#Rank tracker search algo update
Text
Rank tracker search algo update
RANK TRACKER SEARCH ALGO UPDATE UPDATE
As with the previous anti-spam updates, it is mainly targeting low-quality sites that trick users into providing personal information or installing malware.Įxtraordinary volatility in the SERPs over the weekend. Google has announced the release of a new version of its anti-spam algorithm. Google announced the rollout of the next Core Algorithm update, which began on November 17th. "It involved a rebalancing of various factors we consider in generating local search results
RANK TRACKER SEARCH ALGO UPDATE UPDATE
Google said that this local update began November 30th and ran through December 8th. This comes a few days after the unconfirmed January 11th update. These updates are unrelated to crawl spike activity.įriday and Saturday, January 14th and 15th there may have been another unconfirmed Google search ranking algorithm update. It seems we are seeing again signs of another Google search ranking algorithm update occurring yesterday and today, January 19th and 20th. 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. Google strengthens The Pirate (DMCA) Penalty Algorithm. Goolge is releasing its page experience update to the desktop results. This is most likely unrelated to the desktop version of the page experience update that started on Thursday, February 22nd. Unconfirmed Google Search ranking algorithm update that seemed to touch down on Thursday, February 24th. The update includes more in-depth details, visuals, and comparisons with competitor products. Google has launched an update on product reviews shown in Search. It will take 1-2 weeks to be fully rolled out. Google has started the rollout of a new broad core update. Google announced that it will only crawl, and utilize the first 15MB of a page for ranking purposes. Released the July 2022 product reviews update for English-language product reviews. Google starts its 2 week rollout of its Helpful Content Updater, a sitewide signal targeting websites that have a relatively high amount of unsatisfying or unhelpful content, where the content is written for search engines first.
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Top Guidelines Of Seo checker
https://stationtool.com/blog/topseo
When you get started with your backlink generation functions, the very first issues you must have a look at are classified as the backlinks of your competitor’s web page. Being familiar with the backlink system within your competitor site is a great gain though escalating your web site.
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Besides, the wise crawling algo checks a lot more important and dynamic webpages extra usually, so some internet pages can even be recrawled and inbound links up-to-date each day. Dwell backlink record stats
Search engine optimisation means “Search Engine Optimization”. This can be the apply of accomplishing higher rankings for your website using a number of strategies, all derived from tests and research in order that what we do basically works.
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How important is SEO Powersuite's search algo subscription? https://www.reddit.com/r/SEO/comments/e8v955/how_important_is_seo_powersuites_search_algo/
Hey There,
So i understand SEO powersuite is available currently for a good discount. But i do see they only provide 6 months of search algo data updates. After that you need to pay every month to get those search engine updates.
Now my question is, what if I pay for those updates maybe 1 month and then dont use the tool for a couple of months and then pay again for a month. So basically on and off after the first 6 months is over.
Will I still be able to use rank tracker when I want to with the latest search engine data?
submitted by /u/mickeyprime1
[link] [comments] December 11, 2019 at 12:51AM
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What Is A Search Engine Algorithm? and How It’s Work?
what is a Search Engine Algorithm? it as “a process or set of rules” to be followed in calculations or other problem-solving operations, especially by a search engine.
8 major Google algorithm updates :
1. Panda: February 24, 2011
2. Penguin: April 24, 2012
3. Hummingbird: August 22, 2013.
4. Pigeon: July 24, 2014( US); December 22, 2014 (UK, Canada, Australia )
5. Mobile: April 21, 2015
6. RankBrain: October 26, 2015
7. Possum: September 1, 2016.
8. Fred: March 8, 2017.
01. Panda: February 24,
Hazards:
Duplicate, plagiarized or thin content; user-generated spam; keyword stuffing.
How it works:
Panda assigns a so-called “quality score” to web pages; this score is then used as a ranking factor. Initially, Panda was a filter rather than part of Google’s ranking algo, but in January 2016, it was officially incorporated into the core algorithm. Panda rollouts have become more frequent, so both penalties and recoveries now happen faster.
How to adjust:
Run regular site checks for content duplication, thin content, and keyword stuffing. To do that, you’ll need a site crawler, like SEO PowerSuite’s Website Auditor.
( If you have an e-commerce site and cannot afford to have 100 percent unique content, try to use original images where you can, and utilize user reviews to make product descriptions stand out from the crowd.)
02. Penguin: April 24 , 2012
Hazards :
Spammy or irrelevant links; links with over-optimized anchor text.
How it works:
Google Penguin’s objective is to down-rank sites whose links it deems manipulative. Since late 2016, Penguin has been part of Google’s core algorithm; unlike Panda, it works in real-time.
How to adjust:
Monitor your link profile’s growth and run regular audits with a backlink checker like SEO SpyGlass. In the tool’s Summary dashboard, you’ll find a progress graph for your link profile’s growth. Look out for any unusual spikes: those are reason enough to look into the backlinks you’ve unexpectedly gained.
03. Hummingbird:August 22, 2013
Hazards: Keyword stuffing; low-quality content
How it works:
Hummingbird helps Google better interpret search queries and provide results that match searcher intent (as opposed to the individual terms within the query). While keywords continue to be important, Hummingbird makes it possible for a page to rank for a query even if it doesn’t contain the exact words the searcher entered. This is achieved with the help of natural language processing that relies on latent semantic indexing, co-occurring terms, and synonyms.
How to adjust:
Expand your keyword research and focus on concepts, not keywords. Carefully research related searches, synonyms, and co-occurring terms. Great sources of such ideas are Google Related Searches and Google Autocomplete. You’ll find all of them incorporated into Rank Tracker’s Keyword Research module.
(Use these insights to understand your audience’s language better and diversify your content.)
04. Pigeon : July 24, 2014 (US); December 22, 2014 (UK, Canada, Australia)
Hazards: Poor on- and off-page SEO.
How it works:
Pigeon affects those searches in which the user’s location plays an important part. The update created closer ties between the local algorithm and the core algorithm: traditional SEO factors are now used to rank local results.
How to Adjust :
Invest effort into on- and off-page SEO. A good starting point is running an on-page analysis with WebSite Auditor. The tool’s Content Analysis dashboard will give you a good idea about the aspects of on-page optimization you need to focus on.
05. MOBILE: April 21, 2015
Hazards: Lack of a mobile version of the page; poor mobile usability.
How it Works :
Google’s Mobile Update (aka Mobilegeddon) ensures that mobile-friendly pages rank at the top of mobile search, while pages not optimized for mobile are filtered out from the SERPs or seriously down-ranked.
How To Adjust :
Go mobile and focus on speed and usability. Google’s mobile-friendly test will help you see which aspects of your page’s mobile version need to be improved. The test in integrated into WebSite Auditor so you can check your pages’ mobile friendliness quickly. You’ll find it in Content Analysis > Page Audit, under the Technical factors tab.
06. RankBrain: October 26, 2015
Hazards :
Lack of query-specific relevance features; shallow content; poor UX.
How It Works :
RankBrain is part of Google’s Hummingbird algorithm. It is a machine learning system that helps Google understand the meaning behind queries, and serve best-matching search results in response to those queries. Google calls RankBrain the third most important ranking factor. While we don’t know the ins and outs of RankBrain, the general opinion is that it identifies relevance features for web pages ranking for a given query, which are basically query-specific ranking factors.
How to Adjust :
Optimize content for relevance and comprehensiveness with the help of competitive analysis. With the help of WebSite Auditor‘s TF-IDF tool, you can discover relevant terms and concepts used by a large number of your top-ranking competitors: those are a brilliant way to diversify your content.
07 Possum: September 1, 2016
Hazards: Tense competition in your target location.
How it Works :
The Possum update ensured that local results vary more depending on the searcher’s location: the closer you are to a business’s address, the more likely you are to see it among local results. Possum also resulted in greater variety among results ranking for very similar queries, like “dentist Denver” and “dentist Denver co.” Interestingly, Possum also gave a boost to businesses located outside the physical city area.
How to Adjust :
Expand your keyword list and do location-specific rank tracking. Local businesses now need to be targeting more keywords than they used to, due to the volatility Possum brought into the local SERPs. As you check your rankings, make sure you’re doing this from your target location (or, better yet, a bunch of them). You can do this in Rank Tracker under Preferences > Preferred Search Engines. Click Add Custom next to Google. Next, specify your preferred location — you can make it as specific as a street address.
08. Fred: March 8, 2017
Hazards: Thin, affiliate-heavy, or ad-centered content.
How it Works :
The latest of Google’s confirmed updates, Fred targets websites that violate Google’s webmaster guidelines. The majority of affected sites are blogs with low-quality posts that appear to be created mostly for the purpose of generating ad revenue.
How to Adjust :
Review Google Search Quality Guidelines and watch out for thin content. If you show ads, make sure the pages they are found on are high-quality and offer relevant ample information. This is basically it: Don’t try to trick Google into thinking your page is about something when it really is a gateway page full of affiliate links. Most publishers make money off ads, and that’s totally legit as long as you are not cheating.
some other google algorithms update :
Ø Mobile Speed Update: 17 Jan, 2018.
Ø Site Diversity Update: 3 June 2019
Fix it: improve situations where sites had more than two organic listings.
Ø BERT Update: 25 Oct 2019
FIX IT :
Google upgraded its algorithm and hardware to understand BERT natural language processing (NLP) model. BERT allows Google to better interpret and understand language searches and thus improve search results.
#digitalmarketing#digitalmarketingcompany#webstagram#seoservice#socialmediamarketing#digital drawing#tranding#education#knowaboutme
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Google's May 2020 Core Update: Winners, Winnerers, Winlosers, and Why It's All Probably Crap
Posted by Dr-Pete On May 4, Google announced that they were rolling out a new Core Update. By May 7, it appeared that the dust had mostly settled. Here’s an 11-day view from MozCast: We measured relatively high volatility from May 4-6, with a peak of 112.6° on May 5. Note that the 30-day average temperature prior to May 4 was historically very high (89.3°). How does this compare to previous Core Updates? With the caveat that recent temperatures have been well above historical averages, the May 2020 Core Update was our second-hottest Core Update so far, coming in just below the August 2018 “Medic” update.
Who “won” the May Core Update?
It’s common to report winners and losers after a major update (and I’ve done it myself), but for a while now I’ve been concerned that these analyses only capture a small window of time. Whenever we compare two fixed points in time, we’re ignoring the natural volatility of search rankings and the inherent differences between keywords. This time around, I’d like to take a hard look at the pitfalls. I’m going to focus on winners. The table below shows the 1-day winners (May 5) by total rankings in the 10,000-keyword MozCast tracking set. I’ve only included subdomains with at least 25 rankings on May 4: Putting aside the usual statistical suspects (small sample sizes for some keywords, the unique pros and cons of our data set, etc.), what’s the problem with this analysis? Sure, there are different ways to report the “% Gain” (such as absolute change vs. relative percentage), but I’ve reported the absolute numbers honestly and the relative change is accurate. The problem is that, in rushing to run the numbers after one day, we’ve ignored the reality that most core updates are multi-day (a trend that seemed to continue for the May Core Update, as evidenced by our initial graph). We’ve also failed to account for domains whose rankings might be historically volatile (but more on that in a bit). What if we compare the 1-day and 2-day data?
Which story do we tell?
The table below adds in the 2-day relative percentage gained. I’ve kept the same 25 subdomains and will continue to sort them by the 1-day percentage gained, for consistency: Even just comparing the first two days of the roll-out, we can see that the story is shifting considerably. The problem is: Which story do we tell? Often, we’re not even looking at lists, but anecdotes based on our own clients or cherry-picking data. Consider this story: If this was our only view of the data, we would probably conclude that the update intensified over the two days, with day two rewarding sites even more. We could even start to craft a story about how demand for apps was growing, or certain news sites were being rewarded. These stories might have a grain of truth, but the fact is that we have no idea from this data alone. Now, let’s pick three different data points (all of these are from the top 20): From this limited view, we could conclude that Google decided that the Core Update went wrong and reversed it on day two. We could even conclude that certain news sites were being penalized for some reason. This tells a wildly different story than the first set of anecdotes. There’s an even weirder story buried in the May 2020 data. Consider this: LinkedIn showed a minor bump (one we’d generally ignore) on day one and then lost 100% of its rankings on day two. Wow, that May Core Update really packs a punch! It turns out that LinkedIn may have accidentally de-indexed their site — they recovered the next day, and it appears this massive change had nothing to do with the Core Update. The simple truth is that these numbers tell us very little about why a site gained or lost rankings.
