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vineri, 29 mai 2015

Mish's Global Economic Trend Analysis

Five Chicago Suburbs Headed for Bankruptcy (More Illinois Cities Will Follow)
Chicago PMI Unexpectedly Crashes: New Orders, Production and Employment Down by More Than 10%
First Quarter GDP -0.7%; GDPNow Second Quarter Forecast +0.8%; Economists Get Zero Accolades; Smoothed Recession Odds
Introducing the Zero Labor Factory (90% Free Actually); Robots at Chili's, Applebees, Panera

Five Chicago Suburbs Headed for Bankruptcy (More Illinois Cities Will Follow)

Posted: 29 May 2015 12:57 PM PDT

Illinois House Bill 298 would allow Illinois municipalities to file for Chapter 9 bankruptcy. That bill is endorsed by Governor Bruce Rauner, and currently rests in the house rules committee.

As soon as Illinois passes Bill 298, a number of Illinois cities are highly likely to file bankruptcy as noted by Bond Buyer in Illinois' Candidates for Municipal Bankruptcy.

If HB298 was enacted, which local governments might use the new bankruptcy option? To help answer this question, our team reviewed audited financial statements that all but the smallest municipalities must file. Most of these financial audits can be found on the state comptroller's local government Finance Warehouse.

Among the indicators we considered were government-wide unrestricted net position and general fund balance. The first indicator shows the degree to which assets held by the government entity as a whole exceed its liabilities and are not locked up in buildings and other illiquid forms. The second indicator, general fund balance, focuses more narrowly on the government's main fund – which is roughly analogous to an individual's checking account. Low or negative general fund balances were cited in the bankruptcies of Vallejo and Stockton, California. It is worth noting that the five municipalities we identified are all located in Cook County, which also faces fiscal challenges. Our list does not include Chicago. Although that city's financial struggles have made frequent headlines, several of its smaller suburbs appear to be in much greater fiscal distress. The five communities we identified are: Maywood, Sauk Village, Blue Island, Country Club Hills and Dalton.

Distress Summary

Maywood: Village of Maywood reported an unrestricted net position of -$47.4 million, and a general fund balance of -$8.2 million. While we found a number of jurisdictions with negative balances, these levels are quite pronounced for a relatively small municipality. With general fund revenues of only $23.3 million and government-wide revenues of $44.1 million, it will take the village a long time to eliminate these shortfalls.

Sauk Village: Sauk Village reported an unrestricted net position of negative $36.7 million – a very large negative position considering that the village had only $29.6 million in assets and government-wide revenues of $13.4 million. Sauk Village also showed a negative general fund balance and unusually high interest costs. The village's $2.1 million of interest expense accounted for over 15% of total revenue. The Village received an adverse audit opinion for its reporting of "Aggregate Remaining Fund Information" and a qualified opinion for its reporting of "Governmental Activities." The Police Pension Fund information was not included and has not been subject to an actuarial evaluation since May 1, 2011.

Blue Island: The City of Blue Island reported an unrestricted net position of negative $15.2 million and a general fund balance of negative $10.5 million in its 2013 financial statements – the latest available. The negative general fund balance is especially pronounced because the city only recorded $16.3 million in general fund revenue during fiscal year 2013. The city's negative net unrestricted position appears to be understated because Blue Island did not report an Other Post-Employment Benefit (OPEB) liability.

Country Club Hills: The City of Country Club Hills has yet to file audited financial statements for the 2013 fiscal year – making it the most delinquent filer among the municipalities we reviewed. The city's 2012 financial statements show a slightly negative unrestricted net position and a large negative general fund balance. Further, the city's auditor was unable to render an opinion on the accuracy of these statements, saying:

Dolton: The Village of Dolton reported a small negative net unrestricted position in its 2013 financial statements – the latest available. Although its general fund balance was positive, the amount was well below Government Finance Officers Association guidelines. Dolton's $1.3 million general fund balance would cover less than a month of general fund expenditures, which were $22.1 million for the 2013 fiscal year. Further, the village reported a $5.2 million general fund deficit. If this deficit persisted into 2014, Dolton may now be facing a negative general fund balance.

Modification to Bill 298 Needed

The Bond Buyer concludes "As Detroit and other cities filing Chapter 9 have found, municipal bankruptcy is an expensive process that transfers community resources to lawyers and financial advisors. While it may be unavoidable, bankruptcy should always be treated as the least best option."

I agree with that statement and that is why I advocate a rules change to Bill 298 that will give bondholders, not pensioners, a secured first lien.

Such a provision would lower borrowing costs to the benefit of taxpayers and it would get public unions to bargain upfront rather than drag processes out for years as happened in Detroit.

For further discussion on Bill 298 and why bondholders should have first lien rights, please see Calpers Wins Pension Lawsuit, Not Good News for Chicago (or Bondholders in General).

In the case of the five cities listed above, bankruptcy appears inevitable although the village administrator of Dolton strongly rebutted the report's findings as noted in a separate Bond Buyer article on Illinois Bankruptcy Candidates.

Bankrupt Candidate Populations

Maywood: 24,160 (2013)
Dolton: 23,333 (2013)
Country Club Hills: 16,866 (2013)
Blue Island: 23,793 (2013)
Sauk Village: 10,549 (2013)

I am aware of at least one other Illinois city potentially ready to file if allowed, and I suspect there are far more waiting in the wings.

Meanwhile, the outlook for the Illinois economy is not a good one. For details, please see Chicago PMI Unexpectedly Crashes: New Orders, Production and Employment Down by More Than 10%

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

Chicago PMI Unexpectedly Crashes: New Orders, Production and Employment Down by More Than 10%

Posted: 29 May 2015 11:46 AM PDT

Unexpected Chicago PMI Crash

Looking for signs of strength? You will not find them in today's Chicago PMI report.

The Bloomberg Consensus estimate was for a 53.1 expansion reading. Instead, the PMI came in at 46.2, well below the bottom of the consensus range of 51.0 to 54.0.

Readings below 50.0 indicate contraction.

New Orders, Production and Employment Down by More Than 10%

For details, let's turn to the Chicago ISM Report that shows Business Barometer Back into Contraction in May.

The Chicago Business Barometer fell sharply back into contraction in May, reversing all of April's gain and casting doubt on the strength of the widely expected bounceback in the US economy in the second quarter. The Barometer fell 6.1 points to 46.2 in May from 52.3 in April. All five components of the Barometer weakened with three dropping by more than 10% and all of them now below the 50 breakeven mark.

