luni, 10 martie 2014

Damn Cool Pics

Damn Cool Pics


When Leonardo DiCaprio Was a Total Badass

Posted: 10 Mar 2014 11:33 AM PDT

























The 2015 Budget Whiteboard

The White House Monday, March 10, 2014
 

The 2015 Budget Whiteboard

In case you missed it, the President released his Fiscal Year 2015 budget last week.

Brian Deese, Deputy Director of the Office of Management and Budget, is pretty handy with a dry-erase marker, and he took some time to sketch out the nuts and bolts of the President's budget.

Want a better sense of exactly what's in the budget? You should probably watch this whiteboard video.

Watch: Brian Deese explains what's in the budget.

Want to know how the President's budget will continue to steadily bring down the deficit for the next ten years? You should watch this whiteboard.

Watch: Brian Deese explains how the President's budget will bring down the deficit.

Take a look, pass it on, and stay tuned for more.

Happy Monday.

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How to Set Up Meaningful (Non-Arbitrary) Custom Attribution in Google Analytics

How to Set Up Meaningful (Non-Arbitrary) Custom Attribution in Google Analytics


How to Set Up Meaningful (Non-Arbitrary) Custom Attribution in Google Analytics

Posted: 09 Mar 2014 04:15 PM PDT

Posted by Tom.Capper

Attribution modeling in Google Analytics (GA) is potentially very powerful in the results it can give us, yet few people use it, and those that do often get misleading results. The built-in models are all fairly useless, and creating your own custom model can easily dissolve into random guesswork. If you’re lucky enough to have access to GA Premium, you can use Data-Driven Attribution, and that’s greatâ€"but if you haven't got the budget to take that route, this post should show you how to get started with the data you already have.

If you've read up on attribution modelling in the past, you probably already know what’s wrong with the default models. If you haven’t, I recommend you read this post by Avinash, which outlines the basics of how they all work.

In short, they’re all based on arbitrary, oversimplified assumptions about how people use the internet.

The time decay model

The time decay model is probably the most sensible out of the box, and assumes that after I visit your site, the effect of this first visit on the chance of me visiting again halves every X days. The below graph shows this relationship with the default seven-day half-life. It plots "days since visit" against "chance this visit will cause additional visit." If it takes seven days for the repeat visit to come around, the first visit's credit halves to 25%. If it takes 14 days for the repeat visit to come around, the first visit's credit halves again, to 12.5%. Note that the graph is steppedâ€"I'm assuming it uses GA's "days since last visit" dimension, which rounds to a whole number of days. This would mean that, for example, if both visits were on the day of conversion, neither would be discounted and both would get equal credit.

There might be some site and userbase out there for which this is an accurate model, but as a starting assumption it’s incredibly bold. As an entire model, it’s incredibly simplisticâ€"surely we don’t really believe that there are no factors relevant in assigning credit to previous visits besides how long ago they occurred? We might consider it relevant if the previous visit bounced, for example. This is why custom models are the only sensible approach to attribution modelling in Google Analyticsâ€"the simple one-size-fits-all models are never going to be appropriate for your business or client, precisely because they’re simple, one-size-fits-all models.

Note that in describing the time decay model, I’m talking about the chance of one visit generating anotherâ€"an important and often overlooked aspect of attribution modelling is that it’s about probabilities. When assigning partial credit for a conversion to a previous visit, we are not saying that the conversion happened partly because of the previous visit, and partly because of the converting visit. We simply don’t know whether that was the case. It could be that after their first visit, the user decided that whatever happened they were going to come back at some point and make a purchase. If we knew this, we’d want to assign that first visit 100% credit. Or it might be that after their first visit, the user totally forgot that our website existed, and then by pure coincidence found it in their natural search results a few days later and decided to make a purchase. In this case, if we knew this, we’d want to assign the previous visit 0% credit. But actually, we don’t know what happened. So we make a claim based on probabilities. For example, if we have a conversion that takes place with one previous visit, what we’re saying if we assign 40% credit to that previous visit is that we think that there is a 40% chance that the conversion would not have happened without the first visit.

