TL;DR – Everyone is talking about the importance of understanding User Intent in SEO, but there are few practical and scalable solutions for incorporating your newly discovered understanding of searcher intent into existing SEO strategies.
In this blog post, we look at the pitfalls and misconceptions of common advice around user intent and propose a better way to understand the intent of your users and to incorporate this understanding into your SEO and content marketing strategies using advanced SEO automation techniques.
The focus from leading SEOs and industry commentators on keyword intent or user intent stems from the convergence of a number of factors of which the most important are the growth in Voice Search, the release and subsequent industry discussion around the Google Search Quality Raters’ guidelines, and a series of Google algorithm updates in recent years (e.g. YMYL updates) that have reinforced SEOs’ belief that producing content that answered your users’ desired intentions was the right way to give users what they want and ultimately would be rewarded by Google with higher prominence in the search results.
The initial leaking and subsequent leaks and official release of the Google Search Quality Raters guidelines and the emphasis on producing high quality content that shows off your Expertise, Authority and Trust (EAT) was an important catalyst in starting this conversation. But for me, it was the update to the guidelines which talked about “Needs Met” ratings and weighing-up how well a page answers a specific query which really accelerated the conversation in the industry around search intent.
To fully understand user intent you need to have a grasp of some of the fundamental concepts and examples that Google uses to train the Google Search Quality Raters – the guys and gals that are used to provide feedback about how effective Google’s latest algorithm changes are.
If you’re comfortable with all of these definitions then please skip ahead.
In the field of search engine optimisation, understanding the keyword intent of your users has become a fundamental SEO best practice. You are essentially trying to ascertain what the intent and motivations of a user(s) are from their underlying keyword query (whether made by voice search or a keyboard) to a search engine. When a user types a keyword phrase into a Google search box (or other search engine) or makes a voice search then he/she is signalling to the search engine what they are looking for. Sometimes, it is obvious what the intentions of the user(s) is likely to be and sometimes not. The science of understanding user intent in search engines is more complex than first meets the eye.
Understanding keyword intent is vital to SEO professionals today and goes way beyond traditional SEO focus on keyword research, which has been centered on finding keywords or groups of keywords with the right combination of low competition and high search volumes, to focusing on the keywords with these characteristics that also have high buyer intent or high commercial intent. (More on how to do this later).
Keyword phrases are often ambiguous in nature, e.g. A search for ‘CFD’ could be about Computational Fluid Dynamics or Contracts for Differences. A search for ‘PCP’ could be for information about personal contract plans or Phencyclidine (a drug commonly known as Angel Dust) – two very different queries with different levels of commercial intent.
Nowadays, search engines do an increasingly good job of interpreting the query and making an educated guess as to what the user really wants and serve him/her appropriate content in the form of a page of universal SERP results or a voice response.
Just because you have interpreted that a query for a specific keyword has a dominant interpretation does not mean you understand the what the user’s intent is. E.g. Take a keyword or voice search for “iPhone”; the Keyword Query is crystal clear, it is pretty evident what the user is looking for information about, but the user’s intentions are not obvious; the user could be looking for news about the latest iPhone, information about where to buy, latest models, pricing, suppliers, images, videos, etc, etc.
Google explains this nuance in some detail in its Raters Guidelines and talks about keywords which have a Dominant Query Interpretation, Common Query Interpretation or Rare Query Interpretation and how this is different from the intent behind the query.
When we are talking about understanding User Intent, we are attempting to understand the user behaviours and motivations of a large group of anonymous users. Are they looking for a specific brand? Are they looking for general facts and information that are non-commercial in nature (e.g. The kind of questions that might come up at school, in your local pub quiz or whilst playing Trivial Pursuit)? Or are they researching a product or service to buy or actually looking to buy something online right now?
SEOs generally classify these type of queries into 3 types of Search Intent;
I believe this approach has many limitations and under-reported pitfalls in practice. There are at least two major pitfalls of splitting your keywords into just 3 types of Search Intent.