April's positive move had suggested that the first quarter slowdown was transitory and had been impacted by the cold snap and port strikes. May's weakness points to a more fundamental slowdown with the Barometer running only slightly above February's 5½-year low of 45.8. The three month average, although little changed on the month at 48.3, is significantly down from 61.3 in Q4 2014 and barring a sharp rebound in June points to continued sluggish growth in the second quarter.

The decline was led by a 13.8% fall in New Orders to 47.5 from 55.1 in April, pushing it into contraction for the third time this year. In line with the lower order intake, both Production and Employment Indicators suffered double-digit losses in percentage terms between April and May, with the latter falling to the lowest since April 2013. Order Backlogs declined more moderately, remaining in contraction for the fourth consecutive month.

There was further evidence that the period of oil driven softer prices has run its course. Prices Paid jumped sharply back into expansion in May to the highest since December.

Chicago PMI

Telling Stats

Unlike strict manufacturing PMI reports, the Chicago PMI is a survey of manufacturing and non-manufacturing (services), tracking all aspects of the Chicago economy.

Here is one more telling stat from the report: "42% of companies said their current inventory level was too high compared with 12% in a comparable question asked in November 2014. 53.2% said stock levels were about right, with less than 5% reporting them as too low."

So don't go looking for an inventory rebuild to lead the way out of this slump.

Recession Call

I don't believe this is a "Chicago Only" problem. But it could be an indication that Illinois will be harder hit by the next recession than other areas.

Nationally, economists are looking for close to 3% annualized growth for second quarter. I am sticking with my recession call made back on January 31.

For comments on current recession odds, first quarter GDP revisions, and second quarter GDP estimates, please see First Quarter GDP -0.7%; GDPNow Second Quarter Forecast +0.8%; Economists Get Zero Accolades; Smoothed Recession Odds from earlier today.

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

First Quarter GDP -0.7%; GDPNow Second Quarter Forecast +0.8%; Economists Get Zero Accolades; Smoothed Recession Odds

Posted: 29 May 2015 10:29 AM PDT

First quarter GDP came in at -0.7% pretty much in line with the Bloomberg Consensus estimate of -0.8%.

First-quarter GDP was revised down about as expected, to minus 0.7 percent vs expectations for minus 0.8 and compared with an initial reading of plus 0.2 percent. Updated source data made for a bigger negative contribution from net exports as imports spiked 5.6 percent from an initial gain of 1.8 percent. The change here is tied to the port strike and the sudden unloading of imports in March. A lower estimate for inventory growth was also a negative. Turning to demand, final sales were revised downward to minus 1.1 percent from minus 0.5 percent.

On the positive side, the contribution from residential fixed investment rose to 5.0 percent from 1.3 percent while the negative contribution from business spending improved 6 tenths to minus 2.8 percent.

The first quarter was definitely weak, showing the first contraction since first-quarter 2014 when GDP fell 2.1 percent in another winter quarter affected by unusually severe weather. The Fed itself has been noting the risk that the pattern of first quarter weakness could reflect how the numbers are crunched by government statisticians to account for seasonal variations. This process may have exaggerated the underlying weakness in the quarter.

Where is GDP currently tracking? Early estimates were in the 3.0 percent range but, due to weak consumer spending, have been slipping to the 2.0 percent range.

Economists Get Zero Accolades

Economists get zero credit for guessing this one correct. Their negative estimate was in arrears after consumer spending unexpectedly collapsed.

This is what the "Blue Chip" economists thought about first quarter GDP on April 2.

GDPNow Estimate for 1st Quarter, April 2

Note the "Blue Chip" consensus at the end of the first quarter was for 1.7% annualized growth. They were off by 2.4 percentage points.

Pathetic.

Bloomberg notes the "port strike and the sudden unloading of imports in March." Question of the day: Had they not unloaded merchandise in March, would they have done so in April?

Of course they would. So instead of whining about the sudden unloading in March, mentally shift -0.4% or so from first quarter to the second quarter.

That brings us to the today's GDPNow Forecast.

Second Quarter GDPNow Estimate

The "Blue Chip" forecasters who were off by a massive 2.4 percentage points at the end of the first quarter are now back at it.

They are looking for 2.9% GDP growth vs. the Atlanta Fed GDPNow model of 0.8%.

Had that port strike settled in April, first quarter would still have been negative due to the revision in final sales to minus 1.1 percent from minus 0.5 percent. And second quarter GDP would now be barely positive according to the GDPNow model.

Smoothed Recession Odds

As of May first, the smoothed recession odds of recession stand at 1.2%.

On Verge of Recession

I think second quarter GDP will come in even lower than GDPNow. Consumers show no inclination to spend, despite economists persistent belief they will.

We are on the verge of recession, if indeed not already in one. First quarter GDP was negative and if for any reason second quarter GDP is negative the US will be in recession.

Regardless of whether or not one believes second quarter GDP will be negative, the odds are far better than 1.2%.

Besides, it does not even take two quarters of negative GDP for there to be a recession. Rather, two quarters of negative GDP is a sufficient but not necessary condition.

The smoothed recession odds model is clearly a joke.

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

Introducing the Zero Labor Factory (90% Free Actually); Robots at Chili's, Applebees, Panera

Posted: 28 May 2015 11:25 PM PDT

In the strive for zero labor factories we are nearly there. Is 90% good enough?

China Daily reports Manufacturing Hub Starts Work on First Zero-Labor Factory.

A manufacturing hub in South China's Guangdong province has begun constructing the city's first zero-labor factory, a signal that the local authorities are bringing into effect its "robot assembling line" strategy.

Dongguan-based private company Everwin Precision Technology Ltd is pushing toward putting 1,000 robots in use in its first phase of the zero-labor project, China National Radio reported. It said the company has already put first 100 robots on the assembly line.

"The 'zero-labor factory' does not mean we will not employ any humans, but what it means is that we will scale down the size of workers by up to 90 percent," said Chen Qixing, the company's board chairman.

After the work on smart factory started, Chen predicted that instead of 2,000 workers, the current strength of the workforce, the company will require only 200 to operate software system and backstage management.

"It is necessary to replace human workers with robots, given the severe labor shortage and mounting labor costs," said Di Suoling, head of Dongguan-based Taiwan Business Association.

Manufacturers in the PRD have been hit by a shortage of an estimated 600,000 to 800,000 workers, according to data released after the Spring Festival in February.

Tens of thousands of migrant workers had earlier gone back home to inlands for a family get-together and some of them decided to settle down in their hometown where the living costs are much less than the coastal cities.

Shortage of Labor?

There is no shortage of labor. There is no shortage of skills either. Rather, there is a shortage of people willing to work for what factory owners are willing to pay.

And with cheap money everywhere you look, there is plenty of money at low rates to buy robots.

Meanwhile, back in the US, McDonald's employees think they are worth $15 an hour for taking orders and handing people a sack of crap.

