SEO
Is Google headed towards a continuous “real-time” algorithm?
30-second summary:
- The present reality is that Google presses the button and updates its algorithm, which in turn can update site rankings
- What if we are entering a world where it is less of Google pressing a button and more of the algorithm automatically updating rankings in “real-time”?
- Advisory Board member and Wix’s Head of SEO Branding, Mordy Oberstein shares his data observations and insights
If you’ve been doing SEO even for a short while, chances are you’re familiar with a Google algorithm update. Every so often, whether we like it or not, Google presses the button and updates its algorithm, which in turn can update our rankings. The key phrase here is “presses the button.”
But, what if we are entering a world where it’s less of Google pressing a button and more of the algorithm automatically updating rankings in “real-time”? What would that world look like and who would it benefit?
What do we mean by continuous real-time algorithm updates?
It is obvious that technology is constantly evolving but what needs to be made clear is that this applies to Google’s algorithm as well. As the technology available to Google improves, the search engine can do things like better understand the content and assess websites. However, this technology needs to be interjected into the algorithm. In other words, as new technology becomes available to Google or as the current technology improves (we might refer to this as machine learning “getting smarter”) Google, in order to utilize these advancements, needs to “make them a part” of its algorithms.
Take MUM for example. Google has started to use aspects of MUM in the algorithm. However, (at the time of writing) MUM is not fully implemented. As time goes on and based on Google’s previous announcements, MUM is almost certainly going to be applied to additional algorithmic tasks.
Of course, once Google introduces new technology or has refined its current capabilities it will likely want to reassess rankings. If Google is better at understanding content or assessing site quality, wouldn’t it want to apply these capabilities to the rankings? When it does so, Google “presses the button” and releases an algorithm update.
So, say one of Google’s current machine-learning properties has evolved. It’s taken the input over time and has been refined – it’s “smarter” for lack of a better word. Google may elect to “reintroduce” this refined machine learning property into the algorithm and reassess the pages being ranked accordingly.
These updates are specific and purposeful. Google is “pushing the button.” This is most clearly seen when Google announces something like a core update or product review update or even a spam update.
In fact, perhaps nothing better concretizes what I’ve been saying here than what Google said about its spam updates:
“While Google’s automated systems to detect search spam are constantly operating, we occasionally make notable improvements to how they work…. From time to time, we improve that system to make it better at spotting spam and to help ensure it catches new types of spam.”
In other words, Google was able to develop an improvement to a current machine learning property and released an update so that this improvement could be applied to ranking pages.
If this process is “manual” (to use a crude word), what then would continuous “real-time” updates be? Let’s take Google’s Product Review Updates. Initially released in April of 2021, Google’s Product Review Updates aim at weeding out product review pages that are thin, unhelpful, and (if we’re going to call a spade a spade) exists essentially to earn affiliate revenue.
To do this, Google is using machine learning in a specific way, looking at specific criteria. With each iteration of the update (such as there was in December 2021, March 2022, etc.) these machine learning apparatuses have the opportunity to recalibrate and refine. Meaning, they can be potentially more effective over time as the machine “learns” – which is kind of the point when it comes to machine learning.
What I theorize, at this point, is that as these machine learning properties refine themselves, rank fluctuates accordingly. Meaning, Google allows machine learning properties to “recalibrate” and impact the rankings. Google then reviews and analyzes and sees if the changes are to its liking.
We may know this process as unconfirmed algorithm updates (for the record I am 100% not saying that all unconfirmed updates are as such). It’s why I believe there is such a strong tendency towards rank reversals in between official algorithm updates.
It’s quite common that the SERP will see a noticeable increase in rank fluctuations that can impact a page’s rankings only to see those rankings reverse back to their original position with the next wave of rank fluctuations (whether that be a few days later or weeks later). In fact, this process can repeat itself multiple times. The net effect is a given page seeing rank changes followed by reversals or a series of reversals.
