Instead of trying to apply a one-size-fits-all solution, she looks at attribution through a strategic lens. For instance, she doesn’t prescribe a single attribution model to clients. Instead, she examines how each channel—whether it’s Meta ads or podcast sponsorships—requires a tailored attribution approach. Different marketing tactics need different attribution models, and the key is to match the strategy with the appropriate attribution method.
Key takeaway: In a multi-channel marketing world, no single attribution model can capture the full picture. Each strategy requires its own attribution method, and marketers must adapt their approach to fit the unique data and behavior of each channel. Rather than chasing a universal solution, focus on aligning attribution models with specific strategies to measure performance more effectively.
The Limitations of Multi-Touch Attribution for Conversion Path Insights
When it comes to understanding the path to conversion, Barbara isn’t convinced that multi-touch attribution (MTA) delivers much value. She points out a critical flaw in how MTA handles mobile data—especially for brands reliant on platforms like Meta and Google. Many conversions today happen on mobile, but when someone clicks an ad on a platform like Facebook and later purchases on Safari, MTA models often treat this as two separate users. This breaks the accuracy of UTM tracking and creates a fragmented view of the customer journey.
Barbara has encountered this firsthand with clients in the DTC space, where mobile interactions dominate. Unless a customer completes a purchase immediately after clicking the ad within the same app session, MTA models fail to capture the true journey. This gap grows even larger when cross-device usage comes into play, such as users moving from their phone to a desktop for the final purchase. The result? MTA data that presents an incomplete and often misleading story.
Another key issue Barbara raises is around UTM models misattributing conversions to first-session visits. She recalls a situation where an MTA model suggested that most conversions happened during the very first visit after a user searched for the brand. This scenario is highly unlikely, as customers typically engage with multiple touchpoints before finally converting. It’s clear that while MTA may try to connect these dots, it frequently falls short, leaving marketers to question the validity of the data.
For Barbara, this brings an important lesson: MTA data needs to be taken with a grain of salt. While it can provide some directional insights, it’s not the full picture, especially when it comes to mobile-heavy interactions and complex customer journeys. Marketers have to critically assess whether the data aligns with their understanding of customer behavior or risk drawing false conclusions.
Key takeaway: MTA struggles with capturing mobile and cross-device conversions, often leading to fragmented or misleading data. Marketers should approach MTA insights with caution and complement them with other attribution models to get a more accurate picture of the customer journey.
Is Incrementality the Golden Alternative to MTA?
Barbara cuts through the hype around incrementality, a method often painted as the “golden alternative” to multi-touch attribution (MTA). While she acknowledges its value, she’s quick to point out that there is no one-size-fits-all solution when it comes to attribution. Each method has its place, but they also come with limitations. Incrementality, which measures the sales that wouldn’t have happened without marketing efforts, is no exception. It’s a valuable tool for optimizing spend, but Barbara cautions against seeing it as the perfect answer for every situation.
One of the key challenges with incrementality is that it often requires cutting off certain campaigns to measure their true impact—something not every marketing team can afford to do. Marketing leaders expect campaigns to run continuously, and stopping a campaign, even temporarily, can mean a revenue hit. This makes it difficult for teams to implement incrementality tests regularly, despite their potential to provide deeper insights into what’s really driving conversions.
Barbara also highlights another practical hurdle: not all marketing strategies are suitable for incrementality testing. For example, a long-term brand-building campaign, like a podcast sponsorship, isn’t something you can easily pause to measure its incremental impact. Even geo-based testing, a common incrementality method, can be tricky for smaller campaigns or those with modest returns. Selecting the right geographic area and getting meaningful data can prove difficult for these scenarios.
However, Barbara sees real potential for incrementality when it comes to channels like social and display advertising, particularly for brands with six-figure monthly budgets. In these cases, adding incremental tests to the marketing roadmap can provide valuable insights. But it requires a dedicated marketing and data team to manage the process, and not every company has the resources or support to make it happen.
