Ultimate SEO Guides - User Intent
User intent has not always been seen as important in SEO, but this has certainly changed in the past few years. At Authoritas we have user intent within our software to help you tailor your content to the buyers’ stage, whether they are just researching for a product or service or if they are in the position and ready to purchase from you.
We seem to have talked about User Intent a lot this year and will no doubt be doing the same next year as we plan to roll-out some advanced new tools to help SEOs produce content that Google will rank well!
In February, Laurence O’Toole shared how the Authoritas team thinks about User Intent and why we avoid calling it ‘Keyword Intent. This blog post is a good primer and answers many of the common questions around User Intent.
In May 2020, we released new user intent features within the keyword ranking module of our software to help you tailor your content to the buyers’ stage and deliver the content Google wants to serve for a specific user intent.
We touched upon user intent again in our recently published on-page SEO guide and we delved into it even further when Ben Garry, Dave Davies and Ric Rodriguez joined us for Tea Time SEO on User Intent in October 2020 and shared their insights with us.
Ben Garry and Dave Davies joined us for Tea Time SEO in 2020 and shared their insights with us. Ben touches upon the different types of user intent, whereas Dave shares how to use the knowledge behind what user intent is and how to make better predictions about how it’s used by the search engines.
A big thank you to our co-authors for giving up their time and sharing their expertise with us.
Watch the episode again
Chapter 1. Introduction and Beginner’s Guide to User Intent
What is User Intent?
User intent, also known as search intent, is the human purpose behind the text of search query. In other words, a user’s intent is what they are hoping to achieve from their search.
Taking user intent seriously means recognising that search queries are not the end goal of a search. The search terms someone enters into Google or Bing are simply a way of achieving an end goal like buying a new pair of shoes or finding the answer to a question that’s been on their mind.
When we talk about ‘meeting’ or ‘satisfying’ user intent, we mean putting content in front of users in organic search that helps them to achieve their end goals. While we only go into intent for more traditional search in this guide, the principles of user intent apply more widely to other search engines, such as Amazon and YouTube.
Why is User Intent important in SEO?
User intent is important for SEOs to take seriously for at least two reasons. The first is that it’s increasingly difficult to rank well in Google without meeting it. Google’s goal is to give human users the most satisfying search experience so that they keep coming back for more.
The second main reason to pay attention to user intent is because meeting user intent is vital if we want our visitors to engage with us in a meaningful way. If someone lands on your site via Google and can’t find what they’re looking for, they’ll bounce back to the search results. However, if you can show them what they want to find, they’re more likely to stay on your website and convert.
Since the Panda update in 2010, a series of algorithm updates have increased the quality of the content that Google surfaces, making Google much better at unpicking a searcher’s language and context to identify what they’re trying to achieve. Now, with natural language processing souping up its algorithms further, Google is excellent at showing results that meet user intent.
The bottom line is that if you try to optimise a landing page for a group of keywords without addressing their underlying intent, your landing page simply isn’t going to rank well. If you want high rankings and, therefore, higher traffic, you have to take the intent behind your target topics seriously.
The 3 (well, 4) main User Intent categories
Google openly acknowledges three broad categories of user intent: Do, Know and Go.
The wider SEO industry tends to refer to these categories as transactional, informational and navigational. Many SEO resources and tools also include a fourth main category: research (or commercial research).
These four categories are not the only facets of user intent to be aware of, but they’re a start. In reality, there are countless nuances almost as varied as search queries themselves. However, understanding the distinctions between these four is all you need to start thinking about user intent strategically.
Transactional searches are those that have the end goal of a commercial interaction, whether that’s buying a product or getting a quote. These searches tend to be the most competitive and the highest priorities in an SEO strategy. Shopping ads are a great indication of transactional intent.
For product searches, ecommerce categories and product pages dominate. Transactional intent for services can be trickier, but the service providers usually rise to the top alongside a few articles. Watch out for blog posts and informational content on your own site that could compete with your service pages for these rankings.
Though not formally recognised by Google, commercial research is another category, as the types of pages that rank for these terms don’t really appear in other categories. Commercial research refers to users’ desire to compare products and services, typically to find the best-reviewed or the cheapest options. This type of search is often dominated by industry publishers and comparison sites, and is very difficult for the selling sites to break into.
Commercial research is often dominated by industry publishers and comparison sites, and is very difficult for the selling sites to break into.