If we did think that there was a 40% chance of a conversion being caused by an initial visit, we’d want to assign 40% credit to “Position in Path” exactly matching “First interaction” (meaning visits that were the user's first visit). If you want to use “Position in Path” as your sole predictor of the chance that a visit generated the conversion, you can. Provided you don’t pull the percentages off the top of your head, it’s better than nothing. If you want to be more accurate, there’s a veritable smorgasbord of additional custom credit rules to choose from, with any default model as your starting point. All we have to do now is figure out what numbers to put in, and realistically, this is where it gets hard. At all costs, do not be tempted to guessâ€"that renders the entire exercise pointless.

Tested assumptions

One tempting approach is simply to create a model based to a greater or lesser extent on assumptions and guesswork, then test the conclusions of that model against your existing marketing strategy and incrementally improve your strategy in this manner. This approach is probably better than nothing for improving your market strategy, and testing improvements to your strategy is always worthwhile, but as a way of creating a realistic attribution model this starting point is going to set you on a long, expensive journey.

The ideal solution is to do this process in reverseâ€"run controlled experiments to build your model in the first place. If you can split your users into representative segments, then test, for example,

  • the effect of a previous visit on the chance of a second visit
  • the effect of a previous non-bounce visit on the chance of a second visit
  • the effect of a previous organic search visit on the chance of a second visit

and so on, you can start filling in your custom credit rules this way. If your tests are done well, you can get really excellent results. But this is expensive, difficult, and time consuming.

The next-best alternative is asking users. If users don’t remember having encountered your brand before, that previous visit they had probably didn’t contribute to their conversion. The most sensible way to do this would be an (optional but incentivised) post-conversion questionnaire, where a representative sample of users are asked questions like:

  • How did you find this site today?
  • Have you visited this site before?
    • If yes:
      • How many times?
      • How did you find it?
      • Did this previous visit impact your decision to visit today?
      • How long ago was your most recent visit?

The results from questions like these can start filling in those custom credit rules in a non-arbitrary way. But this is still somewhat expensive, difficult and time-consuming. What if you just want to get going right away?

Deconstructing the Data-Driven Attribution model

In this blog post, Google offers this explanation of the Data-Driven Attribution model in GA Premium:

“The Data-Driven Attribution model is enabled through comparing conversion path structures and the associated likelihood of conversion given a certain order of events. The difference in path structure, and the associated difference in conversion probability, are the foundation for the algorithm which computes the channel weights. The more impact the presence of a certain marketing channel has on the conversion probability, the higher the weight of this channel in the attribution model.The underlying probability model has been shown to predict conversion significantly better than a last-click methodology. Data-Driven Attribution seeks to best represent the actual behaviour of customers in the real world, but is an estimate that should be validated as much as possible using controlled experimentation.” (my emphasis)

Similarly, this paper recommends a combination of a conditional probability approach and a bagged logistic regression model. Don't worry if this doesn't mean much to youâ€"I’m going to recommend here using a variant of the much simpler conditional probability method.

I'd like to look first at the kind of model that seems to be suggested by Google's explanation above of their Data Driven Attribution feature. For example, say we wanted to look at the most basic credit rule: How much credit should be assigned to a single previous visit? The basic logic outlined in the explanation from Google above would suggest an approach something like this:

  • Find conversion rate of new visitors (let’s say this is 4%)
  • Find conversion rate of returning visitors with one previous visit (let’s say this is 7%)
  • Credit for previous visit = ((7-4)/7) = 43%

To me, this model is somewhat flawed (though I’m fairly sure that this flaw lies in my application of Google’s explanation of their Data-Driven Attribution rather than in the model itself). For example, say we had a large group of repeat visitors who were only coming to the site because of a previous visit, but that were converting poorly. We’d want to assign credit for these (few) conversions to the previous visits, but the model outlined above might assign them low or negative credit; this is because even though conversions among this group are caused by previous visits, their conversion rate is lower than that of new visitors. This is just one example of why this model can end up being misleading.

