Like all great literature, this post started as a bad joke on Twitter on a Friday night:
If you know me, then this kind of behavior hardly surprises you (and I probably owe you an apology or two). What's surprising is that Google's Matt Cutts replied, and fairly seriously:
Matt's concern that even my painfully stupid joke could be misinterpreted demonstrates just how confused many people are about the algorithm. This tweet actually led to a handful of very productive conversations, including one with Danny Sullivan about the nature of Google's "Hummingbird" update.
These conversations got me thinking about how much we oversimplify what "the algorithm" really is. This post is a journey in pictures, from the most basic conception of the algorithm to something that I hope reflects the major concepts Google is built on as we head into 2014.
The Google algorithm
There's really no such thing as "the" algorithm, but that's how we think about it—as some kind of monolithic block of code that Google occasionally tweaks. In our collective SEO consciousness, it looks something like this:
So, naturally, when Google announces an "update", all we see are shades of blue. We hear about a major algorithm update ever month or two, and yet Google confirmed 665 updates (technically, they used the word "launches") in 2012—obviously, there's something more going on here than just changing a few lines of code in some mega-program.
Inputs and outputs
Of course, the algorithm has to do something, so we need inputs and outputs. In the case of search, the most fundamental input is Google's index of the worldwide web, and the output is search engine result pages (SERPs):
Simple enough, right? Web pages go in, [something happens], search results come out. Well, maybe it's not quite that simple. Obviously, the algorithm itself is incredibly complicated (and we'll get to that in a minute), but even the inputs aren't as straightforward as you might imagine.
First of all, the index is really roughly a dozen data centers distributed across the world, and each data center is a miniature city unto itself, linked by one of the most impressive global fiber optic networks ever built. So, let's at least add some color and say it looks something more like this:
Each block in that index illustration is a cloud of thousands of machines and an incredible array of hardware, software and people, but if we dive deep into that, this post will never end. It's important to realize, though, that the index isn't the only major input into the algorithm. To oversimplify, the system probably looks more like this:
The link graph, local and maps data, the social graph (predominantly Google+) and the Knowledge Graph—essentially, a collection of entity databases—all comprise major inputs that exist beyond Google's core index of the worldwide web. Again, this is just a conceptualization (I don't claim to know how each of these are actually structured as physical data), but each of these inputs are unique and important pieces of the search puzzle.
For the purposes of this post, I'm going to leave out personalization, which has its own inputs (like your search history and location). Personalization is undoubtedly important, but it impacts many areas of this illustration and is more of a layer than a single piece of the puzzle.
Relevance, ranking and re-ranking
As SEOs, we're mostly concerned (i.e. obsessed) with ranking, but we forget that ranking is really only part of the algorithm's job. I think it's useful to split the process into two steps: (1) relevance, and (2) ranking. For a page to rank in Google, it first has to make the cut and be included in the list. Let's draw it something like this:
In other words, first Google has to pick which pages match the search, and then they pick which order those pages are displayed in. Step (1) relies on relevance—a page can have all the links, +1s, and citations in the world, but if it's not a match to the query, it's not going to rank. The Wikipedia page for Millard Fillmore is never going to rank for "best iPhone cases," no matter how much authority Wikipedia has. Once Wikipedia clears the relevance bar, though, that authority kicks in and the page will often rank well.
Interestingly, this is one reason that our large-scale correlation studies show fairly low correlations for on-page factors. Our correlation studies only measure how well a page ranks once it's passed the relevance threshold. In 2013, it's likely that on-page factors are still necessary for relevance, but they're not sufficient for top rankings. In other words, your page has to clearly be about a topic to show up in results, but just being about that topic doesn't mean that it's going to rank well.
Even ranking isn't a single process. I'm going to try to cover an incredibly complicated topic in just a few sentences, a topic that I'll call "re-ranking." Essentially, Google determines a core ranking and what we might call a "pure" organic result. Then, secondary ranking algorithms kick in—these include local results, social results, and vertical results (like news and images). These secondary algorithms rewrite or re-rank the original results:
To see this in action, check out my post on how Google counts local results. Using the methodology in that post, you can clearly see how Google determines a base set of rankings, and then the local algorithm kicks in and not only adds new features but re-ranks the original results. This diagram is only the tip of the iceberg—Bill Slawski has an excellent three-part series on re-ranking that covers 40 different ways Google may re-rank results.
