http://vervedevelopments.com/Blog/query-classification-understanding-user-intent.html
What exactly is the function of a search engine? In simplest terms it acquires, stores and returns information (from the web). Ok, simple enough. But we're talking about people here, people seeking information. How they interact with the search engine is often a huge problem. What is the user intent? A good way of starting to pick apart that puzzle is by classification of query types. And that's what we're going to be looking at today.
To understand what a user truly wants when searching for something you'd need to ask each user what it is they are after. While that works for the offline mom and pop store, it isn't at all feasible for a search engine. Thus an automated approach needs to be taken. What's more limiting, is that they have to infer intent from very few words and little in the way of (explicit) interaction.
Why would that be important to SEOs? Well, that's even easier. If we understand how people search for things, we are in a far better position to actually target our programs to ensure the highest, most relevant, levels of traffic for our sites and our clients. Understanding how search engines attack the problem, can be VERY useful in our own targeting and programming.
The basics of query classification and beyond
One of the reasons a search engine looks at classifying queries is to better understand user intent. To do that they will look at the search task process as such;
- Enter query
- Retrieve results
- Scan results
- View results (actual pages returned)
- Refine query (if needed)
Interestingly searchers have certainly evolved over the years beyond mere information needs, into commercial and navigational (seeking known entity) as well. We also can consider that, unlike say.. a library, web searchers are looking for a wide variety of mediums (text, images, multimedia) as well as from various locales (work, home, mobile). Search engines perform social networking functions. They Act as dictionaries, spell checkers and thesauruses.
This is why classification has become more and more important to search engineers over the years. Understanding, as close as possible, the intent, is paramount. The interesting part is the ever changing landscape of exactly what the intent is.
Some studies make the case that users are prone to a higher level keyword approach to simply get near the vicinity, preferring to click through at that point and search the local site for the exact information need. These have been referred to as 'teleporting queries'.
Classification of query types, among SEOs at least, have generally come in three flavours;
- Informational
- Transactional
- Navigational
But, these are simply broad categorizations that we should play hard and fast with. Many queries fall into more than one category and that's actually quite important for SEOs to understand. The reason we care about these is that they play a strong roll in keyword research and ultimately targeting and content programs.
The following table gives you a sense of the various classification types in this area (click for full size);
A different mind set
So let's go beyond the traditional understanding of query types. We have looked at the core types so far, but another paper I came across broke things down a little differently. The reason I decided to bring this up is because we need to understand things aren't always the same.
Here's a chart from the paper which helps understand this approach;
|
1. Navigational |
My goal is to go to specific known website that I already have in mind. The only reason I'm searching is that it's more convenient than typing the URL, or perhaps I don't know the URL. |
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|
2. Informational |
My goal is to learn something by reading or viewing web pages |
|
|
**2.1.1 Directed |
I want to learn something in particular about my topic |
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|
2.1.2 Open |
Closed I want to get an answer to a question that has a single, unambiguous answer. |
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|
2.2 |
Undirected I want to learn anything/everything about my topic. A query for topic X might be interpreted as "tell me about X." |
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|
2.3 Advice |
I want to get advice, ideas, suggestions, or instructions. |
|
|
2.4 Locate |
My goal is to find out whether/where some real world service or product can be obtained |
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|
2.5 List |
My goal is to get a list of plausible suggested web sites (I.e. the search result list itself), each of which might be candidates for helping me achieve some underlying, unspecified goal |
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|
3. Resource |
My goal is to obtain a resource (not information) available on web pages |
|
|
3.1 Download |
My goal is to download a resource that must be on my computer or other device to be useful |
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|
3.2 Entertainment |
My goal is to be entertained simply by viewing items available on the result page |
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|
3.3 Interact |
My goal is to interact with a resource using another program/service available on the web site I find |
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|
3.4 Obtain |
My goal is to obtain a resource that does not require a computer to use. I may print it out, but I can also just look at it on the screen. I'm not obtaining it to learn someinformation, but because I want to use the resource itself. |
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This approach certainly helps to give you the idea of what search engineers are looking at. Does it really matter if it is the classical informational/transactional/navigational or resource? Of course not. What we're doing here today is seeking to get a feel for how classification may be done.
To a search engineer, on the larger level, there are two simple aspects to a query; intent and satisfaction. Each person using a search engine has a goal and classification helps break down these goals into bite sized pieces.
Not to be taken too seriously is some of the data this particular group found in their research;
I say not to take it to heart because as we all know a single data set never tells us the entire picture. This was actually taken from Alta Vista data, soooooo... take it for what it is.
Associating Goals with Queries
If we consider goals as understanding intent, then we can break associations into two areas familiar to most of the search geeks reading my ramblings over the years; implicit and explicit.
Explicit – in most cases these would be navigational or more simplified queries.
Implicit – would be less obvious queries or system elements such as Google's [I'm feeling lucky] feature.
Another common element we see in query classification is building machine learning approaches based on training sets. As you'd imagine, the larger the data set of query data you have, the better associations you can make between queries and intent/satisfaction. Just because behavioural data may not be overly-valuable for ranking elements, doesn't mean it's off the table altogether.
In the paper I cited earlier, they talk about using behavioural data to seek out telling signals the user might give;
- the query itself
- results returned by the search engine
- results clicked by the user
- further searches or other actions by the user
There are actually other actions that can be tracked such as;
- dwell time (on page clicked on)
- scrolling
- page depth
- saving the page to favourites
- Explicit SERP actions (such as Google's +1)
You get the idea. Behavioural data, on a large scale, can bring a great deal of data to further understand the goals and potential intentions of users through implicit and explicit data. We can also see this in recommendation engine elements (Google Suggest, refinements etc.).
What Can SEOs Learn From Query Classification
To begin with, let us look at the core goal of classification; assessing user intent. That of course should be obvious as far as why we, as SEOs, would want to also understand this. There are no tools out there that really give us this. Which means, to some extent, we have to look at potential query spaces and establish what user goals we're trying to service. If you use the classic informational/transactional/navigational approach or the above 'resource' model, is inconsequential. What we need to do is align targeting and content programs to best serve these needs.
When do we look at it? For the most part understanding query classification plays into one of the first elements of an SEO program; keyword research. Out SEO programs live and die from the efficacy of the keyword research. Keyword research is focused on matching user intent with our (ranking) targets.
It should be noted that most queries are informational in nature. Even quasi-classifications such as seeking information on a product prior to purchase. In fact, much of the research seems to show that navigational queries are often seeking information about a product and the query is often refined to reflect this. As such we must consider having a content program that reflects this.
Below are some tables from a recent research paper that shows the percentages of each (in the traditional model) for various topic areas.
The main goal here today was to give you a sense of how search engines are dealing with this so that you can start to adapt your own keyword research and content programs accordingly. If you're interested in more detailed planning, be sure to sign up for the SEO Training Dojo as we will be putting this (and much more) into a keyword research section being posted in the next week or so.
I hope you enjoyed the ride... I know I did.
Papers used/cited in this post;
Understanding User Goals in Web Search
Classifying Web Queries by Topic and user intent(direct link to PDF)