A series of rank reversals impacting almost all pages ranking between position 5 and 20 that align with across-the-board heightened rank fluctuations
This trend, as I see it, is Google allowing its machine learning properties to evolve or recalibrate (or however you’d like to describe it) in real-time. Meaning, no one is pushing a button over at Google but rather the algorithm is adjusting to the continuous “real-time” recalibration of the machine learning properties.
It’s this dynamic that I am referring to when I question if we are heading toward “real-time” or “continuous” algorithmic rank adjustments.
What would a continuous real-time google algorithm mean?
So what? What if Google adopted a continuous real-time model? What would the practical implications be?
In a nutshell, it would mean that rank volatility would be far more of a constant. Instead of waiting for Google to push the button on an algorithm update in order to rank to be significantly impacted as a construct, this would simply be the norm. The algorithm would be constantly evaluating pages/sites “on its own” and making adjustments to rank in more real-time.
Another implication would be a lack of having to wait for the next update for restoration. While not a hard-fast rule, if you are significantly impacted by an official Google update, such as a core update, you generally won’t see rank restoration occur until the release of the next version of the update – whereupon your pages will be evaluated. In a real-time scenario, pages are constantly being evaluated, much the way links are with Penguin 4.0 which was released in 2016. To me, this would be a major change to the current “SERP ecosystem.”
I would even argue that, to an extent, we already have a continuous “real-time” algorithm. In fact, that we at least partially have a real-time Google algorithm is simply fact. As mentioned, In 2016, Google released Penguin 4.0 which removed the need to wait for another version of the update as this specific algorithm evaluates pages on a constant basis.
However, outside of Penguin, what do I mean when I say that, to an extent, we already have a continuous real-time algorithm?
The case for real-time algorithm adjustments
The constant “real-time” rank adjustments that occur in the ecosystem are so significant that they refined the volatility landscape.
Per Semrush data I pulled, there was a 58% increase in the number of days that reflected high-rank volatility in 2021 as compared to 2020. Similarly, there was a 59% increase in the number of days that reflected either high or very high levels of rank volatility:
Simply put, there is a significant increase in the number of instances that reflect elevated levels of rank volatility. After studying these trends and looking at the ranking patterns, I believe the aforementioned rank reversals are the cause. Meaning, a large portion of the increased instances in rank volatility are coming from what I believe to be machine learning continually recalibrating in “real-time,” thereby producing unprecedented levels of rank reversals.
Supporting this is the fact (that along with the increased instances of rank volatility) we did not see increases in how drastic the rank movement is. Meaning, there are more instances of rank volatility but the degree of volatility did not increase.
In fact, there was a decrease in how dramatic the average rank movement was in 2021 relative to 2020!
Why? Again, I chalk this up to the recalibration of machine learning properties and their “real-time” impact on rankings. In other words, we’re starting to see more micro-movements that align with the natural evolution of Google’s machine-learning properties.
When a machine learning property is refined as its intake/learning advances, you’re unlikely to see enormous swings in the rankings. Rather, you will see a refinement in the rankings that align with refinement in the machine learning itself.
Hence, the rank movement we’re seeing, as a rule, is far more constant yet not as drastic.
The final step towards continuous real-time algorithm updates
While much of the ranking movement that occurs is continuous in that it is not dependent on specific algorithmic refreshes, we’re not fully there yet. As I mentioned, much of the rank volatility is a series of reversing rank positions. Changes to these ranking patterns, again, are often not solidified until the rollout of an official Google update, most commonly, an official core algorithm update.
Until the longer-lasting ranking patterns are set without the need to “press the button” we don’t have a full-on continuous or “real-time” Google algorithm.
However, I have to wonder if the trend is not heading toward that. For starters, Google’s Helpful Content Update (HCU) does function in real-time.
Per Google:
“Our classifier for this update runs continuously, allowing it to monitor newly-launched sites and existing ones. As it determines that the unhelpful content has not returned in the long-term, the classification will no longer apply.”
How is this so? The same as what we’ve been saying all along here – Google has allowed its machine learning to have the autonomy it would need to be “real-time” or as Google calls it, “continuous”:
“This classifier process is entirely automated, using a machine-learning model.”
