What is User Intent?

In relation to Google and search engines, user intent is the most important factor when trying to rank for a product or service keyword online. In the early days of Google SEOs would use backlinks and plain text updates to rank for specific keywords. Now that search engines have become more sophisticated, they have incorporated these concepts of “user intent” into their keyword ranking algorithms.

Understanding how people search for a particular product or service, the “user intent” is often linked to one of the four types of keyword intent. These include the Navigational, Commercial, Informational & Transactional keywords commonly used to reach a specific goal or function. In Search Marketing, the type of keyword intent measured as well as the actions taken on a page can be used to define the successful alignment of user intent.

Queries with Multiple Meanings

Query interpretations can be classified into three areas depending on how common or competitive the queries are. Dominant interpretations of a query should be clear, while Common Interpretations can have multiple meanings. Minor Interpretations cover most if not all of the less common queries, particularly relating to local search queries.

Dominant Interpretations

This is typically what most users meant when they performed a specific search query. An example would be “Buy New Nike Shoes”, where the intent is clear to purchase Nike Shoes, specifically new ones. Most, if not all of the search results page would be saturated with Nike’s latest releases as the anchor for intent (with the exception of Google Ads).

Common Interpretations

Every query can have multiple Common Interpretations, where Google is unable to provide a result that meets the certainty threshold to dominantly fulfill search intent so the results become varied estimates of likely interpretations, or intent. This occurs with complex keywords that have multiple meanings.

Minor Interpretations

These are the least common interpretations, typically dependent on local or niche search terms. An example could be “T-Shirt stores near South Jersey”, where the query is specific enough that it could almost be taken literally.

Do Know Go

Separating search queries into relevant action categories can be helpful for standardizing the type of results that are delivered on Google. The Do, Know, Go concept is a way of segmenting query types based on the types of goals associated with them. The interpretations of search intent are not explicitly stated by the search engines but must be reverse-engineered by testing the limitations of the SERP.

Do (Transactional Queries)

Users are looking for specific action
Purchase keywords are common in this category such as “Buy”, “Shop”, or “Compare”
Include content such as Color, Size, Material & Type of Product on PLPs (for eCommerce sites)
Includes transactional keywords such as “Lock installer”, “Amazon Coupons”, or “Cheap Vacations”
Implies an action with a tangible result, not a question like “Do Red Dresses Make Mermaids Look Silly”

Know (Informational Queries)

Queries related to information seeking, research or reviews from legitimate sources such as news articles
Includes terms such as “Who is Donald Trump?”, “What is TayAi?”, “How to build a Bird House”
Occurs when someone wants to “learn more” about a subject
Strong correlation with “micro moments”, when a user needs to satisfy a specific query there and then, with urgency or time as a dominant factor. Examples would be stock prices or transit delay information.
Queries are nearly always Informational in intent, regardless of urgency
Can be simple or complex queries, such as “Adam Sandler imdb”, or “How long does it take to watch every Adam Sandler movie in existence”
Frequently Asked Questions or Contact Information would be considered a Know query

Go (Navigational Queries)

Identifying high-level page intent, with specific brands or websites included in the keywords to solidify the appropriate user experience, such as “Nike shopping cart”
Content includes brand or known-entity content that is easily recognizable and trustworthy
User intent is defined by navigating to the correct destination, such as “Official Nike Website”
Easiest queries to win after establishing brand awareness
Can be used as a measure of performance for brand SEO

The search engine components of keyword intent combine psychological elements of common communication to help design the algorithms behind the calculation of “user intent”. Standardizing the intent of web pages will future proof from any algorithm updates so long as the content remains truthful and relevant. Using this framework is helpful for optimizing pages that may seem ambiguous or irrelevant to the target keywords set out by the search strategist. User intent measures can be used to troubleshoot, diagnose and optimize pages for conversion rate optimization as well as organic search optimization.

How can User Intent inform Product Landing Pages (PLPs)

Encourage user engagement
Decrease time to purchase
Promote product discovery

Defining the “Commercial” High Intent Keywords

Optimizing the PLPs for user intent across large eCommerce web design platforms can take time, especially when technical debt is involved. Treating each PLP and PDP with the appropriate SEO attention, keeping search intent as the focal point of supporting each customer journey will improve the performance of any eCommerce website.

What Defines the “Informational” Research Intent Keywords

Optimizing the PLPs across large eCommerce web design platforms can take time, especially when technical challenges are uncovered during PLP updates. Treating each PLP and PDP with the appropriate SEO attention will maintain the performance of any eCommerce website.

What Defines the “Transactional” Purchase Intent Keywords

What Defines the “Navigational” Search Intent Keywords

Breadcrumbs are one of the easiest ways to display parent or sub-categories that the PLP belongs to. When the site architecture is set correctly and PDPs have appropriate tags, there are fewer technical debts when deploying new PLPs. This allows for a cycle of optimization using keyword research, user data, and behavioral metrics to continuously build new PLPs based on trending relevancy.

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