Key takeaway: Incrementality is a powerful tool for understanding the true impact of marketing, but it’s not a one-size-fits-all solution. Marketers should consider adding incrementality tests to their roadmap, particularly for high-budget campaigns, but must balance the complexity and resource requirements involved.
Balancing Experimentation, Measurement and Execution
Barbara offers an insightful take on balancing experimentation with execution, especially when resources are stretched. For marketers at small companies, the pressure to keep things moving often means tests and experiments take a backseat. Larger companies like Spotify may have entire teams dedicated to growth pods, running controlled tests and dissecting outcomes. But startups? They don’t always have that luxury. Barbara stresses the need for balance—sometimes it’s better to trust your instincts and keep going, rather than pausing to validate every move with data.
The challenge, though, is in recognizing when measurement is absolutely critical. For startups, quick wins can matter more than getting every detail right. Barbara believes that in some cases, the cost of over-analyzing a strategy outweighs the benefit of knowing the exact numbers behind its success. If something’s clearly working, why not run with it instead of slowing down to prove incremental gains?
On the flip side, as companies scale, the need for more precise measurement becomes impossible to ignore. For example, brands like Ahrefs may have the luxury of focusing on overarching metrics like revenue. They’ve earned that. But for growth-stage companies trying to hit aggressive targets, relying solely on revenue figures doesn’t cut it. You need to understand which channels or tactics are driving the results. Without that clarity, you can’t make smart decisions on where to allocate resources.
Barbara’s view is practical: experiment when you can, but don’t get paralyzed by the need for proof. The key is knowing when deeper insights are essential to keep momentum going, and when gut instinct is enough to push forward.
Key takeaway: Marketers need to strike a balance between testing and taking action. Measurement matters, but not every decision requires a microscope. When you know something’s working, it’s okay to push forward without having all the data.
Comparing MTA, MMM, and Incrementality in Marketing Attribution
When asked about the roles of different attribution methodologies, Barbara largely agrees with the common breakdown of multi-touch attribution (MTA), media mix modeling (MMM), and incrementality testing. Each method has its place, but understanding where they excel and their limitations is key for marketers.
MTA, Barbara notes, works well at the bottom of the funnel, providing insights into the path to conversion through behavioral analytics. However, she adds a critical nuance: MTA requires clicks to be effective. While it shines for click-based actions like ads or emails, many bottom-funnel activities, such as case studies or customer testimonials, don’t generate clicks but still play a significant role in driving conversions. MTA will miss these contributions, which can create a blind spot for marketers relying solely on this model.
As for MMM, Barbara concurs that it demands a large dataset and substantial budget to be effective—though she’s less certain about the exact threshold, acknowledging that it requires a significant budget to draw correlations effectively. MMM is useful for high-level strategy and long-term budget allocation, as it shows which channels correlate with business outcomes. However, it’s not designed for daily optimization or proving direct causality. In her view, MMM is ideal for brands looking to understand broad trends rather than micro-level interactions.
Incrementality testing is another valuable tool but comes with its own challenges. Barbara emphasizes the importance of the conversion window and the type of event being measured. For instance, tracking higher-occurrence actions, such as leads or white paper downloads, might yield more actionable insights than waiting for the rare occurrence of a sale. She notes that incrementality helps prove causality, but the results are often point-in-time, which limits their long-term application. The challenge, especially for B2B companies, is gathering enough data to make this method worthwhile.
Each attribution model has its strengths and limitations, and Barbara encourages marketers to pick the method that aligns best with their specific goals, budget, and available data.
Key takeaway: Each attribution model—MTA, MMM, and incrementality—has its own strengths and gaps. MTA is effective for click-based, bottom-funnel activities but misses non-click interactions. MMM helps with long-term budgeting but lacks the granularity for daily optimization. Incrementality testing provides causality insights but requires careful event selection and may only offer point-in-time results. Choosing the right approach depends on your marketing objectives and resources.
