In an age where information is abundant yet often overwhelming, the key to effective searching lies in understanding how to refine your queries.
Imagine typing a simple question into a search engine only to be bombarded with thousands of irrelevant results—frustrating, right? This is where query refinements come into play, transforming vague inquiries into precise answers that cater to user intent. By delving into the mechanics of query refinements, we can uncover how they enhance our search experiences and empower content creators to connect more effectively with their audiences.
This is an in-depth guide to everything about query refinements and search filters. Here’s what you’ll find:
- Introduction to Query Refinements
- Overview of the importance of query refinements in information retrieval.
- Understanding Query Refinements
- Definition and purpose of query refinements.
- The role of natural language processing (NLP) in enhancing search functionality.
- Types of Query Refinements
- FiltersExplanation of different content types (Images, Maps, News, etc.).
- TopicsDiscussion on sub-topics and their relevance to user searches.
- How Query Refinements Work
- Technical overview of the query refinement process.
- Dynamic generation and precomputation of refinements.
- Mathematical Foundations of Search Queries
- Introduction to vector representation of words and phrases.
- Key mathematical functions used in search engines.
- User Experience: Mobile vs Desktop
- Differences in query refinement presentation on mobile devices compared to desktops.
- Distinguishing Query Refinements from Keyword Clustering
- Comparison between query refinements and keyword clustering strategies.
- Benefits of Leveraging Query Refinements for Content Creation
- Improved keyword targeting and content strategy.
- Enhanced content structure and SEO performance.
- Better user experience through relevant content.
- Accessing Query Refinements through URL Parameters
- Explanation of active and passive URL parameters.
- Basic and additional parameters used by Google for refining searches.
- Practical Applications: Improving Organic Traffic with Query Parameters
- SERP and competitive analysis using query parameters.
- Content ideation and digital PR opportunities through refined searches.
- FAQ & Conclusion
- Recap of the significance of query refinements in enhancing search experiences and content strategies.
Understanding query refinements
Information retrieval has always been challenging. Translating human language to computer language takes a lot of work. It doesn’t stop there because it’s not always easy to know what exactly you are searching for in the first place.
Consider your behaviour when using a search engine. You are likely to do one of two types of searches.
- You know precisely what you need (e.g. Levi’s 501s size 32)
- You have an idea of what you want but maybe not the exact phrase (e.g. Les Paul; do you want a guitar, or are you looking for information about the person)
While modern search engines can easily handle the first search type, the second type is more complicated. This is where query refinements come into play.
When your query is vague, has multiple meanings or is too broad, query refinements are designed to filter the results to answer the intent of your query better, thus providing you with a more pleasant experience. For example, the query “jaguar” could refer to the animal, the car, or the sports team, leading to ambiguity in the search results.
Even after you’ve assembled the result set for your search engine and tweaked the rankings, your users might still type queries that are too broad. For example, if users search for a health condition, it is not clear what kind of information they are looking for. Are they looking for information about symptoms, treatments, or risk factors? However, if they can select refinements, or categories of search results, they can narrow their searches and get to the answers quickly. -From Google’s documentation on programmatic search engines
What are query refinements?
A query refinement is a natural language processing (NLP) technique that enhances search functionality by offering users structured navigation options to narrow down and refine their search results. This powerful search engine feature divides results into logical groups, presenting clickable options or “pills” at the top of the search results page. By enabling users to filter or modify their original query quickly, query refinements streamline the information-seeking process and significantly improve the overall search experience..
Types of query refinements
There are different ways to filter a Google search to be more precise or to expand in new directions. In broad strokes, there are two types of query refinements: Filters and Topics.
Filters
These refinements change the medium of the content you are searching. There are six main types:
- Images
- Maps
- News
- Shopping
- Video
- Web (default)
Topics
Topics attempt to create sub-topics or adjacent subjects that are likely related and helpful for continuing the original search. For example. If we search for “hotel in Paris” we get the following Search Engine Results Page (SERP)
The topic refinements are extensive:
- 5-star
- Cheapest
- Luxury
- Near Effiel Tower
- Etc.
As you can imagine, filters are fairly static from search to search, but topics will differ for every search. Interestingly, as you’ll see in the how it works section below, the topics are dynamic and will update based on your search journey.
How query refinements work?
While we don’t know precisely how Google populates query refinements, below is the general process used to build a system that reacts to users’ queries and helps them refine their search.
Technical Overview
Most query refinement systems will have the following built into its flow:
- Dynamic Generation: Refinements are generated in real time based on current search trends and the user’s original query.
- Precomputation: Some systems precompute possible refinements based on past user queries and store them for quick lookup. This allows for faster response times when presenting refinements to users.
- Categorization or clustering: Search results are clustered based on common attributes or themes. For example, a search for “oscar” might create clusters for “Academy Awards,” “Oscar the name,” and “oscar the fish.”
