How ChatGPT and Perplexity are Rewriting the Rules of Search Intent

Search intent was previously easier to comprehend. The user entered a query on a search engine, went through a page of search results, and selected a link that seemed to meet their needs. Marketers categorized the queries into informational, navigational, commercial, and transactional intent and built content for all these different types.

With the development of conversational AI, people are able to ask for solutions, seek clarification, provide additional context, challenge a solution, and make amendments to their initial request during a single interaction. This makes the search intent not necessarily limited to a single short query and can evolve throughout the course of the conversation.

The Evolution of Search Intent into a Dialogue

Typically, traditional searches require consumers to express their requirements through keywords. An individual searching for customer retention strategies would do something like “customer retention strategies B2B” and proceed to assess the options available. This is the principal way through which marketers determine what the individual is after.

Conversational searches operate differently. An individual seeking customer retention strategies would start by asking how the business could cut down on customer churn before providing further information regarding the industry, business, customers, and problems faced. In the following step, they may ask what strategies will be feasible given that the company has a small sales force.

From Keywords to Problems

With the shift described above, marketers will have to consider another topic of study. Keywords remain important, yet they do not provide a full understanding of what a consumer seeks. The question of the problem that lies under the surface of a consumer’s query becomes crucial.

Very often consumers do not seek information just for the sake of getting information. They can make an attempt to select a certain technology, justify an investment, get some insight into changes in the market, solve some operational problems, and convince other stakeholders. In order to be effective, marketing content should meet those needs rather than focus on keywords of queries.

Several Intents in One Query

Often a conversation-type query includes multiple intents. A person asking about CRM software may need just a definition at first glance; yet the same query implies the desire to compare technologies, figure out what the requirements are for implementation, calculate the price, and see if the technology is appropriate for a certain type of business.

The described issue makes marketers think twice before creating content based on a single keyword. Content created to solve the whole problem can be much more valuable.

Many Intents in One Query

The simple query can have many levels of intent behind it. The person who asks for the definition of CRM software may be looking for an explanation of the matter at first. But this same query can be used to learn something about the platform comparison, implementation process, cost estimation, and whether the technology is suitable for specific business needs.

This poses some difficulties for classic content strategy. While a page targeting one keyword can give an answer to the basic question, the content built on the topic of making a decision can handle all other issues too.

How Follow-Up Questions Expose the True Need

One of the key distinctions between conversational AI and search is how follow-up questions are handled. The person doesn’t have to start a brand new search once their first answer sparks yet another question. They can keep the conversation going.

This means that marketers need to think about follow-up questions when writing content. For example, if a piece of content talks about a certain strategy, then it also needs to talk about when that strategy would work, when it wouldn’t work, what it needs in terms of resources, and other options available for it. The idea isn’t to provide all possible answers in one big piece of content.

Example: How One Search Reveals Multiple Intents

Consider a B2B buyer researching cybersecurity software:

• Initial question: “What does endpoint security do?”

• Follow-up: “How can it protect a remote workforce?”

• Context: “We have 150 employees working across three countries.”

• Comparison: “Which endpoint security features should we compare?”

• Decision: “What should we check before choosing a provider?”

The conversation starts with an informational question but gradually reveals problem-solving, comparison, implementation, and decision-making intent. The buyer's underlying need becomes clearer with each follow-up question.

The New Search Process is No Longer Linear

The typical search process usually follows a funnel-like approach. The consumer begins by asking an information-based question, proceeds to comparison, considers solutions, and finally searches for the product or service provider.

A conversational artificial intelligence system makes the process shorter and even more non-linear. The consumer jumps from a general question to a more comparative question in a matter of minutes. The user is able to ask for pros, cons, alternatives, implementation, and purchase-related questions all in the same session.

This does not mean that the consumer’s journey ends. It just means that the process through which marketers must supply consumers with information is different.

Content Must Show Understanding

As search gets conversational, general information loses its relevance. The buyer can make an AI system provide an explanation of a simple concept within seconds. Thus, the content should offer something more than just a superficial definition.

Understanding is shown by practical information, original analysis, examples, models, and application in business. It should help the reader grasp not only the essence of the concept, but its importance, the time and situations when it must be applied, and the decisions it impacts.

It is crucial for brands that want to establish themselves as experts. The goal is not only to get a spot on a search page for a certain keyword. The goal is to become an authoritative resource providing information to support buyer queries.

Content Planning Needs to Begin with the Intent Network Approach

Instead of developing content for separate keywords, businesses can focus on mapping out questions related to one business problem. The main keyword serves as the center, while secondary questions show the various angles from which the buyers look at the main topic.

For instance, a business that is producing content on the topic of cybersecurity software can consider questions on such issues as risk assessment, implementation, user acceptance, compliance, vendor selection, pricing, and management. 

This approach allows for more effective content development because marketers will not have to repeat themselves in developing content for the same search phrases but will fill the knowledge gaps of the buyers.

The New Objective is to Be Helpful Before the Customer is Ready to Purchase

Searches using conversational search techniques reward a more complete knowledge base of what customers need to know. Customers will not be ready to buy at the first mention of a topic; they are likely only trying to find out whether it is an issue that requires any action.

Here lies the possibility for brands to create brand awareness through helpful information. If a brand is known to always help customers understand difficult topics, then its expertise is tied to the topic long before even the discussion about buying happens.

Search intent is no longer something solely for the SEO department. It is an insight into how customers perceive their problems and come up with solutions. With ChatGPT and Perplexity leading the shift from keyword searches to conversations, it is now easier than ever to change intent between questions.

But the answer for B2B marketers is not to turn their backs on keywords. It’s to look past them. Recognize the issue behind the search, the questions that come after the search, and the decision being made by the prospect. Then produce content to help make that whole thought process work, not just to answer the actual search term itself.

The brands that evolve will do more than optimize for what people are searching for. They will produce content based on what people are trying to learn. This is where the evolution of search intent is taking things: from the start to the full conversation.