When you're buying bottled water you want to look on the bottom for symbols such as HDP, HDPE and PP. Those symbols mean that the plastic doesn't release harmful toxins into the water. Taking a few seconds to look could make all the difference.
Reddit user vsky lost his grandmother six years ago and he was recently cleaning out her house. He found a safe that wasn't mentioned in her will and decided to crack it open. Once he got it open he found some pretty cool stuff inside.
We're excited to announce the results of Moz's biannual Search Engine Ranking Correlation Study and Expert Survey, a.k.a. Ranking Factors.
Moz's Ranking Factors study helps identify which attributes of pages and sites have the strongest association with ranking highly in Google. The study consists of two parts: a survey of professional SEOs and a large correlation study.
This year, with the help of Moz's data scientist Dr. Matt Peters, new data partners, and over 150 search marketing professionals, we were able to study more data points than in any year past. All together, we measured over 170 correlations and collected over 15,000 data points from our panel of SEO experts.
We want to especially thank our data partners. SimilarWeb, Ahrefs, and DomainTools each gave us unparallelled access and their data was essential to helping make this study a success. It's amazing and wonderful when different companies—even competitors—can come together for the advancement of knowledge.
You can see all of our findings within the study now. In the coming days and weeks we'll dive into deeper analysis as to what we can learn from these correlations.
Search Engine Ranking Correlation Study
Moz's Ranking Correlation Study measures which attributes of pages and websites are associated with higher rankings in Google's search results. This means we look at characteristics such as:
Keyword usage
Page load speed
Anchor text
...and over 170 other attributes
To be clear, the study doesn't tell us if Google actually uses these attributes in its core ranking algorithm. Instead, it shows which features of pages and sites are most associated with higher rankings. It's a fine, but important, distinction.
While correlation studies can't prove or disprove which attributes Google considers in its algorithm, it does provide valuable hints. In fact, many would argue that correlation studies are even more important than causation when working with today's increasingly complex algorithms.
For the study, Dr. Peters examined the top 50 Google results of 16,521 search queries, resulting in over 700,000 unique URLs. You can read about the full methodology here.
Here's a sample of our findings:
Example: Page-Level Link-Based Features
The features in the chart below describe link metrics to the individual ranking page (such as number of links, PageRank, etc.) and their correlation to higher rankings in Google.
Despite rumors to the contrary, links continue to show one of the strongest associations with higher rankings out of all the features we studied. While this doesn't prove how Google uses links in its algorithm, this information combined with statements from Google and the observations of many professional marketers leads us to strongly believe that links remain hugely important for SEO.
Link-based features were only one of the features categories we examined. The complete correlation study includes 12 different categories of data.
10 Ranking Factors summary findings
We continue to see lower correlations between on-page keyword use and rankings. This could likely be because Google is smarter about what pages mean (through related keywords, synonyms, close variants, and entities) without relying on exact keyword phrases. We believe matching user intent is of the utmost importance.
While page length, hreflang use, and total number of links all show moderate association with Google rankings, we found that using HTTPS has a very low positive correlation. This could indicate it's the "tie-breaker" Google claims. Negatively associated factors include server response time and the total length of the URL.
Despite rumors to the contrary, the data continues to show some of the highest correlations between Google rankings and the number of links to a given page.
While there exists a decent correlation between exact-match domains (domains where the keyword matches the domain exactly, e.g. redwidgets.com) and rankings, this is likely due to the prominence of anchor text, keyword usage, and other signals, instead of an algorithmic bias in favor of these domains.
Our study showed little relationship with the type of top-level domain (.com, .org, etc.) and rankings in Google.
While not quite as strong as page-level link metrics, the overall links to a site's root and subdomain showed a reasonably strong correlation to rankings. We believe links continue to play a prominent role in Google's algorithm.
Always controversial, the number of social shares a page accumulates tends to show a positive correlation with rankings. Although there is strong reason to believe Google doesn't use social share counts directly in its algorithm, there are many secondary SEO benefits to be gained through successful social sharing.
Engagement metrics from SimilarWeb showed that pages with lower bounce rates, higher pageviews, and better time-on-site metrics were associated with higher rankings.
Ranking Factors Expert Survey
While correlation data can provide valuable insight into the workings of Google's algorithm, we often learn much more by gathering the collective wisdom of search marketing experts working at the top of their game.