For the record, continuous does not mean ever-changing. In the case of the HCU, there’s a logical validation period before restoration. Should we ever see a “truly” continuous real-time algorithm, this may apply in various ways as well. I don’t want to let on that the second you make a change to a page, there will be a ranking response should we ever see a “real-time” algorithm.
At the same time, the “traditional” officially “button-pushed” algorithm update has become less impactful over time. In a study I conducted back in late 2021, I noticed that Semrush data indicated that since 2018’s Medic Update, the core updates being released were becoming significantly less impactful.
Data indicates that Google’s core updates are presenting less rank volatility overall as time goes on
Subsequently, this trend has continued. Per my analysis of the September 2022 Core Update, there was a noticeable drop-off in the volatility seen relative to the May 2022 Core Update.
Rank volatility change was far less dramatic during the September 2022 Core Update relative to the May 2022 Core Update
It’s a dual convergence. Google’s core update releases seem to be less impactful overall (obviously, individual sites can get slammed just as hard) while at the same time its latest update (the HCU) is continuous.
To me, it all points towards Google looking to abandon the traditional algorithm update release model in favor of a more continuous construct. (Further evidence could be in how the release of official updates has changed. If you look back at the various outlets covering these updates, the data will show you that the roll-out now tends to be slower with fewer days of increased volatility and, again, with less overall impact).
The question is, why would Google want to go to a more continuous real-time model?
Why a continuous real-time google algorithm is beneficial
A real-time continuous algorithm? Why would Google want that? It’s pretty simple, I think. Having an update that continuously refreshes rankings to reward the appropriate pages and sites is a win for Google (again, I don’t mean instant content revision or optimization resulting in instant rank change).
Which is more beneficial to Google’s users? A continuous-like updating of the best results or periodic updates that can take months to present change?
The idea of Google continuously analyzing and updating in a more real-time scenario is simply better for users. How does it help a user looking for the best result to have rankings that reset periodically with each new iteration of an official algorithm update?
Wouldn’t it be better for users if a site, upon seeing its rankings slip, made changes that resulted in some great content, and instead of waiting months to have it rank well, users could access it on the SERP far sooner?
Continuous algorithmic implementation means that Google can get better content in front of users far faster.
It’s also better for websites. Do you really enjoy implementing a change in response to ranking loss and then having to wait perhaps months for restoration?
Also, the fact that Google would so heavily rely on machine learning and trust the adjustments it was making only happens if Google is confident in its ability to understand content, relevancy, authority, etc. SEOs and site owners should want this. It means that Google could rely less on secondary signals and more directly on the primary commodity, content and its relevance, trustworthiness, etc.
Google being able to more directly assess content, pages, and domains overall is healthy for the web. It also opens the door for niche sites and sites that are not massive super-authorities (think the Amazons and WebMDs of the world).
Google’s better understanding of content creates more parity. Google moving towards a more real-time model would be a manifestation of that better understanding.
A new way of thinking about google updates
A continuous real-time algorithm would intrinsically change the way we would have to think about Google updates. It would, to a greater or lesser extent, make tracking updates as we now know them essentially obsolete. It would change the way we look at SEO weather tools in that, instead of looking for specific moments of increased rank volatility, we’d pay more attention to overall trends over an extended period of time.
Based on the ranking trends we already discussed, I’d argue that, to a certain extent, that time has already come. We’re already living in an environment where rankings fluctuate far more than they used to and to an extent has redefined what stable rankings mean in many situations.
To both conclude and put things simply, edging closer to a continuous real-time algorithm is part and parcel of a new era in ranking organically on Google’s SERP.
Mordy Oberstein is Head of SEO Branding at Wix. Mordy can be found on Twitter @MordyOberstein.
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SEO
How Compression Can Be Used To Detect Low Quality Pages
The concept of Compressibility as a quality signal is not widely known, but SEOs should be aware of it. Search engines can use web page compressibility to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords, making it useful knowledge for SEO.
