Ultimate SEO Guides - User Intent

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.

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.

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.

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.

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?

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 …

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: