Google has begun the rollout of the third version of the products reviews update, a search ranking algorithm update targeted at ranking product review related content on the web that is most helpful and useful to searchers. The first product reviews update was launched on April 8, 2021, the second was launched on December 1, 2021 and now the third has been released on March 23, 2022. The new is named the March 2022 product reviews update.

Google product reviews update. The Google product reviews update aims to promote review content that is above and beyond much of the templated information you see on the web. Google said it will promote these types of product reviews in its search results rankings.

Google is not directly punishing lower quality product reviews that have “thin content that simply summarizes a bunch of products.” However, if you provide such content and find your rankings demoted because other content is promoted above yours, it will definitely feel like a penalty. Technically, according to Google, this is not a penalty against your content, Google is just rewarding sites with more insightful review content with rankings above yours.

Technically, this update should only impact product review content and not other types of content.

What has changed. With the third release of the product reviews update, Google said this update “builds on” the work of the first two product review updates to enhance Google’s “ability to identify high quality product reviews.” “This will make it easier for us to get sound purchasing advice in front of users, and to reward creators who are earnest in being helpful,” Alan Kent of Google added.

Google listed the following criteria for what matters with the product reviews update:

• Include helpful in-depth details, like the benefits or drawbacks of a certain item, specifics on how a product performs or how the product differs from previous versions
• Come from people who have actually used the products, and show what the product is physically like or how it’s used
• Include unique information beyond what the manufacturer provides — like visuals, audio or links to other content detailing the reviewer’s experience
• Cover comparable products, or explain what sets a product apart from its competitors

The rollout. This rollout will happen over the “next few weeks,” Google said. These, and core updates, normally take a few weeks to rollout, so that should be no surprise. You should expect the bulk of the ranking volatility to happen in the earlier stages of this rollout.

What is impacted. Google said this update may impact those who “create product reviews in any language.” Google said the initial rollout will be “English-language product reviews” but they have seen “positive effects” from this update in the past and the search company “plans to open up product review support for more languages” in the future.

Initially, Google said this update is looking at “English-language product reviews across many sites.”

Please note that these updates can be very big, almost as big as core updates.

Previous advice on the product reviews update. The “focus overall is on providing users with content that provides insightful analysis and original research, content written by experts or enthusiasts who know the topic well,” Google said about this update. That is similar advice to the core update recommendations mentioned above, but here is a list of “additional useful questions to consider in terms of product reviews.” Google recommends your product reviews cover these areas and answer these questions. Do your product reviews…

• Express expert knowledge about products where appropriate?
• Show what the product is like physically, or how it is used, with unique content beyond what’s provided by the manufacturer?
• Provide quantitative measurements about how a product measures up in various categories of performance?
• Explain what sets a product apart from its competitors?
• Cover comparable products to consider, or explain which products might be best for certain uses or circumstances?
• Discuss the benefits and drawbacks of a particular product, based on research into it?
• Describe how a product has evolved from previous models or releases to provide improvements, address issues, or otherwise help users in making a purchase decision?
• Identify key decision-making factors for the product’s category and how the product performs in those areas? For example, a car review might determine that fuel economy, safety, and handling are key decision-making factors and rate performance in those areas.
• Describe key choices in how a product has been designed and their effect on the users beyond what the manufacturer says?
• Provide evidence such as visuals, audio, or other links of your own experience with the product, to support your expertise and reinforce the authenticity of your review.
• Include links to multiple sellers to give the reader the option to purchase from their merchant of choice.

Google also linked to its blog post from earlier this year named providing better product information for shoppers.

