If you are like us, you are surely torn between a certain annoyance at the number of soliciting publications on ChatGPT and AI (we see you as a LinkedIn influencer) and the beauty of what the tool already allows you to do... not to mention the arrival of Bard, Google's AI!
Where the future is now being built in terms of traffic acquisition is that big search engines are already working on integrating AI capabilities into query processing.
Bing, Google... you don't need a crystal ball to see that the ranking in the SERPs will change significantly, as well as the way to (well) advertise on certain keywords.
AI and the evolution of search engine results pages (SERPs)
With the integration of advanced AI technologies like GPT (Generative Pretrained Transformer), search engines are becoming more and more sophisticated in their understanding of content and search queries.
Let's take a complex query like “easy dessert recipe without lactose and gluten free for 4 people“.
An AI like GPT will be able to understand this request in a more profound and nuanced way, taking into account things like the user's level of cooking skill, the exact number of people to serve, and the user's implicit food preferences.
For example, it could understand that the user is looking for a recipe that is not only lactose and gluten free, but also suitable for a beginner in cooking (since the user requested an “easy” recipe). It could also understand that the user needs a recipe for exactly 4 people, which could affect the amounts of ingredients suggested in the recipe.
At this stage, you will tell us, we are still very close to what we Google can do today... but what comes next makes the difference:
Thanks to AI, a search engine will be able to understand the undertones or intentions implicit in the request. For example, if there is a trend in the user's searches that suggests an interest in vegan recipes, the search engine will take into account this element, favor vegan dessert recipes, even if the user did not explicitly ask for “free of animal products” in its original request.
This means that SEO strategies are going to have to yet evolve. Traditional keyword optimization techniques remain important, but it is also becoming crucial to understand and respond to the intentions and subtle contexts of users. Les high-quality content that meets these needs will likely have an advantage in the search engine rankings.
The integration of an AI like GPT or Bard could therefore allow for a more nuanced understanding of content and search queries, by being able to manage natural language in a more sophisticated way and to take into account more complex contexts.
Another example: an e-commerce search
Let's say a user is looking for the “best beginner SLR camera.”
At present, he can already identify the main keywords very well: “best”, “camera”, “SLR”, “beginner”... and provide a list of products or blog posts that recommend different SLR cameras for this profile.
With more advanced AI, the search engine could go further. He will be able to understand that the user is probably new to photography and promote results that explain camera features in a simple and accessible way. It could also understand that the user is probably interested in a camera that offers a good balance between image quality and ease of use.
In addition, AI is able to take into account more contextual information.
For example, if the user previously searched for”photography courses for beginners”, the search engine could favor cameras that are recommended by photography course sites. In the case of seeking information on a specific type of photography, such as landscape photography or portrait photography, the engine will be able to bring up cameras that are particularly suited to this type of photography in the first place.
Finally, AI will be able to rely on information about user buying behavior to refine results. Quality after-sales service, warranty period...
As you can see, the integration of more artificial intelligence into the way current search engines work, coupled with a search history that allows a query to be contextualized and linked to a user profile, will change the online experience... and through it, the type of organic and paid content to be developed.
What are the impacts on sponsored results?
Search engines and advertising platforms are already using AI and machine learning extensively to understand user intent, personalize search results, and target ads.
However, as these technologies advance, their ability to understand and interpret nuances and context is going to get thinner and thinner.
In the context of sponsored results, for example, advances in AI will be able to allow for even more granular personalization and more precise targeting.
Imagine, for example, a system that could not only target ads based on a user's interests and buying behavior, but also depending on factors such as the mood of the user (inferred from its online interactions), the time of day, or even global or local events in real time.
In addition, AI will also be able to help optimize the creation and presentation of the ads themselves. For example, it can be used to test different versions of an ad and determine which is the most effective for a specific user or user segment.
It will also be able to help determine the best time to present an ad to a user, based on their online behavior and content consumption habits.
What about the choice of keywords to target?
It's a safe bet that bidding strategies will change. The increasing emphasis placed by AIs on understanding context and personalization could mean that approaches based on “short tail” or generic keywords may become less effective.
- Towards a long-tail bidding : with AI able to understand and respond to increasingly specific and contextual queries, advertisers will surely find more value in focusing on “long tail” keywords. These keywords, which are often less competitive, can target users who are further along the buying journey and therefore more likely to convert.
- Context based bidding : with increasing contextualization, it will surely be beneficial for advertisers to think beyond the keywords themselves and to consider more the context in which these keywords are used. This may mean tailoring ads based on factors such as the user's location, time of day, or the type of device used.
- Custom bidding : As AI allows for more granular ad personalization, advertisers will also be able to start customizing their bidding strategies. Example: being prepared to pay more to reach users who have shown a particular interest in their products or who have a history of previous purchases.
To go further: https://searchengineland.com/ai-powered-search-paid-placements-395084
What do you think? Are you already thinking about an organic and paid acquisition strategy adapted to the changes we are experiencing today?