The first is that it over-simplifies and over-generalises the type of queries and intentions a user has and dumps anything that is not obviously a Navigational or Transactional query into one big useless Informational bucket. As an SEO, you don’t know whether a commercial keyword with high commercial intent has been sent into the Informational or Transactional bucket.
The second is that this also ignores the possibility that keyword queries don’t fit neatly into one bucket at all, as transactional and informational keywords often have branded modifiers.
I propose an alternative 4 Type Model of Search Intent that facilitates a more practical implementation of User Intent for SEO:
By splitting queries that have a commercial or monetizable informational search from a search that is more factual and non-commercial in nature you can actually focus on optimising for the terms that are likely to convert into sales and not just the keywords that you feel might be relevant or have a high search volume.
Furthermore, by making a distinction between Commercial Research and Transactional you are getting a better picture of the buyer journey and the content that a user is seeking out at different stages of their buyer journey. This will help you when you start planning your content strategy.
Sometimes the intent behind a keyword search is not at all clear and consequently Google and other search engines will offer a raft of methods for the user to quickly further refine their original query. For example, in Google there are lots of SERP features that are dedicated to understanding what a user’s purpose is so that they can provide the best results as close to instantly as possible.
Here are some of the common examples you see in a typical SERP:
Of course when you search for images you get a host of other related query refinement options, such as image size, image colour, usage rights, time, and a range of dynamic filters based on the type of query you have made.
Whilst these features are designed to help users, they also help Google understand the distribution of a search query (e.g. What paths do the majority of users take) and whether there is a dominant query interpretation that they can rely on, that they can serve results to for the majority of users.
What is the purpose of understanding Search Intent?
We are really trying to ascertain what the users’ motives behind their search phrase.
We’ve already seen the importance that Google places on understanding this and serving the appropriate content that it thinks users want to see. SEOs need to do this too, so we can give users great, relevant content that answers a keyword or group of closely related keyword phrases and questions. We know that if we can do this, then users will be happy and Google should be happy to reward our content with higher rankings and SERP features.
How do we know what Google wants our websites to look like? Well fortunately Google gives us plenty of clues and they fall into 3 main areas:
Look at the tools Google builds to help users, they are all pointing us to focus on certain things:
There are some big pointers for marketers that are very relevant to the user intent topic and I just want to touch on the main ones below.
This refers to the specific part of the Google Search Quality Raters guidelines that trains Quality Raters on the attributes and factors to look for to help them rate a page from the lowest grade (e.g. It contains misleading or dangerous content) to the highest grade (e.g. The majority of users would find this page helpful and useful). Whilst Google has made it clear that no individual Quality Rater’s score can adversely or positively impact a page’s ability to rank; these ratings are used internally by Google to score itself on how well it is doing in surfacing the best quality content to its users. So this makes these guidelines essential reading, not just for SEOs, for anyone who is looking to produce and rank high quality content for users.
Expertise, Authoritativeness and Trustworthiness are the hallmarks of a good quality page or website. Google uses thousands of trained contractors from around the world to give it feedback on the accuracy and utility of its ranking algorithms. Quality Raters are trained to assess the quality of a ranking page and to give it a rating. They look at:
● The purpose of the page
● The quality and amount of main content on the page
● Information about who is responsible for the website and for producing the main content. This is exceptionally important for YML (“Your Money Your Life”) content in sectors like Health and Finance.
This refers to the specific part of the Google Search Quality Raters guidelines that asks Quality Raters to assess how well a search result answers the user’s intent.
This requires the rater to first understand the dominant query interpretation for the keyword phrase (if there is no dominant query interpretation then the rater cannot give the highest ‘Fully Meets’ rating) and then secondly, to assess how well the content block or ranking page provides the type and format of content that would satisfy a user searching for this term.
When we talk about understanding user intent then these are the crucial steps you have to take yourself during your SEO keyword research to give yourself a chance of producing content that matches the user’s intentions and serves the kind of content that he or she needs.
Once you have gauged the user intent for a basket of related keywords then you must evaluate your content and see how it aligns with what you expect the user is looking for – you can do this in isolation or better still you can look at the top ranking pages and compare the quality, amount and format of main content to your mapped pages.