Informational intent is a broad user intent category, encompassing all types of questions and research that don’t have a direct connection to a commercial interaction. Featured snippets, People Also Ask boxes and non-branded Knowledge Panels are all common signs of informational intent, but there are countless other specialist features that can pop up, such as news stories, lyrics and even Pokemon!
Topics with informational intent rarely result in immediate conversions, but can still be valuable for businesses looking to engage their audiences early on so that they’re more likely to make a purchase later.
Any site can rank for informational searches as long as they have some authority on the topic. You should prioritise blogs and other longform content for these terms, as ecommerce pages almost never rank and service pages will have a more difficult time.
Navigational searches are used to getting searchers to a known destination. A common example is someone searching a company name to get to the website because they don’t know the URL or because searching is quicker. Knowledge Panels belonging to companies and managed through Google My Business are common for this type of search. Map Packs are also common with people looking to find a known physical destination. It’s vital that the correct business locations appear in Map Packs for their own navigational searches.
In most cases, the most well-known company of the searched name ranks. Controlling navigational search gets tricky if a larger company shares your name or has a similar name.
Nuances to be aware of
User intent is infinitely more varied than those four broad categories can demonstrate. The majority of topics have mixed intent, with elements of different categories ranking highly, which means that there is no substitute for digging into individual topics to understand what each entails.
Some tools, like Authoritas, help you to understand how the four broad categories interact over real search results, as this example from an online art marketplace shows:
Important nuances also exist within the individual categories themselves. When working on Nottingham law firm Richard Nelson LLP’s tax investigation offering, Ben realised that there were big differences in the types of informational keywords he could target.
The search term [tax investigations] is largely informational, with a People Also Ask box appearing high in its results, and we were able to get a general tax investigation FAQ page ranking top with sitelinks. Incidentally, their main tax investigation service page has never ranked for this term because it’s too commercial in nature and doesn’t match the intent that Google has identified.
Ben soon realised that although the FAQ page performed well for broad terms, it didn’t rank well for its individual questions because Google wanted to show users more detailed information than a broad FAQ could provide. This was a significant nuance in the type of informational intent we were dealing with.
In response, he created new pages for the most popular of these individual questions and linked to them from the FAQ page. Lo and behold, they started to see better rankings for those pages because they matched Google’s nuanced informational intent much better than the FAQ page did.
Interestingly, nuanced search intent also appeared to affect the main tax investigation service page. Ben and his team only started ranking well for keywords like [tax investigation solicitors] once they created this underlying informational content and referred to it on the service page. Although it was important to have a page with commercial intent so that we could actually convert transactional users, it became evident that Google still required a measure of informational quality to rank for transactional searches. We suspect that the sensitive nature of the topic contributed to its greater need for quality information.
Spotting user intent in the wild
There is no substitute for doing your own search result research when optimising for a particular topic. The organic results, ads and SERP features that surface for a query show you what kind of intent Google has identified, and therefore the intent you should aim to meet on your landing page.
Take the below example of the query [running shoes]. The language of the query itself doesn’t tell us what intent to expect – it could be any combination of transactional and informational. The search results give a clearer picture.
There are signs of Google recognising a transactional intent behind the query in the top ranking site – an ecommerce category page – and the shopping ads on the right. Thus, your category page should be your main landing page for this query and similar, not your running shoes guide.
That’s not the end of the story, though. There is also a local map pack showing places near me that sell running shoes. This local result is a nuance to the transactional intent, and suggests that Ben may be able to gain more visibility if Ben optimises his physical store page, or that his ecommerce pages may be more likely to rank in places close to his stores or offices.
How to start thinking strategically about user intent for SEO
Having identified the intent for a topic, you can now begin to consider where that fits into your SEO strategy. If your focus is driving more users to ecommerce categories and products to generate sales, you know to look for topics and keywords like [running shoes] that have a strong transactional intent.
An awareness of search intent also enables you to think about how to attract visitors to our site who may not be ready to convert. Ben’s tax investigation example earlier showed how it’s possible to target queries with different kinds of intent that are all related to the same service, helping our site to appear at every stage of the customer journey, from awareness through to action.
Ben’s primary recommendation for a strategic use of user intent is to identify opportunities where your site is well-positioned to fully satisfy the intent of the searcher behind the query. This was Impression’s approach to using Richard Nelson LLP’s tax law expertise in satisfying informational intent for that topic area.
Then, over the long-term, consider how your site can satisfy different kinds of intent spread across the customer journey, remembering that it may take many touchpoints and minor interactions before someone is ready to become a customer. The more consistently your site can satisfy their intent when they find you in search, the more likely they are to become a loyal client or customer further down the line.