Special inputs: penalties and disavowals
There are also special inputs (for lack of a better term). For example, if Google issues a manual penalty against a site, that has to be flagged somewhere and fed into the system. This may be part of the index, but since this process is managed manually and tied to Google Webmaster Tools, I think it's useful to view it as a separate concept.
Likewise, Google's disavow tool is a separate input, in this case one partially controlled by webmasters. This data must be periodically processed and then fed back into the algorithm and/or link graph. Presumably, there's a semi-automated editorial process involved to verify and clean this user-submitted data. So, that gives us something like this:
Of course, there are many inputs that feed other parts of the system. For example, XML sitemaps in Google Webmaster Tools help shape the index. My goal it to give you a flavor for the major concepts. As you can see, even the "simple" version is quickly getting complicated.
Updates: Panda, Penguin and Hummingbird
Finally, we have the algorithm updates we all know and love. In many cases, an update really is just a change or addition to some small part of Google's code. In the past couple of years, though, algorithm updates have gotten a bit more tricky.
Let's start with Panda, originally launched in February of 2011. The Panda update was more than just a tweak to the code—it was (and probably still is) a sub-algorithm with its own data structures, living outside of the core algorithm (conceptually speaking). Every month or so, the Panda algorithm would be re-run, Panda data would be updated, and that data would feed what you might call a Panda ranking factor back into the core algorithm. It's likely that Penguin operates similarly, in that it's a sub-algorithm and separate data set. We'll put them outside of the big, blue oval:
I don't mean to imply that Panda and Penguin are the same—they operate in very different ways. I'm simply suggesting that both of these algorithm updates rely on their own code and data sources and are only periodically fed back into the system.
Why didn't Google just re-write the algorithm to account for the Panda and/or Penguin intent? Part of it is computational—the resources required to process this data are beyond what the real-time infrastructure can probably handle. As Google gets faster and more powerful, these sub-algorithms may become fully integrated (and Panda is probably more integrated than it once was). The other reason may involve testing and mitigating impact. It's likely that Google only updates Penguin periodically because of the large impact that the first Penguin update had. This may not be a process that they simply want to let loose in real-time.
So, what about the recent Hummingbird update? There's still a lot we don't know, but Google has made it pretty clear that Hummingbird is a fundamental rewrite of how the core algorithm works. I don't think we've seen the full impact of Hummingbird yet, personally, and the potential of this new code may be realized over months or even years, but now we're talking about the core algorithm(s). That leads us to our final image:
Image credit for hummingbird silhouette: Michele Tobias at Experimental Craft.
The end result surprised even me as I created it. This was the most basic illustration I could make that didn't feel misleading or simplistic. The reality of Google today far surpasses this diagram—every piece is dozens of smaller pieces. I hope, though, that this gives you a sense for what the algorithm really is and does.
Additional resources
If you're new to the algorithm and would like to learn more, Google's own "How Search Works" resource is actually pretty interesting (check out the sub-sections, not just the scroller). I'd also highly recommend Chapter 1 of our Beginner's Guide: "How Search Engines Operate." If you just want to know more about how Google operates, Steven Levy's book "In The Plex" is an amazing read.
Special bonus nonsense!
While writing this post, the team and I kept thinking there must be some way to make it more dynamic, but all of our attempts ended badly. Finally, I just gave up and turned the post into an animated GIF. If you like that sort of thing, then here you go...
Hi Dr. Pete,
There are definitely a very wide range of features that Google might be looking at when constructing a set of search results in response to a query that might cause some results to be boosted, others reduced in rankings, and still others removed completely.
Many of these may initially occupy a place in results that might be initially decided upon based upon an IR score looking at relevance for a query and an importance metric such as PageRank, and then be altered based upon a very wide range of factors, that often rely upon certain thresholds and confidence scores.
There's a semantic analysis that might take place through a phrase-based indexing approach, where meaningful co-occurring phrases might be identified in the top n results (where "n" might vary but include the top 10 or 100 or 1000 results, clustered for different meanings), and where pages that include a number of those co-occurring phrases might be boosted (or reduced if their numbers are statistically too high and might indicate scraping).