The Relationship between Google’s Page Quality and Google’s ‘Needs Met’ Ratings. It is important just to clarify that these are two different things.
Section 15.0 of Google’s guidelines state:
“The Page Quality rating slider does not depend on the query. Do not think about the query when assigning a Page Quality rating to the Landing Page”.
“The Needs Met rating is based on both the query and the result. You must carefully think about the query and user intent when assigning a Needs Met rating.”
Confusing these two things, is one of the most common pitfalls of SEO advice on understanding user intent. But it’s not the only area which can trip you up; let’s look at other issues that you need to factor into any keyword or audience research around search intent.
You only have to search for ‘SEO Search/User/Keyword Intent’ in Google and you will find numerous articles from well-respected and well-meaning SEOs extolling the virtues and adding an in-depth understanding of your potential buyer’s intent to your SEO keyword research and optimisation strategy.
Whilst I wholeheartedly agree with the short and long-term benefits of doing this, I have not found one article yet where I agree with their suggested approach! Most ‘conventional wisdom’ on this topic seems to be unworkable, impractical and difficult to scale (especially for Global Heads of SEO and their teams across international boundaries and for large eCommerce sites trying to optimise at scale.
These are the common pitfalls that I see in the advice that I have read elsewhere:
1. Classifying intent into only 3 categories of Navigational, Informational, Transactional (NIT). As I mentioned earlier, having a Commercial Research category is much more helpful (and not just for ecommerce sites) and it helps you go after traffic that converts (which is our Mission statement by the way!).
So think NIRT not NIT!
2. Definition of N, I , R, T too narrow or not fully defined. Often I read very narrow definitions of Navigational terms that are limited to company names and brand names, and sometimes people. I like to think about Entities. Anyone who’s been observing what Google has been doing with its KnowledgeGraph and KnowledgePanel results in the SERPs can realise the importance of understanding entities. (But that’s a whole other blog post!)
3. User Intent Classification is not binary. Some keywords do fit neatly into a classification of N, I, R or T. But the majority have a mixture of two, three or even all four intent elements. Having a scoring model that shows the percentage of intent for each category gives you a better picture of what the SERP is really like and what actions you might need to take to compete effectively.
4. Confusing query interpretation and user intent. A good way I have found of avoiding this is to think in terms of questions your potential customers have (rather than keywords which can have an ambiguous query interpretation) and map these to your pages where these questions can be answered and then to the head terms that these pages rank for (or you want them to rank for).
5. Ambiguous queries, e.g. “CFD”, “PCP”, ”Waterloo”. Sometimes there is no dominant query interpretation and Google will show a mixed SERP result. If you are optimising for these terms then you may have less SERP real estate to go after than a term where Google is clear about what most users are looking for and consequently shows consistent results about a topic.
6. Dictionary based approach e.g. “buy”, “compare”, “how to”. Reliance on keyword modifiers –
You can easily end up relying too heavily on the words in keyword phrase (e.g. ‘buy’, ‘review’, or brand name, etc) to help you make the decision of where the keyword sits. Just because a keyword phrase has the keyword ‘buy’ in it does not make it a transactional keyword query. This is the advice that is given most often by leading SEO and which I think is way off the mark, clunky, archaic and often laughable.
It typically looks something like this. If you are lucky they’ve split general informational queries from commercially-oriented research queries – but often they only recommend 3 search intent categories.
And the advice goes something along these lines….Take your keyword research and find any keywords that contain a brand name, company name or website and tag them as Navigational. Then take any keywords that look like they might be questions (e.g. Who, what, how, why words) and classify them as Informational. Look for any keywords with adjectives or superlatives and classify them as Commercial Research and finally look for keyword phrases with keywords that imply a high buyer intent such as ‘buy’, ‘discount’, etc.
If this is how Google did User Intent, then we’d all be using Bing!