New advice. Google added three new points of new advice for this third-release of the products reviews update:

• Are product review updates relevant to ranked lists and comparison reviews? Yes. Product review updates apply to all forms of review content. The best practices we’ve shared also apply. However, due to the shorter nature of ranked lists, you may want to demonstrate expertise and reinforce authenticity in a more concise way. Citing pertinent results and including original images from tests you performed with the product can be good ways to do this.
• Are there any recommendations for reviews recommending “best” products? If you recommend a product as the best overall or the best for a certain purpose, be sure to share with the reader why you consider that product the best. What sets the product apart from others in the market? Why is the product particularly suited for its recommended purpose? Be sure to include supporting first-hand evidence.
• If I create a review that covers multiple products, should I still create reviews for the products individually? It can be effective to write a high quality ranked list of related products in combination with in-depth single-product reviews for each recommended product.  If you write both, make sure there is enough useful content in the ranked list for it to stand on its own.

Not a core update. Google also previous said that product reviews updates are not the same as core updates. This is a standalone update they’re calling the product reviews update. This is separate from Google’s regular core updates, the company told us. Nonetheless, Google did add that the advice it originally provided for core updates, “about producing quality content for those is also relevant here.” In addition to that advice, Google provided additional guidance specific to this update.

Why we care. If your website offers product review content, you will want to check your rankings to see if you were impacted. Did your Google organic traffic improve, decline or stay the same?

Long term, you are going to want to ensure that going forward, that you put a lot more detail and effort into your product review content so that it is unique and stands out from the competition on the web.

Also, those impacted by previous core updates, that put in the work, may be rewarded by this March 2022 product reviews update.

The post Google releases March 2022 product reviews update with additional ranking criteria appeared first on Search Engine Land.

On February 8, 2022, Google released a document to the U.S. Copyright Office saying “when a site is demoted [by the Pirate update], the traffic Google Search sends it drops, on average, by 89% on average.” This is a statement about Google’s efforts to remove sites that “received a large number of valid removal notices” as DMCA requires, hence the Google Pirate update from 2012.

The Pirate update. The Pirate update, which Google originally called the DMCA update, looked at if a site had a large number of DMCA takedown requests and if so, it demoted the site. Google officially only confirmed updating this algorithm once after its launch in 2012, that was in 2014. It is without a question that Google runs this algorithm update periodically to catch new sites that may be copying copyrighted content from others. But Google clearly does not announce each time the search company runs this update.

Google said in the new PDF document “we have developed a ‘demotion signal’ for Google Search that causes sites for which we have received a large number of valid removal notices to appear much lower in search results.”

89% demotion. This PDF document written by Google states that when sites are demoted by this algorithm, the sites hit on average see 89% less Google Search traffic as a result. The document states “When a site is demoted, the traffic Google Search sends it drops, on average, by 89% on average.”

Redirect tricks. Google also said that it has a flag named “still-in-theaters/prerelease” that will pick up on when a site hit by this update redirects to a new domain without that flag. Google said, “We have also made it much harder for infringing sites to evade demotion by redirecting people to a new domain.” Google added “we have added a “still-in-theaters/prerelease” flag for DMCA notices involving this category of content to enhance the Search demotion signal.”

This report comes via TorretFreak, a publication that tracks the latest news about copyright, privacy and related topics. Also, a hat tip to @GlennGabe for notifying me of this.

Why we care. If a site gets too many (how many is unknown) DMCA takedown requests, it can lead to that site being hit by this Pirate update. If a site gets hit by the Pirate update, you can expect, on average, 89% less traffic from Google Search to that site. And no, redirecting that site to a new domain name won’t seem to help you.

So if you run into any clients during an audit that have these notices, which you might see Search Console notification, then maybe check the Google transparency report for the domain and then maybe request reconsiderations after cleaning up the issues.

The post Google’s Pirate Update can cause 89% drop in search traffic for offending site appeared first on Search Engine Land.

As Google continues to leverage more artificial intelligence and machine learning in Google Search, one may wonder in what ways does AI and machine learning help Google Search perform its daily tasks. Since 2015, when Google introduced its first AI into search named RankBrain, Google has continued to deploy AI systems to better understand language and thus improve the search results Google presents to its searches.

