5 Reasons Why Your Business Needs Consolidated Marketing Data

Where Do These Conversions Come From?

Using multiple tools in campaigns, and thus various metrics and attribution models, makes it hard to quickly and easily determine what (and to what extent) influences the final result. The Customer Journey complicates things even more. Why? First, the customer needs to become aware of us (one set of metrics), then engage (a different set of metrics), make a purchase, and ideally return and recommend us to others (yet another set of metrics). At each of these stages, we use tools with different indicators, and these tools interact with each other. Additionally, many actions have delayed effects. So, what exactly drives the result and to what extent?

Even if you manage to align everything to a single tool-based metric, the data often still cannot be directly compared. For instance, comparing conversion data from Meta Ads and Google Ads should theoretically add up to millions of dollars. Yet somehow, it never does. It turns out that platforms may attribute the same conversions to themselves (even in last-click models).

Is it worth it? The answer is always yes, because data gets you closer to the truth about what works and what doesn’t.

Parallel Worlds

Sometimes, it seems like marketing operates within companies as a department that no one understands. Boards and sales teams don’t really know what marketing does. And frankly, they don’t want to know or understand. Marketing is often seen as a black box where you allocate a budget and later expect results. It’s also the first area where budgets are cut during crises (which is often a self-defeating move). On the other hand, marketing teams rarely feel the need to explain their activities or results. When asked, “What exactly do you do?” they often respond, “Don’t worry about it.”

This issue also extends to collaboration between marketing departments and agencies. The expectations expressed by the marketing department and what the agency understands and delivers are sometimes like parallel worlds. A recent example: an agency was tasked with increasing overall sales for a brand. The agency’s approach: maximizing ROAS (Return on Ad Spend) at the expense of cannibalizing other channels. The tool reports looked great, but the company’s sales targets were not met. What went wrong?

Another case: the goal is to acquire customers with high repeat purchase potential, i.e., customers who can be monetized over time. Each year, the company spends a significant budget on Black Friday campaigns, even though customers acquired this way are notoriously disloyal. Yet, the first-purchase conversion metrics look fantastic. The individual tool reports also look great. The problem? The customer doesn’t return.

What if communication with the agency was based on business metrics, which the agency could then translate into specific campaign goals?

Bartłomiej Pucek once used an analogy in one of his newsletters that stuck with me. We should not only focus on doing more things to move faster but also on doing more things that move us in the right direction.

In English, there are two distinct terms for this: speed (how fast) and velocity (speed in a specific direction).

A/B Testing as a More Effective Form of Optimization

An experiment conducted by Peter Skillman, known as the „Marshmallow Challenge,” proved that iterative improvement leads to better results than trying to create the perfect project from the outset.

Teams that attempted to meticulously plan and create a perfect structure from the beginning often failed or had their towers collapse under the weight of the marshmallow.

Teams that adopted an iterative approach—build, test, improve, and then build again—achieved significantly better results.

The problem is that modern marketing is far more complex than a single marshmallow. Effective campaigns use a wide array of tools. This can be compared to a symphony orchestra, with marketers acting as conductors orchestrating the entire process.

Orchestration is the process of arranging a musical composition for various instruments. A composer or arranger decides which instruments will play certain parts to achieve the desired sound effect. Similarly, a conductor relies on their ear (preferably perfect pitch), and a marketer relies on data analysis. With a consolidated analytical model, we input adjusted values from individual tools and observe the final marketing outcome and its impact on business results.

Zero-Party and First-Party Data as a Competitive Advantage

In today’s reality (GDPR, consent mode, etc.), it has become clear that nothing enhances campaign effectiveness like the collection of proprietary data—meaning, even more data.

Often, we use this data to segment customers and conduct personalized communication. But which segments are worth focusing on, which traffic is more profitable, which part of the funnel deserves more investment, and which data is worth collecting? These are questions worth asking, but many marketers don’t. A model that integrates metrics from top to bottom—from business metrics to impressions across different tools—can help answer them.

When data is skillfully analyzed and interpreted, it can become a real competitive advantage.

The Market No Longer Works for Marketers

The complexity of data (gone are the days when you could create a media plan in Excel!) exceeds our cognitive abilities. The language and data we use are becoming increasingly abstract to others. Fortunately, AI is helping us with this (and that’s a good thing). However, the rise of artificial intelligence also brings a cost to the industry, which can be summed up in one sentence:

AI is displacing—and will increasingly displace—those who configure and optimize individual tools. This is already happening.

Optimal tool configuration will become the baseline, not the ceiling. Everyone will have easy access to this knowledge, meaning it will no longer provide a competitive advantage. With just a few clicks, AI can help you optimally configure a tool.

The winners (or perhaps survivors) will be those who can effectively combine tools with human behaviors—something AI, at least for now, cannot do. They will have a broader perspective. And this is where an integrated data model can significantly help.

Key Takeaways

These are just a few reasons why it’s worth collecting and consolidating data now. If you feel like this is “your problem” as well, it’s high time to start building an analytical model that will make it easier (though not necessarily easy) to identify the impact of individual tools and activities on overall results—not just marketing outcomes but also their contribution to the company’s performance.

• Using multiple tools and metrics makes it difficult to determine what influences the results (whether marketing or business-related). Inconsistent data can lead to erroneous conclusions. The solution (though still imperfect) is to create an analytical model.
• A lack of understanding of marketing activities by other departments, along with miscommunication between marketing teams and agencies, often leads to ineffective actions. These may look good in reports but fail to support key business goals—often due to inconsistent metrics.
• Iterative testing and refining campaigns (A/B testing) leads to better results than trying to create the perfect project from the outset. It’s worth testing, gathering data, and using it for optimization.
• Collecting and analyzing proprietary data allows for better customer segmentation and more effective campaigns. Data, or knowledge, can become a competitive advantage.
• The growing role of AI in marketing means that optimal tool configuration will become standard practice, and companies that effectively combine tools with human behavior insights will have the edge.
• Consolidating marketing data from different sources allows for a better understanding of the impact of individual actions on overall company results, enabling more informed strategic decision-making.

Where to start? How can you simplify this process? That’s a topic for another post 🙂