How do we define “normal”?
Let’s take a deeper look at the MarketWatch data. Marketwatch gained 19% in the 1-day stats, but lost 2% in the 2-day numbers. The problem here is that we don’t know from these numbers what MarketWatch’s normal SERP flux looks like. Here’s a graph of seven days before and after May 4 (the start of the Core Update): Looking at even a small bit of historical data, we can see that MarketWatch, like most news sites, experiences significant volatility. The “gains” on May 5 are only because of losses on May 4. It turns out that the 7-day mean after May 4 (45.7) is only a slight increase over the 7-day mean before May 4 (44.3), with MarketWatch measuring a modest relative gain of +3.2%. Now let’s look at Google Play, which appeared to be a clear winner after two days: You don’t even need to do the math to spot the difference here. Comparing the 7-day mean before May 4 (232.9) to the 7-day mean after (448.7), Google Play experienced a dramatic +93% relative change after the May Core Update. How does this 7-day before/after comparison work with the LinkedIn incident? Here’s a graph of the before/after with dotted lines added for the two means: While this approach certainly helps offset the single-day anomaly, we’re still showing a before/after change of -16%, which isn’t really in line with reality. You can see that six of the seven days after the May Core Update were above the 7-day average. Note that LinkedIn also has relatively low volatility over the short-range history. Why am I rotten-cherry-picking an extreme example where my new metric falls short? I want it to be perfectly clear that no one metric can ever tell the whole story. Even if we accounted for the variance and did statistical testing, we’re still missing a lot of information. A clear before/after difference doesn’t tell us what actually happened, only that there was a change correlated with the timing of the Core Update. That’s useful information, but it still begs further investigation before we jump to sweeping conclusions. Overall, though, the approach is certainly better than single-day slices. Using the 7-day before-vs-after mean comparison accounts for both historical data and a full seven days after the update. What if we expanded this comparison of 7-day periods to the larger data set? Here’s our original “winners” list with the new numbers: Obviously, this is a lot to digest in one table, but we can start to see where the before-and-after metric (the relative difference between 7-day means) shows a different picture, in some cases, than either the 1-day or 2-day view. Let’s go ahead and re-build the top 20 based on the before-and-after percentage change: Some of the big players are the same, but we’ve also got some newcomers — including sites that looked like they lost visibility on day one, but have stacked up 2-day and 7-day gains. Let’s take a quick look at Parents.com, our original big winner (winnerer? winnerest?). Day one showed a massive +100% gain (doubling visibility), but day-two numbers were more modest, and before-and-after gains came in at just under half the day-one gain. Here are the seven days before and after: It’s easy to see here that the day-one jump was a short-term anomaly, based in part on a dip on May 4. Comparing the 7-day averages seems to get much closer to the truth. This is a warning not just to algo trackers like myself, but to SEOs who might see that +100% and rush to tell their boss or client. Don’t let good news turn into a promise that you can’t keep.
Why do we keep doing this?
If it seems like I’m calling out the industry, note that I’m squarely in my own crosshairs here. There’s tremendous pressure to publish analyses early, not just because it equates to traffic and links (frankly, it does), but because site owners and SEOs genuinely want answers. As I wrote recently, I think there’s tremendous danger in overinterpreting short-term losses and fixing the wrong things. However, I think there’s also real danger in overstating short-term wins and having the expectation that those gains are permanent. That can lead to equally risky decisions. Is it all crap? No, I don’t think so, but I think it’s very easy to step off the sidewalk and into the muck after a storm, and at the very least we need to wait for the ground to dry. That’s not easy in a world of Twitter and 24-hour news cycles, but it’s essential to get a multi-day view, especially since so many large algorithm updates roll out over extended periods of time. Which numbers should we believe? In a sense, all of them, or at least all of the ones we can adequately verify. No single metric is ever going to paint the entire picture, and before you rush off to celebrate being on a winners list, it’s important to take that next step and really understand the historical trends and the context of any victory.
Who wants some free data?
Given the scope of the analysis, I didn’t cover the May 2020 Core Update losers in this post or go past the Top 20, but you can download the raw data here. If you’d like to edit it, please make a copy first. Winners and losers are on separate tabs, and this covers all domains with at least 25 rankings in our MozCast 10K data set on May 4 (just over 400 domains). Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!
https://www.businesscreatorplus.com/googles-may-2020-core-update-winners-winnerers-winlosers-and-why-its-all-probably-crap/
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Google's May 2020 Core Update: Winners, Winnerers, Winlosers, and Why It's All Probably Crap
Posted by Dr-Pete
On May 4, Google announced that they were rolling out a new Core Update. By May 7, it appeared that the dust had mostly settled. Here’s an 11-day view from MozCast:
We measured relatively high volatility from May 4-6, with a peak of 112.6° on May 5. Note that the 30-day average temperature prior to May 4 was historically very high (89.3°).
How does this compare to previous Core Updates? With the caveat that recent temperatures have been well above historical averages, the May 2020 Core Update was our second-hottest Core Update so far, coming in just below the August 2018 “Medic” update.
Who “won” the May Core Update?
It’s common to report winners and losers after a major update (and I’ve done it myself), but for a while now I’ve been concerned that these analyses only capture a small window of time. Whenever we compare two fixed points in time, we’re ignoring the natural volatility of search rankings and the inherent differences between keywords.
This time around, I’d like to take a hard look at the pitfalls. I’m going to focus on winners. The table below shows the 1-day winners (May 5) by total rankings in the 10,000-keyword MozCast tracking set. I’ve only included subdomains with at least 25 rankings on May 4:
Putting aside the usual statistical suspects (small sample sizes for some keywords, the unique pros and cons of our data set, etc.), what’s the problem with this analysis? Sure, there are different ways to report the “% Gain” (such as absolute change vs. relative percentage), but I’ve reported the absolute numbers honestly and the relative change is accurate.
The problem is that, in rushing to run the numbers after one day, we’ve ignored the reality that most core updates are multi-day (a trend that seemed to continue for the May Core Update, as evidenced by our initial graph). We’ve also failed to account for domains whose rankings might be historically volatile (but more on that in a bit). What if we compare the 1-day and 2-day data?
Which story do we tell?
The table below adds in the 2-day relative percentage gained. I’ve kept the same 25 subdomains and will continue to sort them by the 1-day percentage gained, for consistency:
Even just comparing the first two days of the roll-out, we can see that the story is shifting considerably. The problem is: Which story do we tell? Often, we’re not even looking at lists, but anecdotes based on our own clients or cherry-picking data. Consider this story:
If this was our only view of the data, we would probably conclude that the update intensified over the two days, with day two rewarding sites even more. We could even start to craft a story about how demand for apps was growing, or certain news sites were being rewarded. These stories might have a grain of truth, but the fact is that we have no idea from this data alone.
Now, let’s pick three different data points (all of these are from the top 20):
From this limited view, we could conclude that Google decided that the Core Update went wrong and reversed it on day two. We could even conclude that certain news sites were being penalized for some reason. This tells a wildly different story than the first set of anecdotes.
There’s an even weirder story buried in the May 2020 data. Consider this:
LinkedIn showed a minor bump (one we’d generally ignore) on day one and then lost 100% of its rankings on day two. Wow, that May Core Update really packs a punch! It turns out that LinkedIn may have accidentally de-indexed their site — they recovered the next day, and it appears this massive change had nothing to do with the Core Update. The simple truth is that these numbers tell us very little about why a site gained or lost rankings.
How do we define “normal”?
Let’s take a deeper look at the MarketWatch data. Marketwatch gained 19% in the 1-day stats, but lost 2% in the 2-day numbers. The problem here is that we don’t know from these numbers what MarketWatch’s normal SERP flux looks like. Here’s a graph of seven days before and after May 4 (the start of the Core Update):
Looking at even a small bit of historical data, we can see that MarketWatch, like most news sites, experiences significant volatility. The “gains” on May 5 are only because of losses on May 4. It turns out that the 7-day mean after May 4 (45.7) is only a slight increase over the 7-day mean before May 4 (44.3), with MarketWatch measuring a modest relative gain of +3.2%.
Now let’s look at Google Play, which appeared to be a clear winner after two days:
You don’t even need to do the math to spot the difference here. Comparing the 7-day mean before May 4 (232.9) to the 7-day mean after (448.7), Google Play experienced a dramatic +93% relative change after the May Core Update.
How does this 7-day before/after comparison work with the LinkedIn incident? Here’s a graph of the before/after with dotted lines added for the two means:
While this approach certainly helps offset the single-day anomaly, we’re still showing a before/after change of -16%, which isn’t really in line with reality. You can see that six of the seven days after the May Core Update were above the 7-day average. Note that LinkedIn also has relatively low volatility over the short-range history.
Why am I rotten-cherry-picking an extreme example where my new metric falls short? I want it to be perfectly clear that no one metric can ever tell the whole story. Even if we accounted for the variance and did statistical testing, we’re still missing a lot of information. A clear before/after difference doesn’t tell us what actually happened, only that there was a change correlated with the timing of the Core Update. That’s useful information, but it still begs further investigation before we jump to sweeping conclusions.
Overall, though, the approach is certainly better than single-day slices. Using the 7-day before-vs-after mean comparison accounts for both historical data and a full seven days after the update. What if we expanded this comparison of 7-day periods to the larger data set? Here’s our original “winners” list with the new numbers:
Obviously, this is a lot to digest in one table, but we can start to see where the before-and-after metric (the relative difference between 7-day means) shows a different picture, in some cases, than either the 1-day or 2-day view. Let’s go ahead and re-build the top 20 based on the before-and-after percentage change:
Some of the big players are the same, but we’ve also got some newcomers — including sites that looked like they lost visibility on day one, but have stacked up 2-day and 7-day gains.
Let’s take a quick look at Parents.com, our original big winner (winnerer? winnerest?). Day one showed a massive +100% gain (doubling visibility), but day-two numbers were more modest, and before-and-after gains came in at just under half the day-one gain. Here are the seven days before and after:
It’s easy to see here that the day-one jump was a short-term anomaly, based in part on a dip on May 4. Comparing the 7-day averages seems to get much closer to the truth. This is a warning not just to algo trackers like myself, but to SEOs who might see that +100% and rush to tell their boss or client. Don’t let good news turn into a promise that you can’t keep.
Why do we keep doing this?
If it seems like I’m calling out the industry, note that I’m squarely in my own crosshairs here. There’s tremendous pressure to publish analyses early, not just because it equates to traffic and links (frankly, it does), but because site owners and SEOs genuinely want answers. As I wrote recently, I think there’s tremendous danger in overinterpreting short-term losses and fixing the wrong things. However, I think there’s also real danger in overstating short-term wins and having the expectation that those gains are permanent. That can lead to equally risky decisions.
Is it all crap? No, I don’t think so, but I think it’s very easy to step off the sidewalk and into the muck after a storm, and at the very least we need to wait for the ground to dry. That’s not easy in a world of Twitter and 24-hour news cycles, but it’s essential to get a multi-day view, especially since so many large algorithm updates roll out over extended periods of time.
Which numbers should we believe? In a sense, all of them, or at least all of the ones we can adequately verify. No single metric is ever going to paint the entire picture, and before you rush off to celebrate being on a winners list, it’s important to take that next step and really understand the historical trends and the context of any victory.
Who wants some free data?
Given the scope of the analysis, I didn’t cover the May 2020 Core Update losers in this post or go past the Top 20, but you can download the raw data here. If you’d like to edit it, please make a copy first. Winners and losers are on separate tabs, and this covers all domains with at least 25 rankings in our MozCast 10K data set on May 4 (just over 400 domains).
Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!