There's an approach that might identify named entities within a query that could be used to boost results from one or more domains that might have been associated with that particular named entity so that those results are boosted. For example, when you searched for something like [spaceneedle hours] in the recent past, the first 8 or so results were from "spaceneedle.com" and those results inferred that Google was performing a site search on that site for searchers who might be requesting such a search.
In addition to providing personalizations to results (looking at search history data from you or people "like" you), or adding social results (as derived from looking at sources such as Google Plus), Google might also rerank contextual results based upon your preferred country, preferred language, time of the year, time of day, and so on.
It's possible that Google performs some predictive analysis of an initial query that you might perform to send your query to a specific data center (for reasons that have less to do with load balancing and more to do with whether or not that data center might contain the information that they query is asking for). For instance, I performed a search for "gas station" in Mandarin Chinese with my Google location set in central New Jersey to try to get an idea of where Google might return results from, and it definitely wasn't returning those from a data center in the United States, which could have been a problem if my car was running on empty. :)
One of the issues that I have with correlation studies is that we don't know what thresholds there might be with some signals, what confidence levels Google might set, and where they are set at. One Google patent notes that the title of a place in local search is treated differently based upon its length, so that places with shorter names might have the queries re-written to also include a geographic signal (zip code, city and state name, etc.), and places with longer names wouldn't have that information added to the query.
Even the new Hummingbird update from Google appears to rely upon the relevance and importance signals that we are familiar with in initially ranking results, but in re-writing queries may look to a semantic analysis that might include looking at co-occurring words within search results for alternative synonyms or substitutes, and how other words within the initial query might be semantically related to those potential alternatives within re-written queries. This isn't so much of a re-ranking of results as it is an analysis of a searcher's initial query to try to return higher quality results based upon user data from sources such as query log files and click log files.
So - easy as pie, right? ;)
Thanks for chiming in, Bill - I really appreciate it. The very fact that you can write a three-part series on re-ranking (and, for those who haven't read it, it covers 50 different re-ranking concepts) shows just how incredibly complicated the algorithm has become.
Your point about the correlations reminds me of a recent discussion about Eric Enge's experimental study on +1s as a ranking factor. One thing that we all kind of landed on in discussion is the idea that many of these factors are become corroborative layers more than "pure" ranking factors. In other words, Google is smart enough to know that they shouldn't rank a page well just because it has a big chunk of +1s - that's just too easy to game. However, if that page has a bunch of +1s on top of a strong link profile, good on-page relevance, strong citations, etc., then those +1s may kick in and have a real impact (or could down the road, if they don't now). We want to view ranking factors in isolation, but the reality is that Google is adding them as layers. Real sites with strong brands have natural clusters of ranking factors - they aren't islands unto themselves. Any one factor in isolation is more than likely a spam signal.
A propos Bill's observations, where's the query in your iteratively more complex diagram Dr. Pete?
Obviously Google requires some sort of query to produce "the result," and to that extent fair enough if we're to assume its existence in this diagram. But (again, looking above at Bill's comments), the algorithm doesn't necessarily start doing its thing after processing the query, but as soon as it receives the query.
Let me put this another way. You say that "first Google has to pick which pages match the search, and then they pick which order those pages are displayed in." Well, no. First Google has to (try to) figure out what the searcher is driving at.
Not trying to quibble, but making this point because it's an increasingly important one. SEOs have been ignoring the query like, forever - which is perfectly in line with a focus on keywords rather than the concepts underlying those keywords (that is, looking only at keywords means that one is only looking a component of a query).
Google doesn't magically know what a query is about (quick - "best apple accessories" - an iPad cover or a pie pan?), and puts a lot of a processing power behind trying to figure that out.
Are Google's efforts to understand a searcher's intent part "the algorithm" or are we to infer that the phrase really means "the ranking algorithm?" Okay, from my perspective an admittedly rhetorical question: I think, especially with the arrival of Hummingbird, that Google's "algorithm" (insofar as an algorithm is a process and set of rules) should be thought of holistically as computer logic applied to determine user intent and to return results, including the ranking of those results if required.