The survey itself is famously grueling–over 100 questions covering every aspect of Google's ranking algorithm. This year, we sent the invitation-only survey to 150 industry professionals.
Stay tuned for a deeper dive into the Expert Survey later this week. We're honored to have the participation of so many knowledgeable professionals.
In the meantime, you can freely view all the findings and results right now:
Ranking Factors wouldn't be possible without the contribution of dozens of very talented people, but we'd especially like to thank Dr. Matt Peters, Kevin Engle, Rand Fishkin, Casey Coates, Trevor Klein, and Kelly Cooper for their efforts, along with our data partners and all the survey participants.
What ranking factors or correlations stand out to you? Leave your thoughts in the comments below.
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Empathy doesn't involve feeling sorry for someone. It is our honest answer to the question, "why did they do what they did?"
The useful answer is rarely, "because they're stupid." Or even, "because they're evil." In fact, most of the time, people with similar information, similar beliefs and similar apparent choices will choose similar actions. So if you want to know why someone does what they do, start with what they know, what they believe and where they came from.
Dismissing actions we don't admire merely because we don't care enough to have empathy is rarely going to help us make the change we seek. It doesn't help us understand, and it creates a gulf that drives us apart.
I don't fully understand from your latest post what is the significance of congress labeling China a currency manipulator? Does it matter? Also you say they shouldn't, aren't they obviously a manipulator? Can you elaborate?
Thanks for all the great insights! Martin
Significance
The significance of Congress labeling China a currency manipulator is the likelihood that president Obama would have to hike tariffs on a number of Chinese imports. This would likely result in a tit-for-tat response by China.
No one wins trade wars. Instead trade suffers.
Manipulators
China does manipulate currency to force yuan down.
The US has undergone QE to force the dollar down.
The US and China both manipulate interest rates for the same reason.
The EU manipulates interest rates and also launched an inane QE policy.
Japan does all three: Currency interventions, QE, Interest rate manipulation.
No one should point fingers here. They are all guilty
Mike "Mish" Shedlock http://globaleconomicanalysis.blogspot.com
According to conventional wisdom, wars are easy to start and difficult to end. Similarly Beijing's devaluation, the biggest one-day currency move since 1993, represents the latest skirmish in an emerging battle which, analysts warn, may be hard to reverse.
"This shows how desperate the government is over the state of the economy," said Fraser Howie, a China analyst and co-author of Red Capitalism. "If they were trying, as the central bank said it was, to bring the exchange rate back into line with market expectations then they have failed miserably as the market is now just expecting further devaluation."
In late March, Chinese Premier Li Keqiang told the Financial Times: "We don't want to see further devaluation of the Chinese currency, because we can't rely on devaluing our own currency to boost exports.
"We don't want to see a scenario in which major economies trip over each other to devalue their currencies," Mr Li continued. "That will lead to a currency war, and if China feels compelled to devalue the RMB in this process, we don't think this will be something good for the international financial system."
Another problem is that depreciation is likely to exacerbate capital flight, which has already become a serious issue this year for the first time in more than a decade.
"The devaluation in the renminbi is not large enough to improve China's export competitiveness, but it is large enough to create a sense that Beijing may have fundamentally shifted its currency policy," said Stuart Allsopp, head of country risk and financial markets strategy at BMI Research.
"The risk now is that investors see the yuan [another term for the renminbi] as a one-way bet weaker and start to position against the currency, raising the prospect of more substantial yuan weakness and more economic uncertainty."
Yuan vs. US Dollar
That may not look like much but it is a surprise 2% move. And it comes amidst a persistent trade deficit with China.
Economists expected bounce in productivity, and got one, but it was a bit weaker than than the Bloomberg Consensus Estimate of 1.6%.
A bounce back for output gave first-quarter productivity a lift, up a quarter-to-quarter 1.3 percent vs a revised decline of 1.1 percent in the first quarter. The bounce in output also held down unit labor costs which rose 0.5 percent vs 2.3 percent in the first quarter.
Output in the second quarter rose 2.8 percent vs a depressed 0.5 percent in the first quarter. Compensation rose 1.8 percent, up from 1.1 percent in the first quarter, while hours worked were little changed, up 1.5 percent vs 1.6 in the first quarter.