Robots at Chili's, Applebees, Panera

High wages means fewer jobs. CNN accurately reports Robots will Replace Fast-Food Workers.

Panera Bread (PNRA) is the latest chain to introduce automated service, announcing in April that it plans to bring self-service ordering kiosks as well as a mobile ordering option to all its locations within the next three years. The news follows moves from Chili's and Applebee's to place tablets on their tables, allowing diners to order and pay without interacting with human wait staff at all.

In a widely cited paper released last year, University of Oxford researchers estimated that there is a 92% chance that fast-food preparation and serving will be automated in the coming decades.

Delivery drivers could be replaced en masse by self-driving cars, which are likely to hit the market within a decade or two, or even drones. In food preparation, there are start-ups offering robots for bartending and gourmet hamburger preparation. A food processing company in Spain now uses robots to inspect heads of lettuce on a conveyor belt, throwing out those that don't meet company standards, the Oxford researchers report.

Darren Tristano, a food industry expert with the research firm Technomic, said digital technology will "slowly, over time, create efficiency and labor savings" for restaurants. He guessed that work forces would only drop as a result by 5% or 10% at a maximum in the decades to come, however, given the expectations that customers have for the dining experience.

"If you look at the thousands of years that consumers have been served alcohol and food by people, it's hard to imagine that things will change that quickly," he said.

I think Darren Tristano is in fantasyland. The higher the wage, the bigger the incentive to get rid of people.

Central banks have mush for brains in their attempts force wages and prices up in this type of environment.

Question of the Day

How much do you tip a human server, when the server did not even take your order? The question will eventually be moot when robots bring food to the table.

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

Damn Cool Pics

Horrible Accidents That Happened On Famous Movie Sets

Posted: 29 May 2015 04:03 PM PDT

Sometimes making a movie can be dangerous work.

Is Brand a Google Ranking Factor? - Whiteboard Friday - Moz Blog

Is Brand a Google Ranking Factor? - Whiteboard Friday

Posted on: Friday 29 May 2015 — 02:15

Posted by randfish

A frequently asked question in the SEO world is whether or not branding plays a part in Google's ranking algorithm. There's a short answer with a big asterisk, and in today's Whiteboard Friday, Rand explains what you need to know.

For reference, here's a still of this week's whiteboard. Click on it to open a high resolution image in a new tab!

Video Transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week I'm going to try and answer a question that plagues a lot of marketers, a lot of SEOs and that we ask very frequently. That is: Is brand or branding a ranking factor in Google search engine?

Look, I think, to be fair, to be honest, that the technical answer to this question is no. However, I think when people say brand is powerful for SEO, that is a true statement. We're going to try and reconcile these two things. How can brand not be a ranking factor and yet be a powerful influencer of higher rankings in SEO? What's going to go on there?

What is a ranking factor, anyway?

Well, I'll tell you. So when folks say ranking factor, they're referring to something very technical, very specific, and that is an algorithmic input that Google measures directly and uses to determine rank position in their algorithm.

Okay, guess what? Brand almost certainly is not this.

Google doesn't try and go out and say, "How well known is Coca-Cola versus Pepsi versus 7 Up versus Sprite versus Jones Cola? Hey, let's rank Coca-Cola a little higher because they seem to have greater brand awareness, brand affinity than Pepsi." That is not something that Google will try and do. That's not something that's in their algorithm.

However, a big however, many things that are in Google's ranking algorithm correlate very well with brands.

Those things are probably used by Google in both direct and indirect ways.

So when you see sites that have done a great job of branding and also have good SEO best practices on them, you'll notice kind of a correlation, like boy, it sure does seem like the brands have been performing better and better in Google's rankings over the last four, five, or six years. I think this is due to two trends. One of those trends is that Google's algorithmic inputs have started favoring things that brands are better at and that what I'd call generic sites or non-branded sites, or businesses that have not invested in brand affinity have not done well.

Those things are things like links, where Google is rewarding better links rather than just more links. They're things around user and usage data, which Google previously didn't use a whole lot of signals around that. Same story with user experience. Same story with things like pogo sticking, which is probably one of the ways that they're measuring some of that stuff.

If we were to scatter plot it, we'd probably see something like this, where the better your brand performs as a brand, the higher and better it tends to perform in the rankings of Google search engine.

How does brand correlate to ranking signals?

Now, how is it that these brand signals that I'm talking about correlate more directly to ranking signals? Like why does this impact and influence? I think if we understand that, we can understand why we need to invest in brand and branding and where to invest in it as it relates to the web marketing kinds of things that we do for SEO.

One very clearly and very frankly is links. So when we talk about the links that Google wants to measure, wants to count today, those are organic, editorially earned links. They're not manipulative. They weren't bought. They tend not to be cajoled, they're earned.

Because of that, one of the best ways that folks have been earning links is to get people to come to their website and then have some fraction, some percentage of those folks naturally link to them without having to do any extra effort. It's basically like, "Hey, you made this great piece of content or this great product or great service or great data. Therefore, I'm going to reference it." Granted, that's a small percentage of people. There's still only maybe two or three out of a hundred folks who might visit your website on the Internet who actually have the power or ability to link to you because they control content on the web as opposed to just social sharing.

But when that happens, in a lot of cases folks go and they say, "Hmm, yeah, this content's good, but I've never heard of this brand before. I'm not sure if I should recommend it. It looks good, but I don't know them." Versus, "Oh, I love these folks. This is like one of my favorite companies or brands or products or experiences, and this content is great. I am totally going to link to it." Because that happens, even if that difference is small, even if the percent goes from 1% to 2%, well now, guess what? For every hundred visits, you're earning twice the links of your non-branded competitor.

Social signals

These are pretty much exactly the same thing. Folks who visit content, who have experiences with a company, with a product, or with a service, if they're familiar and comfortable with the brand, if they want to evangelize that brand, then guess what? You're going to get more social sharing per visit, per exposure than you would ordinarily, and that's going to lead to a cycle of more social sharing which leads to visits which probably leads to links.

User and usage data

It's also true that brand is going to impact user and usage data. So one of the most interesting patents, which we'll probably be talking about in a future Whiteboard Friday, was brought up recently by Bill Slowsky and looked at user and usage data. It was just granted to Google in the last month. It talked about how Google would look at the patterns of where web visitors would go and what their search experiences would be like. It would potentially say, "Hey, Google would like to reward sites that are getting organic traffic, not just from search, but traffic of all kinds on a particular topic."

So if it turns out that lots of people who are researching a vacation to Costa Rica end up going to Oyster.com, well, Google might say, "Hey, you know what? We've seen this pattern over and over again. Let's boost Oyster.com's rankings because it seems like people who look for this kind of content end up on this site. Not necessarily directly through us, through Google. They might end up on it through social media, through organic web links, through direct visits, through e-mail marketing, whatever it is."