So, truthfully, I was viewing the illustration as the "answer" and the query more as the "question" (sitting outside of this conceptualization, to some degree). You and Bill are absolutely right, though - the reality is far more interactive than that. The question itself has to be parsed and interpreted, and many aspects of the algorithm have a query-specific aspect. There are keyword-targeted penalties, for example, and Hummingbird is reshaping how Google parses queries.
I'm not sure how to represent that in this diagram or even if it quite fits the diagram in the way I conceived it, but I absolutely agree that it's an important concept.
Thanks Bill, Dr. Pete, and Aaron for this dialogue. While I greatly enjoyed the original post that provides a visualization for some of the ranking concepts that many have a hard time putting together in their head, it is this specific reply chain that really starts to get at the heart of algorithmic ranking theory.
If a page is relevant to a query, how Relevant is it? Can Authority and Trust transcend relevance? If so, to what point? Is it a sliding scale? Are there thresholds?
These are all questions we've been asking since the beginning and the concrete answers are becoming more elusive with every additional layer of complexity.
Philosophically speaking maybe this is good as more attention can be focused on true user-centric marketing and less on the technical deciphering of a formula that in all honesty is likely learning and optimizing itself anyway.
But for those of us that still enjoy the scientific pursuit of understanding how things work and not just THAT it works, this discussion is relevant and worthwhile.
Bill, in this statement I believe you are touching upon something much deeper here:
"One of the issues that I have with correlation studies is that we don't know what thresholds there might be with some signals, what confidence levels Google might set, and where they are set at."
Unfortunately the vast majority of SEO specialists just want to be given definitive rules to follow:
How many links do I need? How many blog posts should I create? How many Likes should get?
This line of thinking fits nice and neat into a business model that just sells SEO Packages with set deliverables. But as you guys have pointed out, it is much more fluid and complex than that.
Many of us would agree that Relevance, Authority, Personalization, and other broader ranking concepts are rarely boolean equations where you either pass or fail. Confidence Levels, standard deviations of error, and reciprocal influence modeling must also play a role.
If Google applies predictive analysis to determine the intent of a search query, how confident are they in that calculation?
For the sake of conversation, hypothetically if one page was 70% relevant to the query but 80% authoritative should it rank higher than a page that was 85% relevant but only 20% authoritative?
Now add in other factors. Is the information unique and exactly what the user is looking for but the site has a poor user experience in terms of performance, layout, hard to read, ads above the fold, or accessibility issues? If this triggered a penalty such as Panda does that trump all other relevance and authority signals?
This is even more complex when Google may only be 50% confident that they know the intent of the user.
The question isn't whether or not these things have an impact but rather how much of an impact they have.
Still speaking in overly simplistic terms, if there was a checklist of 200 factors of which you have completely failed in 20% of them can the 80% that you do very well in make up for it? To this day this Min/Max strategy is still being used by an alarming number of SEO consultants.
I believe that there are a few "deal breakers" and "top tier" signals but most of them likely will not have a significant impact by themselves. This historically hasn't worked out well in the past. The heavy reliance on Links allowed the Google Bombing phenomenon.
It appears that some of the ranking upgrades for relevance and trust over the last 10+ years are now working together and possibly influencing each other (as Dr Pete alluded to with the +1s example).
For example:
- PageRank looked at links to calculate and transfer value to quantify Importance.
- LSI determined patterns between related concepts to help refine Relevance.
- Hilltop determined connections between Authoritative sites / pages and the "experts" they linked to. Later the concept of Trust has expanded using a number of additional signals.
By themselves many of these algorithmic add-ons have had varying degrees of success until loopholes were found to artificially manipulate these signals. However, in the last few years it appears that the formula has become advanced enough to look at these factors collectively while adapting to and detecting abnormalities in natural growth patterns whether it is social engagement, link profiles, or content creation.
Thanks for the discussion guys.
I'd also add that there's unlikely to be one ruleset.
I see the algorithm a bit like https://blueroommusicstudio.com/wp-content/uploads/2011/11/sliders.jpg
Some of the sliders are set high, some of the sliders are set low - it depends on the type of query - as in subject, search volume.