My best solution

Figuring out from our data whether a repeat visitor came because of a previous visit or independently of a previous visit is hard. I’ll be honest: I don’t know how Google does it. My best solution is an approximation, but a non-arbitrary one. The idea is using the percentage of traffic that is either branded or direct as an indicator for brand familiarity. Going back again to how much credit should be assigned to a single previous visit, my solution looks like this:

  • Calculate the percentage of your new visitor traffic is direct, branded organic or branded PPC (let’s say it’s 50%)
    • Note: Obviously most of your organic is (not provided), so I recommend multiplying your total organic traffic by the % of your known keyword traffic that is branded. As (not provided) approaches 100%, you’ll have to use PPC data to approximate your branded organic traffic levels.
  • Calculate the percentage of your 2nd-time-visitor traffic is direct, branded organic or branded PPC (let’s say it’s 55%)
  • Based on the knowledge that only 50% (in this case) of people without previous visits use branded/direct, approximate that without their first visit we’d only have seen (100%-55%)*(100/50)=90% of these 2nd time visitors.
  • Given this, 10% of visitors came because of a previous visit, so we should assign 10% credit for 2nd time visits to the first visit.

We can use similar logic applied to users with 3+ visits to calculate the credit deserved by “middle interactions”.

This method is far from perfectâ€"that’s why I recommended two others above it. But if you want to get started with your existing data in a non-arbitrary way, I think this is a non-ridiculous way to get started. If you’ve made it this far and you have any ideas of your own, please post them in the comments below.


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Photo: A Presidential High Five

 
 
 
 


  Featured

Photo: A Presidential High Five

Photo: President Obama high-fives a child after speaking about education in Florida.

President Barack Obama high-fives a youngster at Coral Reef Senior High School, Fla., March 7, 2014. (Official White House Photo by Pete Souza)

 
 

  Top Stories

Weekly Address: Time for Congress to Raise the Minimum Wage for the American People

In this week's address, President Obama highlighted the momentum building across the country to give Americans a raise and reiterated his call for Congress to increase the minimum wage from $7.25 to $10.10. If you agree, then add your name here.

READ MORE

The 2014 Economic Report of the President

This morning, the Council of Economic Advisers is releasing the 2014 Economic Report of the President, which discusses the progress that has been made in recovering from the worst recession since the Great Depression, and President Obama's agenda to build on this progress by creating jobs and expanding economic opportunity.

READ MORE

A World-Class Education for Every Student in America

President Obama and the First Lady visited Coral Reef High School in Miami on Friday to discuss the President's plan to equip all Americans with the education they need to compete in the 21st century economy.

READ MORE


 
 
  Today's Schedule

All times are Eastern Time (ET)

8:30 AM: The Vice President meets with President-elect Michelle Bachelet of Chile

9:30 AM: The Vice President meets with President Sebastián Piñera of Chile

10:00 AM: The President receives the Presidential Daily Briefing

11:15 AM: The President participates in an Ambassador Credentialing Ceremony

12:00 PM: Press Briefing by Press Secretary Jay Carney WATCH LIVE

5:00 PM: The President hosts 2012-2013 NCAA Division I Men's and Women's Champions WATCH LIVE

6:00 PM: The Vice President meets with President Ollanta Humala of Peru

7:15 PM: The Vice President and Dr. Biden attend a dinner hosted by President Piñera of Chile

 
 

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Seth's Blog : Will they switch for cheaper?

 

Will they switch for cheaper?

In fact, most people switch for better.

Without a doubt, there's a slot in every market for the cheap enough, good enough alternative.

But rapid growth and long-term loyalty come from being better instead.

When your product or your service doesn't measure up, the answer probably isn't to lower your price or offer a refund to the disappointed customer. Instead, the alternative is to invest in making it better. So much better that people can't help but talk about it—and so much better that they would truly miss it if it were gone.

       

 

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Mish's Global Economic Trend Analysis

Mish's Global Economic Trend Analysis


Word of Thanks

Posted: 09 Mar 2014 06:43 PM PDT

Every day numerous people send links to articles, edit my typos, comment on the blog, monitor comments on my blog, translate articles, etc.

It's a community and I appreciate the global involvement and global help.

Each day I get between 50 and 200 emails from readers. I respond to most of them. But even though I spend 2 hours or more every day reading and answering emails, I cannot respond to all of them.

Frequently, I offer a one word response: thanks.

That does not do justice to how I feel, especially to those who send something every day or nearly every day.

