In a world saturated with data, marketers face the immense challenge of proving their impact. We sit down with Anastasia Braitsik, a global leader in SEO, content marketing, and data analytics, to cut through the noise. She offers a masterclass on transforming marketing from a perceived cost center into a proven growth engine. This conversation explores the power of marketing mix modeling to untangle complex campaign performance, the necessity of a holistic measurement strategy, and the practical steps teams can take to reallocate budgets with confidence. We’ll also dive into the critical importance of high-quality data, the common pitfalls of confusing correlation with causation, and how data-driven insights can finally align marketing with finance and leadership, fostering a shared language of results.
Many marketers struggle to prove the value of diverse campaigns, from TV ads to email blasts. How does marketing mix modeling specifically help untangle this complexity, and what are the most critical data inputs, like sales or economic trends, needed to build an effective model?
It’s a challenge I see constantly. You’re running a dozen different initiatives, and when sales go up, everyone wants to take credit, but no one can truly prove it. Marketing mix modeling, or MMM, is the statistical referee in that situation. It’s designed to look back at all your historical data—everything you’ve done—and mathematically isolate the contribution of each element. Instead of a jumbled mess, you get a clear picture of how your TV ads, digital campaigns, and even in-store promotions individually moved the needle. To make this work, the model needs solid, clean inputs. You absolutely must have your core sales and revenue data aligned, along with accurate media spend for each channel. But it’s also crucial to include external factors; things like major holidays, economic shifts, or even competitor activity can have a huge impact, and a good model accounts for that context.
Relying solely on one metric, like digital clicks, can skew a marketing strategy. How do you recommend combining marketing mix modeling with other methods, such as attribution models or lift tests, to create a more holistic and accurate picture of overall performance?
That’s a critical point. If you only focus on last-click attribution, for instance, you’ll inevitably over-invest in channels that are good at closing a sale and completely undervalue the ones that build the initial awareness. You end up chasing short-term wins while your brand equity slowly erodes. That’s why I always advocate for a blended approach. Think of it as seeing both the forest and the trees. MMM gives you that high-level, “forest” view of how all your channels work together over the long term, including offline efforts like TV or sponsorships. Then, you can use more granular tools like digital attribution models or A/B lift tests to understand the “trees”—specific customer journeys or the direct impact of a digital campaign variation. By combining these methods, you get a complete, balanced perspective that honors both immediate conversions and long-term brand health.
Imagine a team discovers a high-performing channel through analytics. Beyond simply increasing its budget, what practical steps should they take to reallocate funds from underperforming areas, and how can they use “what if” scenarios to forecast the impact of these changes on revenue?
Discovering a high-performer is an exciting moment, but the next step requires strategy, not just a bigger check. The first thing to do is identify the underperformers with confidence. The data should clearly show which channels are not pulling their weight. Then, the magic of modeling comes into play with “what if” scenarios. Before moving a single dollar, you can simulate the outcome. For example, you can model: “What happens to our total revenue if we reallocate just 10% of our budget from display ads to paid search?” The model will forecast the potential gains based on past performance. This isn’t just a guess; it’s a data-informed projection that allows you to plan with confidence, avoid costly surprises, and get buy-in from leadership by showing them the expected financial impact of your decision.
High-quality data is non-negotiable for accurate analysis. For a company just starting this journey, what are the first three data sources they must clean up and consolidate, and what common pitfalls should they look out for during this crucial preparation phase?
This is the foundation of everything. Without good data, the most sophisticated model is useless. For a company just beginning, I’d say the first three non-negotiable data sources are: first, clean, consistent sales and revenue data. This is your ultimate source of truth. Second, accurate media spend tracking across every single channel. You need to know exactly what you spent and when. Third, I would emphasize campaign metadata—things like flight dates, ad formats, and which creative was used. A common pitfall is having incomplete or messy data; numbers that are outdated or live in different, disconnected spreadsheets. If your inputs are unreliable, your insights will be too. You have to put in the foundational work to get this right before you can expect any meaningful analysis.