I’ve seen so much advice that over-simplifies this process of understanding user intent and at worse produces erroneous results that are difficult to implement at scale (especially if you are looking at international SEO optimisation).
You don’t have to think for long to come up with other shortcomings of this approach. Take these two examples:
E.g. “How to buy the best discounted HP Spectre laptop at Best Buy?”
Try breaking down a user query like this:
This contains brand, company and product names, has ambiguous terms like ‘buy’, contains question phrases like ‘how to’, transactional terms like ‘discounted’ and contains supposed research terms like ‘best’.
E.g. Take ‘buy laptop’. This advice would classify the keyword query as Transactional. But in reality this is likely to be a query from a user that is much earlier in their buyer journey. You only have to look at the type of SERP result page that Google serves to come to this conclusion. Yes, it contains Ads and Shopping results but it also contains universal features that help the user conduct further research and narrow their purchase options.
If Google worked out its users’ intent using this method then we’d all be using Bing!
To be 100% sure you can take this one stage further and visiting the top ranking organic results. In all instances the top 10 organic ranking pages are category pages that are designed to further help the user narrow their search by brand, model, feature, specification, and price. Google recognises this in its Search Quality Raters guidelines as it says in section 21.0 on “Product Queries: Importance of Browsing and Researching”;
“However, most product queries don’t obviously specify one type of intent. Keep in mind that many users enjoy browsing and visually exploring products online, similar to window shopping in real life. Give high Needs Met ratings to results that allow users to research, browse, and decide what to purchase.
Users may not always plan to buy products online that they are browsing and researching, for example, cars or major appliances. Even though the ultimate goal may be to purchase a product, many other activities may take place first: researching the product (reviews, technical specifications), understanding the options that are available (brands, models, pricing), viewing and considering various options (browsing), etc.”
For this reason, I would classify ‘buy laptop’ as a Research query because there is buyer intent but it is not specific enough to be transactional in my opinion. The buyer has plenty more research to do before making a purchase decision, such as researching which specs would suit their needs, which brands are the best reviewed and so forth.
7. Forgetting explicit or implicit Local Intent ? e.g. “Dry cleaners”, “used car”. Given the majority of searches are now performed on mobile devices, it is fair to say that users expect to have their SERP results geolocated whether they explicitly say “near me” or add a location to their query. When Google thinks there is local intent for a query it will show a relevant local universal SERP feature which may vary depending upon whether Google believe the intent is N, I, R or T or a mixture of them.
So when talking about user intent – still think in terms of N, I, R, T but add a Local Flag to indicate that this is something that the consumer could also purchase in their local area. Clearly for pureplay ecommerce businesses this might allow you to focus on optimising for keywords with low local intent.
e.g. The ‘Local Pack’ here shows a number of local business with the same name. They therefore look like local branches of a brand and reinforces the Navigational aspect of the user’s intent.
e.g. The KnowledgePanel with a Local Business Listing gives a clear indication of navigational intent.
e.g. The Map displayed here indicates a broader informational query than the other local SERP features.
e.g. ‘Discover more places’ offers a user a range of shops to explore for further research.
e.g. The Local Pack here shows a number of different, local stores where you could purchase a pram.
8. Ambiguous stages of buyer journey, e.g. “Mortgage rates”, “buy laptop”.
Most attempts at automating keyword intent research do not even consider or take into account where the user is in the buyer journey.
In my opinion, “buy laptop” is not a transactional keyword any more than “buy new car” or “buy new home” or “what car not to buy” are. Yes ‘buy’ does imply some commercial intent – but it is not always a transactional term straight off the bat.
My other issue with this approach is that it assumes that the buyer journey is simply and is easy to follow, when in fact it could start anywhere (often not even on Google) and finish anywhere (often not on Google). Even Google in their defence against the EU’s anti-trust shopping case are trying to argue that platforms such as Amazon have become consumers’ preferred place to search for products and compare prices.