Unlike other bloggers, I seldom offer "hat tips" and the reason is simple. I tend to read emails LIFO (last in first out), and most often when someone sends me a link, another person already has.

So here is my word of thanks. But I also need to do more. For those who contribute every day, I need to offer special thanks.

Spell Checkers

I have three mainstay spell checkers: Randy, Curt, and Mark. What one of them misses, another one of them will catch. The only problem is how long it takes me to find the email pointing out my error. Typically it's not spelling per se, as I spellcheck myself, but rather stuff like saying "an" when I mean "and" or vice versa. When you proof your own article you do not see such things.

Content

I am going to get in trouble over this one because I am sure to leave someone out. Apologies offered in advance.

Every day I get emails from Bran in Spain. Every day, Richard sends numerous links I consider writing about. So does Mark the spellchecker.

Tony, Brisbane Bear, Bigpond, and Hugh send frequent emails about Australia and Asia. Many others, including Dave, Bob, Joe, Tony, Donald, and Mayraj send so much content that it's hard for me to know where to stop listing names.

Blog Monitoring

I have one person, "fedwatcher", who monitors comments and removes anything inappropriate.

I hope I touched all the bases here, but most likely I didn't. If I missed your name, please accept my apology.

Thanks!

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

Spain Modifies Bankruptcy Laws to Prevent Corporate Liquidations; 65,000 Companies, 1.3 Trillion Euro Delinquencies in Play

Posted: 09 Mar 2014 04:53 PM PDT

In the name of saving jobs, the Spanish government has decided to change the rules as to whether or not a business is viable.

In bankruptcy proceedings, companies will no longer be free to decide whether they prefer to liquidate the company.

Instead, corporations will be obliged to accept debt restructuring offerings from creditors on creditor-proposed terms centered around debt-to-equity conversions.

Via translation from El Economista.
According to various judicial, legal and bankruptcy administration sources consulted by elEconomista, companies will no longer be free to decide whether they prefer to liquidate the company.

New rules favor foreign financial institutions including the vulture funds, to take over companies. The decree aims to end the massive liquidation of companies in bankruptcy.
Followup post by el Economista
The Ministry of Economy approved the bankruptcy reform legislation to save viable businesses including a provision that transforms debt into equity.

If creditors representing at least 60% of the financial liability agree, forced conversion of loans into equity may last up to five years. If creditors representing at least 75% of the financial liability agree, forced conversion of loans into equity may last ten years.

Dissenting creditors may choose a haircut equivalent to the nominal amount of the shares that would correspond subscribe or take and, where appropriate, the corresponding premium or assumption.

Judges may override the 60% threshold to as low as 51%.
1.3 Trillion Euro Delinquencies in Play

Via translation Libre Mercado discusses the number of potential forced debt-to-equity conversions.
Deputy Prime Minister Soraya Saenz de Santamaria, said that the new law completes a package of measures that are aimed at companies that, despite their high debt, "can continue to run their business while maintaining their employment."

In Spain, 90% of companies that enter bankruptcy end up in liquidation. At first, this may seem logical and even reasonable: if a company is not viable, what is best for everyone to pay the maximum of their debts, creditors save everything and these goods are reallocated to more productive tasks. The problem is that, perhaps, not every business in competition (and even more in preconcursal phase) should close.

According to the Registry of Forensic Economists (REFOR), 65,000 companies, of them 15,000 "quality" companies with the rest being microenterprises that are "at risk of death because of refinancing problems.

Debt held by businesses is equivalent to 130% of GDP, about 1.3 tillion euros. The Ministry of Economy believes that the new RDL could facilitate the refinancing of up to 10% of this amount: 130 billion euros.
Think these debt-to-equity terms will be extremely one-sided?

Addendum

Reader Bran who lives in Spain sent the following pertinent comment: "1.3 trillion is the total debt held by businesses. The article did not stipulate how much of that is delinquent, but it's at least the 10% they hope to save. Registered delinquent business debt has been rising towards 10%. If they talk of saving 10% there is a lot more at stake than the 130 billion euros they think they can save."

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

Pro-Russia Troops Install Minefields, Border Markers in Crimea; Gazprom Ups Price of Natural Gas 37%, Calls in $2 Billion Gas Debt

Posted: 09 Mar 2014 12:10 PM PDT

The takeover of Crimea by Russia is nearly complete. All that remains is the final vote on March 16. There would not be a vote if the outcome was uncertain.