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Google's May 2020 Core Update: Winners, Winnerers, Winlosers, and Why It's All Probably Crap
Posted by Dr-Pete
On May 4, Google announced that they were rolling out a new Core Update. By May 7, it appeared that the dust had mostly settled. Here’s an 11-day view from MozCast:
We measured relatively high volatility from May 4-6, with a peak of 112.6° on May 5. Note that the 30-day average temperature prior to May 4 was historically very high (89.3°).
How does this compare to previous Core Updates? With the caveat that recent temperatures have been well above historical averages, the May 2020 Core Update was our second-hottest Core Update so far, coming in just below the August 2018 “Medic” update.
Who “won” the May Core Update?
It’s common to report winners and losers after a major update (and I’ve done it myself), but for a while now I’ve been concerned that these analyses only capture a small window of time. Whenever we compare two fixed points in time, we’re ignoring the natural volatility of search rankings and the inherent differences between keywords.
This time around, I’d like to take a hard look at the pitfalls. I’m going to focus on winners. The table below shows the 1-day winners (May 5) by total rankings in the 10,000-keyword MozCast tracking set. I’ve only included subdomains with at least 25 rankings on May 4:
Putting aside the usual statistical suspects (small sample sizes for some keywords, the unique pros and cons of our data set, etc.), what’s the problem with this analysis? Sure, there are different ways to report the “% Gain” (such as absolute change vs. relative percentage), but I’ve reported the absolute numbers honestly and the relative change is accurate.
The problem is that, in rushing to run the numbers after one day, we’ve ignored the reality that most core updates are multi-day (a trend that seemed to continue for the May Core Update, as evidenced by our initial graph). We’ve also failed to account for domains whose rankings might be historically volatile (but more on that in a bit). What if we compare the 1-day and 2-day data?
Which story do we tell?
The table below adds in the 2-day relative percentage gained. I’ve kept the same 25 subdomains and will continue to sort them by the 1-day percentage gained, for consistency:
Even just comparing the first two days of the roll-out, we can see that the story is shifting considerably. The problem is: Which story do we tell? Often, we’re not even looking at lists, but anecdotes based on our own clients or cherry-picking data. Consider this story:
If this was our only view of the data, we would probably conclude that the update intensified over the two days, with day two rewarding sites even more. We could even start to craft a story about how demand for apps was growing, or certain news sites were being rewarded. These stories might have a grain of truth, but the fact is that we have no idea from this data alone.
Now, let’s pick three different data points (all of these are from the top 20):
From this limited view, we could conclude that Google decided that the Core Update went wrong and reversed it on day two. We could even conclude that certain news sites were being penalized for some reason. This tells a wildly different story than the first set of anecdotes.
There’s an even weirder story buried in the May 2020 data. Consider this:
LinkedIn showed a minor bump (one we’d generally ignore) on day one and then lost 100% of its rankings on day two. Wow, that May Core Update really packs a punch! It turns out that LinkedIn may have accidentally de-indexed their site — they recovered the next day, and it appears this massive change had nothing to do with the Core Update. The simple truth is that these numbers tell us very little about why a site gained or lost rankings.
How do we define “normal”?
Let’s take a deeper look at the MarketWatch data. Marketwatch gained 19% in the 1-day stats, but lost 2% in the 2-day numbers. The problem here is that we don’t know from these numbers what MarketWatch’s normal SERP flux looks like. Here’s a graph of seven days before and after May 4 (the start of the Core Update):
Looking at even a small bit of historical data, we can see that MarketWatch, like most news sites, experiences significant volatility. The “gains” on May 5 are only because of losses on May 4. It turns out that the 7-day mean after May 4 (45.7) is only a slight increase over the 7-day mean before May 4 (44.3), with MarketWatch measuring a modest relative gain of +3.2%.
Now let’s look at Google Play, which appeared to be a clear winner after two days:
You don’t even need to do the math to spot the difference here. Comparing the 7-day mean before May 4 (232.9) to the 7-day mean after (448.7), Google Play experienced a dramatic +93% relative change after the May Core Update.
How does this 7-day before/after comparison work with the LinkedIn incident? Here’s a graph of the before/after with dotted lines added for the two means:
While this approach certainly helps offset the single-day anomaly, we’re still showing a before/after change of -16%, which isn’t really in line with reality. You can see that six of the seven days after the May Core Update were above the 7-day average. Note that LinkedIn also has relatively low volatility over the short-range history.
Why am I rotten-cherry-picking an extreme example where my new metric falls short? I want it to be perfectly clear that no one metric can ever tell the whole story. Even if we accounted for the variance and did statistical testing, we’re still missing a lot of information. A clear before/after difference doesn’t tell us what actually happened, only that there was a change correlated with the timing of the Core Update. That’s useful information, but it still begs further investigation before we jump to sweeping conclusions.
Overall, though, the approach is certainly better than single-day slices. Using the 7-day before-vs-after mean comparison accounts for both historical data and a full seven days after the update. What if we expanded this comparison of 7-day periods to the larger data set? Here’s our original “winners” list with the new numbers:
Obviously, this is a lot to digest in one table, but we can start to see where the before-and-after metric (the relative difference between 7-day means) shows a different picture, in some cases, than either the 1-day or 2-day view. Let’s go ahead and re-build the top 20 based on the before-and-after percentage change:
Some of the big players are the same, but we’ve also got some newcomers — including sites that looked like they lost visibility on day one, but have stacked up 2-day and 7-day gains.
Let’s take a quick look at Parents.com, our original big winner (winnerer? winnerest?). Day one showed a massive +100% gain (doubling visibility), but day-two numbers were more modest, and before-and-after gains came in at just under half the day-one gain. Here are the seven days before and after:
It’s easy to see here that the day-one jump was a short-term anomaly, based in part on a dip on May 4. Comparing the 7-day averages seems to get much closer to the truth. This is a warning not just to algo trackers like myself, but to SEOs who might see that +100% and rush to tell their boss or client. Don’t let good news turn into a promise that you can’t keep.
Why do we keep doing this?
If it seems like I’m calling out the industry, note that I’m squarely in my own crosshairs here. There’s tremendous pressure to publish analyses early, not just because it equates to traffic and links (frankly, it does), but because site owners and SEOs genuinely want answers. As I wrote recently, I think there’s tremendous danger in overinterpreting short-term losses and fixing the wrong things. However, I think there’s also real danger in overstating short-term wins and having the expectation that those gains are permanent. That can lead to equally risky decisions.
Is it all crap? No, I don’t think so, but I think it’s very easy to step off the sidewalk and into the muck after a storm, and at the very least we need to wait for the ground to dry. That’s not easy in a world of Twitter and 24-hour news cycles, but it’s essential to get a multi-day view, especially since so many large algorithm updates roll out over extended periods of time.
Which numbers should we believe? In a sense, all of them, or at least all of the ones we can adequately verify. No single metric is ever going to paint the entire picture, and before you rush off to celebrate being on a winners list, it’s important to take that next step and really understand the historical trends and the context of any victory.
Who wants some free data?
Given the scope of the analysis, I didn’t cover the May 2020 Core Update losers in this post or go past the Top 20, but you can download the raw data here. If you’d like to edit it, please make a copy first. Winners and losers are on separate tabs, and this covers all domains with at least 25 rankings in our MozCast 10K data set on May 4 (just over 400 domains).
Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!
0 notes
Text
Google's May 2020 Core Update: Winners, Winnerers, Winlosers, and Why It's All Probably Crap
Posted by Dr-Pete
On May 4, Google announced that they were rolling out a new Core Update. By May 7, it appeared that the dust had mostly settled. Here’s an 11-day view from MozCast:
We measured relatively high volatility from May 4-6, with a peak of 112.6° on May 5. Note that the 30-day average temperature prior to May 4 was historically very high (89.3°).
How does this compare to previous Core Updates? With the caveat that recent temperatures have been well above historical averages, the May 2020 Core Update was our second-hottest Core Update so far, coming in just below the August 2018 “Medic” update.
Who “won” the May Core Update?
It’s common to report winners and losers after a major update (and I’ve done it myself), but for a while now I’ve been concerned that these analyses only capture a small window of time. Whenever we compare two fixed points in time, we’re ignoring the natural volatility of search rankings and the inherent differences between keywords.
This time around, I’d like to take a hard look at the pitfalls. I’m going to focus on winners. The table below shows the 1-day winners (May 5) by total rankings in the 10,000-keyword MozCast tracking set. I’ve only included subdomains with at least 25 rankings on May 4:
Putting aside the usual statistical suspects (small sample sizes for some keywords, the unique pros and cons of our data set, etc.), what’s the problem with this analysis? Sure, there are different ways to report the “% Gain” (such as absolute change vs. relative percentage), but I’ve reported the absolute numbers honestly and the relative change is accurate.
The problem is that, in rushing to run the numbers after one day, we’ve ignored the reality that most core updates are multi-day (a trend that seemed to continue for the May Core Update, as evidenced by our initial graph). We’ve also failed to account for domains whose rankings might be historically volatile (but more on that in a bit). What if we compare the 1-day and 2-day data?
Which story do we tell?
The table below adds in the 2-day relative percentage gained. I’ve kept the same 25 subdomains and will continue to sort them by the 1-day percentage gained, for consistency:
Even just comparing the first two days of the roll-out, we can see that the story is shifting considerably. The problem is: Which story do we tell? Often, we’re not even looking at lists, but anecdotes based on our own clients or cherry-picking data. Consider this story:
If this was our only view of the data, we would probably conclude that the update intensified over the two days, with day two rewarding sites even more. We could even start to craft a story about how demand for apps was growing, or certain news sites were being rewarded. These stories might have a grain of truth, but the fact is that we have no idea from this data alone.
Now, let’s pick three different data points (all of these are from the top 20):
From this limited view, we could conclude that Google decided that the Core Update went wrong and reversed it on day two. We could even conclude that certain news sites were being penalized for some reason. This tells a wildly different story than the first set of anecdotes.
There’s an even weirder story buried in the May 2020 data. Consider this:
LinkedIn showed a minor bump (one we’d generally ignore) on day one and then lost 100% of its rankings on day two. Wow, that May Core Update really packs a punch! It turns out that LinkedIn may have accidentally de-indexed their site — they recovered the next day, and it appears this massive change had nothing to do with the Core Update. The simple truth is that these numbers tell us very little about why a site gained or lost rankings.
How do we define “normal”?
Let’s take a deeper look at the MarketWatch data. Marketwatch gained 19% in the 1-day stats, but lost 2% in the 2-day numbers. The problem here is that we don’t know from these numbers what MarketWatch’s normal SERP flux looks like. Here’s a graph of seven days before and after May 4 (the start of the Core Update):
Looking at even a small bit of historical data, we can see that MarketWatch, like most news sites, experiences significant volatility. The “gains” on May 5 are only because of losses on May 4. It turns out that the 7-day mean after May 4 (45.7) is only a slight increase over the 7-day mean before May 4 (44.3), with MarketWatch measuring a modest relative gain of +3.2%.
Now let’s look at Google Play, which appeared to be a clear winner after two days:
You don’t even need to do the math to spot the difference here. Comparing the 7-day mean before May 4 (232.9) to the 7-day mean after (448.7), Google Play experienced a dramatic +93% relative change after the May Core Update.
How does this 7-day before/after comparison work with the LinkedIn incident? Here’s a graph of the before/after with dotted lines added for the two means:
While this approach certainly helps offset the single-day anomaly, we’re still showing a before/after change of -16%, which isn’t really in line with reality. You can see that six of the seven days after the May Core Update were above the 7-day average. Note that LinkedIn also has relatively low volatility over the short-range history.
Why am I rotten-cherry-picking an extreme example where my new metric falls short? I want it to be perfectly clear that no one metric can ever tell the whole story. Even if we accounted for the variance and did statistical testing, we’re still missing a lot of information. A clear before/after difference doesn’t tell us what actually happened, only that there was a change correlated with the timing of the Core Update. That’s useful information, but it still begs further investigation before we jump to sweeping conclusions.