E.g. A user intending to purchase a new laptop may start with a generic query like ‘buy laptop’ and go straight to their favourite ecommerce store and complete a transaction, but equally they might watch one of the ‘Research Videos’ that Google displays from Tech Radar, then perform another search for ‘HP Spectre’ laptop to view the range on the HP site before returning to Google (or maybe Amazon or elsewhere) to search for ‘Buy HP Spectre 15 inch laptop with SSD’.
Some of these queries are pure generic un-branded research queries with some buyer intent, some are a mix of branded navigational queries with research intent and others are ‘transactional and navigational in nature’. Only, by analysing a complete set of queries across your market can you start to develop a picture of what the SERP landscape looks like.
9. Doesn’t take into account the type of product. You cannot assume the buyer journey is linear or is the same for every type of product. I recently bought a new car online and a few reams of A4 Office LaserJet paper – my buyer journeys were obviously very, very different
There’s more research I plan to do in this area, but I see a big difference in consumers’ buyer behaviour for necessities, commodities and luxury (YMYL) items.
We should also not underestimate the influence of brand in certain purchases especially in markets with large above-the-line advertising spend.
10. User Intent is not static – it will change over time. You cannot say that the presence (or lack of) Ads or Shopping in the SERPs indicates that a term is commercial (or the opposite). This may well be a term that is a perennial commercial term with crystal clear purchase intent, but equally it might be a term that has greater or lesser commercial intent at different times of year (seasonal) or even on a longer cycle (e.g. Tickets for a sporting event that’s only held once every 4 years). Consider Timeliness, e.g. “World Cup Final Tickets”; Seasonality, e.g. “Christmas jumpers” and Freshness, e.g. “Coronavirus”, “Tsunami”.
11. User’s location or device. Google calls this Visit-in-person vs Non-visit-in-person intent – e.g. “Cinnamon” could be a nearby restaurant if you are in the vicinity, but for most users this would be an informational search about the spice.
12. Navigational terms can require more context. e.g. “Little black dress” is a generic term well used in the UK, but it is also a brand with an exact match domain. “Jigsaw” is a well-known clothes brand as well as a toy. This obviously varies by country even where the language is common, as for example these brands might not be so well known in the US, Canada or Australia and the same keyword could therefore have a different user intent N,I,R,T score.
13. Multi-lingual approach. I’ve already been pretty critical of dictionary based models but they also don’t translate well across international borders and are not easily scalable. As a global head of SEO, could you imagine for a moment having to translate and manually build keyword modifiers in every market. Could you really expect a consistent result?
To understand SERP Intent for International SEO you really need a consistent set of rules or machine-learning approach to applying user intent consistently to the keywords that matter to you.
14. Doesn’t take into account the actual SERP results displayed. If you focus on keywords, then you are completely missing out on the type of SERP displayed and the types of sites and ranking pages – whether they are category pages or product pages, articles, news sites, etc or a mixture of the above.
As a consequence, a keyword-based approach does not provide you any actionable insights that you can implement into your content creation plan.
15. A focus on keywords rather than questions. User intent is not always clear even if the query is not ambiguous. But questions tends to be more descriptive of a user’s needs and as a general rule of thumb I find them easier to categorise.
Fortunately, I believe there is a much better way of determining the main motivations of a user at a given stage of the buyer journey for a given query interpretation and the beauty of this is it can be automated. If you have access to an SEO platform or SERPs API with rich Google keyword research and ranking data with lots of universal search SERP features then you can use these SERP features to help you understand search intent.
We’ve conducted some internal testing and found it was very hard to reach consensus on whether a keyword phrase was N, I, R, T. In fact, what we ended up agreeing on that in many instances the user intent was much more nuanced than this and many queries had a mixture of intent; some were N & R, Some N&T, some N&I, etc.
My criticism of current SEO advice is that the definitions are often too narrow. So this reflects my current perspective but I remain open to debate on this!
Navigational
The user is looking for a specific entity: e.g. A company, website, brand, product, event, location, social media handle, historical event, etc. Please note, this definition includes all “brands” that appear in the SERP not just your own.
Informational
The user is looking for general facts or information about a topic that has no commercial intent.