For example.... a product query - products pages are generated by CMS so tend to have the "keyword in the title" or as a page heading, internally linked to using the product name, etc. so I could imagine that on-page optimisation would have a higher relevance.
Longer tail search queries are unlikely to have the same weight of links behind them. So the value of links to these pages may be diminished.
Split/multi-variate testing performed across thousands of SERPs could vary the weighting each of the alleged 200 factors have - with vastly different factors per term type.
I've noticed some insane "glitches in the Matrix" for sites ranking for terms in lower volume and less "quality" areas of the web. I think Google operates on a much simpler ruleset for these.
Dr Pete, repeat your Ranking Factors analysis just on 200 types of shoes - and again on 200 types of holiday destination.
I don't think anybody has ever explained Google's search process this way. Extremely clear and factual way.
Dr. Pete is unbeatable when it comes to conveying the message in the most favorable (and understandable) way.
Hat's off and Thanks.
Yea, especially for us right-brained folks it was finally done in a simple and visual manner. And it didn't turn into a 20 foot long infographic either. Kudos!
Indeed, the joke was painfully stupid. Given your own account, if an average penguin is 4x taller than the 'bird, then Penguin 2.1 should have 8.4x the impact. Bummer.
All joking aside, thanks for this article. It's clear and funny, which makes it both enjoyable and useful. I oughta read more like this. Best regards, Ed.
Haha - you can't argue with the math - 2.1 X 4 = 8.4.
Pete,
I'm calling this piece "a dose of sanity." I get so annoyed at hearing about THE algorithm, as if it's some static entity.
RS
What an amazing piece of content Dr pete, you clearly explain how google algorithm works, we cant depend on mattt cutts everytime, he says something , do something else. Many results were dropped out due to latest humminbird and penguin updates, still gibberish content is on top of SERP.
Dr. Pete, despite the poor illustrations (ha) this is a fantastic way to picture how Google's algo processes queries and delivers results. I'm curious though, If you DID include personalization, where would it be included? I tend to think it'd be another blue bubble above the algo that's now operating on Hummingbird.
Do you agree? Disagree? It may be bigger than that, but that's where I'd place it. I'm curious to know where you and others would place it though.
I think there's definitely a re-ranking aspect to personalization, but it also has independent inputs and overlaps with other layers. You can have social impact "core" ranking and also social influence on personalization, for example. Putting it just one place didn't feel right, so I decided to make excuses instead :)
I agree with Pete and consider Personalization as a re-ranking layer. Every time a query is performed, Google probably is considering a first "basic" SERP, then it adds the Personalization layer taking into account all these "factors":
Be aware, though, that some of those factors are considered also by other elements, which composes the Algo. For instance "Search History" is an important element that Google uses for defining the so-called "Search Entities", which seems being basic in Hummingbird.
I would think that a small block for personalization lies in each of the filters or bubbles feeding data to from all personalization factors to help determine the final results.
Thanks for all the great responses and information, Dr. Pete, Gianluca, and Jared. Personalization may be the one ranking aspect that's just ambiguous enough to not be included in poorly illustrated guides. Something about the on/off switch makes me feel like it might most often be a last-second filter.
Seems like it would be more difficult to eliminate the "personalization" from each level of the algo when somebody chooses to search without it applied. But at the same time, it's already tied into how the algo works like Gianluca said.
Fun stuff, guys. Thanks again for the blog post.
"This is one reason that our large-scale correlation studies show fairly low correlations for on-page factors. Our correlation studies only measure how well a page ranks once it's passed the relevance threshold. In 2013, it's likely that on-page factors are still necessary for relevance, but they're not sufficient for top rankings. In other words, your page has to clearly be about a topic to show up in results, but just being about that topic doesn't mean that it's going to rank well."
Yes, yes, a thousand times yes! Thanks, Dr. Pete. Awesome as usual. You may also enjoy: https://searchengineland.com/how-search-engines-work-really-171556
Brilliant.
I still wonder how Google can process half a billion new and unique queries everyday. That is the part that boggles my mind.
I could understand if when searching Google, a message popped up saying, "Hang on for a few minutes, we are searching 24 gazillion pages. Be back shortly." But to have queries returned in fractions of a second?
