The Quick and Dirty Guide to Google’s QDF & QDD and their importance in SEO
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Query Deserves Freshness (QDF) and Query Deserves Diversity (QDD) are two of Google’s lesser-known algorithms. They are responsible for adjusting the SERPs based on the needs of those in search of newer or more diverse information.
Most SEO’s know Google uses over 200 ranking factors to influence search results. These factors can be grouped into the following major groups:
- Technical SEO: (canonical pages and duplicate content issues, crawling, indexing and filtering pages and sites, server response codes, URL parameter checks, robots.txt, geo-targeting, server hacks, sitemaps)
- On-page SEO (On-site SEO): (meta tags, keywords, domain/subdirectories, schema, internal links, CTAs, mobile optimization)
- Off-page SEO: (social/reviews, external links, mentions/sentiment, impressions, searches, CTR)
- Performance SEO: (site behavior, clicks, bounces, goals, conversions, UI/UX, speed and site quality & performance)
- SERP Analysis: (competitors, QDF and QDD, knowledge graph, local SEO, etc.)
While most ranking factors produce some level of predictability (or correlation) in search results. This time we will talk about QDF and QDD, two Google algorithmic ranking factors that produce more-volatile-than-usual results for certain types of searches.
What is QDF?
“Query Deserves Freshness”, in short QDF, is a component of Google’s ranking algorithm that is responsible for giving relevance to those results that normally need to offer an updated response to the user. In other words, everything that is newsworthy.
Unlike regular search results which don’t usually have a time-sensitive limitation, when using QDF, Google is trying to provide those search results that Google knows are relevant for a given time period. News articles, live events, concerts, conferences, comments, elections, status updates, tweets, are some examples of queries that require a fresh component in the SERPs.
What is QDD?
“Query Deserves Diversity”, short for QDD, is another component of the big G algorithm that responds to a slightly different need. Google knows that sometimes queries are not as clear cut. Search intent can be interpreted differently depending on the user or situation. Google’s QDD aims at providing relevance to certain queries with broader search spectrum.
In some cases where Google isn’t sure what the user is actually looking for, it decides to present a varied set of results aimed at slightly different needs, with only their search term in common. Unlike regular search results, these type of results are aimed at solving different needs.
How does QDF (Query Deserves Freshness) work?
Search queries are constantly moving based on Google tweaks. Since queries are sometimes influenced by trends or whats-happening topics, it’s necessary for Google to consider this type of intent when displaying search results.
From photos, to texts to all sorts of media, Google’s engines are constantly copying and indexing everything that happens on the web. They do this in order to make sure every time a user searches for something specific, they can provide relevant, up to date results.
By providing relevant search results, more users rely on Google for search, and thus Google has more data that lets them provide even more relevant results.
QDF model takes several factors including:
- Search volume
- News coverage
- Blog coverage
Because Google has been around for many years, while amassing a significant amount of information, it has enough data to understand millions of search parents. When a search happens that doesn’t follow Google’s patterns, a sort of alert is activated telling Google that it needs to pay more attention to this query. Once this situation is identified, Google decides to re-crawl certain parts of the web along with their own index in ways aimed at reorganizing their search results. It basically is algorithmically reindexing information according to real time inputs.
I short, anytime this type of search happens, QDF acts by showing search results that wouldn’t normally be shown. if the search query took place in a different moment.
How does QDD (Query Deserves Diversity) work?
In a similar manner, QDD works by algorithmically identifying outlier search patterns that don’t fit expected behavior.
You’ve probably heard of Google Analytics, one of their most-used products after search. It’s a snippet of code, usually set up by webmasters, that in short can help them respond the five Ws of every website and webpage where it’s installed. Who is visiting, what are they doing, where are they clicking, when are they clicking, arriving, leaving, etc. Why are they clicking, arriving, leaving in that moment.
While QDD is able to better understand user behavior from Google Analytics implementations (A topic that we’ll talk at a later time). The user-feedback loop in search results has been in place for several years, is usually enough for Google to understand that some search results need a varied set of results.
