AI not only changes Google Ads, but also how Google understands search.
Previously, keywords and locations were central to visibility. Today, too, the intention, context and relevance play an increasingly important role.
With AI, we see that Google is moving more closely towards voice-based answers and recommendations. In practice, Google is more trying to understand why the user is seeking, not just what is written in the search box.
This means that the way we work with Google Ads, SEO and product content needs to be adapted.
Search terms alone are no longer enough
Previously, products could rank well with quite generic descriptions:
- high quality
- good comfort
- modern design
- popular products
The problem is that such descriptions say little about who the product actually fits, or what need it solves.
In AI-driven search results, this type of information is likely to be even more important, but relevance is not only created through text. Google also uses product data and other signals to understand what a product is and who it fits for. Take the following search:
“What running shoes fit for long runes on asphalt if I have knee problems?”
Here, Google needs to understand much more than just the words in the search. In order to make relevant recommendations, the system also needs to understand the needs of the user and the products most relevant.
Relevance and user needs become more important
The difference is made clearly in how products are described. Traditional product texts often focus on general product advantages, while more user-oriented content explains who the product is suitable for, what needs it solves, what situations it is used in and why it is relevant.
For a running shoe, it can be about overprobing, asphalt running, knee loading or long distances.
The goal is not just to rank by keywords, but to give Google enough context to understand when a product actually meets the user's needs. The description is one part of the picture, but Google also uses signals such as product attributes, categorisation and other product data to assess relevance.
Product feed becomes a more important part of marketing
While many still consider product feeds as technical, in practice they have become a central part of marketing.
Google has long used the product feeds to understand what a product is and when it is relevant to different searches. With AI developments, the quality and level of detail of these data are becoming increasingly important.
A good example is “Gore-Tex”. For many, it is just a material, but it can also give Google valuable context about properties such as weather protection, breathing and use.
The richer and more precise product data, the better the basis Google gets to match products with the appropriate searches.
What does this mean for your business?
Too many people have to adjust the current working order. Much indicates that enterprises combining good product data with content that responds to user needs will be stronger in the future.
This often means:
- improving product descriptions;
- enriching the product feeds
- work more strategically with attributes
- Connect content closer to searchintion
- think less in keywords and more in need
Google visibility is increasingly about understanding and meeting user needs. Search terms are still important, but they're only one part of the picture. Relevance, context and good product data are becoming increasingly important to succeed in both organic and paid search results.