Commercial Research Intent
The user is researching a product or service and is looking for relevant data about what to buy and where to buy it to inform their decision.
Transactional Intent
The user has narrowed their research and is now looking to purchase a specific product or service.
This is of greater use to an SEO or online marketer as it makes a greater distinction between general queries that are non-commercial in nature (e.g. How tall is Barack Obama?) and research queries that have commercial intent but are not necessarily transactional queries where someone is looking to buy online immediately (e.g. “Best family cars 2020”).
These ‘Commercial Research’ queries are extremely important as they are the stage of the sales funnel where consumers are self-educating and self-directing, refining their research and starting their buyer journey with predominantly non-branded queries. If you can provide good answers to consumers questions at this stage of the buyer journey then you set yourself up well to succeed when the consumer is finally looking to transact whether online or off-line at your local branch or store.
Take a look at the following 4×4 matrix. The intent behind some search queries do fit nice and neatly into one category. E.g.
Navigational – Tesco, Audi
Informational – How tall is Big Ben?
Research – What are the best family cars? What are the safest child car seats to buy?
Transactional – Buy office A4 laser jet paper
But for the overwhelming majority of keyword queries there is an element of mixed intent which this matrix attempts to illustrate.
I am definitely not saying, that you should classify your keywords from your keyword research into 16 different categories. But I am saying that just as Keyword Queries are often ambiguous and there is no dominant query interpretation, User Intent too is often not binary and sometimes there is no dominant user intent.
What I am saying is that by automating the analysis of your keywords/questions by using the SERP you can understand the full picture and get a much better sense of how to group keyword sets around the buyer journey and plan and create your content accordingly.
If you look at this matrix, you can make some obvious observations:
There’s little point in making a distinction between N/I and I/N or I/R and R/I and so forth.
I/T, T/I – Doesn’t really exist in the real-world. Using my definitions at least, keyword intent cannot be informational and transactional as informational is defined as being non-commercial in nature.
I – Is dominated by Wikipedia, Google Answer Boxes and Direct Answers for non-commercial terms. Obviously, this depends on the type of site and users you are optimising for – but for most sites you can ignore these (after all you’re probably competing with Wikipedia and other major internet sites for prominence) and concentrate on commercial research queries.
I/R, R/I – I call this the “SERP of Ambiguity”. This is where Google shows a mixture of commercial and non-commercial content and sites. You often see this for queries that the Government is ranking for like “tax returns” and “Car MOTs”. There is of course some value in gaining Featured Snippets in this area – but again I would focus on pure commercial research keyword sets and return to this when that opportunity has been thoroughly exhausted.
Generally, as an SEO, I only care about the 3 coloured areas (Blue, Amber, Green) in the matrix:
N , N/I, I/N, N/R, R/N – Focusing on displaying your branded content and answering buyer’s potential questions around your company, your brand, your services and delivery, your competitors and the product brands you stock.
R, R/T, T/R – huge value in Featured Snippets, Organic FAQs and PAA and ranking your in-depth articles, videos, buyer guides, and category pages.
N/T, T/N, T – huge value in Featured Snippets, Organic FAQs and PAA and ranking your category pages and product pages.
A word about local intent. This is not a separate category of intent. Users exhibit explicit or implicit local intent in all sorts of query whether they are N, I, R or T in nature.
Google is very adept at spotting this local intent (even if it is not obvious in the keyword query) and will show SERP features and query refinement features that make this clear and encourage users to local in their local area. Good examples, are often high value or bulky items that a consumer is likely to want to shop locally for.
As SEOs we’ve been doing keyword research for 20 years and are well versed in categorising keywords into different keyword groups or keyword tags, and in splitting our keywords into brand and non-brand to help understand our SEO performance properly. Understanding search user intent is just an extension of this which then gives us clues as to the type of content we should be adding to our sites to satisfy the user need.