How would hypothesize that such a dramatic mass of data is processed so quickly?
Google "Caffeine Update" to see solved your questions :)
Hi Dr Pete,
I rememeber seeing the tweet a few weeks back and having a little giggle and then thinking, what if you are being trolled by Matt haha Anyhow great to see how you have visually constructed the algo updates! Good Job! :)
Pannda, Penguine, Caffeine & now Hummingbird.
Got Scolded by my boss reading this article during working hours. But, it doesn't matter. I enjoyed reading this post.
Awesome and Entertaining. Thanks Dr. Pete.
Interesting title! So much interest in the hummingbirds lately. Glad to know I'm not the only one that needed more explanations on the algorithm. It gives me a more organized way of thinking about this.
This blog light my path towards seo skills and change my thinking and concepts about algorithms and its impact on search logic.
I read this; this morning. Thats awesome that Matt Cutts follows you. :) Have you linked him to this post!?!
This blog light my path towards seo skills and change my thinking and concepts about algorithms and its impact on search logic.
I kept thinking about the Matrix while reading this.
thanks Pete,
Seems like a very decent starting point in understanding Google's complexity.
I think it's good to realize that all factors can be split into many many variables.
O and yeah, you can't have a good post without a .GIF included !
Thanks Dr Pete, I'm going to use this often!
Thanks Pete,
I have been explaining the difference between Pengiun/Panda and the Hummingbird updates in this way.
Great to have something simple and visual to reference.
Dave
Love the simplicity in writing that you have used to explain such a daunting thing. Its Brilliant!
Dr. Pete,
Poorly illustrated huh?! Well this is a marvelous piece that is very difficult to commend enough. Thanks for shedding the light.
Just came across this post after last Tuesday's phenomenal workshop! What a terrific visual representation.
Thank you!
After Penguin, panda, Hummingbird Matt Cutts surely received so many Angry tweets by the Webmasters. Dr. Pete really do a Cool Prediction about the Google Algorithm.
The problem with so many great posts is that the reality is different. In my niche, the top ranking website for the top search terms have tens of thousands of spam links to internal pages. Hundreds of bought facebook 'likes' and similar social media signals. Google is not picking up the spam and despite reporting the problem, nothing ever happens. Meanwhile, the guys paying it straight with content and non black hat tactics watch or rankings burn. So thanks for the post which is excellent but until Google do the basics they say they are policing then its no more than great reading.
Thanks Dr. Peter, Just Wow! It;s perfect to view and measure the updates in my opinion..... I got the formula to go with genuine way in my online business promotion's.
With all due respect to Matt Cutts...
he wasn't paying attention to your tweet very well. You were speaking of impact rather than relatedness.
But being a birder myself, I have to ask, is a penguin (average) really 4X taller than a hummingbird? 0__0
Nice stuff you got here :D
Not bad, wish I had the time to produce this stuff. Thanks.
Stunningly clear description of what is by (google) necessity a pretty opaque subject! Added real clarity to this, I will be passing this on to my fellow students and lecturers at Manchester Met at #dsmmcm1314
The BEST post in all of the SEO Blog World! I love it when people can simplify complicated things, It makes the world a lot better.
Yes I agree with Dr. Pete. The visualization made easy to understand the how the google algorithm works. This section Relevance, ranking and re-ranking is awesome.
Yup....nice take here Doc....and I'd buy the T-Shirt too!!
Dr. Pete,
Why this title "A poorly Illustrated Guide"................?
Its AMAZING :)
I concur--Dr. Pete possibly meant that is more simplistic than some of the models out there, but it' pretty darn good. Einstein said "If you can't explain it simply, you don't understand it well enough."
It was kind of a riff on the whole trend toward "Ultimate", "Complete", etc. guides to things. I thought maybe I'd under-promise and see what happened :)
Nice Strategy. You tried to 'under-promise' by mentioning it 'poor guide'. But we readers still read it 'ultimate' because it is mentioned 'posted by Dr. Peter J. Meyers' :)
This could be on tshirt;) We could bet what would be the next animal-algo-name... elephant?