- Scoring: Refinements are scored and ranked based on cluster size and relevance to the original query (using cosine similarity). This helps determine which refinements are most likely valuable for the user.
- Query Modification: When a user clicks a refinement, it either filters the existing results or modifies the original query to produce more targeted results
The process is similar to creating roads and bridges that connect neighbourhoods, towns and cities.
Making mathematical sense out of words
The above is all good and gives us the general scheme of a query refinement system. We must remember that a search engine follows a mathematical equation. Let’s turn the above into math.
Every word and phrase can be translated into a vector. A vector is a point on a grid that can have multiple dimensions. These layers of dimensions go beyond 3D and give machines the ability to be incredibly precise. The main thing to remember is that each word and phrase takes up a position in a mathematical space.
Once a text is translated into a vector, a system can measure the distance between two words, phrases, or entire documents. Below are some standard mathematical functions search engines use to calculate similarity.
- Cosine similarity
- K-Means
- TF-IDF
- Levenshtein Distance
- Vector Space Model (VSM)
- Okapi BM25
- Sorensen-Dice Similarity
- Tversky Similarity
- Overlap Similarity
Learn more about the equations listed above here.
Once all the math is done, it’s a matter of grouping and categorizing similar items into a baseline categorization. Search engines can then use user data to refine the options presented as a query refinement.
Query Refinement: Mobile vs Desktop Experience
Before moving on, note how query refinements work on mobile versus desktop.
The system is generally the same, but the user interface is different. Because mobile devices have limited screen real estate, fewer refinements are presented to users.
Let’s look at “hotels in Paris” on a mobile device.
While we can see several of the desktop refinement options, a number of the topic filters are tucked away in the map pack. This helps keep the screen organized and helps user narrow down their search in the map pack, which is where Google wants people to go (booking through Google can earn revenue for big-G).
How are query refinements different from clustering keywords?
While there are many similarities between the two, query refinements are a search engine process used to help people navigate vague, complex or ambiguous searches. Keyword clustering, on the other hand, is a strategy employed by SEOs to help their content rank more effectively on search engines. As I’ll outline below, smart SEOs use query refinements to improve their strategy.
Benefits of leveraging query refinements for content creation
Now that we understand query refinements let’s turn a proverbial page and learn why and how to use them to our advantage.
Improved keyword targeting and content strategy
Query refinements provide insights into how search engines organize topics and how users might refine their original query. This allows SEOs and content strategists to identify popular subtopics, discover new keyword variations and understand user intent and interests at a granular level. By analyzing query refinements, you can uncover highly relevant keywords that may not appear in traditional keyword research tools. This allows you to create more targeted content that aligns closely with user needs.
Enhanced content structure
The patterns in query refinements can inform how to structure and organize content. This helps create a logical content hierarchy that matches how users think about and search for information. There are a few ways to do this:
- Use common refinements as subheadings or section topics
- Create dedicated pages or sections for frequently refined attributes (e.g. men’s/women’s/kids’ versions of products)
- Develop content that addresses different intents revealed by refinements
Improved SEO performance
When you Leverage query refinements in your content strategy, you can boost SEO results. Because query refinements help you discover the entire topic space, you’ll have more coverage and rank higher since your content will be more relevant.
Better user experience
Since query refinements reveal what additional information or options users are looking for, your content will likely provide a better experience. Addressing query refinements in your content will help you:
- Reduce bounce rates by providing more relevant information
- Increase time on site as users find what they need
- Improve conversion rates by addressing user needs more comprehensively
Data-driven content planning
Lastly, analyzing query refinements provides data-driven insights for content planning. You’ll be able to identify content gaps, prioritize popular concepts and adjust to seasonal trends. All of these benefits help you create content that is more likely to rank well and serve your visitors more effectively.
Accessing query refinements
URL parameters or query string parameters
Before we get into the fun part, a primer is needed.
URL parameters are extra bits of information added to the end of a URL. They come in the form of a key-value pair. There are two types of parameters: active and passive.
- Active: These are content-modifying parameters that change how a page behaves.
- Passive: These parameters are used for tracking, such as determining where a user came from.
Parameters are always found after a ‘?’, which signifies the start of a query string. Queries are separated by ‘&’. You can string as many parameters together as you want. Lastly, parameters are always strings, so you’ll need to convert them to other data types if you need them.
URL parameters in Google Search
Now that we’ve addressed that, Google has provided a way for us to access query refinements using query parameters.
First, some of the basics. But be aware that Google updates query strings. While I’ll do my best to keep this list accurate, the best way to understand the parameters is to test them.
Basic query parameters used by Google
Below is a list of the keys Google uses when you search.
