SEO
What Are The Differences? (Festive Flashback)
Celebrate the Holidays with some of SEJ’s best articles of 2023.
Our Festive Flashback series runs from December 21 – January 5, featuring daily reads on significant events, fundamentals, actionable strategies, and thought leader opinions.
2023 has been quite eventful in the SEO industry and our contributors produced some outstanding articles to keep pace and reflect these changes.
Catch up on the best reads of 2023 to give you plenty to reflect on as you move into 2024.
Chatbots are taking the world by storm.
SEO pros, writers, agencies, developers, and even teachers are discussing the changes that this technology will cause in society and how we work in our day-to-day lives.
ChatGPT’s release on November 30, 2022 led to a cascade of competition, including Bard and Bing, although the latter runs on OpenAI’s technology.
If you want to search for information, need help fixing bugs in your CSS, or want to create something as simple as a robots.txt file, chatbots may be able to help.
They’re also wonderful for topic ideation, allowing you to draft more interesting emails, newsletters, blog posts, and more.
But which chatbot should you use and learn to master? Which platform provides accurate, concise information?
Let’s find out.
What Is The Difference Between ChatGPT, Google Bard, And Bing Chat?
ChatGPT | Bard | Bing | |
Pricing | ChatGPT’s original version remains free to users. ChatGPT Plus is available for $20/month. | Free for users who joined the waitlist and are accepted. | Free for users who are accepted after joining the waitlist. |
API | Yes, but on a waitlist. | N/A | N/A |
Developer | OpenAI | Alphabet/Google | OpenAI |
Technology | GPT-4 | LaMDA | GPT-4 |
Information Access | Training data with a cutoff date of 2021. The chatbot does state that it has been trained beyond this year, although it won’t include that information. | Real-time access to the data Google collects from search. | Real-time access to Bing’s search data. |
Wait! What Is GPT? What Is LaMDA?
ChatGPT uses GPT technology, and Bard uses LaMDA, meaning they’re different “under the hood.” This is why there’s some backlash against Bard. People expect Bard to be GPT, but that’s not the intent of the product.
Also, although Bing has chosen to collaborate with OpenAI, it uses fine-tuning, which allows it to tune responses for the end user.
Since Bing and Bard are both available on such a wide scale, they have to tune the responses to maintain their brand image and adhere to internal policies that aren’t as restrictive in ChatGPT – at the moment.
GPT: Chat Generative Pre-trained Transformer
GPTs are trained on tons of data using a two-phase concept called “unsupervised pre-training and then fine-tuning.” Imagine consuming billions of data points, and then someone comes along after you gain all of this knowledge to fine-tune it. That’s what is happening behind the scenes when you prompt ChatGPT.
ChatGPT had 175 billion parameters that it has used and learned from, including:
- Articles.
- Books.
- Websites.
- Etc.
While ChatGPT is limited in its datasets, OpenAI has announced a browser plugin that can use real-time data from websites when responding back to you. There are also other neat plugins that amplify the power of the bot.
LaMDA Stands For Language Model For Dialogue Applications
Google’s team decided to follow a LaMDA model for its neural network because it is a more natural way to respond to questions. The goal of the team was to provide conversational responses to queries.
The platform is trained on conversations and human dialog, but it is also apparent that Google uses search data to provide real-time data.
Google uses an Infiniset of data, which are datasets that we really don’t know much about at this point, as Google has kept this information private.
Since these bots are learning from sources worldwide, they also have a tendency to provide false information.
Hallucinations Can Happen
Chatbots can hallucinate, but they’re also very convincing in their responses. It’s important to heed the warning of the developers.
Google tells us:
Bing also tells us:
If you’re using chatbots for anything that requires facts and studies, be sure to crosscheck your work and verify that the facts and events actually happened.
There have been times when these hallucinations are apparent and other times when non-experts would easily be fooled by the response they receive.
Since chatbots learn from information, such as websites, they’re only as accurate as the information they receive – for now.
With all of these cautions in mind, let’s start prompting each bot to see which provides the best answers.
ChatGPT Vs. Bard Vs. Bing: Prompt Testing And Examples
Since technical SEO is an area I am passionate about, I wanted to see what the chatbots have to say when I put the following prompt in each:
What Are The Top 3 Technical SEO Factors I Can Use To Optimize My Site?
ChatGPT’s Response
ChatGPT provides a coherent, well-structured response to this query. The response does touch on three important areas of optimization:
- Site speed.
- Mobile responsiveness.
- Site architecture.
When prompted to provide more information on site speed, we receive a lot of great information that you can use to begin optimizing your site.