Several months ago we sent Google a number of questions around how Google uses its AI in search, including RankBrain, neural matching, BERT and Google’s latest AI breakthrough – MUM. We’ve come up with more of an understanding of when Google uses AI, which AI does what in Google Search, how these various AI algorithms may work together, how they have changed over the years and what, if anything, search marketers need to know when it comes to how Google uses AI in search.

We spoke with Danny Sullivan, the Public Liaison for Google Search, to help with the answers to many of these questions. In short, RankBrain, neural matching and BERT are used in Google’s ranking system across many, if not most, queries and look at understanding the language of both the query and content it is ranking. However, MUM is not currently used for ranking purposes, it is currently only used for COVID vaccine naming and powers the related topics in videos results.

It starts by writing content for humans

You hear it all the time from Google representatives and from many SEOs: write content for humans. In the older days of SEO, when the algorithms were maybe simpler, you would have many SEOs who would craft content for each and every search engine (back then there were dozens of different search engines). Now, there is primarily Google, with a little bit of Bing and some ruffling from DuckDuckGo – but the algorithms are much more complex and with machine learning and AI, the algorithms understand language more like a human would understand language.

So the advice Google has given is write for humans, and that you can’t optimize your site for BERT or any AI. If you write content that humans understand, then the algorithms and AI search engines use will also understand it. In short, this article is not aimed at trying to give you SEO tips on how to optimize your sites for any specific AI, but rather to communicate how Google uses AI in Google Search.

Overview of AI used in Google Search

RankBrain. It starts with RankBrain, Google’s first attempt at using AI in search dates back to 2015. Google told us RankBrain helps Google understand how words are related to concepts and can take a broad query and better define how that query relates to real-world concepts. While it launched in 2015 and was used in 15% of queries, Google said it is now, in 2022, widely used in many queries and in all languages and regions. RankBrain does specifically help Google rank search results and is part of the ranking algorithm.

• Year Launched: 2015
• Used For Ranking: Yes
• Looks at the query and content language
• Works for all languages
• Very commonly used for many queries

Here is an example provided by Google of how RankBrain is used, if you search for “what’s the title of the consumer at the highest level of a food chain,” Google’s systems learn from seeing those words on various pages that the concept of a food chain may have to do with animals, and not human consumers. By understanding and matching these words to their related concepts, RankBrain helps Google understand that you’re looking for what’s commonly referred to as an “apex predator.”

Neural matching. Neural matching was the next AI Google released for search, it was released in 2018 and then expanded to the local search results in 2019. In fact, we have an article explaining the differences between RankBrain and neural matching over here. Google told us neural matching helps Google understand how queries relate to pages by looking at the entire query or content on the page and understanding it within the context of that page or query. Today, neural matching is used in many, if not most, queries, for all languages, in all regions, across most verticals of search. Neural matching does specifically help Google rank search results and is part of the ranking algorithm.

• Year Launched: 2018
• Used For Ranking: Yes
• Looks at the query and content language
• Works for all languages
• Very commonly used for many queries

Here is an example provided by Google of how neural matching is used, if you search for “insights how to manage a green,” for example. Google said “if a friend asked you this, you’d probably be stumped.” “But with neural matching, we’re able to make sense of this quizzical search. By looking at the broader representations of concepts in the query — management, leadership, personality and more — neural matching can decipher that this searcher is looking for management tips based on a popular, color-based personality guide,” Google told us.

BERT. BERT, Bidirectional Encoder Representations from Transformers, came in 2019, it is a neural network-based technique for natural language processing pre-training. Google told us BERT helps Google understand how combinations of words express different meanings and intents, including looking at the sequence of words on a page, so even seemingly unimportant words in your queries are counted for. When BERT launched, it was used in 10% of all English queries but expanded to more languages and used in almost all English queries early on. Today it is used in most queries and is supported in all languages. BERT does specifically help Google rank search results and is part of the ranking algorithm.