The Buyer Journey is not linear and can start and finish anywhere (and often not just on Google). It also varies considerably depending upon the type of product/service being researched or purchased. Google recognises this in its advice around YMYL (Your Money Your Life Queries) where consumes are potentially going to spend a lot of money or buy/research something with significant health or wealth implications. Google wants to hold providers to the highest standards to appear for these YMYL queries. Think how different the search buyer journey is for someone buying a luxury item like a family car, or a necessity like a child car seat compared to a commodity item like a ream of A4 paper.
The buyer journey is different and the content that Google serves at the top echelons of the SERPs is different.
We also agreed that actually it doesn’t matter what you or I, or any other SEO for that matter thinks, What matters is what Google thinks the predominant user intent is! Building your own keyword lists with keyword modifiers won’t tell you this but analysing the SERP will. If you can second-guess what Google thinks the user intent is then you are well on your way to understanding what type and format of content you should be producing to fully meet that intent. This is the key to the very best rankings in 2020 and beyond.
Whilst there are many drawbacks as SEOs to Google’s ever changing SERP features and layout (and we may bemoan the lack of the good old days when there were just 10 blue links) one of the main benefits of the SERP today is that there are a multitude of SERP features that give us clues as to the nature of the SERP intent.
For example:
The user is looking for a specific entity: e.g. A company, website, brand, product, event, location, social media handle, historical event, etc.
Signals include: EMD, Multiple pages ranking, Organic Site Links, Knowledge Panel content, links and see also, Knowledge Panel Local Business Listing, Wikipedia page, Social media, Local Pack shows branches, Top Nav promotes News.
The user is looking for general facts or information about a topic that has no commercial intent.
Signals include:Google Answer Boxes, Knowledge Panel content and links, Wikipedia, types of ranking TLDs and pages, Featured Snippets, etc.
The user is researching a product or service and is looking for relevant data about what to buy and where to buy it to inform their decision.
Signals include:
Ads, Shopping, Rich Snippets, Videos, Images, Types of ranking sites, eCommerce category and product pages, Local Pack, Order of Top Nav, Guided Search Filters, Complementary Results, Similar Products, etc.
The user has narrowed their research and is now looking to purchase a specific product or service.
Ads, Shopping, Rich Snippets, Videos, Images, Types of ranking sites, eCommerce category and product pages, Local Pack, Order of Top Nav, Guided Search Filters, Complementary Results, Similar Products, etc.
If you have access to a SERP Keyword Ranking API like ours, then you can work with your developer to score these SERP features and create your own custom User intent score.
{‘ads’: 0, ‘currencies’: 1, ‘direct_answer’: 0, ‘event_finder’: 0, ‘flight_finder’: 0, ‘hotel_finder’: 0, ‘knowledge_graph_with_url’: 1, ‘knowledge_graph_no_url’: 0, ‘map’: 0, ‘people_also_search_for’: 0, ‘place’: 0, ‘research_guide’: 0, ‘shopping’: 0, ‘keyword_equal_domain’: 0, ‘keyword_in_domain’: 0, ‘domain_repeats’: 0, ‘social_media’: 0, ‘wikipedia’: 0, ‘rating’: 0}
In each case we score the SERP for a keyword query across all 4 areas N, I , R, T and then also give a DI score for what we believe the Dominant Intent is. We also flag whether there is high Local Intent.
Figure.1 Authoritas User Intent Score – based on keywords
Figure.1 Authoritas User Intent Score – based on questions
Having a model like this allows you to scale your analysis and focus your efforts on evaluating how closely your current content matches the user intent.
You can then take it one stage further and this is an area we are experimenting with at the moment which is showing some early promise.
We call this “The Smell of the SERP”. This was a phrase first coined by Laurent Bourelly, a leading SEO in France and a strong proponent of the “Cocon Semantique” SEO technique.
I like this analogy as it is basically asking you to judge what the SERP smells (looks) like for a query, what type of sites are ranking, what types of content are they displaying (text heavy articles, category relisting pages, videos, images, tools, etc).
Sometimes a smell can be very clear and distinctive and other times it may be a mixture of competing smells; a SERP is similar sometimes the user intent and the type of content returned is crystal clear, sometimes it is a mixture of content signifying that the query interpretation and/or user intent is either ambiguous or there are a number of main paths that a user may intend to go from here.