When you're unbranded, one of the few ways that you can get traffic is through unbranded search. Search is one of those few channels that does drive traffic, or historically anyway did drive traffic to a lot of non-branded, less branded sites. Brands tend to earn traffic from a wide variety of sources. If you can start earning traffic from lots of sources and have the retention and the experience to drive people back again and again, well, probably you're going to benefit from some of these potential algorithmic shifts and future looking directions that Google's got.

Click-through rates

Same story a little bit when it comes to click-through rate. Now, we know from experience and testing that click-through rate is or appears to have a very direct impact on rankings. If lots of people are performing a search and they click on your website in position number four or five, and they're not clicking on position one, two, or three, you can bet that you're going to be moving up those rankings very, very quickly.

Granted there is some manipulative services out there that try and automate this. Some of them work for a little while. Most of them get shut down pretty quick. I wouldn't recommend investing in those. But I do recommend investing in brand, because when you have a recognizable brand, searchers are going to come here and they're going to go, "Oh, that one, maybe I haven't heard of it. That one, I've heard of it. That one, I haven't heard of it."

Guess what they're clicking on? The one they're already familiar with. The one they have a positive association with already. This is the power of brand advertising, and I think it's one of the big reasons why you've seen case studies from folks like Seer Interactive, talking about how a radio ad campaign or a billboard ad campaign seemed to have a positive lift in their SEO work as well. This phenomenon is going to mean that you're benefiting from every searcher who looks for something, even if you rank further down, if you're the better known brand.

So is brand a ranking factor? No, it's not. Is brand something that positively impacts SEO? Almost certainly in every niche, yes, it is.

All right. Looking forward to some great comments. I'll try and jump in there and answer any questions that I can. If you have experiences you want to share, we'd love to hear from you. Hopefully, we'll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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Your Daily SEO Fix: Week 2

Posted on: Thursday 28 May 2015 — 13:30

Posted by Trevor-Klein

Last week, we began posting short (< 2-minute) video tutorials that help you all get the most out of Moz's tools. Each tutorial is designed to solve a use case that we regularly hear about from Moz community members—a need or problem for which you all could use a solution.

Today, we've got a brand-new roundup of the most recent videos:

How to Examine and Analyze SERPs Using New MozBar Features
How to Boost Your Rankings through On-Page Optimization
How to Check Your Anchor Text Using Open Site Explorer
How to Do Keyword Research with OSE and the Keyword Difficulty Tool
How to Discover Keyword Opportunities in Moz Analytics

Let's get right down to business!

Fix 1: How to Examine and Analyze SERPs Using New MozBar Features

The MozBar is a handy tool that helps you access important SEO metrics while you surf the web. In this Daily SEO Fix, Abe shows you how to use this toolbar to examine and analyze SERPs and access keyword difficulty scores for a given page—in a single click.

Fix 2: How to Boost Your Rankings through On-Page Optimization

There are several on-page factors that influence your search engine rankings. In this Daily SEO Fix, Holly shows you how to use Moz's On-Page Optimization tool to identify pages on your website that could use some love and what you can do to improve them.

Fix 3: How to Check Your Anchor Text Using Open Site Explorer

Dive into OSE with Tori in this Daily SEO Fix to check out the anchor text opportunities for Moz.com. By highlighting all your anchor text you can discover other potential keyword ranking opportunities you might not have thought of before.

Fix 4: How to Do Keyword Research with OSE and the Keyword Difficulty Tool

Studying your competitors can help identify keyword opportunities for your own site. In this Daily SEO Fix, Jacki walks through how to use OSE to research the anchor text for competitors websites and how to use the Keyword Difficulty Tool to identify potential expansion opportunities for your site.

Fix 5: How to Discover Keyword Opportunities in Moz Analytics

Digesting organic traffic that is coming to your site is an easy way to surface potential keyword opportunities. In this Daily SEO Fix, Chiaryn walks through the keyword opportunity tab in Moz Analytics and highlights a quick tip for leveraging that tool.

Looking for more?

We've got more videos in last week's round-up! Check it out here.

Don't have a Pro subscription? No problem. Everything we cover in these Daily SEO Fix videos is available with a free 30-day trial.

Sounds good. Sign me up!

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Seth's Blog : Do-able

Do-able

Lean entrepreneurs can talk about the minimum viable product, but far more important is the maximum do-able project.

Given the resources you have (your assets, your time, your patience), what's the biggest thing it's quite likely you can pull off?

Our culture is organized around the people who get on base, who reliably keep their promises, who deliver. "Quite likely," is a comforting story indeed. [HT to Bernadette.]

Domino's could have offered five-minute pizza delivery, and sometimes, without a doubt, they could have pulled that off. But promising something they could do virtually every time earned them a spot on the speed dial of millions of phones.

Aiming too high is just as fearful a tactic as aiming too low. Before you promise to change the world, it makes sense to do the hard work of changing your neighborhood.

Do what you say, then do it again, even better.

We need your dreams, but we also need your deeds.

• Email to a friend •

joi, 28 mai 2015

Mish's Global Economic Trend Analysis

April Greek Capital Flight €5 Billion; Eurozone Liabilities Hit €115 Billion
Swede Has Had Enough
"Bond Girl" on Chicago and the Quality of Credit Analysis in the Municipal Bond Market

April Greek Capital Flight €5 Billion; Eurozone Liabilities Hit €115 Billion

Posted: 28 May 2015 12:26 PM PDT

Chalk up another €5 billion in capital flight from Greece in April. Total eurozone exposure to Greek currency liabilities now sits at €115 Billion, not counting accelerated capital flight in recent weeks.

The following two charts produced with data from EuroCrisis Monitor.

Greece Target2 Imbalance Since February 2008

Greece Target2 Imbalance Detail Since June 2014

The chart shows a rise of €2 billion but that does not count cash.

Target2 Explanation

For a refresher course on Target2, please see Reader From Europe Asks "Can You Please Explain Target2?"

Intra-Eurosystem Liabilities

The latest Intra-Eurosystem Liabilities from the Bank of Greece are €114.95 billion as shown below.

Change From Last Month

Last month, eurozone exposure to Greek liabilities was €96.427 billion of Target2 imbalances plus another €14.028 billion net liabilities related to the allocation of euro banknotes.

"The past week in May was more challenging compared to the previous ones in the month, with daily outflows of 200 to 300 million euros in the last few days," a senior Greek banker said yesterday.

In the last week alone, it seems likely another €2 billion was pulled from Greek banks. The total May drain will not be reported until June 10.