- &q= The main search query
- &hl= Controls the interface language
- &gl= Specifies the search region
- &tbs= Used for various time-based and other filters
- &ei= Represents the search session start time
If we wanted to search for ‘pool tables for sale’ and see the results for the United States, we could use this www.google.com/search?q=pool+table+for+sale&gl=us
Here’s a Google Sheet that contains a complete list of Google domains, hl= and nl= parameters.
Other Useful Parameters
Additional parameters that can be useful for SEO.
- &num= Controls the number of results per page
- &start= Specifies the starting point for results (for pagination)
- &safe= Sets the SafeSearch filter level
- &source= Limits results to a specific news source (for Google News)
- &before= and &after= Filters results by date
While Google includes other parameters in a URL, such as sourceid, these are either unparsable or don’t seem to have a useful purpose for SEO.
Interesting search parameters
tbs=li:1 (verbatim search; tell Google not to guess misspellings)
tbas=0 Reset all specific settings from the previous search
Pro tip: If you want to learn more about the various parameters that Google adds to a URL, check out Unfurl. The tool visualizes the query parameters and explains their use where known. You can also use the tool to view other URLs that have parameters (like social media links).
Query parameters to research query refinements
I will steal a bit from Malte Landwehr, who wrote an excellent piece on Medium detailing these parameters.
- tbm= Vertical search for images, videos, etc.
- &tbm=isch Images -> redirects to &udm=2 (example)
- &tbm=app Apps -> Does something but can’t figure out what
- &tbm=lcl Maps but not the same as maps.google.com (example). Note: You’ll notice in this example that I’ve used the Google domain for Seychelles. This should show you how powerful your physical location is for ranking on maps and location-based searches (like near me).
- &tbm=pts Patents (combine with &assignee= for more interesting results ;)) -> This redirects to patents.google.com. (example)
- &tbm=shop Shopping -> Redirects to udm=28 (example)
- &tbm=nws News (example)
- &tbm=bks Books (example)
- &tbm=vid Videos -> Redirects to udm=7 (example)
- udm= This parameter introduced by Google seems similar to tbm but offers more flexibility. Here are some of what’s known. Check out Malte’s article for further details.
- &udm=1 Places (example). This one displays differently depending on your country (the first example is Austria, and here’s a US example)
- &udm=2 Images (example)
- &udm=3 Products (example) and you get more of a Google Shopping experience in the US (example)
- &udm=5 Lodging (example)
- &udm=6 Learn (example)
- &udm=7 Videos (example)
- &udm=8 Jobs (example)
- &udm=9 Product sites (example)
- &udm=10 Job sites (example)
- &udm=11 Places sites (example)
- &udm=13 Airline options (example)
- &udm=14 Web (clean results without extra features) (example)
- &udm=15 Attractions (example)
- &udm=18 Forums (example)
- &udm=28 Shopping (example)
- &udm=31 Flight sites (example)
- &udm=32 Trains (example)
- &udm=33 Buses (example)
- &udm=34 Transportation sites (example)
- &udm=36 Books (example)
- &udm=37 Products -> Does something but can’t figure out what (example)
- &udm=39 Short Videos -> different form udm=7 (example)
- &udm=44 Visual matches -> Does something but can’t figure out what (example)
- &udm=48 Exact matches -> Does something but can’t figure out what (example)
- &udm=51 Homework -> Does something but can’t figure out what (example)
After udm=53 the URL redirects to a URL without the udm parameter which suggests that the higher numbers aren’t reserved.
Key point: While you can access filters using the UI, some filters aren’t available depending on your search term and location. Using udm= or tbm= along with gl= and country-level Google domains allows you to force Google to display your desired filter except when there aren’t any results for your search.
You’ll get this message when searching for jobs ‘SEO Berlin” from Toronto using the google.com domain.
How to use Google’s URL parameters to improve your organic traffic
Okay, what can we do with this information now that we have this framework behind us?
Serp and competitive analysis
First, you should run your query through the various tbm and udm values to better understand your content opportunities. Secondly, forcing Google to show, for example, product results for a keyword where Google would typically not suggest that filter can lead to interesting insights.
Udm=14 is one of the more helpful options. This value allows you to see search results without all the clutter. Using udm=14 strips away AI-generated content and extra features, providing cleaner web results and a chance for focused analysis. Beyond udm=14, look at udm=2 (images) and udm=7 (video) to better understand the existing multimedia opportunities.
Content ideation & digital PR
Udm=18 can give you a great way to search for content ideas, discover more about your customers and find niche forums. For example, a search for pool table yields:
- AzBilliards Forums
- MAACA (forum about arcade games from out of Montreal)
- The Snooker Forum
- Pinball Info
- Darts Nutz Forum
- And other niche places on the internet
That search also uncovers a wealth of content ideas:
- Is it worth buying a cheap pool table?