Overall, though, the approach is certainly better than single-day slices. Using the 7-day before-vs-after mean comparison accounts for both historical data and a full seven days after the update. What if we expanded this comparison of 7-day periods to the larger data set? Here’s our original “winners” list with the new numbers:
Obviously, this is a lot to digest in one table, but we can start to see where the before-and-after metric (the relative difference between 7-day means) shows a different picture, in some cases, than either the 1-day or 2-day view. Let’s go ahead and re-build the top 20 based on the before-and-after percentage change:
Some of the big players are the same, but we’ve also got some newcomers — including sites that looked like they lost visibility on day one, but have stacked up 2-day and 7-day gains.
Let’s take a quick look at Parents.com, our original big winner (winnerer? winnerest?). Day one showed a massive +100% gain (doubling visibility), but day-two numbers were more modest, and before-and-after gains came in at just under half the day-one gain. Here are the seven days before and after:
It’s easy to see here that the day-one jump was a short-term anomaly, based in part on a dip on May 4. Comparing the 7-day averages seems to get much closer to the truth. This is a warning not just to algo trackers like myself, but to SEOs who might see that +100% and rush to tell their boss or client. Don’t let good news turn into a promise that you can’t keep.
Why do we keep doing this?
If it seems like I’m calling out the industry, note that I’m squarely in my own crosshairs here. There’s tremendous pressure to publish analyses early, not just because it equates to traffic and links (frankly, it does), but because site owners and SEOs genuinely want answers. As I wrote recently, I think there’s tremendous danger in overinterpreting short-term losses and fixing the wrong things. However, I think there’s also real danger in overstating short-term wins and having the expectation that those gains are permanent. That can lead to equally risky decisions.
Is it all crap? No, I don’t think so, but I think it’s very easy to step off the sidewalk and into the muck after a storm, and at the very least we need to wait for the ground to dry. That’s not easy in a world of Twitter and 24-hour news cycles, but it’s essential to get a multi-day view, especially since so many large algorithm updates roll out over extended periods of time.
Which numbers should we believe? In a sense, all of them, or at least all of the ones we can adequately verify. No single metric is ever going to paint the entire picture, and before you rush off to celebrate being on a winners list, it’s important to take that next step and really understand the historical trends and the context of any victory.
Who wants some free data?
Given the scope of the analysis, I didn’t cover the May 2020 Core Update losers in this post or go past the Top 20, but you can download the raw data here. If you’d like to edit it, please make a copy first. Winners and losers are on separate tabs, and this covers all domains with at least 25 rankings in our MozCast 10K data set on May 4 (just over 400 domains).
Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!
https://ift.tt/2AjddzJ
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Text
Google's May 2020 Core Update: Winners, Winnerers, Winlosers, and Why It's All Probably Crap
Posted by Dr-Pete
On May 4, Google announced that they were rolling out a new Core Update. By May 7, it appeared that the dust had mostly settled. Here’s an 11-day view from MozCast:
We measured relatively high volatility from May 4-6, with a peak of 112.6° on May 5. Note that the 30-day average temperature prior to May 4 was historically very high (89.3°).
How does this compare to previous Core Updates? With the caveat that recent temperatures have been well above historical averages, the May 2020 Core Update was our second-hottest Core Update so far, coming in just below the August 2018 “Medic” update.
Who “won” the May Core Update?
It’s common to report winners and losers after a major update (and I’ve done it myself), but for a while now I’ve been concerned that these analyses only capture a small window of time. Whenever we compare two fixed points in time, we’re ignoring the natural volatility of search rankings and the inherent differences between keywords.
This time around, I’d like to take a hard look at the pitfalls. I’m going to focus on winners. The table below shows the 1-day winners (May 5) by total rankings in the 10,000-keyword MozCast tracking set. I’ve only included subdomains with at least 25 rankings on May 4:
Putting aside the usual statistical suspects (small sample sizes for some keywords, the unique pros and cons of our data set, etc.), what’s the problem with this analysis? Sure, there are different ways to report the “% Gain” (such as absolute change vs. relative percentage), but I’ve reported the absolute numbers honestly and the relative change is accurate.
The problem is that, in rushing to run the numbers after one day, we’ve ignored the reality that most core updates are multi-day (a trend that seemed to continue for the May Core Update, as evidenced by our initial graph). We’ve also failed to account for domains whose rankings might be historically volatile (but more on that in a bit). What if we compare the 1-day and 2-day data?
Which story do we tell?
The table below adds in the 2-day relative percentage gained. I’ve kept the same 25 subdomains and will continue to sort them by the 1-day percentage gained, for consistency:
Even just comparing the first two days of the roll-out, we can see that the story is shifting considerably. The problem is: Which story do we tell? Often, we’re not even looking at lists, but anecdotes based on our own clients or cherry-picking data. Consider this story:
If this was our only view of the data, we would probably conclude that the update intensified over the two days, with day two rewarding sites even more. We could even start to craft a story about how demand for apps was growing, or certain news sites were being rewarded. These stories might have a grain of truth, but the fact is that we have no idea from this data alone.
Now, let’s pick three different data points (all of these are from the top 20):
From this limited view, we could conclude that Google decided that the Core Update went wrong and reversed it on day two. We could even conclude that certain news sites were being penalized for some reason. This tells a wildly different story than the first set of anecdotes.
There’s an even weirder story buried in the May 2020 data. Consider this:
LinkedIn showed a minor bump (one we’d generally ignore) on day one and then lost 100% of its rankings on day two. Wow, that May Core Update really packs a punch! It turns out that LinkedIn may have accidentally de-indexed their site — they recovered the next day, and it appears this massive change had nothing to do with the Core Update. The simple truth is that these numbers tell us very little about why a site gained or lost rankings.
How do we define “normal”?
Let’s take a deeper look at the MarketWatch data. Marketwatch gained 19% in the 1-day stats, but lost 2% in the 2-day numbers. The problem here is that we don’t know from these numbers what MarketWatch’s normal SERP flux looks like. Here’s a graph of seven days before and after May 4 (the start of the Core Update):
Looking at even a small bit of historical data, we can see that MarketWatch, like most news sites, experiences significant volatility. The “gains” on May 5 are only because of losses on May 4. It turns out that the 7-day mean after May 4 (45.7) is only a slight increase over the 7-day mean before May 4 (44.3), with MarketWatch measuring a modest relative gain of +3.2%.
Now let’s look at Google Play, which appeared to be a clear winner after two days:
You don’t even need to do the math to spot the difference here. Comparing the 7-day mean before May 4 (232.9) to the 7-day mean after (448.7), Google Play experienced a dramatic +93% relative change after the May Core Update.
How does this 7-day before/after comparison work with the LinkedIn incident? Here’s a graph of the before/after with dotted lines added for the two means:
While this approach certainly helps offset the single-day anomaly, we’re still showing a before/after change of -16%, which isn’t really in line with reality. You can see that six of the seven days after the May Core Update were above the 7-day average. Note that LinkedIn also has relatively low volatility over the short-range history.
Why am I rotten-cherry-picking an extreme example where my new metric falls short? I want it to be perfectly clear that no one metric can ever tell the whole story. Even if we accounted for the variance and did statistical testing, we’re still missing a lot of information. A clear before/after difference doesn’t tell us what actually happened, only that there was a change correlated with the timing of the Core Update. That’s useful information, but it still begs further investigation before we jump to sweeping conclusions.
Overall, though, the approach is certainly better than single-day slices. Using the 7-day before-vs-after mean comparison accounts for both historical data and a full seven days after the update. What if we expanded this comparison of 7-day periods to the larger data set? Here’s our original “winners” list with the new numbers:
Obviously, this is a lot to digest in one table, but we can start to see where the before-and-after metric (the relative difference between 7-day means) shows a different picture, in some cases, than either the 1-day or 2-day view. Let’s go ahead and re-build the top 20 based on the before-and-after percentage change:
Some of the big players are the same, but we’ve also got some newcomers — including sites that looked like they lost visibility on day one, but have stacked up 2-day and 7-day gains.
Let’s take a quick look at Parents.com, our original big winner (winnerer? winnerest?). Day one showed a massive +100% gain (doubling visibility), but day-two numbers were more modest, and before-and-after gains came in at just under half the day-one gain. Here are the seven days before and after:
It’s easy to see here that the day-one jump was a short-term anomaly, based in part on a dip on May 4. Comparing the 7-day averages seems to get much closer to the truth. This is a warning not just to algo trackers like myself, but to SEOs who might see that +100% and rush to tell their boss or client. Don’t let good news turn into a promise that you can’t keep.
Why do we keep doing this?
If it seems like I’m calling out the industry, note that I’m squarely in my own crosshairs here. There’s tremendous pressure to publish analyses early, not just because it equates to traffic and links (frankly, it does), but because site owners and SEOs genuinely want answers. As I wrote recently, I think there’s tremendous danger in overinterpreting short-term losses and fixing the wrong things. However, I think there’s also real danger in overstating short-term wins and having the expectation that those gains are permanent. That can lead to equally risky decisions.
Is it all crap? No, I don’t think so, but I think it’s very easy to step off the sidewalk and into the muck after a storm, and at the very least we need to wait for the ground to dry. That’s not easy in a world of Twitter and 24-hour news cycles, but it’s essential to get a multi-day view, especially since so many large algorithm updates roll out over extended periods of time.
Which numbers should we believe? In a sense, all of them, or at least all of the ones we can adequately verify. No single metric is ever going to paint the entire picture, and before you rush off to celebrate being on a winners list, it’s important to take that next step and really understand the historical trends and the context of any victory.
Who wants some free data?
Given the scope of the analysis, I didn’t cover the May 2020 Core Update losers in this post or go past the Top 20, but you can download the raw data here. If you’d like to edit it, please make a copy first. Winners and losers are on separate tabs, and this covers all domains with at least 25 rankings in our MozCast 10K data set on May 4 (just over 400 domains).
Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!
0 notes
Text
Google's May 2020 Core Update: Winners, Winnerers, Winlosers, and Why It's All Probably Crap
Posted by Dr-Pete
On May 4, Google announced that they were rolling out a new Core Update. By May 7, it appeared that the dust had mostly settled. Here’s an 11-day view from MozCast:
We measured relatively high volatility from May 4-6, with a peak of 112.6° on May 5. Note that the 30-day average temperature prior to May 4 was historically very high (89.3°).
How does this compare to previous Core Updates? With the caveat that recent temperatures have been well above historical averages, the May 2020 Core Update was our second-hottest Core Update so far, coming in just below the August 2018 “Medic” update.
Who “won” the May Core Update?
It’s common to report winners and losers after a major update (and I’ve done it myself), but for a while now I’ve been concerned that these analyses only capture a small window of time. Whenever we compare two fixed points in time, we’re ignoring the natural volatility of search rankings and the inherent differences between keywords.
This time around, I’d like to take a hard look at the pitfalls. I’m going to focus on winners. The table below shows the 1-day winners (May 5) by total rankings in the 10,000-keyword MozCast tracking set. I’ve only included subdomains with at least 25 rankings on May 4:
Putting aside the usual statistical suspects (small sample sizes for some keywords, the unique pros and cons of our data set, etc.), what’s the problem with this analysis? Sure, there are different ways to report the “% Gain” (such as absolute change vs. relative percentage), but I’ve reported the absolute numbers honestly and the relative change is accurate.
The problem is that, in rushing to run the numbers after one day, we’ve ignored the reality that most core updates are multi-day (a trend that seemed to continue for the May Core Update, as evidenced by our initial graph). We’ve also failed to account for domains whose rankings might be historically volatile (but more on that in a bit). What if we compare the 1-day and 2-day data?
Which story do we tell?
The table below adds in the 2-day relative percentage gained. I’ve kept the same 25 subdomains and will continue to sort them by the 1-day percentage gained, for consistency:
Even just comparing the first two days of the roll-out, we can see that the story is shifting considerably. The problem is: Which story do we tell? Often, we’re not even looking at lists, but anecdotes based on our own clients or cherry-picking data. Consider this story:
If this was our only view of the data, we would probably conclude that the update intensified over the two days, with day two rewarding sites even more. We could even start to craft a story about how demand for apps was growing, or certain news sites were being rewarded. These stories might have a grain of truth, but the fact is that we have no idea from this data alone.
Now, let’s pick three different data points (all of these are from the top 20):
From this limited view, we could conclude that Google decided that the Core Update went wrong and reversed it on day two. We could even conclude that certain news sites were being penalized for some reason. This tells a wildly different story than the first set of anecdotes.