The ECB is attempting to stem the flow by not upping emergency liquidity assistance (ELA) as noted yesterday in Run on Greek Banks Accelerates; ECB Halts Emergency Funding Hike; Untangling the Lies

Everyone Prepared?
When the ECB and Germany say they are prepared for Grexit, do they include taxpayers who will have to foot the bill for default?

My friend Lars from Norway pinged me with this observation today...

Greek GDP is about €180 billion. Public sector is 60% of the total. That makes the private sector contribution to GDP about €72 billion.

Total public sector debt is close to €500 billion (not €320 billion as quoted by the mainstream media). So a private sector with €72 billion final sales will have to service a debt load of €500 billion.

Isn't the conclusion obvious?

Regards

Lars

Since June of 2014, Greek banks shed about €70 billion in deposits, an amount roughly equivalent to Greek private GDP.

Not to worry, everything is clearly under control.

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

Swede Has Had Enough

Posted: 28 May 2015 10:11 AM PDT

A Swedish man reached the absolute end of what he can take anymore and profanely complains about Swedish politicians. The man is the founder of a new political party called Riksdemokraterna.

Warning: graphic language.

Link if video does not play: Swede Has Had Enough

My comment: Beggar-thy-neighbor policies, deflationary conditions, and the rise of extremist political parties all go hand in hand.

Discontent is spreading in spite of the alleged recovery.

What happens when the next recession hits?

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

"Bond Girl" on Chicago and the Quality of Credit Analysis in the Municipal Bond Market

Posted: 28 May 2015 12:31 AM PDT

On May 13, Moody's shocked the municipal bond market by downgrading Chicago to junk.

At that time S&P rated Chicago five notches higher, the widest spread between bond raters in history.

Kristi Culpepper, AKA "Bond Girl" comments on the event in What Chicago's Fiscal Emergency says about the Quality of Credit Analysis in the Municipal Bond Market.

In a sense, Moody's was only validating the bond market's opinion of the city's creditworthiness — the bonds had already been trading at junk levels for several months. This should have been a straightforward event for the chattering class to process intellectually. Rating actions tend to lag the market rather than lead it.

Oddly, however, Moody's downgrade sparked a debate over whether Moody's was being "fair" to Chicago.

How could Moody's cut the city to junk when the other rating agencies rate the city so much higher? (That has obviously never happened before in an era of ratings shopping and superdowngrades.) Wouldn't having a diverse economy and large tax base cancel out the costs associated with machine politics? (It's not like this is Chicago's third fiscal crisis in the past century.)

This was probably the first instance in the history of the capital markets that a rating agency was accused of having too radical an attitude toward risk.

There is a conversation to be had about how politics influences the perception of financial commitments and whether bond structures can further evolve to protect bondholders. If the general obligation pledge — absent a statutory lien, which few states have — lacks teeth in court, why isn't it obsolete? Why is this bond structure still the foundation for credit analysis? Does the general obligation pledge allow governments to over-commit themselves financially in certain political contexts? I would submit to you that this absolutely the case with Chicago.

What financial risks does Chicago pose to investors?

Let's examine Chicago's credit profile and you can decide whether or not the city's bonds are speculative investments.

From Nuveen:

Chicago's combined annual debt and pension costs are substantially higher than any [of the ten largest US cities] when these obligations are indexed to total governmental revenue. Chicago's fiscal 2015 debt service and annual pension costs account for 44.8% of fiscal 2013 governmental revenue. San Jose is the next closest city at 27.8%. The nine cities other than Chicago averaged 22.4% of revenue.

Most municipal market analysts assume that the city will address its unfunded pension liabilities and relatively high debt burden by increasing residents' property taxes by nearly 50%.

Chicago officials have been unwilling to raise property taxes for at least a decade.

If officials lack the political will to raise taxes when their bonds are trading at 300 basis points (3%) over the AAA benchmark, will there ever be a resolution short of insolvency?

As I described at length in my earlier essay, How Chicago Has Used Financial Engineering to Paper Over its Massive Budget Gap, the city has also been using long-term debt to: (1) finance everyday expenses and maintenance; (2) finance judgments and settlements, including police brutality cases and retroactive wage increases and pension contributions for unionized employees; (3) restructure the city's existing debt to extend the the maturities on its bonds far out into the future, in order to avoid having to pay the debt as it was coming due; and (4) provide slush funds for the city's 50 alderman to undertake projects in their respective areas (i.e., pork).

Chicago has incurred literally billions of dollars of debt where residents have nothing to show for it.

The municipal bond market has not seen a liquidity problem of this magnitude for a local government borrower since the financial crisis. And S&P calls this situation "short-term interference."

According to the Chicago Tribune: Chicago's population grew by only 82 residents last year, giving it the dubious distinction of being the slowest-growing city among the top 10 US cities with one million or more residents.

"Texas, as an example, has been a magnet for a lot of lower-paying jobs and has the benefit of lower housing costs. If you're making $15 an hour, the difference between making it where a house costs $100,000 and $300,000 is great."

Few Assets Left to Sell

Chicago has already blown through the reserves it established from the Skyway and lease of its parking meters. It could try to hawk Midway Airport, but that has already failed three times.

The city's other tax districts have their own problems

The Chicago Board of Education is also heavily indebted and its recent downgrade likewise triggered events of default. These will force the school system to pay penalty interest rates ranging from 9% to 13.5% and make swap termination payments. The board has significant unfunded pension liabilities and a $1 billion deficit.

All of the recent insolvencies in the municipal bond market have combined protracted fiscal mismanagement with a reliance on innovative financial products (e.g., interest rate swaps and pension obligation bonds). This epiphany continues to elude many market participants, especially those who believe credit analysis is as simple as financial ratios.

Perhaps Chicago will successfully navigate through this storm, but it is insane to disregard the risk involved.

Damning Report

There is much more in Culpepper's report, and all of it damning.

Chicago is on the verge of shrinking. Meanwhile, Illinois is already losing jobs to Indiana, Texas, and Wisconsin. A number of Illinois cities are on the verge of bankruptcy (more on that point in a subsequent post).

And what does Illinois have to show for all this?

Nothing!

Bankruptcy is the only sensible answer.

Mike "Mish" Shedlock
http://globaleconomicanalysis.blogspot.com

Damn Cool Pics

If You Ever Doubted the Existence of Dinosaurs Then You’ve Never Seen the Shoebill Stork

Posted: 28 May 2015 10:24 AM PDT

This is the Shoebill Stork. A very big predatory bird that lives in the swamps of Africa.

Velociraptors are still among us...

In all seriousness, tt's a beautiful creature....but it terrifies me.