A t-shirt that would have to be reprinted ever few days with all these changes :)
but still be awesome;) google tshirts collection:)
A great post thanks really insightful and a great tool for helping explain a bit more of what the algorithm does more than "magic"
Thanks for making the Google filtering process easy to understand Dr. Pete. I like the gif.
Thanks for the article, I will be sharing with my digital team, one thing I have noticed and this is for my own searches Google of present (could be hummingbird) is giving me strange results, results that are not what I was expecting and I am having to refine and re-refine to get results that are useful. Does anyone else currently have the some problems?
Yes! I get junk results on the first page of search results and sometimes the second page. After that it seems to normalize, and I have zero clue why.
Thanks for taking the time to put that together. Your comment about them not wanting to release Penguin updates in real time reminded me of another big shakeup around a holiday this summer. Have they learned that lesson? I'm wondering if they'll schedule an update for November.
The beginning is so cool - I don't know whats cooler - the question or the completly seriously answer of Matt Cutts. We cant really interpret a tweet but it sounds a bit ... pissed :)
So - a really cool react by the way to say: it is because so many people are talking about it.
Respect for that cool reaction.
Based on a follow-up tweet, I think there's real concern within Google that people are misinterpreting what they've said about Hummingbird (and, frankly, a lot of things). I'm not sure that Matt was angry so much as reacting to internal pressure within the company that's probably pretty serious.
They could solve quite easily that problem if they stopped talking like oracles...
Agreed! Often times their "best practices" fine print don't line up with what's actually happening.
Yes, but they behave as oracles, because we hang on every word....
We hang on every word because we use their product. Just like any service provider we trust and we love.
*they behave as oracles, because we hang on every word*
may be a reason
I'm not sure that Matt was angry so much as reacting to internal pressure
^^ at least the smile on his twitter pic makes us beleive he was not that angree :)
that was a really nice gifs there. Honestly speaking, i don't trust matt's comments nowadays as there are many terms which gives pathetic non-relevant results even after penguin 2.1 and hummingbird!
Nothing to say much about it, only one word for it AWESOME. This should be added in moz beginner guide to SEO.
Very detailed and does a great job. Now trying to compete with the big guys like angieslist/yelp/or thumbtack is a mission on its own. These guys are top and wont go down!
Dr. Pete nice post I could imagine how difficult those Gif's were to relate across the board. Excellent articulation and about not seeing the full impact of Hummingbird yet. I couldn't agree with you more Dr. Pete I believe that Hummingbird will soon show its true Capaobilities.
I love everything about this post, especially the possibility that Matt Cuts might completely lack a sense of humor.
Thank you for putting this together. There's too many explanations that dance around the true complexity of what under Google's hood.
Hi Dr Pete,
Thanks, this really is helpful, you've added colour and clarity to what was for me a very grey and fuzzy algo cloud.
Thanks Dr. Pete! This is going to be a great tool to explain Google's algorithm to a client. While it may be surface level, it does paint a great picture that Google is more than your search + their result.
VERY well done Pete! I'm honestly astonished at how simple you made it but also how fluidly you explained it. I've always tried to facilitate an expression of Google's algo into something visual, but I always end up making it more complex than it needs to be. Sure there are more points to the equation, but you simplified it to the core-est ideals that I think anyone could understand, well done! I'm definitely going to pass this around to my clients etc.
wouldnt it be cool to have a embed code for the https://d2eeipcrcdle6.cloudfront.net/blog-posts/illustrated-algo-ani.gif so people with blogs can steal/'embed" the image on their own blog posts and hey, you get a link to this page :D
Nice effort ;)
Great Post about how latest Google Algorithm actually works. You illustrated presentation is just awesome and explain everything very well.
Thumbs up for Dr. Pete :(Y)
Entertaining & informative post Dr. Pete. Still find it funny Matt Cutts twitter response.
Great post. I can see where you'd think the visuals make it seem too simplistic but when paired with the accompanying text I think its an excellent and easily digestible breakdown of how "the algorithm" works. A more detailed graph would def be interesting to see but this provides a nice base.
This is one of the your best. Don't read entire post if an image can say all the things.
Your presentation told all the story. Hammingbird just work like a filter just before display result in SERP.