Troops Install Minefields, Border Markers in Crimea

Bloomberg reports Pro-Russia Forces Occupy Ukraine as Separatist Vote Looms.
Pro-Russian forces advanced in Ukraine's Crimean peninsula, ignoring Western calls to halt a military takeover before the region's separatist referendum. The U.S. estimates Russia now has 20,000 troops confronting a smaller Ukrainian force there. Ukraine has stepped up its eastern border defenses in the worst standoff between it and the West since the Cold War.

Pro-Russian units planted minefields in the Kherson region, north of Crimea on Ukraine's mainland, and began to install border markers between the two regions, news website Khersonskie Vesti reported today. Ukraine's border control service said Russian forces now control 13 border bases as well as the ferry crossing across the Kerch Strait to Russia, preventing guards from inspecting trucks arriving in Crimea.

Authorities on the peninsula ordered an anti-aircraft regiment in the city of Yevpatoriya to lay down its arms or its base would be taken over, news service Interfax reported.

The peninsula, where Russian speakers comprise a majority, will join Russia once parliament in Moscow passes the necessary legislation and there's nothing the West can do, according to Sergei Tsekov, the deputy speaker of Crimea's parliament.

"There's no comeback, and the U.S. or Europe can't impede us," Tsekov said by phone on March 7 from Moscow, where he met Russian officials to discuss the region's future. "Crimea won't be part of Ukraine anymore. There are no more options."

The U.S. and European allies will impose sanctions if there isn't a quick resolution, Obama said at the White House on March 6.
Russia Calls in $2 Billion Gas Debt, Gazprom Ups Price of Natural Gas 37%.

A Bloomberg video claims Russia Calls in $2 Billion Gas Debt. In addition, Ukraine Sees Gazprom Charging 37% More for Gas in Second Quarter.
Ukraine faces a 37 percent increase in the price it pays for Russian natural gas after OAO Gazprom canceled a discount and threatened to cut supplies, Ukrainian Energy Minister Yuri Prodan told reporters today.

Ukraine will pay about $368.50 per 1,000 cubic meters of the fuel in the second quarter, Prodan said. Russia agreed last year to cut the price it charges Ukraine to $268.50. Gazprom rescinded the discount last week and said Ukraine risks a repeat of 2009, when the Moscow-based company reduced shipments during a pricing dispute.

Ukraine needs to import about 30 billion cubic meters of gas this year, of which a third may come from Slovakia, Prodan said March 5. Gazprom said March 7 in a statement it's owed $1.89 billion by Ukrainian state gas company NAK Naftogaz Ukrainy for supplies already received.
Minimal Sanctions

It is likely the US issues some sanctions. They probably will not be meaningful. as noted on March 5, Russia Has Upper Hand in Ukraine, No Meaningful Sanctions Coming.
It was easy enough to impose sanctions on Iran because there was no meaningful trade with Iran other than oil. And global oil supply can come from anywhere.

Secondly, and unlike the US which has little trade with Russia, Germany, the UK, and other European countries do have meaningful trade with Russia.

Finally, Germany gets 30% of its natural gas supply from Russia. Impose severe sanctions and Russia can shut down those supply lines, most of which happen to run through Ukraine.

Obama can pretend to put down a tough stance, but don't expect any meaningful reaction globally. Events are already firming up along those lines.
Germany and the UK already rejected major sanctions. Europe gets much of its natural gas from Russia and hoarding supplies is now underway.

See Natural Gas Hoarding in Europe Thanks to US Sanction Proposals; Boehner vs. McCain; LNG the Solution.



The chart is from Gazprom and Morgan Stanley.

It is mathematically impossible to be more than 100% dependent on a supplier, at least over the long-term. Certainly, for short periods of time imports can exceed usage, but over the long haul they cannot.

That said, I do not know the timeframe for the chart. It's possible the chart is reflective of recent hoarding.

Regardless, the chart shows the huge dependencies some European countries have for Russian natural gas.

I feel sorry for those who will soon be living in a nation they do not feel part of. But there is little that can be done about it. 

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