Looking at year-on-year rates, growth in productivity is very slight at only plus 0.3 percent while costs do show some pressure, up 2.1 percent in a reading, along with the rise in compensation, that will be welcome by Federal Reserve officials who are hoping that gains in wages will help offset weakness in commodity costs and help give inflation a needed boost.
The dry spell of productivity in this economic expansion is even worse than previously thought, according to new data released Tuesday.
The average annual rate of productivity growth from 2007 to 2014 was revised down to 1.3% per year from the prior estimate of 1.4%, the Labor Department said Tuesday.
This is well below the long-term rate of 2.2% per year from 1947 to 2014.
Productivity in 2013 was especially weak, revised down to unchanged from the prior estimate of a 0.9% gain. Productivity even dipped below zero for three quarters in 2013. That hadn't been seen since 1982.
Former Fed chairman Alan Greenspan said Monday that weak productivity is the most serious problem that confronts the U.S.
Federal Reserve Chairwoman Janet Yellen has also called productivity since the recession "disappointing," even before the downward revisions.
The flipside of the weakness in productivity growth is that it is one reason for the Fed may be eager to get starting raising interest rates in September because weaker productivity lowers potential growth and it doesn't not take much to generate inflation.
There is widespread debate among economists about the causes of weak productivity. Some blame the lack of capital investment. Some question the government's measuring skills.
Joel Naroff, president of Naroff Economic Advisors, said productivity is low because workers have learned that in this expansion working harder doesn't get them anything in return.
"If firms want to drive up productivity, they will have to do it the old fashioned way, by providing incentives to work harder," Naroff said.
Workers Learn Not to Work Hard?
The above comments on productivity are incredibly funny. The most ridiculous notion is the Fed will hike because low productivity means higher inflation.
Good grief. The Fed wants higher inflation. And inflation (as measured by the CPI) has consistently been under the Fed's 2% target.
Of course the CPI measurement is a piss poor measure of inflation in the first place, but it is what the econo-clowns at the Fed believe in.
And what about notion "workers learn not to work hard"? That seems ridiculous as well. What if the workers simply are not any good, or are good and cannot get much better?
Retail Store Productivity
Have retail stores expanded so much that companies have to hire marginal workers just to staff them? How many in retail have totally useless college degrees and a lack of real skills?
What skills are even needed to take an order for a burger, or scan an item at the register? That question we can answer: almost none.
Productivity would go up if a scanner could ring up an entire basket at one, and the person behind the register was made redundant.
The NPR claims the most common job in 29 of 50 US states is truck driving. This seems a bit overboard, and depends on how jobs are categorized, but here is the chart.
As with retail, short of automating trucks and firing truckers, there is scant room for increased productivity.
Software mapping of the best routes is not likely to get much better. When trucks are fully automated, productivity will soar, but the industry will lose millions of jobs.
Are Measurements Accurate?
Many question the measurements. So do I. But which way do they go? Up or down?
With so many working in retail and trucking, until masses of people are fired, productivity just may stagnate.
Alan Greenspan said Monday that weak productivity is the most serious problem that confronts the U.S.
I disagree. The most serious problem clearly is a debt overload everywhere one looks (students, households, subprime auto loans, corporations going into debt to buy back their own shares, etc.)
For that blame the Fed. One can also blame the Fed for its loosey-goosey monetary policy and nonsensical 2% inflation target.
Increases in productivity are inherently price-deflationary: More goods produced quicker should result in falling prices as well as higher standards of living. That's a good thing.
But the Fed does not want falling prices. It wants unit labor costs to rise. The catch is the higher the unit costs, the more incentive there is to get rid of workers.
The Fed just may not want the next major advancement in productivity because of what it will do to jobs and prices. But that is just around the corner led by fully automated trucks, buses, and cabs.
Mike "Mish" Shedlock http://globaleconomicanalysis.blogspot.com
Consumers at the low end of projection (those living paycheck to paycheck, forecast a mere 0.62% increase in their spending, down from 1.28% last month.
The median forecast is 3.46%, down from 4.29% last month.
The high end forecast is 9.01%, down from 9.62% last month.
The absolute numbers are not that important because month-to-month numbers swing a bit.
Yet, it's easy to see a change starting late last year in the median and low projections. The high end projections have been in decline for even longer.
These trends are very recessionary looking, but the Fed does not believe its own surveys.
Mike "Mish" Shedlock http://globaleconomicanalysis.blogspot.com
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