Data strength: The fuel that unlocks the potential of AI
Insights from Google, with Claes Eriksson, Head of Measurement, Google Nordics.
AI creates new opportunities for companies to streamline marketing, improve the customer experience and make more data-driven decisions. But the value of technology is largely determined by the quality of the available data. Claes Eriksson, Head of Measurement Google Nordics, describes how companies can build a strong data foundation to get better results from AI, strengthen their measurement capabilities and lay the groundwork for long-term growth.
There is no doubt that artificial intelligence (AI) is a revolutionary technology for companies seeking competitive advantages in a changing world. But while most marketers and business leaders focus on the AI models themselves, many forget what actually drives these systems. The truth is simple but decisive: strong data has a dual role to play today. It is both the necessary foundation for activating and optimizing advertising in a new AI era and the absolute prerequisite for being able to measure and evaluate marketing investments reliably, as well as for the ability to create more advanced applications in marketing. Without a robust data foundation, AI optimization becomes ineffective and measurement misleading.
The efficiency of AI technology is directly linked to the information it learns from. In today’s digital landscape, in which privacy regulations and the phasing-out of third-party cookies limit traditional tracking, a company’s own first-party data is its most valuable asset. When this data is combined with AI, advertisers gain access to outstanding pattern recognition and predictive insights, tailored to unique business goals. To succeed fully, organizations must break down internal silos and connect their data, teams and business results. For this reason, we are presenting a concrete four-step plan to build a strong data structure, activate your campaigns and measure your return. Strong data is the foundation for both successful AI activation and accurate measurement.
Four steps to greater data strength and growth
1. Connect your data sources
The journey begins by establishing a secure and stable pipeline for your data sources to the advertising platforms. The goal is to simplify and unify a traditional fragmented landscape in which valuable information is isolated in different silos.
“Enhance your data strength by replacing older third-party tags with modern first-party solutions for data collection. This allows you to manage your tracking in a secure, sustainable and privacy-friendly manner, directly via your own domain.”
“Centralize the data, gather all sources in one place. Use tools that simplify this process through ready-made workflows for direct connection to advertising systems, which facilitates management.”
2. Maximize data quality
When the data is flowing freely, you must ensure you are gathering the right signals. AI tools are only as smart as the data they are fed. If the data is incorrect, the AI learns the wrong lessons and optimizes for incorrect results, which becomes a silent killer of efficiency.
Marketers should focus on four central types of signal:
Build your data strength by integrating hashed customer data (such as encrypted email addresses or phone numbers) directly into your advertising infrastructure. By sharing this type of first-party data, AI models can more accurately match and attribute conversions to different devices and platforms.
Transaction and behavior signals: Measure the actual value of a purchase to prevent it being counted twice, and gain deeper insights into the customer journey on your website so that AI can optimize for your most valuable business goals.
3. Activate your data and AI
High data quality is the key to optimally activating AI and transforming your signals into actual business results. High data quality functions as critical input for Google’s AI and makes it possible to reach the right customers more efficiently. It is a question of letting AI use your own customer signals to steer bidding and target audience selection in completely new ways.
Target audiences: Find your most valuable customer segments and expand reach to similar target audiences.
Bidding: AI-driven bidding uses your data to identify new keywords and channels, optimizing the budget in real time.
Channel optimization: The data enables sophisticated attribution models that fairly distribute value to every touchpoint in the customer journey.
4. Demonstrate effect through modern measurement
The real effect of your data strength becomes clear in measurement. Like before the AI revolution, strong data remains the foundation for understanding what actually works and for measuring investments more effectively. By going beyond simple last-click metrics, you can measure the business-critical key performance indicators (KPIs) that actually matter, such as sales and profit. A modern measurement toolbox combines three interacting components.
Attribution: Advanced attribution models, such as data-driven attribution, help you understand which media touchpoints contribute most to the customer journey.
Incrementality tests: Through controlled experiments (such as geo- and conversion tests), you can measure the actual, incremental effect of your campaigns and see if an ad actually caused a conversion.
Marketing Mix Modeling (MMM): Statistical models analyze investments against sales, which helps you optimize budgets across all channels, both digital and physical.
The way forward is to invest in future growth. Building data strength is not just about fine-tuning ongoing campaigns, it is about strategically preparing the organization for the future. AI anchored in a high-quality first-party data strategy drives intelligent optimization, ensuring that every invested krona yields maximum benefit. For forward-thinking business leaders, superior data strength is no longer an option – it is an absolute prerequisite for unlocking the full potential of modern AI and driving profitable growth.
A decisive factor for building strong data and robust measurement is the organization. We often see that the infrastructure for measurement and data strategy is quickly reduced to purely technical questions and decisions. Even though technology is a prerequisite, decisions must always be based on business priorities and commercial value. To maximize marketing return on investment and justify investments, close collaboration between marketing and IT is required, with both parties sharing a common understanding of both business goals and technical choices.
By taking control of your own data strength, you stop guessing and start investing. This is not just the key to getting the most out of AI – it is the only way to prove marketing’s actual value on the bottom line.