Although the following research paper demonstrates a successful use of on-page features for detecting spam, the deliberate lack of transparency by search engines makes it difficult to say with certainty if search engines are applying this or similar techniques.
What Is Compressibility?
In computing, compressibility refers to how much a file (data) can be reduced in size while retaining essential information, typically to maximize storage space or to allow more data to be transmitted over the Internet.
TL/DR Of Compression
Compression replaces repeated words and phrases with shorter references, reducing the file size by significant margins. Search engines typically compress indexed web pages to maximize storage space, reduce bandwidth, and improve retrieval speed, among other reasons.
This is a simplified explanation of how compression works:
- Identify Patterns:
A compression algorithm scans the text to find repeated words, patterns and phrases - Shorter Codes Take Up Less Space:
The codes and symbols use less storage space then the original words and phrases, which results in a smaller file size. - Shorter References Use Less Bits:
The “code” that essentially symbolizes the replaced words and phrases uses less data than the originals.
A bonus effect of using compression is that it can also be used to identify duplicate pages, doorway pages with similar content, and pages with repetitive keywords.
Research Paper About Detecting Spam
This research paper is significant because it was authored by distinguished computer scientists known for breakthroughs in AI, distributed computing, information retrieval, and other fields.
Marc Najork
One of the co-authors of the research paper is Marc Najork, a prominent research scientist who currently holds the title of Distinguished Research Scientist at Google DeepMind. He’s a co-author of the papers for TW-BERT, has contributed research for increasing the accuracy of using implicit user feedback like clicks, and worked on creating improved AI-based information retrieval (DSI++: Updating Transformer Memory with New Documents), among many other major breakthroughs in information retrieval.
Dennis Fetterly
Another of the co-authors is Dennis Fetterly, currently a software engineer at Google. He is listed as a co-inventor in a patent for a ranking algorithm that uses links, and is known for his research in distributed computing and information retrieval.
Those are just two of the distinguished researchers listed as co-authors of the 2006 Microsoft research paper about identifying spam through on-page content features. Among the several on-page content features the research paper analyzes is compressibility, which they discovered can be used as a classifier for indicating that a web page is spammy.
Detecting Spam Web Pages Through Content Analysis
Although the research paper was authored in 2006, its findings remain relevant to today.
Then, as now, people attempted to rank hundreds or thousands of location-based web pages that were essentially duplicate content aside from city, region, or state names. Then, as now, SEOs often created web pages for search engines by excessively repeating keywords within titles, meta descriptions, headings, internal anchor text, and within the content to improve rankings.
Section 4.6 of the research paper explains:
“Some search engines give higher weight to pages containing the query keywords several times. For example, for a given query term, a page that contains it ten times may be higher ranked than a page that contains it only once. To take advantage of such engines, some spam pages replicate their content several times in an attempt to rank higher.”
The research paper explains that search engines compress web pages and use the compressed version to reference the original web page. They note that excessive amounts of redundant words results in a higher level of compressibility. So they set about testing if there’s a correlation between a high level of compressibility and spam.
They write:
“Our approach in this section to locating redundant content within a page is to compress the page; to save space and disk time, search engines often compress web pages after indexing them, but before adding them to a page cache.
…We measure the redundancy of web pages by the compression ratio, the size of the uncompressed page divided by the size of the compressed page. We used GZIP …to compress pages, a fast and effective compression algorithm.”
High Compressibility Correlates To Spam
The results of the research showed that web pages with at least a compression ratio of 4.0 tended to be low quality web pages, spam. However, the highest rates of compressibility became less consistent because there were fewer data points, making it harder to interpret.
Figure 9: Prevalence of spam relative to compressibility of page.
The researchers concluded:
“70% of all sampled pages with a compression ratio of at least 4.0 were judged to be spam.”
But they also discovered that using the compression ratio by itself still resulted in false positives, where non-spam pages were incorrectly identified as spam:
“The compression ratio heuristic described in Section 4.6 fared best, correctly identifying 660 (27.9%) of the spam pages in our collection, while misidentifying 2, 068 (12.0%) of all judged pages.