• Year Launched: 2019
• Used For Ranking: Yes
• Looks at the query and content language
• Works for all languages but Google said BERT “plays critical role in almost every English query”
• Very commonly used for many queries

Here is an example provided by Google of how BERT is used, if you search for “if you search for “can you get medicine for someone pharmacy,” BERT helps us understand that you’re trying to figure out if you can pick up medicine for someone else. Before BERT, we took that short preposition for granted, mostly surfacing results about how to fill a prescription,” Google told us.

MUM. MUM, Multitask Unified Model, is Google’s most recent AI in search. MUM was introduced in 2021 and then expanded again at the end of 2021 for more applications, with a lot of promising uses for it in the future. Google told us that MUM helps Google not just with understanding languages but also generating languages, so it can be used to understand variations in new terms and languages. MUM is not used for any ranking purposes right now in Google Search but does support all languages and regions.

• Year Launched: 2021
• Used For Ranking: No
• Not query or languages specific
• Works for all languages but Google not used for ranking purposes today
• Used for a limited number of purposes

Currently, MUM is used to improve searches for COVID-19 vaccine information, and Google said it is “looking forward to offering more intuitive ways to search using a combination of both text and images in Google Lens in the coming months.”

AI used together in search but may be specialized for search verticals

Danny Sullivan from Google also explained that while these are individual AI-based algorithms, they often work together to help with ranking and understanding the same query.

Google told us that all of these AI systems “are used to understand language including the query and potentially relevant results,” adding that “they are not designed to act in isolation to analyze just a query or a page.” Previously, it may have been assumed and understood that one AI system may have looked more at understanding the query and not the content on the page, but that is not the case, at least not in 2022.

Google also confirmed that in 2022 RankBrain, neural matching, and BERT are used globally, in all languages that Google Search operates in.

And when it comes to web search versus local search versus images, shopping and other verticals, Google explained that RankBrain, neural matching, and BERT are used for web search. Other modes or verticals of Google Search such as images or shopping mode use separate, specialized AI systems, according to Google.

What about core updates and AI

As explained above, Google uses RankBrain, neural matching, and BERT in most queries you enter into Google Search, but Google also has core updates. The Google broad core updates that Google rolls out a few times per year is often noticed by site owners, publishers, and SEOs more than when Google releases these larger AI-based systems.

But Google said these all can work together, with core updates. Google said these three, RankBrain, neural matching, and BERT are the larger AI systems they have. But they have many AI systems within search and some within the core updates that Google rolls out.

Google told us they do have other machine learning systems in Google Search. “RankBrain, neural matching, and BERT are just some of our more powerful and prominent systems,” Google said. Google added, “there are other AI elements that can impact core updates that don’t pertain to those specific three AI systems.”

The post How Google uses artificial intelligence In Google Search appeared first on Search Engine Land.

A little less than two months ago Search Engine Land made the decision to stop publishing versions of our content using Google’s Accelerated Mobile Pages. For us, it boiled down to wanting to simplify our reporting and our desire to end the process of hosting Search Engine Land content on 3rd party servers.

Read next: We’re turning off AMP pages at Search Engine Land

Since then, a lot has happened, but the bottom line is we have seen very little disruption to our traffic and have reaped the benefit of having a clearer picture of our audience analytics.

What happened to traffic? For us, it is difficult to draw any major conclusions about traffic changes since we turned off AMP. Search Engine Land is a media website that primarily produces journalism, so we are very much tied to the news that emerges. As you would expect, when big news like core updates or major Google Ads changes happens our traffic jumps. But as the news dies down during the holiday season we usually see month-to-month declines. That why year-over-year benchmarking is generally favored by news organizations.

We did not see any year-over-year declines in traffic that we could tie to AMP aside from the loss of pageviews to a handful of pieces that routinely spike for organic traffic. For example, an older article about Google SERP Easter Eggs ranks highly for us and usually spikes a few times during the year (including Easter time!). Mobile traffic to that post was previously going to the AMP version. However, we turned off AMP at a time that piece was spiking on mobile and did not see that traffic shift back to our native page. The page itself has never really driven quality traffic so the lost traffic isn’t really a problem.