Why Startups Should be Focused on Pouring Gas in the Fire
For startups, the key to early growth is finding what works and doubling down. Barbara emphasizes that startups don’t need to overwhelm themselves with complex measurement systems right out of the gate. Instead, the focus should be on identifying a single channel that’s delivering results and maximizing its potential. As she puts it, “pour gasoline into the fire” once you find a channel that’s already working. The goal isn’t to start entirely new channels but to improve what’s already driving growth, particularly with paid media or other amplifying efforts.
Barbara also advises that startups shouldn’t get caught up in chasing a “one source of truth.” The idea of a perfect, unified reporting system that tracks every touchpoint and channels attribution flawlessly is a myth. Startups with limited resources are better off focusing on measuring the activities they are actively doing. This might mean something simple like asking new customers, “How did you find out about us?” for B2C companies or “What problem do you think we can help you with?” for B2B leads. These basic but valuable insights can reveal a lot about both attribution and effective positioning.
The challenge for startups is not just in finding the right marketing channel but in staying agile enough to capitalize on it fully. Channels change, competitors enter, and privacy regulations evolve. Barbara’s advice is clear: don’t take any channel for granted, and extract as much value from it as possible while it’s working. This approach is much more practical for startups that lack the bandwidth to invest in full-scale measurement or analytics from the start.
Ultimately, Barbara suggests relying on qualitative data and intuition in the early stages. Gathering direct feedback from users and leads can serve as a form of attribution, helping startups understand what’s working without needing a dedicated data science team. As companies grow, they can build on this foundation and explore more complex measurement systems, but in the beginning, simplicity and focus should be the guiding principles.
Key takeaway: Startups should focus on a single channel that’s working and maximize its potential rather than trying to scale too many channels or engage in complex attribution systems. Qualitative insights from customers can serve as a form of measurement, allowing startups to gather the data they need without overcomplicating their approach.
The Value and Limitations of Self-Reported Attribution
When it comes to self-reported attribution, the debate is often polarized. Some marketers fully embrace it as a valuable tool, while others dismiss it, arguing that human memory is flawed and unreliable. Barbara’s perspective strikes a balance. She acknowledges that, yes, people may not perfectly recall every touchpoint in their journey, but that doesn’t mean self-reported attribution is useless. In fact, she points out that if a customer remembers a specific touchpoint, even if it wasn’t their first interaction, it still reveals something important about that touchpoint.
Barbara likens self-reported attribution to customer interviews. Just as customer feedback might be influenced by bias or imperfect memory, self-reported touchpoints provide subjective but still meaningful insights. If a customer identifies a certain ad, blog post, or social media interaction as the moment they remember, it signals that the touchpoint made a lasting impression. It may not be a perfect reflection of the entire journey, but it offers a glimpse into what stood out to the customer along the way.
She suggests that while self-reported data shouldn’t be treated as definitive, it can complement other attribution models. Relying solely on MTA or data-driven models without considering the human element can lead to blind spots. By integrating self-reported attribution, marketers can better understand which touchpoints resonate with customers, even if they’re not always the first or last in the journey.
Ultimately, Barbara’s take is pragmatic. Self-reported attribution may not capture every nuance of a customer’s journey, but it still adds value by highlighting memorable interactions. In combination with other methods, it can help build a more complete picture of what influences customer behavior.
Key takeaway: While self-reported attribution is not without flaws, it provides useful insights into which touchpoints resonate with customers. Rather than dismissing it entirely, marketers should use it as a complementary tool alongside other attribution models to better understand customer behavior and identify memorable interactions.
