joi, 17 iulie 2014

Building Better Content By Improving Upon Your Competitors

Building Better Content By Improving Upon Your Competitors


Building Better Content By Improving Upon Your Competitors

Posted: 16 Jul 2014 05:17 PM PDT

Posted by Bill.Sebald

In rock n' roll music, stealing is expected. Led Zepplin allegedly lifted from lots of earlier blues and folk artists. The famous I-IV-V chord progression of The Wild One's song "Wild Thing" was used only a couple years later on "Mony, Mony." My favorite example of musical larceny - "Let It Be" by The Beatles, "Farmhouse" by Phish, and "No Woman, No Cry" by Bob Marley are built around the exact same chord progression. Yet in all these cases, the songs were tweaked enough to stand on their own in meaning, served as distinct entities, and inspired unique feelings from the listener. Granted record company execs often disapproved, but some artists were often flattered to see interpretations of their riffs and progressions. At the end of the day, this is what spawned (and advanced) the rock music genre. Sometimes stealing is the engine of innovation.

"Your idea isn't new. Pick an idea; at least 50 other people have thought of it. Get over your stunning brilliance and realize that execution matters more." —Mark Fletcher of Bloglines.com.

In marketing, we don't just "steal" the minds of consumers, we sometimes steal - and interpret - from our competitors. Sometimes we're lazy about it, and sometimes we're perceived as originals. Remember one of the immutable laws of marketing - always appear to be first. Well then why not be first to make someone's content strategy more effective (for your own gain)?

Wait - so do I condone being a pickpocket, cat burglar, or politician? No. What I'm suggesting is reviewing what inspires you, analyzing why it was successful, and inspiring yourself to make something better. Better for us, better for our clients, and better for their customers.

Oh no; is this another "Content Is King" post?

I'm not a huge fan of that phrase anymore. SEO has gone through some serious developmental stages in its lifetime. Once the hype was all about "keyword density," then "anchor text," then "duplicate content;" now I feel like our latest bandwagon concept is the semi-vague "content is king."

These are certainly all valid concepts in SEO, but without proper context, they often fall short of sound advice. They become blind directives. So here we are in 2014, with many business executives nodding along, "yes - content is king. I've read that a trillion times. We need to crank out 100 posts a month. Go, go go..." But I think this is a problem. Now that SEO is mainstream, there's so much "good content" that the noise ceiling has simply been raised. I've said it before, "Fair-quality copy is becoming the new Google spam." I go into pitches now where businesses can't understand why their legacy content isn't getting searches. In other words, they ask why "content is king" isn't producing results. It's usually because content was treated as a homogeneous tactic where a marketing or SEO strategy wasn't put in place to link the pieces together.

I think it's time SEOs put that phrase to rest, and start thinking in terms of how a traditional content marketer would think about it. "Content that is unique in value, strong in expertise, provides a necessary point-of-view, and leads the pack in terms of usefulness is more than king - it's fundamental to success." A bit of a mouthful (and less sexy), not to mention harder to develop, but it really needs to be adopted.

So if you would, please keep that in mind during this post. Continue on!

What are your competitors doing?

Content ideas come from lots of sources. Some are vapid (like content topic generators) and some are interpreted (like reviewing customer poll results). Often a simple interview with your sales or service team can teach you plenty about the mindset of your consumer. Studying on-page product reviews can also be inspiring. Focus groups, experiments; all this and more can help produce pieces of content that can be strung together and tracked in order to build a truly converting funnel.

We all know the most effective content is inspired by data, versus "crazy ideas" with no concrete evidence quickly thrown against the wall. While this occasionally has some SEO benefit (arguably less and less with Panda updates), it rarely does much for your conversion funnel. It takes that extra digging that some aren't quick to execute (at least in my experience). But what happens when your competitor is willing to do the work?

That's where you can learn some interesting things. Marketing espionage!

Granted, most competitors don't want to share their data with you, no matter how much beer you try to bribe them with (believe me, I've tried). We have tools like SEMrush to estimate search metrics, and services like Hitwise and Compete to get more online visitor data. While that is certainly helpful, it's still directional. But we're marketers - so what do we do? We get creative.

