Identity instead of Illusion: Why AI in Marketing Fails Without First-Party Identity

The Essentials in Brief:

AI is omnipresent in digital marketing. Everyone is talking about it, everyone wants to get involved. There are plenty of ideas, but besides a clear strategy what is missing in many cases is a solid data basis. It is not a question of a lack of data. On the contrary: advertisers have terabytes of user behaviour, click paths and transactions at their disposal. Nevertheless, many AI initiatives fizzle out. Instead of individual customer engagement, generic campaigns are created. Instead of precise attribution, uncertainty remains. Why is that? The reason is simpler than many think: there is a lack of consistent understanding of who is actually behind a contact point and, therefore, how the available data is connected.

When customers become mere pixels

In the data base, one and the same person often appears as five different separate data points. A visit to the website generates a new cookie ID. The app, on the other hand, only recognises the device and, with a bit of luck, a mobile advertising ID. Emails track clicks, but not real people. The data set grows, but the knowledge base remains fragmented as the data does not grow together. 
If this fragmented data is fed into AI, the AI has a huge amount of data, but it cannot recognise the patterns that are actually relevant because data that actually belongs together and should be viewed in combination or in relation to each other is dumped separately. As a result, the AI analyses patterns but does not recognise individuals, and the derived personalisation strikes a chord with the recipient by chance, if at all, and now AI budgets are being burned in addition to advertising budgets.
In this state, the dream of intelligent, adaptive, data-driven marketing is hardly realisable.

Why data currency is essential

Many systems still work with batch processing. Data records are reconciled and merged overnight, and segments are updated weekly. But user expectations are not static – they arise in the moment. Someone who found a product interesting yesterday may have already bought it today, yet hours later they are still receiving advertisements for exactly the same product. Simply because the data has not yet been processed and the data record with the product interest has not yet been merged with that of the conversion.
Today, identity must be resolved in real time. It's not just about speed, but also relevance: when personalised offers are displayed exactly when there is interest, marketing doesn't feel like advertising – it feels like good service.

Precision instead of probabilities

The solution is known as identity resolution, which has long been part of many analytics tools. Identity resolution is now being touted as a feature in large parts of the martech and adtech software landscape. Often probabilistic or AI-driven. The sensible counter-model to the often vague assignments is and remains deterministic identity resolution. Here, there is no estimation, only verification. Clear identification signals create a robust profile – across domains, sessions and devices.
The added value is particularly evident where login rates are low: a realistic recognition rate of over 50 per cent for anonymous users is not a vision of the future, but already technically possible. This not only increases conversion rates, but also confidence in data-based decisions.

The silent gap in the martech stack

Many companies invest large sums in specialised tools: analytics, CDP, CRM, personalisation, campaign management. Each of these is powerful in its own right but often without any connecting logic. The data is isolated, and user identity remains fragmented. This results in a lack of ROI and the replacement of the chosen product.
But what is actually missing is a central authority that organises, links and makes fragmented data sets available. A kind of intelligent data layer that complements existing applications instead of replacing them: the company's own real-time ID graph.

A strategic imperative, not a technical exercise

When designing AI strategies, the focus is often on tools, budgets and use cases. What is rarely discussed is the underlying data quality. If you don't have consistent user level data at your disposal, you shouldn't expect miracles from machine learning models.
Identity is not a feature. It is the operating system for all data-driven marketing. And it determines whether promises are turned into results.