There’s an even weirder story buried in the May 2020 data. Consider this:
LinkedIn showed a minor bump (one we’d generally ignore) on day one and then lost 100% of its rankings on day two. Wow, that May Core Update really packs a punch! It turns out that LinkedIn may have accidentally de-indexed their site — they recovered the next day, and it appears this massive change had nothing to do with the Core Update. The simple truth is that these numbers tell us very little about why a site gained or lost rankings.
How do we define “normal”?
Let’s take a deeper look at the MarketWatch data. Marketwatch gained 19% in the 1-day stats, but lost 2% in the 2-day numbers. The problem here is that we don’t know from these numbers what MarketWatch’s normal SERP flux looks like. Here’s a graph of seven days before and after May 4 (the start of the Core Update):
Looking at even a small bit of historical data, we can see that MarketWatch, like most news sites, experiences significant volatility. The “gains” on May 5 are only because of losses on May 4. It turns out that the 7-day mean after May 4 (45.7) is only a slight increase over the 7-day mean before May 4 (44.3), with MarketWatch measuring a modest relative gain of +3.2%.
Now let’s look at Google Play, which appeared to be a clear winner after two days:
You don’t even need to do the math to spot the difference here. Comparing the 7-day mean before May 4 (232.9) to the 7-day mean after (448.7), Google Play experienced a dramatic +93% relative change after the May Core Update.
How does this 7-day before/after comparison work with the LinkedIn incident? Here’s a graph of the before/after with dotted lines added for the two means:
While this approach certainly helps offset the single-day anomaly, we’re still showing a before/after change of -16%, which isn’t really in line with reality. You can see that six of the seven days after the May Core Update were above the 7-day average. Note that LinkedIn also has relatively low volatility over the short-range history.
Why am I rotten-cherry-picking an extreme example where my new metric falls short? I want it to be perfectly clear that no one metric can ever tell the whole story. Even if we accounted for the variance and did statistical testing, we’re still missing a lot of information. A clear before/after difference doesn’t tell us what actually happened, only that there was a change correlated with the timing of the Core Update. That’s useful information, but it still begs further investigation before we jump to sweeping conclusions.
Overall, though, the approach is certainly better than single-day slices. Using the 7-day before-vs-after mean comparison accounts for both historical data and a full seven days after the update. What if we expanded this comparison of 7-day periods to the larger data set? Here’s our original “winners” list with the new numbers:
Obviously, this is a lot to digest in one table, but we can start to see where the before-and-after metric (the relative difference between 7-day means) shows a different picture, in some cases, than either the 1-day or 2-day view. Let’s go ahead and re-build the top 20 based on the before-and-after percentage change:
Some of the big players are the same, but we’ve also got some newcomers — including sites that looked like they lost visibility on day one, but have stacked up 2-day and 7-day gains.
Let’s take a quick look at Parents.com, our original big winner (winnerer? winnerest?). Day one showed a massive +100% gain (doubling visibility), but day-two numbers were more modest, and before-and-after gains came in at just under half the day-one gain. Here are the seven days before and after:
It’s easy to see here that the day-one jump was a short-term anomaly, based in part on a dip on May 4. Comparing the 7-day averages seems to get much closer to the truth. This is a warning not just to algo trackers like myself, but to SEOs who might see that +100% and rush to tell their boss or client. Don’t let good news turn into a promise that you can’t keep.
Why do we keep doing this?
If it seems like I’m calling out the industry, note that I’m squarely in my own crosshairs here. There’s tremendous pressure to publish analyses early, not just because it equates to traffic and links (frankly, it does), but because site owners and SEOs genuinely want answers. As I wrote recently, I think there’s tremendous danger in overinterpreting short-term losses and fixing the wrong things. However, I think there’s also real danger in overstating short-term wins and having the expectation that those gains are permanent. That can lead to equally risky decisions.
Is it all crap? No, I don’t think so, but I think it’s very easy to step off the sidewalk and into the muck after a storm, and at the very least we need to wait for the ground to dry. That’s not easy in a world of Twitter and 24-hour news cycles, but it’s essential to get a multi-day view, especially since so many large algorithm updates roll out over extended periods of time.
Which numbers should we believe? In a sense, all of them, or at least all of the ones we can adequately verify. No single metric is ever going to paint the entire picture, and before you rush off to celebrate being on a winners list, it’s important to take that next step and really understand the historical trends and the context of any victory.
Who wants some free data?
Given the scope of the analysis, I didn’t cover the May 2020 Core Update losers in this post or go past the Top 20, but you can download the raw data here. If you’d like to edit it, please make a copy first. Winners and losers are on separate tabs, and this covers all domains with at least 25 rankings in our MozCast 10K data set on May 4 (just over 400 domains).
Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!
0 notes
Text
Google's May 2020 Core Update: Winners, Winnerers, Winlosers, and Why It's All Probably Crap
Posted by Dr-Pete
On May 4, Google announced that they were rolling out a new Core Update. By May 7, it appeared that the dust had mostly settled. Here’s an 11-day view from MozCast:
We measured relatively high volatility from May 4-6, with a peak of 112.6° on May 5. Note that the 30-day average temperature prior to May 4 was historically very high (89.3°).
How does this compare to previous Core Updates? With the caveat that recent temperatures have been well above historical averages, the May 2020 Core Update was our second-hottest Core Update so far, coming in just below the August 2018 “Medic” update.
Who “won” the May Core Update?
It’s common to report winners and losers after a major update (and I’ve done it myself), but for a while now I’ve been concerned that these analyses only capture a small window of time. Whenever we compare two fixed points in time, we’re ignoring the natural volatility of search rankings and the inherent differences between keywords.
This time around, I’d like to take a hard look at the pitfalls. I’m going to focus on winners. The table below shows the 1-day winners (May 5) by total rankings in the 10,000-keyword MozCast tracking set. I’ve only included subdomains with at least 25 rankings on May 4:
Putting aside the usual statistical suspects (small sample sizes for some keywords, the unique pros and cons of our data set, etc.), what’s the problem with this analysis? Sure, there are different ways to report the “% Gain” (such as absolute change vs. relative percentage), but I’ve reported the absolute numbers honestly and the relative change is accurate.
The problem is that, in rushing to run the numbers after one day, we’ve ignored the reality that most core updates are multi-day (a trend that seemed to continue for the May Core Update, as evidenced by our initial graph). We’ve also failed to account for domains whose rankings might be historically volatile (but more on that in a bit). What if we compare the 1-day and 2-day data?
Which story do we tell?
The table below adds in the 2-day relative percentage gained. I’ve kept the same 25 subdomains and will continue to sort them by the 1-day percentage gained, for consistency:
Even just comparing the first two days of the roll-out, we can see that the story is shifting considerably. The problem is: Which story do we tell? Often, we’re not even looking at lists, but anecdotes based on our own clients or cherry-picking data. Consider this story:
If this was our only view of the data, we would probably conclude that the update intensified over the two days, with day two rewarding sites even more. We could even start to craft a story about how demand for apps was growing, or certain news sites were being rewarded. These stories might have a grain of truth, but the fact is that we have no idea from this data alone.
Now, let’s pick three different data points (all of these are from the top 20):
From this limited view, we could conclude that Google decided that the Core Update went wrong and reversed it on day two. We could even conclude that certain news sites were being penalized for some reason. This tells a wildly different story than the first set of anecdotes.
There’s an even weirder story buried in the May 2020 data. Consider this:
LinkedIn showed a minor bump (one we’d generally ignore) on day one and then lost 100% of its rankings on day two. Wow, that May Core Update really packs a punch! It turns out that LinkedIn may have accidentally de-indexed their site — they recovered the next day, and it appears this massive change had nothing to do with the Core Update. The simple truth is that these numbers tell us very little about why a site gained or lost rankings.
How do we define “normal”?
Let’s take a deeper look at the MarketWatch data. Marketwatch gained 19% in the 1-day stats, but lost 2% in the 2-day numbers. The problem here is that we don’t know from these numbers what MarketWatch’s normal SERP flux looks like. Here’s a graph of seven days before and after May 4 (the start of the Core Update):
Looking at even a small bit of historical data, we can see that MarketWatch, like most news sites, experiences significant volatility. The “gains” on May 5 are only because of losses on May 4. It turns out that the 7-day mean after May 4 (45.7) is only a slight increase over the 7-day mean before May 4 (44.3), with MarketWatch measuring a modest relative gain of +3.2%.
Now let’s look at Google Play, which appeared to be a clear winner after two days:
You don’t even need to do the math to spot the difference here. Comparing the 7-day mean before May 4 (232.9) to the 7-day mean after (448.7), Google Play experienced a dramatic +93% relative change after the May Core Update.
How does this 7-day before/after comparison work with the LinkedIn incident? Here’s a graph of the before/after with dotted lines added for the two means:
While this approach certainly helps offset the single-day anomaly, we’re still showing a before/after change of -16%, which isn’t really in line with reality. You can see that six of the seven days after the May Core Update were above the 7-day average. Note that LinkedIn also has relatively low volatility over the short-range history.
Why am I rotten-cherry-picking an extreme example where my new metric falls short? I want it to be perfectly clear that no one metric can ever tell the whole story. Even if we accounted for the variance and did statistical testing, we’re still missing a lot of information. A clear before/after difference doesn’t tell us what actually happened, only that there was a change correlated with the timing of the Core Update. That’s useful information, but it still begs further investigation before we jump to sweeping conclusions.
Overall, though, the approach is certainly better than single-day slices. Using the 7-day before-vs-after mean comparison accounts for both historical data and a full seven days after the update. What if we expanded this comparison of 7-day periods to the larger data set? Here’s our original “winners” list with the new numbers:
Obviously, this is a lot to digest in one table, but we can start to see where the before-and-after metric (the relative difference between 7-day means) shows a different picture, in some cases, than either the 1-day or 2-day view. Let’s go ahead and re-build the top 20 based on the before-and-after percentage change:
Some of the big players are the same, but we’ve also got some newcomers — including sites that looked like they lost visibility on day one, but have stacked up 2-day and 7-day gains.
Let’s take a quick look at Parents.com, our original big winner (winnerer? winnerest?). Day one showed a massive +100% gain (doubling visibility), but day-two numbers were more modest, and before-and-after gains came in at just under half the day-one gain. Here are the seven days before and after:
It’s easy to see here that the day-one jump was a short-term anomaly, based in part on a dip on May 4. Comparing the 7-day averages seems to get much closer to the truth. This is a warning not just to algo trackers like myself, but to SEOs who might see that +100% and rush to tell their boss or client. Don’t let good news turn into a promise that you can’t keep.
Why do we keep doing this?
If it seems like I’m calling out the industry, note that I’m squarely in my own crosshairs here. There’s tremendous pressure to publish analyses early, not just because it equates to traffic and links (frankly, it does), but because site owners and SEOs genuinely want answers. As I wrote recently, I think there’s tremendous danger in overinterpreting short-term losses and fixing the wrong things. However, I think there’s also real danger in overstating short-term wins and having the expectation that those gains are permanent. That can lead to equally risky decisions.
Is it all crap? No, I don’t think so, but I think it’s very easy to step off the sidewalk and into the muck after a storm, and at the very least we need to wait for the ground to dry. That’s not easy in a world of Twitter and 24-hour news cycles, but it’s essential to get a multi-day view, especially since so many large algorithm updates roll out over extended periods of time.
Which numbers should we believe? In a sense, all of them, or at least all of the ones we can adequately verify. No single metric is ever going to paint the entire picture, and before you rush off to celebrate being on a winners list, it’s important to take that next step and really understand the historical trends and the context of any victory.
Who wants some free data?
Given the scope of the analysis, I didn’t cover the May 2020 Core Update losers in this post or go past the Top 20, but you can download the raw data here. If you’d like to edit it, please make a copy first. Winners and losers are on separate tabs, and this covers all domains with at least 25 rankings in our MozCast 10K data set on May 4 (just over 400 domains).
Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!