How to get a birdseye view of a content play (with common SEO tools)

It's time to lift the hood. I like to start with  Screaming Frog. Most SEOs know this tool. If you don't, it's a spider that emulates what a search engine spider might find. In my experience there's no better way to find the topics a website is targeting than with a "screaming" crawl.

Filter down to HTML, and you'll find the URL, Title Tag, Meta Description, H1, and sometimes the Meta Keyword data. If you already have your own keywords and entities in mind, and want to see what a competitor is doing with them, it's as simple as searching for them in Screaming Frog (or an excel export) and scanning for it.

Click for a larger image:

Consider this totally random "shammy" example in the screenshot above. If I worked in the shammy business, through a quick scan I might be interested to know that at least one of my competitors found value enough in creating a section around an iPad cloth. Is that a segment I never considered?

Don't have Screaming Frog? The site:operator is a less powerful option. You can't export into a spreadsheet without a scrape. 

Ubersuggest or keywordtool.io can be used in clever "quick and dirty" way - put in a keyword you think there's opportunity for, and add "who," "what," "where," "why," or "how" to the query. Your fragmented query will often show some questions people have asked Google. After all, plenty of great content is used to answer a query. Search some of these queries in Google and see what competitor content shows up! At the very least, this is a nice way to find more competitors who are active with creating content for their users.

At this point you should be taking notes, jotting down ideas, observations, potential content titles, and questions you want to research. Whether in a spreadsheet or the back of a napkin, you're now brainstorming with light research. Let your brain-juice flow. You should also be looking for connections between the posts you are finding. Why were they written? How do they link together? What funnels are the calls-to-action suggesting? Take notes on everything, Sherlock!

Collect the right data

Next, step it up with more quantifying data.Time to trim the fat.

Search data

By entering and measuring your extracted in Google's Keyword Planner, you'll see not only is there interest in an iPad cleaner (where an "iPad Shammy" might make sense with its own strategy), but some searcher interest in the best ways to clean an iPad. That could be fun, playful content to write - even for a shammy retailer. It could tie directly to products you already sell, or possibly lead you into carrying new products.

Click for a larger image:

Estimated searches don't tell the whole story. We know plenty of keywords and metrics from this tool are either interpolated or missing. I've found that small estimated searches can sometimes still lead to more highly-converting volume than expected. Keep that in mind. 

Social data

What searches enter into Google's search box isn't the only indicator of value. Ultimately if nobody likes a certain topic or item your content, they aren't going to share or link to it. Wouldn't it be great to have another piece of evidence before you get to structuring a strategy and writing copy? That evidence may lie with your competitors' social audience.

At this point you have keyword ideas, content titles, sample competitor URLs, and possible strategies sketched out. There are some great tools for checking out what is shared in the social space. TopsySocial Crawlytics, and Buzzsumo are solid selections. You can look up the social popularity of a given URL or domain, and in some cases drill down to influencers. If it's heavily shared, that may suggest perceived value. 

Click for a larger image:

Look at the image above. If my agency is a competitor of yours, you might be interested that one of my posts got 413 social shares. It was a post called "Old School SEO Tests In Action (A 2014 SEO Experiment)". You can dig in to see the debates boiling through the comments or the reactions through social media. You can go so far as see who shared the post, how influential these people are, and what kind of topics they usually share. This helps qualify the shares.

With these social metrics I believe It's reasonably safe to infer people in the SEO space care about experiments, learning about things that move rankings, and that most believe older tactics aren't worth pursuing. With very little time at all, you might be able to come up with ways to improve upon this post or ideas for your own follow up. Maybe even a counter argument? Looking at who the post resonated with, you could presume my target audience was SEOs with a goal of providing industry insights. With a prominent lead generation form on this post, you might even suspect a secondary interest was as a source of new client leads.

If you surmised any of these things from the social data, you're 100% right! This was certainly a thought out post with those goals in mind.   

Backlink data

Let's examine link popularity and return to the shammy industry. Specifically let's look at a pretty unique item - a shammy for Apple products -  https://www.klearscreen.com/detail.aspx?ID=11.