If you can classify the user intent into meaningful buckets, recognise that a keyword phrase may have mixed intent and understand the type of content that is dominating the SERP for a basket of related terms with the same intent signals then you can determine how closely your content fits with what Google wants to show and make the kind of meaningful adjustments that will boost your pages the SERPs.
This is a practical way of understanding user intent, scoring your own content against the intent and your competitors and implementing a solution that will help you give users the content they are looking for (or at least that Google thinks they are looking for).
Here’s a generic example of the different types of ranking pages – of course you may want to classify different predominant page types that are pertinent to your industry.
You can now use our Page & Content API to analyses these page types for the top 10 SERP lisings for every ranking keyword.
Blog Post
Article
Category / Product Listing Page (PLP)
Document
Forum/Q&A/Community
Image
Parked
Product Detail Page (PDP)
Search
Social Media
Spam
Standard Web Page
Video
Wiki
Remember you also have to look at the main content of a page. Think like a Google Search Quality Rater!
Now you have worked out the user intent, mapped questions or keyword phrases to your key pages, worked out the Smell of the SERP versus your top competing pages, you can start your ‘Forensic SEO’ analysis of how your page stacks up against the competition.
We’ll do another in-depth post on this, but in the meantime these are some of the things that I would consider looking at.
Main Content – Text
How much text is there? Is it in long paragraphs and looking to answer a query in-depth? Or are there short snippets and this page is more like a category page on an ecommerce site helping the user refine their search to land on another internal page that will answer their question?
Main Content – Images – How many? Placement and prominence on the page, Alt tags, etc.
Main Content – Videos – How many? Placement and prominence on the page.
Main Content – Tools – is there a calculator or other tool on the top ranking page?
Main Content – SEO Content & Meta data factors – Meta title, description, keyword and synonym usage, etc.
Main Content – Technical SEO factors – speed, mobile friendly, Schema mark-up, etc?
Main Content – Authority – Is it obviously written by someone with expertise on the subject? Does it answer related consumers’ questions (ideally nicely marked up with FAQ Schema)? Does it link out to other relevant authoritative sites? Is it fresh and up-to-date.
Main Content – Links – Does it have external references from relevant, authoritative people or sites?
Main Content – UX Factors – is the page nicely designed and easy to use on a mobile?
Main Content – Needs Met – would the majority of users be satisfied to discover this page for each query.
I’d love to think what you think about this approach to understanding consumers’ search intent and whether you are planning to incorporate this kind of analysis into your SEO strategies.
The rollout of SGE will create unprecedented risks to your hard earned organic traffic, as well as new opportunities to succeed.
You need to be ready. The only question is, whether you want to be ready now or later?
Unlock the power of keyword clustering with our new feature, using variable SERP similarity analysis to group similar keywords. Set your own thresholds to refine your SEO and content strategies, making sure every keyword counts. Streamline your efforts, avoid content overlap, and gain competitive insights with ease.
The monkey is the contextual part of our brains. Those dominant in monkey traits are really interested in their position in the world. This audience cares about their status, recognition and purpose.
The SERPs are ever-changing, sometimes the changes are subtle almost imperceptible and other times they are marked reflecting a change of approach by Google to the way in which it wants to present itself to its users much like a Chameleon adapting to changes in the environment!
Less than a month after Jumpshot’s parent company Avast announced it was no longer going to be collecting and selling consumer data and closing its web analytics subsidiary Jumpshot; then Hitwise one of its key customers followed suit and announced that it too is to close down with immediate effect.
OK, that was the first thing that came to mind for me and I’m sure for many SEOs like me, who have been using ‘question answering’ as a technique to rank for Universal Search results such as Featured Snippets, People Also Ask and Organic FAQs.
This week we’re pleased to announce that we’re releasing a new module – the Keyword Explorer tool. Along with other “explorer” tools in the platform (and ones we’re developing for page and link analysis) this is free to use – in this case to allow you to explore keyword and content ideas.