Using all of the aforementioned features, the classification accuracy after the ten-fold cross validation process is encouraging:
95.4% of our judged pages were classified correctly, while 4.6% were classified incorrectly.
More specifically, for the spam class 1, 940 out of the 2, 364 pages, were classified correctly. For the non-spam class, 14, 440 out of the 14,804 pages were classified correctly. Consequently, 788 pages were classified incorrectly.”
The next section describes an interesting discovery about how to increase the accuracy of using on-page signals for identifying spam.
Insight Into Quality Rankings
The research paper examined multiple on-page signals, including compressibility. They discovered that each individual signal (classifier) was able to find some spam but that relying on any one signal on its own resulted in flagging non-spam pages for spam, which are commonly referred to as false positive.
The researchers made an important discovery that everyone interested in SEO should know, which is that using multiple classifiers increased the accuracy of detecting spam and decreased the likelihood of false positives. Just as important, the compressibility signal only identifies one kind of spam but not the full range of spam.
The takeaway is that compressibility is a good way to identify one kind of spam but there are other kinds of spam that aren’t caught with this one signal. Other kinds of spam were not caught with the compressibility signal.
This is the part that every SEO and publisher should be aware of:
“In the previous section, we presented a number of heuristics for assaying spam web pages. That is, we measured several characteristics of web pages, and found ranges of those characteristics which correlated with a page being spam. Nevertheless, when used individually, no technique uncovers most of the spam in our data set without flagging many non-spam pages as spam.
For example, considering the compression ratio heuristic described in Section 4.6, one of our most promising methods, the average probability of spam for ratios of 4.2 and higher is 72%. But only about 1.5% of all pages fall in this range. This number is far below the 13.8% of spam pages that we identified in our data set.”
So, even though compressibility was one of the better signals for identifying spam, it still was unable to uncover the full range of spam within the dataset the researchers used to test the signals.
Combining Multiple Signals
The above results indicated that individual signals of low quality are less accurate. So they tested using multiple signals. What they discovered was that combining multiple on-page signals for detecting spam resulted in a better accuracy rate with less pages misclassified as spam.
The researchers explained that they tested the use of multiple signals:
“One way of combining our heuristic methods is to view the spam detection problem as a classification problem. In this case, we want to create a classification model (or classifier) which, given a web page, will use the page’s features jointly in order to (correctly, we hope) classify it in one of two classes: spam and non-spam.”
These are their conclusions about using multiple signals:
“We have studied various aspects of content-based spam on the web using a real-world data set from the MSNSearch crawler. We have presented a number of heuristic methods for detecting content based spam. Some of our spam detection methods are more effective than others, however when used in isolation our methods may not identify all of the spam pages. For this reason, we combined our spam-detection methods to create a highly accurate C4.5 classifier. Our classifier can correctly identify 86.2% of all spam pages, while flagging very few legitimate pages as spam.”
Key Insight:
Misidentifying “very few legitimate pages as spam” was a significant breakthrough. The important insight that everyone involved with SEO should take away from this is that one signal by itself can result in false positives. Using multiple signals increases the accuracy.
What this means is that SEO tests of isolated ranking or quality signals will not yield reliable results that can be trusted for making strategy or business decisions.
Takeaways
We don’t know for certain if compressibility is used at the search engines but it’s an easy to use signal that combined with others could be used to catch simple kinds of spam like thousands of city name doorway pages with similar content. Yet even if the search engines don’t use this signal, it does show how easy it is to catch that kind of search engine manipulation and that it’s something search engines are well able to handle today.
Here are the key points of this article to keep in mind:
- Doorway pages with duplicate content is easy to catch because they compress at a higher ratio than normal web pages.
- Groups of web pages with a compression ratio above 4.0 were predominantly spam.
- Negative quality signals used by themselves to catch spam can lead to false positives.