If you’ve ever tried to optimize your site’s speed before, you know just how important all of these factors are for improving your site speed.
ChatGPT mentions browser caching, but what about server-side caching?
When site speed is impacted by slow responses for database queries, server-side caching can store these queries and make the site much faster – beyond a browser cache.
Bard’s Response
Bard’s responses are faster than ChatGPT, and I do like that you can view other “drafts” from Bard if you like. I went with the first draft, which you can see below.
The information is solid, and I do appreciate that Google uses more formatting and bolds parts of the responses to make them easier to read.
Structured data was a nice addition to the list, and Bard even mentions Schema.org in its response.
To try and keep things similar, I asked Bard, “Can you elaborate on site speed?”
You can certainly find similarities between ChatGPT’s and Bard’s responses about optimization, but some information is a bit off. For example:
“A caching plugin stores static files on the user’s computer, which can improve load time.”
Caching plugins, often installed on your content management system (CMS), will store files on your server, a content delivery network (CDN), in memory, and so on.
However, the response from Bard indicates that the plugin will store static files on the user’s computer, which isn’t entirely wrong, but it’s odd.
Browsers will cache files automatically on their own, and you can certainly manipulate the cache with a Cache-Control or Expires header.
However, caching plugins can do so much more to improve site speed. I think Bard misses the mark a bit, as well as ChatGPT.
Bing’s Response
Bing is so hard to like because, for years, it has missed the mark in search. Is Chat any better? As an SEO and content creator, I love the fact that Bing provides sources in its responses.
I think for content creators that have relied on traffic from search for so long, citing sources is important. Also, when I want to verify a claim, these citations provide clarity that ChatGPT and Google Bard cannot.
The answers are similar to Bard and GPT, but let’s see what it produces when we ask for it to elaborate a little more:
Bing elaborated less than ChatGPT and Bard, providing just three points in its response. But can you spot the overlap between this response and the one from ChatGPT?
- Bing: You should compress your images and use the correct file format (JPEG for photographs, PNG for graphics).
- ChatGPT: You can compress them, reduce their file sizes, and use the correct file format (e.g., JPEG for photos, PNG for graphics).
The responses are going to be very similar for this type of answer, but neither mentioned using a format like WebP. They both seem to be lacking in this regard. Perhaps there’s just more data for optimizing JPEG and PNG files, but will this change?
This is an interesting concept because what if thousands of articles are created to provide the wrong advice, such as eliminating images completely?
Let’s move on to website caching. Bing’s response is a little more in-depth, explaining what caching can help you achieve, such as a lower time to first byte (TTFB).
Winner: Bing. I thought ChatGPT would win this query, but it turns out Bing provides a little more information on caching and wins out in the “technical” arena. Bard and ChatGPT did provide more solutions for improving your site speed.
Who Is Ludwig Makhyan?
All chatbots knew a little something about technical SEO, but how about me? Let’s see what happens when I ask them about myself:
ChatGPT’s Response
ChatGPT couldn’t find any information about me, which is understandable. I’m not Elon Musk or a famous person, but I did publish a few articles on this very blog you’re reading now before the data cutoff date of ChatGPT.
I have a feeling that Bing and Bard will do a little better for this query.
Bard’s Response
Hmm. The first sentence seems a bit familiar. It came directly from my Search Engine Journal bio, word-for-word. The last sentence in the first paragraph also comes word-for-word from another publication that I write for: “He is the co-founder at MAZELESS, an enterprise SEO agency.”
I’m also not the author of either of these books, although I’ve talked about these topics in great detail before.
Unfortunately, pulling full sentences from sources and providing false information means Bard failed this test. You could argue that there are a few ways to rephrase those sentences, but the response could certainly be better.
Bing’s Response
Bing also took my profile information directly, and most of the other information is the same, too. Bing does provide a much shorter response and links to the sources.
From this data, it seems to me that there needs to be a lot of references for chatbots to work from to define a person. But let’s see what these bots can do with a better prompt that is a bit more advanced.
Advanced Prompt: I Want To Become An Authority In SEO. What Steps Should I Take To Reach This Goal?
Up until this point, the prompts have been a bit easy. Let’s find out how each chatbot performs when we use more advanced prompts:
ChatGPT’s Response
Bard’s Response
Bing’s Response
ChatGPT provides me with more “light bulb” moments, explaining that I should learn things like keyword research, on-page optimization, and link building.
Knowledge seemed to be the core of the recommendations from ChatGPT, but it would have been nice if it mentioned anything about getting published.
Overall, these tips are very similar, but ChatGPT was my favorite. Let’s try putting these chatbots to work on some tasks that I’m sure they can perform.
