Rethinking Paid Modern Search with Our Strategic Approach

To guide advertisers through this complexity, we have developed our framework for Paid Modern Search. It connects business and audience understanding, data and campaign management, and performance measurement into a shared roadmap.

At the core are the different environments in which people search, discover, and prepare decisions: traditional search engine results pages (SERPs), AI chatbots based on large language models (LLMs), and social search. They differ in how people express their intent (e.g., significantly longer queries in LLMs than on Google), how they receive information, and how they engage with offers. People frequently switch between these environments.

The three layers in the model show which aspects companies must consider across these search environments:

• Business & Audience – Set priorities: What do we want to achieve, who do we want to reach, and what role do the different search environments play for these people?
• Data & Signal – Use data deliberately: What information and signals do we need to deliver relevant offers and optimize campaigns toward the desired outcomes?
• Measurement – Measure impact: How do we recognize the contribution of our activities to business success, and what decisions do we derive from it?

These layers interlock: Business goals and target audience understanding set the direction. Data and signals support execution. Measurement provides insights that allow us to improve campaigns and re-evaluate priorities.

Thus, the framework provides a shared foundation to categorize new developments and shape Paid Search from strategic planning to performance measurement. Behind it lies a simple logic: do the right thing, execute with precision, and measure correctly.

These internal assumptions are then validated with external market and usage data. What matters is not only where a target audience is present, but also how they use the respective environment and what role it plays in their decision-making process.

For example, a platform may be relevant for inspiration, while concrete offers are compared elsewhere. Mere audience presence alone is not a sufficient basis for an investment decision. Only the connection between business objectives, target audience behavior, and usage context creates a solid foundation for clear priorities.

What you can do now:

• Understand internally: Bring together business goals, audience insights, past findings, and the existing data and measurement base.
• Validate assumptions: Align your assessment of target audiences and search behavior with external market and usage data.
• Set priorities: Evaluate which touchpoints can contribute to your goals and prioritize targeted tests.

This is where the second layer of our framework comes in: Data & Signal. Proprietary product, service, and customer knowledge helps map this context. This includes product attributes, usage scenarios, or insights from customer relationships. Reliable conversion signals, in turn, show which results are valuable to the business.

This information serves different purposes: Product knowledge describes which needs an offer is suitable for. Customer knowledge complements audience understanding. Conversion signals give automated optimization a direction. The quality of this information thus forms a crucial foundation for precise ad delivery and steering.

Therefore, it is not just about providing as much data as possible. What matters is whether the information is complete, up to date, and consistent—and whether it is used effectively in feeds, campaigns, and optimization goals.

What you can do now:

• Audit data: Review what product and customer knowledge as well as CRM and conversion data already exist and how they are being used.
• Improve quality: Verify information for completeness, accuracy, and consistency. Precisely describe offers, usage scenarios, and specific benefits.
• Deploy and test deliberately: Integrate relevant information into product feeds and campaigns, define high-value conversions as optimization goals, and systematically review adjustments.

Which measurement methods are suitable depends on these questions and the available data foundation. Incrementality testing, attribution analysis, and marketing mix modeling offer different perspectives. The key is selecting methods that fit the specific question and evaluating their results holistically.

To translate these insights into concrete actions, organizations need not only technical infrastructure but also a shared understanding of success and clear ownership. A reliable testing and measurement setup helps allocate budget where it delivers the greatest impact.

What you can do now:

• Define success: Determine which business outcomes matter and which decisions the measurement setup should enable.
• Align measurement foundations: Establish consistent definitions, data sources, and ownership, and connect platform metrics to relevant business outcomes.
• Evaluate impact: Select appropriate tests and analyses to assess the contribution of activities and derive budget and optimization decisions.

The three areas of action form a continuous loop. Insights from measurement may reveal that a touchpoint is used differently than assumed, an optimization goal yields little insight into business success, or crucial information is missing.

The framework helps categorize such questions and address them at the right point: in priorities, data and signal quality, or performance measurement. In this way, new search and advertising environments can also be evaluated using the same fundamental questions.

Want to dive deeper into this topic? In our webinar “Modern Search Unpacked,” Samuel Kirchhof, Head of Performance Marketing at Webrepublic, explained the framework with practical examples.

Watch the (German!) recording here.