A common mistake is confusing correlation with causation, especially when a sales spike coincides with a new campaign. Could you share an example of how this might mislead a team and explain how a well-designed model isolates the true drivers of performance?
This is probably one of the most dangerous and common traps in marketing. A team might launch a big social media campaign in the summer, see a huge spike in sales, and immediately conclude the campaign was a massive success. They might then decide to double down on that same strategy next quarter. However, a well-designed model would also account for other variables. It might find that a major competitor went out of business that same month, or that a seasonal trend always causes a summer sales lift in their industry. The model isolates these external factors, separating their impact from the campaign’s true contribution. In this case, it might reveal the campaign only had a minor effect, and the real driver was the market shift. That insight prevents the team from wasting money by repeating a strategy that didn’t actually work.
Focusing only on short-term impact can cause teams to undervalue long-term brand-building efforts. How can an analytics model be designed to capture both immediate returns and the delayed, long-lasting effects of certain marketing activities? Can you provide an example?
This is where a thoughtful approach to modeling is essential. Many standard models are built to measure immediate response—you run an ad today, you see a sale tomorrow. But brand-building doesn’t work like that. Think of a major sponsorship or a sustained TV campaign. Its real value isn’t just in a quick sales lift; it’s in building brand recognition and trust over months or even years. To capture this, a model must be designed to look for these delayed returns. It can incorporate ad-stock variables that account for the decaying effect of advertising over time. So, the model recognizes that a TV ad seen in January might still be influencing a purchase in March. This ensures you see the full, long-term contribution and don’t prematurely cut funding for a powerful brand-building channel just because it doesn’t deliver a huge spike in sales overnight.
For a marketing team with limited resources, what does a successful pilot program for performance measurement look like? Could you walk us through the key steps for a single product or region, from clarifying goals to choosing the right tools for analysis?
You absolutely don’t need a massive budget to get started. The key is to start small and focused. A successful pilot begins with clarifying your most critical goal—are you trying to boost conversions for one specific product, or increase awareness in a single sales region? Pick one clear objective. Next, audit your existing data for that specific product or region. Gather all your sales numbers, marketing spend, and campaign details into one clean, organized dataset. Then, instead of investing in enterprise software, you can start with a simpler internal model or partner with a specialist for a one-off analysis. The goal isn’t perfection; it’s progress. By focusing on a single area, you can generate a few powerful insights, prove the value of the approach, and build momentum to scale your efforts across the entire marketing plan over time.
When marketing provides clear, data-driven proof of its contribution, it can change organizational dynamics. How does this shared language of results help better align marketing with finance or leadership, and what shifts have you seen in how strategic decisions get made?
It completely transforms the conversation. For too long, marketing has been seen as a “cost center,” often struggling to justify its budget with metrics that finance doesn’t always understand. But when you can walk into a meeting and say, “We invested this much in this channel, and our model shows it generated this much in revenue,” you are no longer talking about opinions. You are speaking the language of results, the same language as finance and the C-suite. This builds immense trust and credibility. I’ve seen this shift firsthand: debates over budget cuts turn into strategic discussions about investment opportunities. Decision-making speeds up because everyone is working from a shared, objective set of facts. Marketing earns a seat at the table not as a creative department, but as a verifiable growth engine for the business.
What is your forecast for the future of marketing analytics?
I believe the future of marketing analytics is about moving from complexity to clarity. For years, we’ve been drowning in data and fragmented tools. The next wave isn’t about more data, but about more meaningful, integrated insights that are accessible to everyone, not just data scientists. We’ll see a greater fusion of different measurement techniques, where AI helps automate the process of blending high-level mix models with real-time attribution. Ultimately, the goal is to provide marketers with confident, forward-looking guidance. The question will change from “What happened last quarter?” to “What is the smartest move we can make next week to drive growth?” Analytics will become less of a rearview mirror and more of a GPS for navigating the future.