The Pitfalls of Overemphasizing Attribution Accuracy
Barbara dives into the pitfalls of overemphasizing attribution accuracy, a common issue that can quickly turn marketing meetings into contentious battlegrounds. Too often, teams get wrapped up in debates over who gets credit for conversions, with each department armed with its own set of data. This tension only intensifies when bonuses and incentives are tied to attribution metrics. As Barbara notes, when employee rewards hinge on attribution numbers, discussions devolve into turf wars instead of strategic planning.
Barbara sees this focus on precise attribution as a time sink, pulling teams away from meaningful work that could drive real revenue. She argues that perfect attribution is an unrealistic goal; gaps will always exist, no matter the data. Instead of striving for a single “source of truth,” Barbara encourages teams to choose a few core metrics that align directly with business goals. Attribution should act as a decision-making tool—not a battlefield for validation. In her view, simpler metrics mean clearer priorities, allowing teams to stay focused on revenue-driving actions without getting sidetracked by attribution disputes.
Barbara highlights Uber’s straightforward approach to calculating customer acquisition cost (CAC) as a smart example of simplified data. Rather than dividing credit between teams or tracking each touchpoint in exhaustive detail, Uber averages its total marketing spend across new accounts. This simplicity not only saves time but fosters a spirit of collaboration, as no one team can claim exclusive credit. It’s a model that strips away the complexity, promoting a unified, goal-oriented mindset.
Ultimately, Barbara believes data is only valuable if it creates clarity and alignment. When attribution data leads to finger-pointing or internal friction, it loses its strategic value. She advises companies to prioritize data that genuinely drives decision-making and creates shared understanding. This approach helps teams move from attribution bickering to collective growth efforts—a shift that’s essential, especially for companies in high-growth or resource-limited stages.
Key takeaway: Perfect attribution is a distraction. Focus on simplified, actionable metrics that align with business objectives, using attribution as a tool for guidance rather than a precise measure of success. In marketing, sometimes less data fosters better alignment, enabling teams to focus on the bigger picture: growth.
Rethinking Testing Culture in Marketing
Barbara questions the prevailing assumption that every marketing effort must be rigorously tested to add value. She contends that while testing has its place, a methodology is essential for meaningful results. Many companies dive into testing without a clear plan, treating it as a checkbox rather than a strategic tool. In Barbara’s view, the lack of structured hypotheses and methodologies explains why an estimated 80% of tests fail to yield statistically significant outcomes. It’s not that testing itself is ineffective; rather, tests without purpose or framework waste time and produce inconclusive results, leading to misguided conclusions.
Barbara emphasizes that a solid testing roadmap is foundational, especially when attempting complex attribution models like media mix modeling (MMM), which require budget variations or regional adjustments to accurately assess causality. Without these purposeful changes, determining a true cause-and-effect relationship becomes nearly impossible. The challenge, Barbara explains, is that many marketers chase granular changes—button colors, page layout tweaks—often more as a way to delay decisions than to drive genuine impact. Such minor tweaks rarely contribute to strategic insights and can dilute focus from high-impact initiatives.
Beyond methodological rigor, Barbara’s perspective is refreshingly pragmatic: marketing resources are finite, and time spent must correlate with potential impact. Marketers have limited hours to invest in data analysis, and it’s critical that their efforts align with high-priority goals. Testing, for the sake of testing, often distracts from bolder strategies that could drive more significant outcomes.
In a striking observation, Barbara remarks that “all marketing data is estimated.” She reminds us that every metric—whether it’s user numbers, page views, or engagement rates—is affected by variables like bots, device overlap, cookie blocking, and regional consent laws. This reality underscores the importance of not treating data as gospel; instead, it should be viewed as directional, helping inform decisions without over-inflating certainty. Recognizing data limitations allows marketers to make wiser choices, focusing on patterns and insights rather than absolute figures.
Key takeaway: Not every marketing effort needs to be tested, and when tests are run, they need clear methodology to deliver real insights. Prioritize impact over precision, and remember that marketing data is often an estimate, valuable for guidance rather than absolute truth.