0 notes
Text
Google's May 2020 Core Update: Winners, Winnerers, Winlosers, and Why It's All Probably Crap
Posted by Dr-Pete
On May 4, Google announced that they were rolling out a new Core Update. By May 7, it appeared that the dust had mostly settled. Here’s an 11-day view from MozCast:
We measured relatively high volatility from May 4-6, with a peak of 112.6° on May 5. Note that the 30-day average temperature prior to May 4 was historically very high (89.3°).
How does this compare to previous Core Updates? With the caveat that recent temperatures have been well above historical averages, the May 2020 Core Update was our second-hottest Core Update so far, coming in just below the August 2018 “Medic” update.
Who “won” the May Core Update?
It’s common to report winners and losers after a major update (and I’ve done it myself), but for a while now I’ve been concerned that these analyses only capture a small window of time. Whenever we compare two fixed points in time, we’re ignoring the natural volatility of search rankings and the inherent differences between keywords.
This time around, I’d like to take a hard look at the pitfalls. I’m going to focus on winners. The table below shows the 1-day winners (May 5) by total rankings in the 10,000-keyword MozCast tracking set. I’ve only included subdomains with at least 25 rankings on May 4:
Putting aside the usual statistical suspects (small sample sizes for some keywords, the unique pros and cons of our data set, etc.), what’s the problem with this analysis? Sure, there are different ways to report the “% Gain” (such as absolute change vs. relative percentage), but I’ve reported the absolute numbers honestly and the relative change is accurate.
The problem is that, in rushing to run the numbers after one day, we’ve ignored the reality that most core updates are multi-day (a trend that seemed to continue for the May Core Update, as evidenced by our initial graph). We’ve also failed to account for domains whose rankings might be historically volatile (but more on that in a bit). What if we compare the 1-day and 2-day data?
Which story do we tell?
The table below adds in the 2-day relative percentage gained. I’ve kept the same 25 subdomains and will continue to sort them by the 1-day percentage gained, for consistency:
Even just comparing the first two days of the roll-out, we can see that the story is shifting considerably. The problem is: Which story do we tell? Often, we’re not even looking at lists, but anecdotes based on our own clients or cherry-picking data. Consider this story:
If this was our only view of the data, we would probably conclude that the update intensified over the two days, with day two rewarding sites even more. We could even start to craft a story about how demand for apps was growing, or certain news sites were being rewarded. These stories might have a grain of truth, but the fact is that we have no idea from this data alone.
Now, let’s pick three different data points (all of these are from the top 20):
From this limited view, we could conclude that Google decided that the Core Update went wrong and reversed it on day two. We could even conclude that certain news sites were being penalized for some reason. This tells a wildly different story than the first set of anecdotes.
There’s an even weirder story buried in the May 2020 data. Consider this:
LinkedIn showed a minor bump (one we’d generally ignore) on day one and then lost 100% of its rankings on day two. Wow, that May Core Update really packs a punch! It turns out that LinkedIn may have accidentally de-indexed their site — they recovered the next day, and it appears this massive change had nothing to do with the Core Update. The simple truth is that these numbers tell us very little about why a site gained or lost rankings.
How do we define “normal”?
Let’s take a deeper look at the MarketWatch data. Marketwatch gained 19% in the 1-day stats, but lost 2% in the 2-day numbers. The problem here is that we don’t know from these numbers what MarketWatch’s normal SERP flux looks like. Here’s a graph of seven days before and after May 4 (the start of the Core Update):
Looking at even a small bit of historical data, we can see that MarketWatch, like most news sites, experiences significant volatility. The “gains” on May 5 are only because of losses on May 4. It turns out that the 7-day mean after May 4 (45.7) is only a slight increase over the 7-day mean before May 4 (44.3), with MarketWatch measuring a modest relative gain of +3.2%.
Now let’s look at Google Play, which appeared to be a clear winner after two days:
You don’t even need to do the math to spot the difference here. Comparing the 7-day mean before May 4 (232.9) to the 7-day mean after (448.7), Google Play experienced a dramatic +93% relative change after the May Core Update.
How does this 7-day before/after comparison work with the LinkedIn incident? Here’s a graph of the before/after with dotted lines added for the two means:
While this approach certainly helps offset the single-day anomaly, we’re still showing a before/after change of -16%, which isn’t really in line with reality. You can see that six of the seven days after the May Core Update were above the 7-day average. Note that LinkedIn also has relatively low volatility over the short-range history.
Why am I rotten-cherry-picking an extreme example where my new metric falls short? I want it to be perfectly clear that no one metric can ever tell the whole story. Even if we accounted for the variance and did statistical testing, we’re still missing a lot of information. A clear before/after difference doesn’t tell us what actually happened, only that there was a change correlated with the timing of the Core Update. That’s useful information, but it still begs further investigation before we jump to sweeping conclusions.
Overall, though, the approach is certainly better than single-day slices. Using the 7-day before-vs-after mean comparison accounts for both historical data and a full seven days after the update. What if we expanded this comparison of 7-day periods to the larger data set? Here’s our original “winners” list with the new numbers:
Obviously, this is a lot to digest in one table, but we can start to see where the before-and-after metric (the relative difference between 7-day means) shows a different picture, in some cases, than either the 1-day or 2-day view. Let’s go ahead and re-build the top 20 based on the before-and-after percentage change:
Some of the big players are the same, but we’ve also got some newcomers — including sites that looked like they lost visibility on day one, but have stacked up 2-day and 7-day gains.
Let’s take a quick look at Parents.com, our original big winner (winnerer? winnerest?). Day one showed a massive +100% gain (doubling visibility), but day-two numbers were more modest, and before-and-after gains came in at just under half the day-one gain. Here are the seven days before and after:
It’s easy to see here that the day-one jump was a short-term anomaly, based in part on a dip on May 4. Comparing the 7-day averages seems to get much closer to the truth. This is a warning not just to algo trackers like myself, but to SEOs who might see that +100% and rush to tell their boss or client. Don’t let good news turn into a promise that you can’t keep.
Why do we keep doing this?
If it seems like I’m calling out the industry, note that I’m squarely in my own crosshairs here. There’s tremendous pressure to publish analyses early, not just because it equates to traffic and links (frankly, it does), but because site owners and SEOs genuinely want answers. As I wrote recently, I think there’s tremendous danger in overinterpreting short-term losses and fixing the wrong things. However, I think there’s also real danger in overstating short-term wins and having the expectation that those gains are permanent. That can lead to equally risky decisions.
Is it all crap? No, I don’t think so, but I think it’s very easy to step off the sidewalk and into the muck after a storm, and at the very least we need to wait for the ground to dry. That’s not easy in a world of Twitter and 24-hour news cycles, but it’s essential to get a multi-day view, especially since so many large algorithm updates roll out over extended periods of time.
Which numbers should we believe? In a sense, all of them, or at least all of the ones we can adequately verify. No single metric is ever going to paint the entire picture, and before you rush off to celebrate being on a winners list, it’s important to take that next step and really understand the historical trends and the context of any victory.
Who wants some free data?
Given the scope of the analysis, I didn’t cover the May 2020 Core Update losers in this post or go past the Top 20, but you can download the raw data here. If you’d like to edit it, please make a copy first. Winners and losers are on separate tabs, and this covers all domains with at least 25 rankings in our MozCast 10K data set on May 4 (just over 400 domains).
Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!
0 notes
Text
Google's May 2020 Core Update: Winners, Winnerers, Winlosers, and Why It's All Probably Crap
Posted by Dr-Pete
On May 4, Google announced that they were rolling out a new Core Update. By May 7, it appeared that the dust had mostly settled. Here’s an 11-day view from MozCast:
We measured relatively high volatility from May 4-6, with a peak of 112.6° on May 5. Note that the 30-day average temperature prior to May 4 was historically very high (89.3°).
How does this compare to previous Core Updates? With the caveat that recent temperatures have been well above historical averages, the May 2020 Core Update was our second-hottest Core Update so far, coming in just below the August 2018 “Medic” update.
Who “won” the May Core Update?
It’s common to report winners and losers after a major update (and I’ve done it myself), but for a while now I’ve been concerned that these analyses only capture a small window of time. Whenever we compare two fixed points in time, we’re ignoring the natural volatility of search rankings and the inherent differences between keywords.
This time around, I’d like to take a hard look at the pitfalls. I’m going to focus on winners. The table below shows the 1-day winners (May 5) by total rankings in the 10,000-keyword MozCast tracking set. I’ve only included subdomains with at least 25 rankings on May 4:
Putting aside the usual statistical suspects (small sample sizes for some keywords, the unique pros and cons of our data set, etc.), what’s the problem with this analysis? Sure, there are different ways to report the “% Gain” (such as absolute change vs. relative percentage), but I’ve reported the absolute numbers honestly and the relative change is accurate.
The problem is that, in rushing to run the numbers after one day, we’ve ignored the reality that most core updates are multi-day (a trend that seemed to continue for the May Core Update, as evidenced by our initial graph). We’ve also failed to account for domains whose rankings might be historically volatile (but more on that in a bit). What if we compare the 1-day and 2-day data?
Which story do we tell?
The table below adds in the 2-day relative percentage gained. I’ve kept the same 25 subdomains and will continue to sort them by the 1-day percentage gained, for consistency:
Even just comparing the first two days of the roll-out, we can see that the story is shifting considerably. The problem is: Which story do we tell? Often, we’re not even looking at lists, but anecdotes based on our own clients or cherry-picking data. Consider this story:
If this was our only view of the data, we would probably conclude that the update intensified over the two days, with day two rewarding sites even more. We could even start to craft a story about how demand for apps was growing, or certain news sites were being rewarded. These stories might have a grain of truth, but the fact is that we have no idea from this data alone.
Now, let’s pick three different data points (all of these are from the top 20):
From this limited view, we could conclude that Google decided that the Core Update went wrong and reversed it on day two. We could even conclude that certain news sites were being penalized for some reason. This tells a wildly different story than the first set of anecdotes.
There’s an even weirder story buried in the May 2020 data. Consider this:
LinkedIn showed a minor bump (one we’d generally ignore) on day one and then lost 100% of its rankings on day two. Wow, that May Core Update really packs a punch! It turns out that LinkedIn may have accidentally de-indexed their site — they recovered the next day, and it appears this massive change had nothing to do with the Core Update. The simple truth is that these numbers tell us very little about why a site gained or lost rankings.
How do we define “normal”?
Let’s take a deeper look at the MarketWatch data. Marketwatch gained 19% in the 1-day stats, but lost 2% in the 2-day numbers. The problem here is that we don’t know from these numbers what MarketWatch’s normal SERP flux looks like. Here’s a graph of seven days before and after May 4 (the start of the Core Update):
Looking at even a small bit of historical data, we can see that MarketWatch, like most news sites, experiences significant volatility. The “gains” on May 5 are only because of losses on May 4. It turns out that the 7-day mean after May 4 (45.7) is only a slight increase over the 7-day mean before May 4 (44.3), with MarketWatch measuring a modest relative gain of +3.2%.
Now let’s look at Google Play, which appeared to be a clear winner after two days:
You don’t even need to do the math to spot the difference here. Comparing the 7-day mean before May 4 (232.9) to the 7-day mean after (448.7), Google Play experienced a dramatic +93% relative change after the May Core Update.
How does this 7-day before/after comparison work with the LinkedIn incident? Here’s a graph of the before/after with dotted lines added for the two means:
While this approach certainly helps offset the single-day anomaly, we’re still showing a before/after change of -16%, which isn’t really in line with reality. You can see that six of the seven days after the May Core Update were above the 7-day average. Note that LinkedIn also has relatively low volatility over the short-range history.
Why am I rotten-cherry-picking an extreme example where my new metric falls short? I want it to be perfectly clear that no one metric can ever tell the whole story. Even if we accounted for the variance and did statistical testing, we’re still missing a lot of information. A clear before/after difference doesn’t tell us what actually happened, only that there was a change correlated with the timing of the Core Update. That’s useful information, but it still begs further investigation before we jump to sweeping conclusions.
