User intent is the purpose behind a user’s search query. It can also be thought of as the user’s underlying objective or goal. The task they are trying to accomplish through their search. 

When we talk about personalizing the customer journey, we tend to focus on channels like email, chat, and website. Or the things we’re saying to our customers. But tapping into what they’re looking for is a powerful way to connect with your audience. When a customer comes to your site and conducts a site search, that search shouldn’t just turn up content that matches the words they put in the search box. It should match the intent behind those words.

Businesses are looking for new ways to deliver tailored results — aka personalization — that meet individual customer needs. Dynamic search that leverages user search intent can help them achieve this. 

So let’s learn a little more about user intent, and how AI search helps satisfy it.

What Is User Intent?

In site search, user intent — also known as search intent — is an expression of the user’s goal when performing a search. 

It manifests, in part, as a keyword search query. But this is only one facet of user intent because that search query can be understood in different contexts — what was said is not always what was meant. Those contexts include informational, transactional, or something else entirely. 

The query is an external expression of the user’s need, and it can be imprecise.

A search engine is tasked with identifying and categorizing what a user wants based on four common types of search intent:

  • Informational intent: An informational query tries to find informational content about a topic that may or may not be tied to a specific product or service. An informational keyword search can be broad or specific (e.g., “cake making tips” versus “how to bake a red velvet cake”).
  • Commercial intent: Search intent is focused on finding information about a specific product or product type with the intent to eventually buy it (e.g., “best hiking boots”). 
  • Navigational intent: This type of user intent focuses on finding a specific site or page.  Navigational searches typically target a destination like a social media login page, hub site, or specific website.
  • Transactional intent: The intent with a transactional search is connected to the user’s readiness to buy or convert. They have done their research and are at the decision/buying stage (e.g., “buy Apple AirPods”).

Why User Intent is Tough to Discern

Search intent is often ambiguous, making it difficult for a search engine to personalize a visitor’s experience by recommending relevant content. For example, a user may search for “laptops” with informational intent (they want to learn more about laptops) but then click on a laptop page and buy one, showing transactional intent. 

Differentiating transactional searches from an informational search is one of the things that makes creating a targeted search engine optimization approach (or SEO strategy) so challenging. Providing relevant content for a certain search term can be difficult without more context around why someone might be searching for that keyword.

A graphic illustrates the different kinds of content that can be surfaced to help satisfy different user intents.
When someone searches for “mountain bikes,” are they looking to buy? Are they researching how to fix one? Or do they want to sell one? User intent is often ambiguous.

Understanding user intent helps search engine algorithms serve up the most relevant content for a specific keyword or user query. It also helps with search intent optimization, enabling you to better plan your website content strategy. Traditionally, search personalization required historical data, user profiles, and more.

But what about when you don’t have much information about a user? This is where session-based personalization can address challenges when it comes to interpreting intent, including:

  • User intent can change over time. Without historical data, it’s difficult to understand where a user is in their buying or informational journey since the algorithms can only see what is happening in the current session. 
  • As the “laptop” example demonstrated, keywords can have different interpretations. User intent doesn’t always equal purchase intent when someone searches for a product.
  • User signals can be influenced by external factors like location (e.g., local intent), time of day, and device type which can make user intent around a specific search term or keyword query hard to interpret. 
  • A user’s search intent tends to be informed by their past search activity, browsing behavior, and interactions on external websites, but this data may not be available.

During a singular search session, there is still data — and that can be used to personalize their session. 

AI search can provide dynamic personalization that adjusts to a user’s search results based on their behavior (e.g., behavioral data), interests, and search history. This can be referred to as “small data personalization.” Rather than relying on large amounts of user data, AI search combines things like user intent, keyword phrase analysis, journey analysis, predictive analytics, and natural language processing to produce a customized search result in real time.

Personalization happens when the search engine studies in-session user-specific signals to provide individualized search results and recommendations. 

Personalized search looks different for every line of business, but the goal is always to deliver the most relevant results possible based on an individual customer’s search intent. Here are some use cases that demonstrate what that might look like across different industries.

What small data personalization looks like across industries

Healthcare: A user visits a healthcare provider’s website but isn’t a patient yet. They browse provider pages, scan the page about what insurances are accepted, then do a search for some symptoms they’re experiencing. Customized search results could include a list of providers who accept their insurance, articles and resources about their symptoms, and links to FAQs covering topics the user was browsing about. 