Building Strong Foundations for Effective Marketing Data
Barbara emphasizes that effective marketing data starts with more than just collection—it requires thoughtful naming conventions and organization, consistent structure, and a commitment to making data accessible and usable for everyone on the team. For Barbara, data management isn’t just a backend process; it’s about ensuring that all marketing contacts, especially within paid media, follow standardized naming conventions to enable consistent tracking and reporting. While ad platforms offer some built-in reporting tools, these are often insufficient for in-depth analysis. To maximize data’s potential, all campaign details should be tagged and categorized clearly to serve the brand’s unique needs.
Barbara also highlights an often-overlooked element: data literacy. It’s not enough to have data on hand; the team needs to understand it deeply and use it confidently. Data literacy and ongoing education should be woven into the team’s operations to ensure every team member can analyze, interpret, and act on the insights effectively. She points out that when marketers lack the tools or understanding to work with data, it hinders their ability to execute strategies and make informed decisions, leading to missed opportunities and incomplete analysis.
Another cornerstone of Barbara’s approach is integrating a testing roadmap within the data strategy. She argues that every hypothesis should be linked to specific, measurable outcomes, particularly as each strategy operates differently across the funnel. For instance, a top-of-funnel awareness campaign demands distinct metrics from a bottom-funnel conversion campaign, so attributing success should vary accordingly. Without this strategic attribution, marketers risk oversimplifying the effectiveness of diverse campaigns, losing valuable insights in the process.
Accessibility is the final piece Barbara adds to the puzzle. She notes that, too often, marketers are left out of the data loop, relying on pre-made dashboards that limit their ability to dig deeper. For data to truly drive value, it needs to be accessible—marketers should be able to work directly with the data, modeling and transforming it in ways that align with campaign goals. A closed-off approach, where domain experts are isolated from the data, stifles innovation and limits strategic planning.
Key takeaway: For impactful data-driven marketing, ensure data literacy across the team, establish a structured testing roadmap, and make data accessible for hands-on analysis. A foundation built on clear, consistent data practices enables smarter, strategy-specific insights.
Bridging the Gap Between Data and Marketing Teams
Barbara underscores the value of collaboration between data and marketing teams, noting that both sides struggle in complementary ways. Data teams often grapple with proving their business impact, while marketing teams face challenges in making data-driven decisions. The solution, she suggests, lies in building stronger connections between the two groups—empowering marketers with a solid foundation in data interpretation and providing data teams with the context they need to make their insights actionable.
One example is bigger teams where a cross-functional experimentation platform might be developed and managed by data scientists and engineers. These platforms are integrated systems that include mandatory training for marketers, covering essentials like minimal detectable effect, experiment duration, and hypothesis formulation. Through this structure, marketers can gain a deeper understanding of experimentation fundamentals, making their insights more grounded and strategic.
What makes this setup unique is the direct involvement of data scientists as teachers, guiding marketers through concepts they might otherwise gloss over. Barbara believes this is essential in any data-marketing collaboration. Marketers who think they “know experimentation” often discover gaps in their understanding when exposed to the more rigorous standards set by data professionals. This partnership empowers marketers to frame experiments more precisely and align their objectives with broader data practices, fostering a culture of mutual growth.
Today, as hybrid roles emerge, like data product managers or martech product managers, these professionals are increasingly tasked with acting as translators between technical and creative functions. These individuals bridge knowledge gaps, ensuring that marketers understand the value and limitations of the data at hand, while also giving data teams a clearer view of the marketing lifecycle. Such roles are vital to syncing the efforts of both teams, allowing them to drive strategy forward without getting bogged down in technical or contextual misunderstandings.
Key takeaway: Strengthen the connection between data and marketing teams by encouraging cross-functional training and fostering collaboration. This alignment enables marketers to make data-informed decisions with confidence, while data teams gain clarity on their impact, enhancing business outcomes.