Advanced Prompt: Create A Robots.txt File Where I Block Google Search Bot, Hide My “Private” Folder, And Block The Following IP Address “123.123.123.123”
ChatGPT’s Response
ChatGPT listened to my directions, reiterated them to me, showed me a makefile for the robots.txt, and then explained the parameters to use. I’m impressed.
Bard’s Response
Google! Are you assuming that you’re the only search bot in the world because you’re blocking everyone? Unfortunately, Bard uses the “*” as an agent, meaning every search engine is blocked from going to my site – not just Google.
Interestingly, when I repeated the question to block Bing on a fresh chat, it provided the same answer. But when I asked the question a second time in a row, it provided a much better answer with some comments:
Bing’s Response
Bing tries hard, and I appreciate the explanation that it provides. However, it’s a bit strange. We’re disallowing all bots using “/” and then allowing using “/$,” which allows them to crawl the homepage and nothing else and then denying a certain IP address.
ChatGPT wins this test because it provides a clean and easy way to make your robots.txt file. The other two examples need some fine-tuning and will have undesired consequences if you simply copy and paste them into your robots.txt file.
Advanced Prompt: What Are The Top 3 Destinations In Italy To Visit, And What Should I Know Before Visiting Them?
ChatGPT’s Response
ChatGPT does a nice job with its recommended places and provides useful tips for each that are on the same point. I also like how “St. Mark’s Square” was used, showing the bot being able to discern that “Piazza San Marco” is called “St. Mark’s Square” in English.
As a follow-up question, I asked what sunglasses to wear in Italy during my trip, and the response was:
This was a long shot, as the AI doesn’t know my facial shape, likes and dislikes, or interests in fashion. But it did recommend some of the popular eyewear, like the world-famous Ray-Ban Aviators.
Bard’s Response
Bard did really well here, and I actually like the recommendations that it provides.
Reading this, I know that Rome is crowded and expensive, and if I want to learn about Italian art, I can go to the Uffizi Gallery when I’m in Florence.
Just out of curiosity, I looked at the second draft from Bard, and it was even better than the first.
This is the “things to know” section, which is certainly more insightful than the first response. I learned that the cities are walkable, public transport is available, and pickpocketing is a problem (I was waiting for this to be mentioned).
The third draft was much like the first, but I’m learning something about Bard throughout all of this.
Bard seems to have answers with great insights, but it’s not always the first draft or response that the bot gives. If Google corrects this issue, it might provide even better answers than Bing and ChatGPT.
When I asked about sunglasses to wear, it came up with similar answers as ChatGPT, but even more specific models. Again Bard doesn’t know much about me personally:
Bing’s Response
Bing did very well with its response, but it’s curious that it says, “According to 1,” because it would be much nicer to put the site or publication’s name in the place of the number one. The responses are all accurate, albeit very short.
Bard wins this query because it provides more in-depth, meaningful answers. The bot even recommended some very good places to visit in each area, which Bing failed to do. ChatGPT did do well here, too, but the win goes to Bard.
And for the sunglasses query, you be the judge. Some of the recommendations in the list may be out of range for many travelers:
But I did notice the same Aviator sunglasses in the summary.
Which Chatbot Is Better At This Stage?
Each tool has its own strengths and weaknesses.
It’s clear that Bard lacks in its initial response, although it’s quick and provides decent answers. Bard has a nice UI, and I believe it has the answers. But I also think it has some “brain fog,” or should we call it “bit fog?”
Bing’s sources are a nice touch and something I hope all of these chatbots eventually incorporate.
The platform is nice to use, but I’m hearing ads are being integrated into it, which will be interesting. Will ads take priority in chat? For example, if I asked my last question about Italy, would ads:
- Gain priority in what information is displayed?
- Cause misinformation? For example, would the top pizza place be paid ad from a place with horrible reviews instead of the top-rated pizzeria?
ChatGPT, Bard, and Bing are all interesting tools, but what does the future hold for publishers and users? That’s something I cannot answer. No one can yet.
And There’s Also The Major Question: Is AI “Out Of Control?”
Elon Musk, Steve Wozniak, and over a thousand other leaders in tech, AI, ethics, and more are calling for a six-month pause on AI beyond GPT-4.
The pause is not to hinder progress but to allow time to understand the “profound risks to society and humanity.”
These leaders are asking for time to develop and implement measures to ensure that AI tools are safe and are asking governments to create a moratorium to address the issues.
What are your thoughts on these AI tools? Should we pause anything beyond GPT-4 until new measures are in place?
More Resources:
Featured Image: Legendary4/Shutterstock
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
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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!