To better understand this, you need to understand that every time you search for something, Google is “taking notes” of everything that you’re doing. From where are you pointing your mouse or finger, to how much you’re scrolling down, to how many search results you’re clicking on. And if there’s an Analytics implementation on the visited website, it’s likely they are also using this data in order to improve their ranking algorithm according to website behavior.
What happens behind the scenes is that Google is using this data to identify search patterns that don’t fit users expected behavior. When you search for something and you can’t find it on the first page of Google, it doesn’t mean ‘G’ doesn’t have the information that you’re looking for, it most likely means none of the results are relevant enough for you to click on them. This translates into a query that should probably display different search results.
QDD is able to identify this type of queries on the fly, and thus give you a varied set of results aimed at solving different needs, only related with their search term.
Real world example of QDF
If you’re reading this, it means you’re probably a human, and if you’re a human, you’ve probably heard of the FIFA World Cup. That major sporting event taking place every four years and that pretty much pauses the world’s agenda during gameplay.
We’ll omit the query ‘world cup’ an instead use something related to explain Google’s QDF algorithm.
As you can see from Google Trends chart above, the ‘russia’ search term was receiving a ‘flat’ number of searches for the past 12 months, until the week of June 10-16, 2018, when the World Cup started and ‘russia’ search terms peaked.
This shows how search works and how patterns are algorithmically recognized. Every time an outlier behavior like this happens, Google understands something is happening and that it needs to rewire its search results page in order to provide fresher information.
As seen on the animated SERP above, results for ‘russia’ queries will be very different in 2019, in August when the 2018 World Cup is over, or even in two days time when new matches have been played.
Google is very clever for the time being and understands ‘Russia’ queries relate to the sporting event and not specifically to Russia the country, history, geography, etc. Thus first page results include soccer news and videos of recent or soon to happen matches, as well as any other relevant news developing in real time.
In short, Google understands this is a ‘hot’ topic that deserves some fresh results in order to be truly relevant, thus it decides to display those search results that make more sense in the current context.
This is an extreme example, but this algorithm acts in many search queries.
Real world example of QDD
While QDF relates with a topics freshness, QDD relates with a given queries behavior expectations. We’ll use the word ‘murano’. While ‘murano’ and ‘russia’ are both names of places, one a city in Venice famous for its world class murano chandeliers, the other a whole country, both were getting a similar search pattern (before the Fifa world cup).
Note the numbers to the left are not volume or traffic metrics, but rather arithmetic calculations only useful for trend comparison.
Even tough you’ve probably heard the word ‘Murano’ before, and even if had an idea of what it was before you read it here, you probably haven’t realized the many different connotations it has in search results.
As you can see from the animated SERP above, there’s the Nissan Murano SUV brand, there’s Murano city, but there’s also the wikipedia entry for Murano glass, there’s a few Murano restaurants, museums and factories, and there’s even a Murano city guide.
While not many cities worldwide offer this much variety, this is an extreme example to illustrate Google’s QDD algorithm in action.
Relationship between QDF/QDD
These are two generally independent algorithms that don’t necessarily act all the time. As we have explained before, they mostly act based on outlier behavior. Either triggered by search query spikes, or when unexpected behavior (clicks/scrolling/scanning) happens.
Below are some user-cases where either QDF or QDD may come into place:
- Names: Searches of company names, brands, persons, or places. (QDD/QDF)
- Product searches: Searches of products, but also reviews, other non-commercial or unbiased information. (QDD)
- News: Hot or developing issues around topics (QDF).
QDF and QDD are two algorithms Google uses to rank search results. QDF stands for Query Deserves Freshness and is aimed at providing newsworthy content. QDD stands for Query Deserves Diversity and is aimed at showing a broader set of rules. Both algorithms aimed at providing more relevant search results to the user. They were introduced almost 10 years ago. It’s important to consider keyword intent and to verify QDF and QDD applicability during keyword planning.
As you have seen, QDD/QDF play an integral role in understanding SERPs and how to better optimize for them.
A correct keyword research and mapping should help you identify which keywords are most likely to be affected by these algorithms.
Have you identified the impact of QDF and QDD on your website SEO? Is your keyword plan taking QDD/QDF into consideration?
Leave me your comments or thoughts below.