Safeguarding. Around the time we shut off AMP we also took a few steps that could safeguard us in case the experiment caused a big traffic decline. We increased our publishing volume for starters. We also adjusted the strategy in our newsletters to better optimize for click-through rate. That move was also in response to Apple’s privacy change in iOS 15 that now makes open rates a less reliable metric.

The big win. One of the main reasons for turning off AMP was to better understand our metrics. Despite several failed attempts at AMP stitching in Google Analytics, we never could tell how our audience moves from our AMP pages to our native ones. Users were undoubtedly being double-counted as unique in both the AMP and our native website dashboards. The clearest indicator that this was true is in the change we’ve seen in return visitors since we turned off AMP. The number of sessions by return visitors has jumped by 30% since we made the change, and now we have a far better picture of our most valuable audience set.

Why we care. We went into this experiment knowing there was some risk, but haven’t seen anything to make us reconsider the move. The biggest question mark had always been around the Page Experience Update. AMP pages were as fast as they come, so the worry was that our native pages that don’t benchmark as fast as AMP would lose out. It is true that we saw the percentage of pages with “good” Core Web Vitals scores in Goole Search Console plummet when we turned off AMP, but we do not believe it hurt traffic or rankings. It makes sense because many SEOs are still struggling to tie their own wins or losses directly to the Page Experience Update.

So we’re not looking back. And if you have your story about turning off AMP we’d love to hear it.

Read next: Core Web Vitals: SEOs aren’t sold the work was worth it

The post What happened when we turned off AMP appeared first on Search Engine Land.

U.S. consumers spent $204.5 billion on e-commerce purchases during the holiday season, according to data from Adobe. This represented an 8.6% increase in online spending year-over-year.

The numbers show that e-commerce spenders spread out their shopping more over the months of November and December. For instance, in the weeks before Nov 24, spending was up a whopping 19.2% YoY, while the period between Thanksgiving and Cyber Monday – the so-called “Cyber Week”), spending was down 1.4% from the previous year. Sales were up again in the weeks after Nov 30, growing 5.6% YoY.

Undeterred by supply chain challenges. Disruptions to the supply chain created difficulties for shoppers even though they spent at record volume.

Online shoppers saw over 6 billion out-of-stock messages during the 2021 holidays, according to the Adobe study. This was 253% higher than 2019, in pre-pandemic times. It was also 10% higher than last year.

Less discounting. Online shoppers were ready to buy despite seeing fewer discounts. In the electronics category, for instance, discounts were only 8% off the full price, compared to an average of 21% off in 2020. Computers saw a 10% discount in 2021 compared to 22% off in 2020.

There were marginally higher discounts in 2021 for apparel and toys.

BNPL grew substantially. Buy Now Pay Later (BNPL) saw double-digit growth in the most recent holiday season. 

Revenue on BNPL purchases were up 27% year-over-year and orders were up 10%. On average, shoppers were spending $224 per order, with about three items in their cart.

Why we care. Ecommerce was a major disrupter in the retail industry before the pandemic. It then became a saving grace for many brands and consumers.

Search marketers working for retail or with retail clients, whether on the organic or paid side, see the holiday as make-or-break each year. And the increase in activity is likely to have buoyed campaigns. That’s, of course, if they were not disrupted by major Google updates during the season. Despite outcry, Google issued two major updates during the holiday, one a core update and one geared toward product reviews.

In our view, these purchase numbers support the idea that launching core updates, especially really volatile ones, during the holiday season is unfair to marketers and very similar to changing the rules in the middle of the Super Bowl.

Henry Powderly contributed to this report.

The post E-commerce sales jumped this holiday season as search marketers weathered Google updates appeared first on Search Engine Land.