Finding Balance in a Goal-Oriented Career
Barbara’s approach to career balance is refreshingly simple: she’s driven by personal targets that keep her motivated, but she doesn’t let them control her entire day. As a self-proclaimed goal-oriented person, Barbara thrives on setting specific benchmarks, both in her work and personal life. These aren’t necessarily targets others would find meaningful, but they provide her with structure and a sense of progress, which she enjoys tracking and celebrating.
Beyond her love for targets, Barbara has crafted a lifestyle that integrates work and relaxation seamlessly. She structures her day around what she genuinely feels like doing, often taking breaks for non-work activities—like visiting the local market for treats or spending time with her dog at the park. For her, work isn’t a rigid, nine-to-five obligation but something she tackles when her energy and motivation are aligned. This flexible approach, while unconventional, lets her maintain productivity without the burnout that often accompanies strict schedules.
Barbara’s perspective on balance also acknowledges the reality of off days. There are times she doesn’t feel like working, and instead of forcing herself through it, she gives herself permission to take a break. This freedom allows her to reset and return to work with a clearer, more focused mindset. Her approach demonstrates that being goal-oriented doesn’t mean every moment needs to be hyper-productive; rather, it’s about recognizing when to push and when to pause.
In her own words, Barbara’s work style is about balance, not intensity. By gamifying her personal goals and staying adaptable to her own rhythm, she finds satisfaction in her work without sacrificing her well-being. This combination of structure and flexibility helps her remain motivated, grounded, and ultimately happy in both her career and personal life.
Key takeaway: Balance can mean working when you’re motivated and taking breaks when you’re not. Setting personal targets provides structure, but allowing yourself flexibility fosters both productivity and happiness.
Episode Recap
Attribution is like exploring Amsterdam’s canals—captivating but full of twists, turns, and hidden paths. It’s less about mastering every intricate detail and more about finding the path that pushes you forward. Think of attribution as your compass, revealing which routes actually matter rather than a precise map to every step. Instead of obsessing over tracking every touchpoint, approach attribution with flexibility. Multi-touch attribution (MTA) captures only part of the story, often missing those subtle but powerful interactions that truly shape buyer decisions. By layering MTA with models like rule-based or incrementality testing, you build a more grounded view—one that captures the real drivers of impact.
Multi-channel marketing makes things messy, and expecting a one-size-fits-all model to explain everything is like trying to fit a square peg into a round hole. Each channel behaves differently, so why treat them all the same? MTA handles click-based interactions well but leaves out cross-device and mobile conversions, which means you’re looking at only a slice of the story. Instead, rule-based or incrementality tests can fill in those gaps, showing which channels lift performance in ways that MTA can’t quite capture. Incrementality testing, while not a magic bullet, is a power move for big-budget campaigns that can afford to prioritize causation over correlation.
Startups, in particular, should take a simpler path—pour fuel on the fire of a single channel that’s gaining traction instead of getting lost in attribution matrices. For early-stage companies, qualitative feedback from customers is often the best “attribution” you need, offering actionable insights without the hassle of overcomplicated systems. Larger companies with sprawling, multi-channel strategies might need the full deck of attribution tools, but early on, simplicity wins.
Getting too caught up in attribution accuracy is a slippery slope. Data needs to be practical, not perfect. By shifting focus to the broader outcome rather than obsessing over attribution “accuracy,” you can keep efforts aimed at growth. Measurement matters, yes, but it doesn’t always need a microscope. Sometimes it’s enough to trust that something’s working and push it further, without waiting for all the granular data. It’s about doing more of what works, refining as you go, and using data as a guide—not a ball and chain.
And don’t overlook the foundation. A strong, shared understanding of data across teams makes everything easier. Bridging the gap between data and marketing means everyone can pull insights from the same playbook. Cross-functional alignment makes data not just accessible but actionable, ensuring that your insights don’t just live in spreadsheets—they fuel decisions that push business outcomes forward.
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