Insights from the Marketing Tech Monitor: Composability, AI integration and unified data management.

Composability: flexibility and efficiency in software development

Composability, i.e. the structure of software in individually usable components, is becoming increasingly important in order to be able to implement individual processes flexibly and efficiently. The software developed in this way meets existing applications in the company, which leads to an increased need for integration.¹ 

"Our Media Operations Platform Mercury has a modular & API-first structure. (e.g. “Self-Service MMM”, “Strategic Planning”, “Projects”, “Data Connectors/Data Connection Service” or our “Buying Module”. Our customers only license the components they need within their workflow. Of course, we integrate with the customer's existing systems & with relevant peripheral systems on the market via API. "

Gunnar Neumann
Managing Director

Artificial intelligence: turbo booster for MarTech

The market potential of MarTech will increase from €7 billion to €15.5 billion between 2023 and 2027 with an average growth rate of +18.8%. AI as a “turbo booster” in the direction of “real-time automation” and analytics will provide a further boost to growth.²

"We can clearly see that the market is approaching us much more strongly than a few years ago and that there is a great need and openness for external solutions like ours. On the one hand, the use of our system is an ideal basis for using AI-based solutions in a targeted manner, and on the other hand, we are also working on solutions within the platform in which AI can support our customers in the long term. For example, we are already using AI in the analytics area of our MMM module and are working on gradually integrating even more intelligence into media planning and the planning process. Theoretically, use cases are possible in which media planning and optimization suggestions can be generated automatically. We discuss the practical applicability & potentials closely with our customers on an ongoing basis."

Gunnar Neumann
Managing Director

Unified data management: standardizing and using data

Even in 2024, only just under 25% of companies state that they have already fully unified and aggregated their available data: from merging data to breaking down data silos and organizing master data.

"Merging and aggregating data into a clean data basis is a key pillar of our Media Operations Platform. ID-based & without complex naming conventions, we merge target values, planned, booked, realized & billed values via the native marketing & media process. Our customers can access these via a “bridge” with their own BI solutions or view meaningful dashboards directly within the platform. Data-based decisions are ideally enabled by Mercury. "

Gunnar Neumann
Managing Director

Causal AI: understanding cause-and-effect relationships

The fundamental problem with most AI models is that the model learns from training data and, in this case, the data can hardly be used to answer counterintuitive questions regarding cause-and-effect relationships. Causal models solve this problem via causal interference.

“We are continuously working on optimizing our marketing mix model: Starting with a regression analysis via a Bayesian MMM, we are now working on a causal model that better incorporates the effects of the individual media channels on each other. When developing and applying AI models, it is crucial for us to uncover the correct cause-and-effect relationships using causal discovery and to validate them using causal inference. Only if we consider the correct causal relationships between the various influencing factors in our MMM projects, for example, during model development can we keep our performance promise to our customers and increase media efficiency.”

Torben Seebrandt
Director Data Intelligence