- In this particular test, they discovered that on-page negative quality signals only catch specific types of spam.
- When used alone, the compressibility signal only catches redundancy-type spam, fails to detect other forms of spam, and leads to false positives.
- Combing quality signals improves spam detection accuracy and reduces false positives.
- Search engines today have a higher accuracy of spam detection with the use of AI like Spam Brain.
Read the research paper, which is linked from the Google Scholar page of Marc Najork:
Detecting spam web pages through content analysis
Featured Image by Shutterstock/pathdoc
SEO
New Google Trends SEO Documentation
Google Search Central published new documentation on Google Trends, explaining how to use it for search marketing. This guide serves as an easy to understand introduction for newcomers and a helpful refresher for experienced search marketers and publishers.
The new guide has six sections:
- About Google Trends
- Tutorial on monitoring trends
- How to do keyword research with the tool
- How to prioritize content with Trends data
- How to use Google Trends for competitor research
- How to use Google Trends for analyzing brand awareness and sentiment
The section about monitoring trends advises there are two kinds of rising trends, general and specific trends, which can be useful for developing content to publish on a site.
Using the Explore tool, you can leave the search box empty and view the current rising trends worldwide or use a drop down menu to focus on trends in a specific country. Users can further filter rising trends by time periods, categories and the type of search. The results show rising trends by topic and by keywords.
To search for specific trends users just need to enter the specific queries and then filter them by country, time, categories and type of search.
The section called Content Calendar describes how to use Google Trends to understand which content topics to prioritize.
Google explains:
“Google Trends can be helpful not only to get ideas on what to write, but also to prioritize when to publish it. To help you better prioritize which topics to focus on, try to find seasonal trends in the data. With that information, you can plan ahead to have high quality content available on your site a little before people are searching for it, so that when they do, your content is ready for them.”
Read the new Google Trends documentation:
Get started with Google Trends
Featured Image by Shutterstock/Luis Molinero
SEO
All the best things about Ahrefs Evolve 2024
Hey all, I’m Rebekah and I am your Chosen One to “do a blog post for Ahrefs Evolve 2024”.
What does that entail exactly? I don’t know. In fact, Sam Oh asked me yesterday what the title of this post would be. “Is it like…Ahrefs Evolve 2024: Recap of day 1 and day 2…?”
Even as I nodded, I couldn’t get over how absolutely boring that sounded. So I’m going to do THIS instead: a curation of all the best things YOU loved about Ahrefs’ first conference, lifted directly from X.
Let’s go!
OUR HUGE SCREEN
The largest presentation screen I’ve ever seen! #ahrefsevolve pic.twitter.com/oboiMFW1TN
— Patrick Stox (@patrickstox) October 24, 2024
This is the biggest presentation screen I ever seen in my life. It’s like iMax for SEO presentations. #ahrefsevolve pic.twitter.com/sAfZ1rtePx
— Suganthan Mohanadasan (@Suganthanmn) October 24, 2024
CONFERENCE VENUE ITSELF
It was recently named the best new skyscraper in the world, by the way.
The Ahrefs conference venue feels like being in inception. #AhrefsEvolve pic.twitter.com/18Yjai1Cej
— Suganthan Mohanadasan (@Suganthanmn) October 24, 2024
I’m in Singapore for @ahrefs Evolve this week. Keen to connect with people doing interesting work on the future of search / AI #ahrefsevolve pic.twitter.com/s00UkIbxpf
— Alex Denning (@AlexDenning) October 23, 2024
OUR AMAZING SPEAKER LINEUP – SUPER INFORMATIVE, USEFUL TALKS!