Overall, though, the approach is certainly better than single-day slices. Using the 7-day before-vs-after mean comparison accounts for both historical data and a full seven days after the update. What if we expanded this comparison of 7-day periods to the larger data set? Here’s our original “winners” list with the new numbers:
Obviously, this is a lot to digest in one table, but we can start to see where the before-and-after metric (the relative difference between 7-day means) shows a different picture, in some cases, than either the 1-day or 2-day view. Let’s go ahead and re-build the top 20 based on the before-and-after percentage change:
Some of the big players are the same, but we’ve also got some newcomers — including sites that looked like they lost visibility on day one, but have stacked up 2-day and 7-day gains.
Let’s take a quick look at Parents.com, our original big winner (winnerer? winnerest?). Day one showed a massive +100% gain (doubling visibility), but day-two numbers were more modest, and before-and-after gains came in at just under half the day-one gain. Here are the seven days before and after:
It’s easy to see here that the day-one jump was a short-term anomaly, based in part on a dip on May 4. Comparing the 7-day averages seems to get much closer to the truth. This is a warning not just to algo trackers like myself, but to SEOs who might see that +100% and rush to tell their boss or client. Don’t let good news turn into a promise that you can’t keep.
Why do we keep doing this?
If it seems like I’m calling out the industry, note that I’m squarely in my own crosshairs here. There’s tremendous pressure to publish analyses early, not just because it equates to traffic and links (frankly, it does), but because site owners and SEOs genuinely want answers. As I wrote recently, I think there’s tremendous danger in overinterpreting short-term losses and fixing the wrong things. However, I think there’s also real danger in overstating short-term wins and having the expectation that those gains are permanent. That can lead to equally risky decisions.
Is it all crap? No, I don’t think so, but I think it’s very easy to step off the sidewalk and into the muck after a storm, and at the very least we need to wait for the ground to dry. That’s not easy in a world of Twitter and 24-hour news cycles, but it’s essential to get a multi-day view, especially since so many large algorithm updates roll out over extended periods of time.
Which numbers should we believe? In a sense, all of them, or at least all of the ones we can adequately verify. No single metric is ever going to paint the entire picture, and before you rush off to celebrate being on a winners list, it’s important to take that next step and really understand the historical trends and the context of any victory.
Who wants some free data?
Given the scope of the analysis, I didn’t cover the May 2020 Core Update losers in this post or go past the Top 20, but you can download the raw data here. If you’d like to edit it, please make a copy first. Winners and losers are on separate tabs, and this covers all domains with at least 25 rankings in our MozCast 10K data set on May 4 (just over 400 domains).
Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!
0 notes
Text
Google's May 2020 Core Update: Winners, Winnerers, Winlosers, and Why It's All Probably Crap
Posted by Dr-Pete
On May 4, Google announced that they were rolling out a new Core Update. By May 7, it appeared that the dust had mostly settled. Here’s an 11-day view from MozCast:
We measured relatively high volatility from May 4-6, with a peak of 112.6° on May 5. Note that the 30-day average temperature prior to May 4 was historically very high (89.3°).
How does this compare to previous Core Updates? With the caveat that recent temperatures have been well above historical averages, the May 2020 Core Update was our second-hottest Core Update so far, coming in just below the August 2018 “Medic” update.
Who “won” the May Core Update?
It’s common to report winners and losers after a major update (and I’ve done it myself), but for a while now I’ve been concerned that these analyses only capture a small window of time. Whenever we compare two fixed points in time, we’re ignoring the natural volatility of search rankings and the inherent differences between keywords.
This time around, I’d like to take a hard look at the pitfalls. I’m going to focus on winners. The table below shows the 1-day winners (May 5) by total rankings in the 10,000-keyword MozCast tracking set. I’ve only included subdomains with at least 25 rankings on May 4:
Putting aside the usual statistical suspects (small sample sizes for some keywords, the unique pros and cons of our data set, etc.), what’s the problem with this analysis? Sure, there are different ways to report the “% Gain” (such as absolute change vs. relative percentage), but I’ve reported the absolute numbers honestly and the relative change is accurate.
The problem is that, in rushing to run the numbers after one day, we’ve ignored the reality that most core updates are multi-day (a trend that seemed to continue for the May Core Update, as evidenced by our initial graph). We’ve also failed to account for domains whose rankings might be historically volatile (but more on that in a bit). What if we compare the 1-day and 2-day data?
Which story do we tell?
The table below adds in the 2-day relative percentage gained. I’ve kept the same 25 subdomains and will continue to sort them by the 1-day percentage gained, for consistency:
Even just comparing the first two days of the roll-out, we can see that the story is shifting considerably. The problem is: Which story do we tell? Often, we’re not even looking at lists, but anecdotes based on our own clients or cherry-picking data. Consider this story:
If this was our only view of the data, we would probably conclude that the update intensified over the two days, with day two rewarding sites even more. We could even start to craft a story about how demand for apps was growing, or certain news sites were being rewarded. These stories might have a grain of truth, but the fact is that we have no idea from this data alone.
Now, let’s pick three different data points (all of these are from the top 20):
From this limited view, we could conclude that Google decided that the Core Update went wrong and reversed it on day two. We could even conclude that certain news sites were being penalized for some reason. This tells a wildly different story than the first set of anecdotes.
There’s an even weirder story buried in the May 2020 data. Consider this:
LinkedIn showed a minor bump (one we’d generally ignore) on day one and then lost 100% of its rankings on day two. Wow, that May Core Update really packs a punch! It turns out that LinkedIn may have accidentally de-indexed their site — they recovered the next day, and it appears this massive change had nothing to do with the Core Update. The simple truth is that these numbers tell us very little about why a site gained or lost rankings.
How do we define “normal”?
Let’s take a deeper look at the MarketWatch data. Marketwatch gained 19% in the 1-day stats, but lost 2% in the 2-day numbers. The problem here is that we don’t know from these numbers what MarketWatch’s normal SERP flux looks like. Here’s a graph of seven days before and after May 4 (the start of the Core Update):
Looking at even a small bit of historical data, we can see that MarketWatch, like most news sites, experiences significant volatility. The “gains” on May 5 are only because of losses on May 4. It turns out that the 7-day mean after May 4 (45.7) is only a slight increase over the 7-day mean before May 4 (44.3), with MarketWatch measuring a modest relative gain of +3.2%.
Now let’s look at Google Play, which appeared to be a clear winner after two days:
You don’t even need to do the math to spot the difference here. Comparing the 7-day mean before May 4 (232.9) to the 7-day mean after (448.7), Google Play experienced a dramatic +93% relative change after the May Core Update.
How does this 7-day before/after comparison work with the LinkedIn incident? Here’s a graph of the before/after with dotted lines added for the two means:
While this approach certainly helps offset the single-day anomaly, we’re still showing a before/after change of -16%, which isn’t really in line with reality. You can see that six of the seven days after the May Core Update were above the 7-day average. Note that LinkedIn also has relatively low volatility over the short-range history.
Why am I rotten-cherry-picking an extreme example where my new metric falls short? I want it to be perfectly clear that no one metric can ever tell the whole story. Even if we accounted for the variance and did statistical testing, we’re still missing a lot of information. A clear before/after difference doesn’t tell us what actually happened, only that there was a change correlated with the timing of the Core Update. That’s useful information, but it still begs further investigation before we jump to sweeping conclusions.
Overall, though, the approach is certainly better than single-day slices. Using the 7-day before-vs-after mean comparison accounts for both historical data and a full seven days after the update. What if we expanded this comparison of 7-day periods to the larger data set? Here’s our original “winners” list with the new numbers:
Obviously, this is a lot to digest in one table, but we can start to see where the before-and-after metric (the relative difference between 7-day means) shows a different picture, in some cases, than either the 1-day or 2-day view. Let’s go ahead and re-build the top 20 based on the before-and-after percentage change:
Some of the big players are the same, but we’ve also got some newcomers — including sites that looked like they lost visibility on day one, but have stacked up 2-day and 7-day gains.
Let’s take a quick look at Parents.com, our original big winner (winnerer? winnerest?). Day one showed a massive +100% gain (doubling visibility), but day-two numbers were more modest, and before-and-after gains came in at just under half the day-one gain. Here are the seven days before and after:
It’s easy to see here that the day-one jump was a short-term anomaly, based in part on a dip on May 4. Comparing the 7-day averages seems to get much closer to the truth. This is a warning not just to algo trackers like myself, but to SEOs who might see that +100% and rush to tell their boss or client. Don’t let good news turn into a promise that you can’t keep.
Why do we keep doing this?
If it seems like I’m calling out the industry, note that I’m squarely in my own crosshairs here. There’s tremendous pressure to publish analyses early, not just because it equates to traffic and links (frankly, it does), but because site owners and SEOs genuinely want answers. As I wrote recently, I think there’s tremendous danger in overinterpreting short-term losses and fixing the wrong things. However, I think there’s also real danger in overstating short-term wins and having the expectation that those gains are permanent. That can lead to equally risky decisions.
Is it all crap? No, I don’t think so, but I think it’s very easy to step off the sidewalk and into the muck after a storm, and at the very least we need to wait for the ground to dry. That’s not easy in a world of Twitter and 24-hour news cycles, but it’s essential to get a multi-day view, especially since so many large algorithm updates roll out over extended periods of time.
Which numbers should we believe? In a sense, all of them, or at least all of the ones we can adequately verify. No single metric is ever going to paint the entire picture, and before you rush off to celebrate being on a winners list, it’s important to take that next step and really understand the historical trends and the context of any victory.
Who wants some free data?
Given the scope of the analysis, I didn’t cover the May 2020 Core Update losers in this post or go past the Top 20, but you can download the raw data here. If you’d like to edit it, please make a copy first. Winners and losers are on separate tabs, and this covers all domains with at least 25 rankings in our MozCast 10K data set on May 4 (just over 400 domains).
Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!
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Google's May 2020 Core Update: Winners, Winnerers, Winlosers, and Why It's All Probably Crap
Posted by Dr-Pete On May 4, Google announced that they were rolling out a new Core Update. By May 7, it appeared that the dust had mostly settled. Here’s an 11-day view from MozCast: We measured relatively high volatility from May 4-6, with a peak of 112.6° on May 5. Note that the 30-day average temperature prior to May 4 was historically very high (89.3°). How does this compare to previous Core Updates? With the caveat that recent temperatures have been well above historical averages, the May 2020 Core Update was our second-hottest Core Update so far, coming in just below the August 2018 “Medic” update.
Who “won” the May Core Update?
It’s common to report winners and losers after a major update (and I’ve done it myself), but for a while now I’ve been concerned that these analyses only capture a small window of time. Whenever we compare two fixed points in time, we’re ignoring the natural volatility of search rankings and the inherent differences between keywords. This time around, I’d like to take a hard look at the pitfalls. I’m going to focus on winners. The table below shows the 1-day winners (May 5) by total rankings in the 10,000-keyword MozCast tracking set. I’ve only included subdomains with at least 25 rankings on May 4: Putting aside the usual statistical suspects (small sample sizes for some keywords, the unique pros and cons of our data set, etc.), what’s the problem with this analysis? Sure, there are different ways to report the “% Gain” (such as absolute change vs. relative percentage), but I’ve reported the absolute numbers honestly and the relative change is accurate. The problem is that, in rushing to run the numbers after one day, we’ve ignored the reality that most core updates are multi-day (a trend that seemed to continue for the May Core Update, as evidenced by our initial graph). We’ve also failed to account for domains whose rankings might be historically volatile (but more on that in a bit). What if we compare the 1-day and 2-day data?
Which story do we tell?
The table below adds in the 2-day relative percentage gained. I’ve kept the same 25 subdomains and will continue to sort them by the 1-day percentage gained, for consistency: Even just comparing the first two days of the roll-out, we can see that the story is shifting considerably. The problem is: Which story do we tell? Often, we’re not even looking at lists, but anecdotes based on our own clients or cherry-picking data. Consider this story: If this was our only view of the data, we would probably conclude that the update intensified over the two days, with day two rewarding sites even more. We could even start to craft a story about how demand for apps was growing, or certain news sites were being rewarded. These stories might have a grain of truth, but the fact is that we have no idea from this data alone. Now, let’s pick three different data points (all of these are from the top 20): From this limited view, we could conclude that Google decided that the Core Update went wrong and reversed it on day two. We could even conclude that certain news sites were being penalized for some reason. This tells a wildly different story than the first set of anecdotes. There’s an even weirder story buried in the May 2020 data. Consider this: LinkedIn showed a minor bump (one we’d generally ignore) on day one and then lost 100% of its rankings on day two. Wow, that May Core Update really packs a punch! It turns out that LinkedIn may have accidentally de-indexed their site — they recovered the next day, and it appears this massive change had nothing to do with the Core Update. The simple truth is that these numbers tell us very little about why a site gained or lost rankings.