Bonus:
Wonder what happened to the dinosaurs? This is a baby Blue Heron.

via reddit

Jean-Claude Van Damme Is Still Looks Great At 54

Posted: 28 May 2015 10:06 AM PDT

Jean Claude Van Damme recently turned heads in Los Angeles the other day as he was pumping gas. People were amazed to see that even at 54 years old, he still has a physique that would put most men in their 20's to shame.

Deconstructing the App Store Rankings Formula with a Little Mad Science - Moz Blog

Deconstructing the App Store Rankings Formula with a Little Mad Science

Posted on: Thursday 28 May 2015 — 00:21

Posted by AlexApptentive

This post was originally in YouMoz, and was promoted to the main blog because it provides great value and interest to our community. The author's views are entirely his or her own and may not reflect the views of Moz, Inc.

After seeing Rand's "Mad Science Experiments in SEO" presented at last year's MozCon, I was inspired to put on the lab coat and goggles and do a few experiments of my own—not in SEO, but in SEO's up-and-coming younger sister, ASO (app store optimization).

Working with Apptentive to guide enterprise apps and small startup apps alike to increase their discoverability in the app stores, I've learned a thing or two about app store optimization and what goes into an app's ranking. It's been my personal goal for some time now to pull back the curtains on Google and Apple. Yet, the deeper into the rabbit hole I go, the more untested assumptions I leave in my way.

Hence, I thought it was due time to put some longstanding hypotheses through the gauntlet.

As SEOs, we know how much of an impact a single ranking can mean on a SERP. One tiny rank up or down can make all the difference when it comes to your website's traffic—and revenue.

In the world of apps, ranking is just as important when it comes to standing out in a sea of more than 1.3 million apps. Apptentive's recent mobile consumer survey shed a little more light this claim, revealing that nearly half of all mobile app users identified browsing the app store charts and search results (the placement on either of which depends on rankings) as a preferred method for finding new apps in the app stores. Simply put, better rankings mean more downloads and easier discovery.

Like Google and Bing, the two leading app stores (the Apple App Store and Google Play) have a complex and highly guarded algorithms for determining rankings for both keyword-based app store searches and composite top charts.

Unlike SEO, however, very little research and theory has been conducted around what goes into these rankings.

Until now, that is.

Over the course of five studies analyzing various publicly available data points for a cross-section of the top 500 iOS (U.S. Apple App Store) and the top 500 Android (U.S. Google Play) apps, I'll attempt to set the record straight with a little myth-busting around ASO. In the process, I hope to assess and quantify any perceived correlations between app store ranks, ranking volatility, and a few of the factors commonly thought of as influential to an app's ranking.

But first, a little context

Apple App Store vs. Google Play

Image credit: Josh Tuininga, Apptentive

Both the Apple App Store and Google Play have roughly 1.3 million apps each, and both stores feature a similar breakdown by app category. Apps ranking in the two stores should, theoretically, be on a fairly level playing field in terms of search volume and competition.

Of these apps, nearly two-thirds have not received a single rating and 99% are considered unprofitable. These studies, therefore, single out the rare exceptions to the rule—the top 500 ranked apps in each store.

While neither Apple nor Google have revealed specifics about how they calculate search rankings, it is generally accepted that both app store algorithms factor in:

Average app store rating
Rating/review volume
Download and install counts
Uninstalls (what retention and churn look like for the app)
App usage statistics (how engaged an app's users are and how frequently they launch the app)
Growth trends weighted toward recency (how daily download counts changed over time and how today's ratings compare to last week's)
Keyword density of the app's landing page (Ian did a great job covering this factor in a previous Moz post)

I've simplified this formula to a function highlighting the four elements with sufficient data (or at least proxy data) for our analysis:

Ranking = fn(Rating, Rating Count, Installs, Trends)

Of course, right now, this generalized function doesn't say much. Over the next five studies, however, we'll revisit this function before ultimately attempting to compare the weights of each of these four variables on app store rankings.

(For the purpose of brevity, I'll stop here with the assumptions, but I've gone into far greater depth into how I've reached these conclusions in a 55-page report on app store rankings.)

Now, for the Mad Science.

Study #1: App-les to app-les app store ranking volatility

The first, and most straight forward of the five studies involves tracking daily movement in app store rankings across iOS and Android versions of the same apps to determine any trends of differences between ranking volatility in the two stores.

I went with a small sample of five apps for this study, the only criteria for which were that:

They were all apps I actively use (a criterion for coming up with the five apps but not one that influences rank in the U.S. app stores)
They were ranked in the top 500 (but not the top 25, as I assumed app store rankings would be stickier at the top—an assumption I'll test in study #2)
They had an almost identical version of the app in both Google Play and the App Store, meaning they should (theoretically) rank similarly
They covered a spectrum of app categories

The apps I ultimately chose were Lyft, Venmo, Duolingo, Chase Mobile, and LinkedIn. These five apps represent the travel, finance, education banking, and social networking categories.

Hypothesis

Going into this analysis, I predicted slightly more volatility in Apple App Store rankings, based on two statistics:

Android apps receive significantly more ratings than iOS apps, and thus I assumed the weight of each marginal rating impacted rankings less in Google Play than in the App Store
Google Play does not record an average rating exclusive to the current version of an app, meaning ratings are not reset after each update; and thus, I assumed the less volatile Android ratings equated to less volatile Google Play rankings

Both of these assumptions will be tested in later analysis.

Results

7-Day App Store Ranking Volatility in the App Store and Google Play

Among these five apps, Google Play rankings were, indeed, significantly less volatile than App Store rankings. Among the 35 data points recorded, rankings within Google Play moved by as much as 23 positions/ranks per day while App Store rankings moved up to 89 positions/ranks. The standard deviation of ranking volatility in the App Store was, furthermore, 4.45 times greater than that of Google Play.

Of course, the same apps varied fairly dramatically in their rankings in the two app stores, so I then standardized the ranking volatility in terms of percent change to control for the effect of numeric rank on volatility. When cast in this light, App Store rankings changed by as much as 72% within a 24-hour period while Google Play rankings changed by no more than 9%.

Also of note, daily rankings tended to move in the same direction across the two app stores approximately two-thirds of the time, suggesting that the two stores, and their customers, may have more in common than we think.

Study #2: App store ranking volatility across the top charts

Testing the assumption implicit in standardizing the data in study No. 1, this one was designed to see if app store ranking volatility is correlated with an app's current rank. The sample for this study consisted of the top 500 ranked apps in both Google Play and the App Store, with special attention given to those on both ends of the spectrum (ranks 1–100 and 401–500).