Finally, remember nuance. Understanding the four broad categories of user intent is a good start, but remember how different topics may wrinkle those broad definitions, and be prepared to adapt your landing pages to meet the specific intent that you can identify in your target topics.
Chapter 2 - How User Intent Can Help in the Future
In this chapter, we will be exploring how to use the knowledge behind what user intent is, to make better predictions about how it’s used by the search engines and what you can do to maximize the likelihood that you’ll meet it with your content and formats (and even a bit on how to tell if you’re not).
User intent will not just help in the future. User intent IS the future simply based on the way the algorithms are moving.
This was highlighted in the recent Google Search On, where they stressed the proliferation of various AI implementations that are rolling out in the coming weeks and months.
Regardless of whether you are reading this shortly after that video was recorded, or years after, one truth exists in Google algorithms:
Machine Learning is vastly influential and will be even more so tomorrow
But what does this have to do with user intent?
User Intent And Machine Learning
Training a machine learning system takes place over a variety of steps, and the outline below is a very basic example for both brevity and skill.
‘Brevity’, in that it would be pointless for the piece to get into deep detail, and ‘Skill’, in that we are SEO and not machine learning experts. However, we will be putting the sample below in the context we are talking about, training a machine learning system to understand search and improve upon it.
So, let’s dive in …
Step One – Pre-Training
After a Google engineer (let’s call them Stevie) has decided on a hypothesis for improving search results based on a machine learning system, they will most likely pre-train the model with known data.
Basically, Stevie will give the system large volumes of data with known results. In the case of search, they would give the system queries, results, and a rating of the results on a numeric scale of its success. Is it a good result? Does it fulfill the needs of the user?
So already out of the gate we see the impact of user intent coming into play. The entire system is built on the likelihood of a query meeting it.
One of the likely ways the data would be gathered for this training stage would be to pull queries and their results, determine what a likely successful results page has as its engagement factors. A SERP for “weather victoria bc” would have very different success metrics than “best rowing machines for a home gym”.
They would need vast amounts of data for large numbers of queries, a grading of various resulting elements on a page including features snippets, knowledge panels, those ten blue links, and any other elements.
This would be fed into the system to pre-train it.
What is essentially going on during this stage is that Stevie will have created a hypothesis around improving search results using machine learning – let’s say, in what entities are used on a page. For example, if a page is about tourism in New York, what entities should exist on a page likely to satisfy a user?
The hypothesis that certain entities being present on a page or site could suggest that those resources will provide a higher level of user satisfaction with a result might have started when Stevie noticed while planning a trip that most of the successful pages related to tourism in NYC include the boroughs of Queens, Bronx, Brooklyn, Manhattan and Staten Island. From there Stevie may have considered that there are likely other entities common to satisfying users for the query, and that this is likely true in other cities and perhaps for other queries and query types.
From there an algorithm would be trained with data like:
What you see here is simply all the entities that exist on a resource sent to the algorithm along with the resulting score. The machine learning system would create an algorithm to weight the entities by their type, frequency, location, etc. in a way that reliably produced the resulting score.
This would be repeated thousands … hundreds of thousands … maybe millions of times.
With enough data, and the system set to understand how the input (entities) can get as close to the desired output (the known score).
We can see how user intent drove the initial formation of the system itself. But that’s rather indirect.
Step Two – Training
In stage two there’s a simple adjustment to the way the system is trained. During this stage both the system and the engineers get to learn.
The system gets to continue to refine its understanding of the path to optimal results, and the engineer gets to understand how well (or poorly) it’s doing.
The illustration for this stage would look something like:
In this stage the system is given the data, processes it based on the pre-training, and that data is then compared with the known-right answers and StevieBrain in continued to be trained and the engineer is kept aware of how well or poorly the system is doing.
The goal in this stage is to get it as close to producing results that are better than those that are produced by current algorithms. Once this threshold is crossed and consistently confirmed, the system can be deployed.
But What About User Intent?
At some point we run out of training material, and even if we didn’t – training sets can only go so far. You can’t improve by only trying to do what’s been done.
The system needed to be taught and trained to recognize what we’re looking for, but the only ongoing measurables that can be brought in at the scale required are from the system itself.
Core success metrics then, needs to be built into any deployable machine learning system that might be integrated into search. While these metrics are complex because they involve user interaction with a results page and variables as broad as not just a query but the user’s device, location, time, and countless other features, they can be produced.