- What happened to pool tables in British pubs?
- Should you buy a folding pool table?
- How would I clean a pool table?
There are so many opportunities for you to work with for a year’s worth of content using this one search.
Depending on your topic space, explore the other udm values, as they will help you boost your knowledge of the SERP and find other opportunities. For example, an e-commerce site may want to dive deep into udm=37 (products), while bookstores should review udm=36.
Udm=7 & udm=39 are also gems for discovering content ideas and potential reactive news stories for a digital PR campaign. Similarly, tbm=nws can give you good content ideas and help you find out backlink opportunities you can capture using other digital PR techniques.
Diversified content types
To help your website rank, it’s best to stand out. Most competitors will do a web content search on Google and neglect all other ways you can create content. By moving through the different filters and making a plan to create content for that filter, you’re differentiating.
For example, udm=39 is for short videos. Searching your primary keyword and reviewing this filter will generate content ideas for social media and your website. Unlike seeing these in each platform, you get to see all of them together in one place and categorized by your primary keyword, giving you unparalleled access to insights.
Competitive intel
Say you built an app and want to see when new entrants enter your space. You can create a search query like this tbm=app and then make an RSS feed (or similar tool to check for changes). Heck, you can do this for any search or content type you want. This gives you a rapid way of keeping track of searches you are trying to optimize for.
Pro tip: This trick is best for low-volume keyword/filter combinations. Try using tbm=pts&assignee=google to see patents filed by Google (change the assignee parameter to one of your competitors)
As you can see, udm values are a powerful way to get relevant filtered results for your business.
You might be thinking, can’t I just click the filter instead of adding the parameter to the URL? Yes, you can do that in some cases, but as you’ll see in the video below, sometimes a particular filter isn’t available for the keyword you are researching. While that’s a clue for you not to create specific content for that keyword, searching forums will almost always get you great ideas.
FAQs about query refinements
How do query refinements impact click-through rates (CTR) in search results?
Query refinements can significantly improve click-through rates by providing users with more relevant and targeted search results. By allowing users to narrow down their search, query refinements increase the likelihood of users finding exactly what they’re looking for, thus leading to higher CTRs. This improved relevance can result in users being more likely to click on search results that match their refined intent.
What role do query refinements play in voice search optimization?
Query refinements play a crucial role in voice search optimization by helping to interpret and clarify the natural language queries that are common in voice searches. They assist in understanding user intent, especially for ambiguous or broad voice queries, and can provide more accurate results. Voice search optimization can benefit from query refinements by anticipating and addressing various intents behind spoken queries, leading to more precise and helpful voice search responses.
How can businesses leverage query refinements to improve their local SEO strategies?
Businesses can leverage query refinements to enhance their local SEO strategies by:
- Analyzing location-based refinements to understand local search intent.
- Creating content that addresses specific local query refinements.
- Optimizing for various local attributes suggested by refinements (e.g., “near me,” “best in [city]”).
- Using refinements to identify popular local services or products to focus on.
This approach helps businesses tailor their content and SEO efforts to match local search behaviours and preferences.
Are there any differences in query refinement behaviours between B2B and B2C searches?
Yes, there are differences in query refinement behaviours between B2B and B2C searches:
- B2B searches often involve more specific, industry-related refinements.
- B2C refinements tend to focus more on product features, prices, and consumer-oriented attributes.
- B2B query refinements may include more technical terms or business-specific jargon.
- B2C refinements often reflect personal preferences and consumer trends.
Understanding these differences can help businesses tailor their content and SEO strategies to their specific audience, whether B2B or B2C.
How do query refinements affect the performance of long-tail keywords in SEO campaigns?
Query refinements can significantly boost the performance of long-tail keywords in SEO campaigns by:
- Revealing highly specific long-tail keyword variations that users are interested in.
- Providing insights into user intent for niche topics.
- Helping content creators develop more targeted and relevant content for long-tail searches.
- Improving the chances of ranking for less competitive but highly relevant long-tail keywords
Filtering out the end
As we’ve seen, query refinements are a powerful tool that can supercharge your content strategy and SEO efforts. By understanding and leveraging these search engine features, you can uncover valuable insights into user intent, discover new content opportunities, and create more targeted, relevant content that resonates with your audience. From improving keyword targeting to enhancing user experience, the benefits of incorporating query refinements into your digital marketing approach are substantial.
As you embark on this journey to optimize your content and boost your organic traffic, remember that the world of SEO is constantly evolving. To stay ahead of the curve and maximize the potential of query refinements for your business, consider contacting an experienced SEO consultant. We can help you navigate the complexities of search algorithms, interpret data effectively, and develop a tailored strategy that aligns with your specific goals. Don’t leave your SEO success to chance – take action today and consult with an SEO professional to unlock the full potential of query refinements and elevate your online presence.