Hypothesis

I anticipated rankings to be more volatile the higher an app is ranked—meaning an app ranked No. 450 should be able to move more ranks in any given day than an app ranked No. 50. This hypothesis is based on the assumption that higher ranked apps have more installs, active users, and ratings, and that it would take a large margin to produce a noticeable shift in any of these factors.

Results

App Store Ranking Volatility of Top 500 Apps

One look at the chart above shows that apps in both stores have increasingly more volatile rankings (based on how many ranks they moved in the last 24 hours) the lower on the list they're ranked.

This is particularly true when comparing either end of the spectrum—with a seemingly straight volatility line among Google Play's Top 100 apps and very few blips within the App Store's Top 100. Compare this section to the lower end, ranks 401–)500, where both stores experience much more turbulence in their rankings. Across the gamut, I found a 24% correlation between rank and ranking volatility in the Play Store and 28% correlation in the App Store.

To put this into perspective, the average app in Google Play's 401–)500 ranks moved 12.1 ranks in the last 24 hours while the average app in the Top 100 moved a mere 1.4 ranks. For the App Store, these numbers were 64.28 and 11.26, making slightly lower-ranked apps more than five times as volatile as the highest ranked apps. (I say slightly as these "lower-ranked" apps are still ranked higher than 99.96% of all apps.)

The relationship between rank and volatility is pretty consistent across the App Store charts, while rank has a much greater impact on volatility at the lower end of Google Play charts (ranks 1-100 have a 35% correlation) than it does at the upper end (ranks 401-500 have a 1% correlation).

Study #3: App store rankings across the stars

The next study looks at the relationship between rank and star ratings to determine any trends that set the top chart apps apart from the rest and explore any ties to app store ranking volatility.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

As discussed in the introduction, this study relates directly to one of the factors commonly accepted as influential to app store rankings: average rating.

Getting started, I hypothesized that higher ranks generally correspond to higher ratings, cementing the role of star ratings in the ranking algorithm.

As far as volatility goes, I did not anticipate average rating to play a role in app store ranking volatility, as I saw no reason for higher rated apps to be less volatile than lower rated apps, or vice versa. Instead, I believed volatility to be tied to rating volume (as we'll explore in our last study).

Results

Average App Store Ratings of Top Apps

The chart above plots the top 100 ranked apps in either store with their average rating (both historic and current, for App Store apps). If it looks a little chaotic, it's just one indicator of the complexity of ranking algorithm in Google Play and the App Store.

If our hypothesis was correct, we'd see a downward trend in ratings. We'd expect to see the No. 1 ranked app with a significantly higher rating than the No. 100 ranked app. Yet, in neither store is this the case. Instead, we get a seemingly random plot with no obvious trends that jump off the chart.

A closer examination, in tandem with what we already know about the app stores, reveals two other interesting points:

The average star rating of the top 100 apps is significantly higher than that of the average app. Across the top charts, the average rating of a top 100 Android app was 4.319 and the average top iOS app was 3.935. These ratings are 0.32 and 0.27 points, respectively, above the average rating of all rated apps in either store. The averages across apps in the 401–)500 ranks approximately split the difference between the ratings of the top ranked apps and the ratings of the average app.
The rating distribution of top apps in Google Play was considerably more compact than the distribution of top iOS apps. The standard deviation of ratings in the Apple App Store top chart was over 2.5 times greater than that of the Google Play top chart, likely meaning that ratings are more heavily weighted in Google Play's algorithm.

App Store Ranking Volatility and Average Rating

Looking next at the relationship between ratings and app store ranking volatility reveals a -15% correlation that is consistent across both app stores; meaning the higher an app is rated, the less its rank it likely to move in a 24-hour period. The exception to this rule is the Apple App Store's calculation of an app's current rating, for which I did not find a statistically significant correlation.

Study #4: App store rankings across versions

This next study looks at the relationship between the age of an app's current version, its rank and its ranking volatility.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

In alteration of the above function, I'm using the age of a current app's version as a proxy (albeit not a very good one) for trends in app store ratings and app quality over time.

Making the assumptions that (a) apps that are updated more frequently are of higher quality and (b) each new update inspires a new wave of installs and ratings, I'm hypothesizing that the older the age of an app's current version, the lower it will be ranked and the less volatile its rank will be.

Results

How update frequency correlates with app store rank

The first and possibly most important finding is that apps across the top charts in both Google Play and the App Store are updated remarkably often as compared to the average app.

At the time of conducting the study, the current version of the average iOS app on the top chart was only 28 days old; the current version of the average Android app was 38 days old.

As hypothesized, the age of the current version is negatively correlated with the app's rank, with a 13% correlation in Google Play and a 10% correlation in the App Store.

How update frequency correlates with app store ranking volatility

The next part of the study maps the age of the current app version to its app store ranking volatility, finding that recently updated Android apps have less volatile rankings (correlation: 8.7%) while recently updated iOS apps have more volatile rankings (correlation: -3%).

Study #5: App store rankings across monthly active users

In the final study, I wanted to examine the role of an app's popularity on its ranking. In an ideal world, popularity would be measured by an app's monthly active users (MAUs), but since few mobile app developers have released this information, I've settled for two publicly available proxies: Rating Count and Installs.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

For the same reasons indicated in the second study, I anticipated that more popular apps (e.g., apps with more ratings and more installs) would be higher ranked and less volatile in rank. This, again, takes into consideration that it takes more of a shift to produce a noticeable impact in average rating or any of the other commonly accepted influencers of an app's ranking.

Results

Apps with more ratings and reviews typically rank higher

The first finding leaps straight off of the chart above: Android apps have been rated more times than iOS apps, 15.8x more, in fact.

The average app in Google Play's Top 100 had a whopping 3.1 million ratings while the average app in the Apple App Store's Top 100 had 196,000 ratings. In contrast, apps in the 401–)500 ranks (still tremendously successful apps in the 99.96 percentile of all apps) tended to have between one-tenth (Android) and one-fifth (iOS) of the ratings count as that of those apps in the top 100 ranks.

Considering that almost two-thirds of apps don't have a single rating, reaching rating counts this high is a huge feat, and a very strong indicator of the influence of rating count in the app store ranking algorithms.

To even out the playing field a bit and help us visualize any correlation between ratings and rankings (and to give more credit to the still-staggering 196k ratings for the average top ranked iOS app), I've applied a logarithmic scale to the chart above:

The relationship between app store ratings and rankings in the top 100 apps

From this chart, we can see a correlation between ratings and rankings, such that apps with more ratings tend to rank higher. This equates to a 29% correlation in the App Store and a 40% correlation in Google Play.

Apps with more ratings typically experience less app store ranking volatility

Next up, I looked at how ratings count influenced app store ranking volatility, finding that apps with more ratings had less volatile rankings in the Apple App Store (correlation: 17%). No conclusive evidence was found within the Top 100 Google Play apps.

