5 reasons why your company needs consolidated marketing data

Where Are These Conversions Coming From?

Using multiple tools in campaigns, and thus various metrics and attribution models, makes it difficult to quickly and easily determine what (and to what extent) impacts the final outcome. The Customer Journey only complicates things further. Why? The customer first needs to discover us (one set of metrics), engage (another 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 influence each other. Additionally, many of the actions we take have delayed effects… So, what ultimately „delivers” the result, and to what extent?

Even if you manage to align everything to one tool’s metric, you still often can’t directly compare the data. Just compare conversion data from Meta Ads and Google Ads—when you sum them up, you should already be counting proverbial millions. But somehow, that usually doesn’t happen. It turns out that platforms can attribute the same conversions to themselves (even in last-click attribution).

Is it worth it? This question comes up often, and the answer is always: yes, because data brings you closer to the truth about what works and what doesn’t.

Parallel Worlds

Sometimes I feel that marketing functions in companies as a department that no one understands. Management boards or sales departments don’t really know what marketing does. And they don’t seem to want to know, let alone understand. They perceive marketing as a kind of black box into which you pour a budget and later expect results. And it’s usually the first budget to be cut during a crisis (often a self-defeating move). On the other hand, marketing usually doesn’t feel the need to explain its actions or results. When asked, „What exactly do you do?” they respond with, „Don’t worry about it.”

This also extends to the collaboration between marketing departments and agencies. The expectations expressed by the marketing department and what the agency understands and delivers can sometimes be like proverbial parallel worlds. A recent example: the goal set for an agency handling a tool was to increase overall brand sales. The agency’s approach: squeezing the maximum ROAS at the expense of cannibalizing other channels. The tool’s reports look green. The company’s sales goals are not met. What went wrong?

Another case: the goal of the activities is to acquire customers with the highest possible repeatability, meaning customers who can be monetized over months. Every year, the company spends significant amounts on Black Friday campaigns, despite the fact that customers acquired this way are exceptionally disloyal. But the first sale conversion metrics look great. The reports from individual tools also look good. The only problem is that the customer doesn’t come back.

What if we communicated with the agency based on business metrics, which the agency could then translate into goals for individual campaigns?

At one point, Bartek Pucek, in one of his newsletters, used an analogy that stuck with me. We should not only do more things that allow us to accelerate but, more importantly, do more things that accelerate us in the right direction.

The English language has two distinct terms for this: speed and velocity.

A/B Testing as a More Effective Form of Optimization

An experiment that demonstrated that iterative improvement leads to better results than trying to create a perfect project from the start was conducted by Peter Skillman and is known as the „Marshmallow Challenge.”

Teams that tried to meticulously plan and create the perfect project from the beginning often did not achieve good results, or the tower collapsed under the weight of the marshmallow.

Teams that adopted an iterative approach—building, testing, improving, and then building again—achieved significantly better results.

The problem is that modern marketing is more than just one type of marshmallow. Effective campaigns use a multitude of tools. You could compare it to a symphony orchestra, with marketers as conductors who orchestrate the performance.

Orchestration means arranging a musical composition for different instruments in the orchestra. The composer or arranger decides which instruments will play specific parts of the composition to achieve the desired sound effect. The conductor relies on their ear (ideally absolute pitch), while the marketer relies on data analysis. With a consolidated analytical model, we input the changed values from individual tools and observe the final marketing effect and its impact on business results.

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

In the new reality (GDPR, consent mode, etc.), it has become clear that nothing improves campaign efficiency as much as having your own data collection. In other words, more data.

Often, we use this data to segment customers and conduct personalized communication. But which segments should we focus on, which traffic is more profitable than the rest, which part of the funnel is worth investing more in, and which data should we focus on collecting? These are questions worth asking, but many marketers don’t. The answer to them lies in a model that integrates metrics from top to bottom: from business metrics to impressions in individual tools.

Then, data (and skilled analysis and inference) can become a real competitive advantage.

Market No Longer Works for Marketers

The complexity of data (where are the days when you could make a media plan in Excel!) surpasses our cognitive capabilities. The language and data we use are becoming increasingly abstract to others. In all this, AI is increasingly supporting us (and that’s a good thing). But the development of artificial intelligence also comes at a price for the industry, which can be summed up in one sentence:

AI is displacing, and will increasingly displace, people involved in the configuration and optimization of individual tools. This is already happening.

Optimal tool configuration will become the floor, not the ceiling. Everyone will have easy access to this knowledge, and it will cease to be a competitive advantage. Just a few clicks will be enough to have a tool optimally configured with AI assistance.

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

Key Takeaways

These are just some of the reasons why it’s worth collecting and consolidating data now. If you feel like this is also „your problem,” it’s high time to start building an analytical model that will make it easier (though not easy) to pinpoint the impact of individual tools and actions on overall results, not just in marketing but also in their impact on company performance.

• – Using multiple tools and metrics makes it difficult to determine what influences results (marketing or business). Inconsistent data can lead to incorrect conclusions. The solution (though still imperfect) is to create an analytical model.
• A lack of understanding of marketing activities by other departments and misunderstandings between marketing departments and agencies often lead to ineffective actions. These actions may look good in reports but don’t support key business goals—often due to inconsistent metrics.
• Iterative testing and improving campaigns yield better results than trying to create the perfect project right away. Therefore, it’s worth testing, collecting data, and using it for optimization.
• Collecting and analyzing your own data allows for better customer segmentation and more effective campaigns. Data, in other words, knowledge, can become a competitive advantage.
• The growing role of AI in marketing means that optimal tool configuration is becoming standard, and companies that effectively combine tools with knowledge of human behavior will gain the advantage.
  – Consolidating marketing data from various sources allows for a better understanding of the impact of individual activities on overall company results and makes it easier to make informed strategic decisions.

Where to start? How to simplify your work in this area? That’s a topic for another post 🙂