How do we define “normal”?
Let’s take a deeper look at the MarketWatch data. Marketwatch gained 19% in the 1-day stats, but lost 2% in the 2-day numbers. The problem here is that we don’t know from these numbers what MarketWatch’s normal SERP flux looks like. Here’s a graph of seven days before and after May 4 (the start of the Core Update): Looking at even a small bit of historical data, we can see that MarketWatch, like most news sites, experiences significant volatility. The “gains” on May 5 are only because of losses on May 4. It turns out that the 7-day mean after May 4 (45.7) is only a slight increase over the 7-day mean before May 4 (44.3), with MarketWatch measuring a modest relative gain of +3.2%. Now let’s look at Google Play, which appeared to be a clear winner after two days: You don’t even need to do the math to spot the difference here. Comparing the 7-day mean before May 4 (232.9) to the 7-day mean after (448.7), Google Play experienced a dramatic +93% relative change after the May Core Update. How does this 7-day before/after comparison work with the LinkedIn incident? Here’s a graph of the before/after with dotted lines added for the two means: While this approach certainly helps offset the single-day anomaly, we’re still showing a before/after change of -16%, which isn’t really in line with reality. You can see that six of the seven days after the May Core Update were above the 7-day average. Note that LinkedIn also has relatively low volatility over the short-range history. Why am I rotten-cherry-picking an extreme example where my new metric falls short? I want it to be perfectly clear that no one metric can ever tell the whole story. Even if we accounted for the variance and did statistical testing, we’re still missing a lot of information. A clear before/after difference doesn’t tell us what actually happened, only that there was a change correlated with the timing of the Core Update. That’s useful information, but it still begs further investigation before we jump to sweeping conclusions. Overall, though, the approach is certainly better than single-day slices. Using the 7-day before-vs-after mean comparison accounts for both historical data and a full seven days after the update. What if we expanded this comparison of 7-day periods to the larger data set? Here’s our original “winners” list with the new numbers: Obviously, this is a lot to digest in one table, but we can start to see where the before-and-after metric (the relative difference between 7-day means) shows a different picture, in some cases, than either the 1-day or 2-day view. Let’s go ahead and re-build the top 20 based on the before-and-after percentage change: Some of the big players are the same, but we’ve also got some newcomers — including sites that looked like they lost visibility on day one, but have stacked up 2-day and 7-day gains. Let’s take a quick look at Parents.com, our original big winner (winnerer? winnerest?). Day one showed a massive +100% gain (doubling visibility), but day-two numbers were more modest, and before-and-after gains came in at just under half the day-one gain. Here are the seven days before and after: It’s easy to see here that the day-one jump was a short-term anomaly, based in part on a dip on May 4. Comparing the 7-day averages seems to get much closer to the truth. This is a warning not just to algo trackers like myself, but to SEOs who might see that +100% and rush to tell their boss or client. Don’t let good news turn into a promise that you can’t keep.
Why do we keep doing this?
If it seems like I’m calling out the industry, note that I’m squarely in my own crosshairs here. There’s tremendous pressure to publish analyses early, not just because it equates to traffic and links (frankly, it does), but because site owners and SEOs genuinely want answers. As I wrote recently, I think there’s tremendous danger in overinterpreting short-term losses and fixing the wrong things. However, I think there’s also real danger in overstating short-term wins and having the expectation that those gains are permanent. That can lead to equally risky decisions. Is it all crap? No, I don’t think so, but I think it’s very easy to step off the sidewalk and into the muck after a storm, and at the very least we need to wait for the ground to dry. That’s not easy in a world of Twitter and 24-hour news cycles, but it’s essential to get a multi-day view, especially since so many large algorithm updates roll out over extended periods of time. Which numbers should we believe? In a sense, all of them, or at least all of the ones we can adequately verify. No single metric is ever going to paint the entire picture, and before you rush off to celebrate being on a winners list, it’s important to take that next step and really understand the historical trends and the context of any victory.
Who wants some free data?
Given the scope of the analysis, I didn’t cover the May 2020 Core Update losers in this post or go past the Top 20, but you can download the raw data here. If you’d like to edit it, please make a copy first. Winners and losers are on separate tabs, and this covers all domains with at least 25 rankings in our MozCast 10K data set on May 4 (just over 400 domains). Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!
https://www.businesscreatorplus.com/googles-may-2020-core-update-winners-winnerers-winlosers-and-why-its-all-probably-crap/
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Google's May 2020 Core Update: Winners, Winnerers, Winlosers, and Why It's All Probably Crap
Posted by Dr-Pete
On May 4, Google announced that they were rolling out a new Core Update. By May 7, it appeared that the dust had mostly settled. Here’s an 11-day view from MozCast:
We measured relatively high volatility from May 4-6, with a peak of 112.6° on May 5. Note that the 30-day average temperature prior to May 4 was historically very high (89.3°).
How does this compare to previous Core Updates? With the caveat that recent temperatures have been well above historical averages, the May 2020 Core Update was our second-hottest Core Update so far, coming in just below the August 2018 “Medic” update.
Who “won” the May Core Update?
It’s common to report winners and losers after a major update (and I’ve done it myself), but for a while now I’ve been concerned that these analyses only capture a small window of time. Whenever we compare two fixed points in time, we’re ignoring the natural volatility of search rankings and the inherent differences between keywords.
This time around, I’d like to take a hard look at the pitfalls. I’m going to focus on winners. The table below shows the 1-day winners (May 5) by total rankings in the 10,000-keyword MozCast tracking set. I’ve only included subdomains with at least 25 rankings on May 4:
Putting aside the usual statistical suspects (small sample sizes for some keywords, the unique pros and cons of our data set, etc.), what’s the problem with this analysis? Sure, there are different ways to report the “% Gain” (such as absolute change vs. relative percentage), but I’ve reported the absolute numbers honestly and the relative change is accurate.
The problem is that, in rushing to run the numbers after one day, we’ve ignored the reality that most core updates are multi-day (a trend that seemed to continue for the May Core Update, as evidenced by our initial graph). We’ve also failed to account for domains whose rankings might be historically volatile (but more on that in a bit). What if we compare the 1-day and 2-day data?
Which story do we tell?
The table below adds in the 2-day relative percentage gained. I’ve kept the same 25 subdomains and will continue to sort them by the 1-day percentage gained, for consistency:
Even just comparing the first two days of the roll-out, we can see that the story is shifting considerably. The problem is: Which story do we tell? Often, we’re not even looking at lists, but anecdotes based on our own clients or cherry-picking data. Consider this story:
If this was our only view of the data, we would probably conclude that the update intensified over the two days, with day two rewarding sites even more. We could even start to craft a story about how demand for apps was growing, or certain news sites were being rewarded. These stories might have a grain of truth, but the fact is that we have no idea from this data alone.
Now, let’s pick three different data points (all of these are from the top 20):
From this limited view, we could conclude that Google decided that the Core Update went wrong and reversed it on day two. We could even conclude that certain news sites were being penalized for some reason. This tells a wildly different story than the first set of anecdotes.
There’s an even weirder story buried in the May 2020 data. Consider this:
LinkedIn showed a minor bump (one we’d generally ignore) on day one and then lost 100% of its rankings on day two. Wow, that May Core Update really packs a punch! It turns out that LinkedIn may have accidentally de-indexed their site — they recovered the next day, and it appears this massive change had nothing to do with the Core Update. The simple truth is that these numbers tell us very little about why a site gained or lost rankings.
How do we define “normal”?
Let’s take a deeper look at the MarketWatch data. Marketwatch gained 19% in the 1-day stats, but lost 2% in the 2-day numbers. The problem here is that we don’t know from these numbers what MarketWatch’s normal SERP flux looks like. Here’s a graph of seven days before and after May 4 (the start of the Core Update):
Looking at even a small bit of historical data, we can see that MarketWatch, like most news sites, experiences significant volatility. The “gains” on May 5 are only because of losses on May 4. It turns out that the 7-day mean after May 4 (45.7) is only a slight increase over the 7-day mean before May 4 (44.3), with MarketWatch measuring a modest relative gain of +3.2%.
Now let’s look at Google Play, which appeared to be a clear winner after two days:
You don’t even need to do the math to spot the difference here. Comparing the 7-day mean before May 4 (232.9) to the 7-day mean after (448.7), Google Play experienced a dramatic +93% relative change after the May Core Update.
How does this 7-day before/after comparison work with the LinkedIn incident? Here’s a graph of the before/after with dotted lines added for the two means:
While this approach certainly helps offset the single-day anomaly, we’re still showing a before/after change of -16%, which isn’t really in line with reality. You can see that six of the seven days after the May Core Update were above the 7-day average. Note that LinkedIn also has relatively low volatility over the short-range history.
Why am I rotten-cherry-picking an extreme example where my new metric falls short? I want it to be perfectly clear that no one metric can ever tell the whole story. Even if we accounted for the variance and did statistical testing, we’re still missing a lot of information. A clear before/after difference doesn’t tell us what actually happened, only that there was a change correlated with the timing of the Core Update. That’s useful information, but it still begs further investigation before we jump to sweeping conclusions.
Overall, though, the approach is certainly better than single-day slices. Using the 7-day before-vs-after mean comparison accounts for both historical data and a full seven days after the update. What if we expanded this comparison of 7-day periods to the larger data set? Here’s our original “winners” list with the new numbers:
Obviously, this is a lot to digest in one table, but we can start to see where the before-and-after metric (the relative difference between 7-day means) shows a different picture, in some cases, than either the 1-day or 2-day view. Let’s go ahead and re-build the top 20 based on the before-and-after percentage change:
Some of the big players are the same, but we’ve also got some newcomers — including sites that looked like they lost visibility on day one, but have stacked up 2-day and 7-day gains.
Let’s take a quick look at Parents.com, our original big winner (winnerer? winnerest?). Day one showed a massive +100% gain (doubling visibility), but day-two numbers were more modest, and before-and-after gains came in at just under half the day-one gain. Here are the seven days before and after:
It’s easy to see here that the day-one jump was a short-term anomaly, based in part on a dip on May 4. Comparing the 7-day averages seems to get much closer to the truth. This is a warning not just to algo trackers like myself, but to SEOs who might see that +100% and rush to tell their boss or client. Don’t let good news turn into a promise that you can’t keep.
Why do we keep doing this?
If it seems like I’m calling out the industry, note that I’m squarely in my own crosshairs here. There’s tremendous pressure to publish analyses early, not just because it equates to traffic and links (frankly, it does), but because site owners and SEOs genuinely want answers. As I wrote recently, I think there’s tremendous danger in overinterpreting short-term losses and fixing the wrong things. However, I think there’s also real danger in overstating short-term wins and having the expectation that those gains are permanent. That can lead to equally risky decisions.
Is it all crap? No, I don’t think so, but I think it’s very easy to step off the sidewalk and into the muck after a storm, and at the very least we need to wait for the ground to dry. That’s not easy in a world of Twitter and 24-hour news cycles, but it’s essential to get a multi-day view, especially since so many large algorithm updates roll out over extended periods of time.
Which numbers should we believe? In a sense, all of them, or at least all of the ones we can adequately verify. No single metric is ever going to paint the entire picture, and before you rush off to celebrate being on a winners list, it’s important to take that next step and really understand the historical trends and the context of any victory.
Who wants some free data?
Given the scope of the analysis, I didn’t cover the May 2020 Core Update losers in this post or go past the Top 20, but you can download the raw data here. If you’d like to edit it, please make a copy first. Winners and losers are on separate tabs, and this covers all domains with at least 25 rankings in our MozCast 10K data set on May 4 (just over 400 domains).
Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!
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