Apps with more installs and active users tend to rank higher in the app stores

And last but not least, I looked at install counts as an additional proxy for MAUs. (Sadly, this is a statistic only listed in Google Play. so any resulting conclusions are applicable only to Android apps.)

Among the top 100 Android apps, this last study found that installs were heavily correlated with ranks (correlation: -35.5%), meaning that apps with more installs are likely to rank higher in Google Play. Android apps with more installs also tended to have less volatile app store rankings, with a correlation of -16.5%.

Unfortunately, these numbers are slightly skewed as Google Play only provides install counts in broad ranges (e.g., 500k–)1M). For each app, I took the low end of the range, meaning we can likely expect the correlation to be a little stronger since the low end was further away from the midpoint for apps with more installs.

Summary

To make a long post ever so slightly shorter, here are the nuts and bolts unearthed in these five mad science studies in app store optimization:

Across the top charts, Apple App Store rankings are 4.45x more volatile than those of Google Play
Rankings become increasingly volatile the lower an app is ranked. This is particularly true across the Apple App Store's top charts.
In both stores, higher ranked apps tend to have an app store ratings count that far exceeds that of the average app.
Ratings appear to matter more to the Google Play algorithm, especially as the Apple App Store top charts experience a much wider ratings distribution than that of Google Play's top charts.
The higher an app is rated, the less volatile its rankings are.
The 100 highest ranked apps in either store are updated much more frequently than the average app, and apps with older current versions are correlated with lower ratings.
An app's update frequency is negatively correlated with Google Play's ranking volatility but positively correlated with ranking volatility in the App Store. This likely due to how Apple weighs an app's most recent ratings and reviews.
The highest ranked Google Play apps receive, on average, 15.8x more ratings than the highest ranked App Store apps.
In both stores, apps that fall under the 401–500 ranks receive, on average, 10–20% of the rating volume seen by apps in the top 100.
Rating volume and, by extension, installs or MAUs, is perhaps the best indicator of ranks, with a 29–40% correlation between the two.

Revisiting our first (albeit oversimplified) guess at the app stores' ranking algorithm gives us this loosely defined function:

Ranking = fn(Rating, Rating Count, Installs, Trends)

I'd now re-write the function into a formula by weighing each of these four factors, where a, b, c, & d are unknown multipliers, or weights:

Ranking = (Rating * a) + (Rating Count * b) + (Installs * c) + (Trends * d)

These five studies on ASO shed a little more light on these multipliers, showing Rating Count to have the strongest correlation with rank, followed closely by Installs, in either app store.

It's with the other two factors—rating and trends—that the two stores show the greatest discrepancy. I'd hazard a guess to say that the App Store prioritizes growth trends over ratings, given the importance it places on an app's current version and the wide distribution of ratings across the top charts. Google Play, on the other hand, seems to favor ratings, with an unwritten rule that apps just about have to have at least four stars to make the top 100 ranks.

Thus, we conclude our mad science with this final glimpse into what it takes to make the top charts in either store:

Weight of factors in the Apple App Store ranking algorithm

Rating Count > Installs > Trends > Rating

Weight of factors in the Google Play ranking algorithm

Rating Count > Installs > Rating > Trends

Again, we're oversimplifying for the sake of keeping this post to a mere 3,000 words, but additional factors including keyword density and in-app engagement statistics continue to be strong indicators of ranks. They simply lie outside the scope of these studies.

I hope you found this deep-dive both helpful and interesting. Moving forward, I also hope to see ASOs conducting the same experiments that have brought SEO to the center stage, and encourage you to enhance or refute these findings with your own ASO mad science experiments.

Please share your thoughts in the comments below, and let's deconstruct the ranking formula together, one experiment at a time.

Moz Local Dashboard Updates

Posted on: Wednesday 27 May 2015 — 13:30

Posted by NoamC

Today, we're excited to announce some new features and changes to the Moz Local dashboard. We've updated your dashboard to make it easier to manage and gauge the performance of your local search listings.

New and improved dashboard

55656dd0e6cf54.57413440.jpg

We spent a lot of time listening to customer feedback and finding areas where we weren't being as clear as we ought to. We've made great strides in improving Moz Local's dashboard (details below) to give you a lot more information at a glance.

Geo Reporting

55656e552f9c50.19543051.jpg

Our newest reporting view, geo reporting, shows you the relative strength of locations based on geography. The deeper the blue, the stronger the listings in that region. You can look at your scores broken down by state, or zoom in to see the score breakdown by county. Move your mouse over a region to see your average score there.

Scores on the dashboard

55656e67615e70.00335210.png

We're more clearly surfacing the scores for each of your locations right in our dashboard. Now you can see each location's individual score immediately.

Exporting reports

55656ed3c60e54.90415681.png

Use the new drop-down at the upper-right corner to download Moz Local reports in CSV format, so that you can access your historical listing data offline and use it to generate your own reports and visualizations.

Search cheat sheet

556579b7b0fb79.07843805.png

If you want to take your search game to the next level, why not start with your Moz Local dashboard? A handy link next to the search bar shows you all the ways you can find what you're looking for.

We're still actively addressing feedback and making improvements to Moz Local over time, and you can let us know what we're missing in the comments below.

We hope that our latest updates will make your Moz Local experience better. But you don't have to take my word for it; head on over to Moz Local to see our new and improved dashboard and reporting experience today!

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vineri, 29 mai 2015

Video Transcription

What is a ranking factor, anyway?

How does brand correlate to ranking signals?

Social signals

User and usage data

Click-through rates

Fix 1: How to Examine and Analyze SERPs Using New MozBar Features

Fix 2: How to Boost Your Rankings through On-Page Optimization

Fix 3: How to Check Your Anchor Text Using Open Site Explorer

Fix 4: How to Do Keyword Research with OSE and the Keyword Difficulty Tool

Fix 5: How to Discover Keyword Opportunities in Moz Analytics

Looking for more?

joi, 28 mai 2015

But first, a little context

Study #1: App-les to app-les app store ranking volatility

Hypothesis

Results

Study #2: App store ranking volatility across the top charts

Hypothesis

Results

Study #3: App store rankings across the stars

Hypothesis

Results

Study #4: App store rankings across versions

Hypothesis

Results

Study #5: App store rankings across monthly active users

Hypothesis

Results

Summary

Weight of factors in the Apple App Store ranking algorithm

Weight of factors in the Google Play ranking algorithm

New and improved dashboard

Geo Reporting

Scores on the dashboard

Exporting reports

Search cheat sheet

Pagini

Persoane interesate

Arhivă blog

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