Retail: A shopper visits an online retailer’s website for the first time. They browse the shoe selection, click on a product page about running shoes, then do a search for “running shoes”. Customized search results could include running shoe selections that match their preferences and user intent, along with recommendations for related items like socks and apparel.

Car Insurance:  A user visits a car insurance website and does a search for “auto insurance”.  Customized search results could include an overview of coverage types, tips for saving on premiums, and recommendations based on the user’s location. 

Workplace: An employee visits their company’s intranet and does a search for “employee benefits”. Customized search results could include geographical area, user-specific benefits, FAQs about their specific role or department, and relevant forms. 

Service: A potential customer visits an auto service website and does a search for “oil change”. Customized search results could include locations near the user, company reviews from customers who’ve gotten oil changes, and recommendations based on the user’s make or model (if this information has been entered as part of the search).

6 SERP Features to Satisfy User Search Intent

Session-based personalization provides a unique opportunity to personalize for user intent without requiring personal information or historical data about a user. To achieve this, a dynamic search personalization engine looks at the following criteria:

UI Feature: Auto-complete and auto-correct

User signals can include query suggestions using autocomplete and autocorrect. The search platform does this  by using the context of your website and analyzing browsing history and other signals like search queries from the same session. 

For example, if you sell craft supplies and a user who’s been browsing knitting patterns begins typing “cot” the query might autocomplete as “cotton yarn for knitting.”  

A search bar's drop-down provides options to complete an unfinished or incorrectly spelled query.
Autocomplete can help relieve some of the cognitive burden on the target audience by suggesting relevant, popular choices.

UI Feature: Predictive categories

User browsing behavior also informs user intent and provides clues about what the user is looking for. An AI-driven personalization platform can use machine learning to suggest appropriate categories based on a user’s query. 

For example, if someone’s been reading about launching a social media campaign on a marketing agency’s website, then searches for “paid facebook ads” the system could suggest they visit  the “Paid Media Planning” category to find more information. 

Facets are another search feature that users have come to expect in their search experience. They’re also hugely beneficial for making navigating websites on mobile much easier. They work by filtering broader search queries into more specific categories and can be displayed as relevant tags – or facets – alongside the user’s search results. 

Search facets help users quickly narrow down search parameters to find what they want. For example if a user searches for “snowboard” they might see faceted search tags like “size”,” price”, and “category. 

A graphic illustrates faceted search.
With intelligent facet generation, facets can be generated and shifted according to a unique user’s behavior, making it even easier for them to refine their results.

UX Feature: Natural language processing (NLP)

NLP goes beyond search intent signals by using relevant information to derive actual meaning from queries, recommend synonyms, and matching phrases. 

Screenshot depicts a natural language search query with relevant results.
A natural language search query allows the user to “speak” with search the same way they’d talk to another human. 

UX Feature: Time and location

The user’s current location and time of day can influence user intent. By understanding the context of time and place, it’s easier to determine what content might be most relevant for that user. For example, a user in Israel who is searching for round trip airfare to South Africa on an airliner’s website would get different results than a user conducting the same search from California.

An image depicts transactional query results.
With AI search, websites can match query intent to the unique user performing a search.

UX Feature: User clustering

AI search can also make an “educated guess” about user intent by using refined similarity models to predict a user’s intent from their in-session real-time browsing behavior. 

The system applies a temporary ID to anonymous users (e.g., people who aren’t logged in), and analyzes the information gathered during the session. 

For example, a contractor on a home improvement retail site may be reading about how to ship industrial materials and then do a search for “tool rental”. The results would be geared toward professional contractors (e.g., pro equipment rentals versus DIY rentals).

Give Users What They Want With AI-driven Relevance

By leveraging intent signals from the user’s current context and activity on your website, dynamic search personalization provides users with relevant, personalized results without relying on user profiles and historical data. This not only helps you meet rising customer expectations around good online experiences, it opens the door for new opportunities around user engagement, interaction, and conversion. 

By combining user intent signals with AI-driven user personalization, you can deliver an improved user experience on your website in real time without massive user data sets and complex workflows. You’ll have the ability to provide a truly personalized search experience for your visitors that meets their needs more quickly and accurately than ever before. 

Dive Deeper

Wondering just what relevance looks like? Take a closer look — here’s why we believe relevance drives greatness.

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Relevance is giving the right experience, to the right person, at the right time