A super insightful explanation of how Google Search Ranking works #ahrefsevolve pic.twitter.com/Cd1VSET2Aj
— Amanda Walls (@amandajwalls) October 24, 2024
“would I even do this if Google didn’t exist?” – what a great question to assess if you actually have the right focus when creating content amazing presentation from @amandaecking at #AhrefsEvolve pic.twitter.com/a6OKbKxwiS
— Aleyda Solis ️ (@aleyda) October 24, 2024
Attending @CyrusShepard ‘s talk on WTF is Helpful Content in Google’s algorithm at #AhrefsEvolve
“Focus on people first content”
Super relevant for content creators who want to stay ahead of the ever evolving Google search curve! #SEOTalk #SEO pic.twitter.com/KRTL13SB0g
This is the first time I am listening to @aleyda and it is really amazing. Lot of insights and actionable information.
Thank you #aleyda for power packed presentation.#AhrefsEvolve @ahrefs #seo pic.twitter.com/Xe3A9MGfrr
— Jignesh Gohel (@jigneshgohel) October 25, 2024
— Parth Suba (@parthsuba77) October 24, 2024
@thinking_slows thoughts on AI content – “it’s very good if you want to be average”.
We can do a lot better and Ryan explains how. Love it @ahrefs #AhrefsEvolve pic.twitter.com/qFqWs6QBH5
— Andy Chadwick (@digitalquokka) October 24, 2024
A super insightful explanation of how Google Search Ranking works #ahrefsevolve pic.twitter.com/Cd1VSET2Aj
— Amanda Walls (@amandajwalls) October 24, 2024
This is the first time I am listening to @aleyda and it is really amazing. Lot of insights and actionable information.
Thank you #aleyda for power packed presentation.#AhrefsEvolve @ahrefs #seo pic.twitter.com/Xe3A9MGfrr
— Jignesh Gohel (@jigneshgohel) October 25, 2024
GREAT MUSIC
First time I’ve ever Shazam’d a track during SEO conference ambience…. and the track wasn’t even Shazamable! #AhrefsEvolve @ahrefs pic.twitter.com/ZDzJOZMILt
— Lily Ray (@lilyraynyc) October 24, 2024
AMAZING GOODIES
Ahrefs Evolveきました！@ahrefs @AhrefsJP #AhrefsEvolve pic.twitter.com/33EiejQPdX
— さくらぎ (@sakuragi_ksy) October 24, 2024
Aside from the very interesting topics, what makes this conference even cooler are the ton of awesome freebies
Kudos for making all of these happen for #AhrefsEvolve @ahrefs team pic.twitter.com/DGzk5FSTN8
— Krista Melgarejo (@kimelgarejo) October 24, 2024
Content Goblin and SEO alligator party stickers are definitely going on my laptop. @ahrefs #ahrefsevolve pic.twitter.com/QBsBuY5Yix
— Patrick Stox (@patrickstox) October 24, 2024
This is one of the best swag bags I’ve received at any conference!
Either @ahrefs actually cares or the other conference swag bags aren’t up to par w Ahrefs!#AhrefsEvolve pic.twitter.com/Yc9e6wZPHn— Moses Sanchez (@SanchezMoses) October 25, 2024
SELFIE BATTLE
Some background: Tim and Sam have a challenge going on to see who can take the most number of selfies with all of you. Last I heard, Sam was winning – but there is room for a comeback yet!
Got the rare selfie with both @timsoulo and @samsgoh #AhrefsEvolve
— Bernard Huang (@bernardjhuang) October 24, 2024
THAT BELL
Everybody’s just waiting for this one.
@timsoulo @ahrefs #AhrefsEvolve pic.twitter.com/6ypWaTGDDP
— Jinbo Liang (@JinboLiang) October 24, 2024
STICKER WALL
Viva la vida, viva Seo!
Awante Argentina loco!#AhrefsEvolve pic.twitter.com/sfhbI2kWSH
— Gaston Riera. (@GastonRiera) October 24, 2024
AND, OF COURSE…ALL OF YOU!
#AhrefsEvolve let’s goooooooooooo!!! pic.twitter.com/THtdvdtUyB
— Tim Soulo (@timsoulo) October 24, 2024
–
There’s a TON more content on LinkedIn – click here – but I have limited time to get this post up and can’t quite figure out how to embed LinkedIn posts so…let’s stop here for now. I’ll keep updating as we go along!
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