A separate ML algorithm can be trained and created simply to monitor a user interaction with a result page and produce said score based on the myriad of signals it’s being given. That’s what machines do. This is likely (though not necessarily) what produced the score in our first two stages.
This score would then be used by StevieBrain to judge whether it is improving on itself consistently. To judge its ongoing success once deployed as queries, environments and the world at large changes.
Google has said that user behavior isn’t a ranking factor. User behavior is much more powerful than that. It doesn’t just impact the rankings of your site, that’s too easy to game and too onerous for Google.
John Mueller eluded to the impact of user intent when he answered a related question in 2018:
User intent impacts the entire algorithm.
A poor user experience with your site won’t result in your site being “penalized”. However at scale it will result in an algorithmic shift that would see your site negatively impacted.
So, it’s not a penalty, but it sure may feel like it.
What Can You Do?
You’re probably expecting us to say something cliché like, “Just build content that people like.” But we need to think like machines, because machines understand people at scale as opposed to people living in their own bubble. We have all been surprised by the results of A/B tests because people liked something we could not have predicted, so too are they likely to search with an array of needs, wants and desires that’s well beyond what we can predict.
However, a machine can. Or at least, over time it can learn to. So, let the machine tell you what to do. When you’re thinking about the content to produce, it’s always good to start with the SERP.
What formats and information does Google feel are relevant to the topic. This is what they and their billions of queries think fulfill the user intent. There just might be a bit of insight there. Another wonderful free (for now) tool is AlsoAsked.com. It follows the path of what people also ask on a topic like:
Not everything might be relevant to you, but it’s relevant to somebody and almost always worth knowing. You will never stay ahead of the machines, at least not for long. With ML, they are changing too fast, but one thing is constant, the target answer.
Features, factors, signals and indexes change – but the target remains the same. The user, and the SERP that best meets their intent.
It may seem cliché to say that that needs to be the target but, that’s the only target we have that we can keep up with.
User intent and search intent has been spoken about for some time, but it looks like 2021 will be the year where more resources are dedicated to understanding it and building websites with that in mind.
Therefore it is important to write with the user intent in mind and not focus on the keyword itself. Pages which have tailored their content towards a certain search intent, such as navigational, informational, commercial research or transactional stand a better chance of appearing for a variety of relevant long tail terms that answer the searchers’ queries and their intent.
We have seen that users can often spend a lot of time in the informational phase of their buying journey. But just focusing on producing content that satisfies Informational Intent isn’t going to help your business grow revenue (in most cases). Of course, not everyone who is searching online is in a position to buy either, so you may not get very far if you just focus on content that caters to Transactional intent.
If you are interested in Navigational Intent and improving your brand’s SERP then a good starting point is to review the Tea Time SEO Show on How to get and manage Knowledge Panels with Andrea Volpini and Jason Barnard (The Brand SERPs guy).
We would recommend a balanced approach. For example, structuring one’s content strategy around user intent across the buyer journey helps with allocating resources to the key pages which will be the most relevant for the predominant intent type you are focusing on in each stage of the journey.
This guide to user intent is designed to help you understand further the importance of this aspect of SEO and bring that to the forefront of your SEO efforts in 2021 and the years beyond.
Thank you again to our co-authors for sharing their expertise with us. If you would like to contribute to this or any of our other Ultimate SEO Guide series then do get in touch.
What to do next
About the Co-Authors
Ben is a content specialist at Impression, focusing on on-site SEO and content optimisation, as well as co-hosting Impression’s RankUp SEO podcast. He’s spent his SEO career at Impression, joining while still a student in 2016. Now he splits his time between providing specialist on-page work for Impression’s largest clients and leading SEO strategies. Alongside working from home in the last 8 months, he’s enjoyed walking in the beautiful Vale of Belvoir and spending time with friends in the virtual world of online board games.
Dave Davies holds the regal title of Duke of URL over at Beanstalk Internet Marketing, which he started back in 2004 with his wife, the Lady Mary.
Having survived the economic crash of 2008/2009, both Dave and Mary have experience in what was about to happen in the early COVID-19 days with budgets contracting and consumer behavior changing dramatically. They therefore viewed much of what 2020 had in store as an opportunity. This was the opportunity to learn, to develop new skills, and to work with clients to capture market share while it was on sale.
In this guide, Dave will be exploring how to use the knowledge behind what user intent is, to make better predictions about how it’s used by the search engines and what you can do to maximize the likelihood that you’ll meet it with your content and formats (and even a bit on how to tell if you’re not).