  • Open Site Explorer found 1 link from a retailer.
  • Ahrefs found 8 links from 8 domains, one being a forum conversation on Stackexchange.com, and the others from a retailer.
  • Majestic found 13 links from 6 domains. Similiar to what Ahrefs found.
  • WebMeUp found 30 backlinks from 9 domains.

From this data it looks like the iPad shammy market isn't exactly on fire. Now it doesn't appear iKlear (or Klear Screen) is doing much marketing for this particular product - at least not according to Google. Their other Apple product cleaners seem to get more attention, but perhaps iKlear simply knows this isn't a high demand product. It could be true - after all it hasn't gone viral. It hasn't generated much in the way of online discussions. But it also hasn't been marketed much.

This is why all the data needs to be collected, correlated, and analyzed.  You want the best hypothesis you can get before you start committing your time to a content strategy. Did this just kill a possible content strategy for an iPad Shammy, or is this a huge untapped opportunity? It entirely depends on how you interpret all the data you collect. 

You've got some ideas; now what's the execution?

You just did a lot of work. You can't go off half-cocked throwing up willy-nilly content. Jeepers, no! The next step is the most crucial!

At this point you should have uncovered some great ideas based on your competitor's clues. Now comes the part where you thoughtfully determine how to implement these ideas and craft a strategic roadmap. The options are endless, which could provide a decision-making struggle. From new microsites to overhauling existing content, there's so much you can do with the gems you've dug up.

Remember to examine what your competitors did. How did they plug everything together? 

But sometimes your competitors don't have a discernible content strategy. Instead just fragmented content floating like an island. This is even better for you. Now you have opportunity to not only outshine in the actual content, but put together an actual experience that your users will value, thus providing a likely positive SEO result. Here are three options I tend to build a strategy around most often: 

  • Create a new funnel
  • Create content for off-page SEO
  • Create emphasis content

With fresh metrics, the new funnel is often necessary. Chances are you discovered uncharted territory (at least from your website's perspective). All future or existing content should have pre-conceived goals - there's a top and bottom to every funnel, and maybe some strategic off-ramps leading to forms, contact pages, or products. Remember, you're goal is to be driving the reader through an experience, eliciting emotions and appealing to their needs of which you've already built a hypothesis upon. This new funnel can dip into your current website or run parallel (ie, a microsite, sub-domian, or otherwise disconnected grouping). The greatest thing about digital marketing is that nothing is in stone. It's so easy to test these funnels and redesign with collected data when necessary.

Off-page is also very common (right link builders?). Find something that is popular, and go share it with sites more popular than yours. Maybe you can even start generating new popularity and create a segment of its own. Build a strategy to take this burgeoning topic and let the widest audience know about it. Get branding, mind share, links, and ideally profit like a beast.

The "emphasis content" (as I call it) has been a solid go-to plan for me when I discover small pockets of opportunity; notably the stuff that may have a smaller impact and isn't worth a month long content strategy. If I were to create my own iPad shammy play, based on what I'm seeing so far, I'd probably think about a page or two as emphasis content. 

This content is like an independent port of entry or landing page, either to an existing funnel or a direct money maker. In a previous post I talked about  creating niche collection pages for eCommerce. That could serve as emphasis content to a parent collection, but I'm usually thinking of heavier use of text in this case. Where you really take your goal, slice it up, and provide nice, beefy communication about it.

This play can be nuclear. By creating these one-off pages based on all the metrics discussed above, it's usually much easier to do targeted outreach and social marketing. A well placed page, providing well placed internal links (ideally off popular pages), can pass PageRank and context like a dream, A tool like  Alchemy API can help you see the relevance of pages and help you determine the best place to publish this page

Summary

A content strategy doesn't go far if it's phoned in. Take all the help you can get, even if it's from a competitor. Learn from businesses who took steps before you. They may have very well discovered the holy grail. Competitive research has always been a part of any marketing campaign, but scratching the surface only gets you superficial results. Look deeper to uncover more than just a competitor's marketing plan, but the very reason why the competitor may be beating you in search. Then, hopefully you'll become the rock star others are trying to copy from. That's a good problem to have.


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Demystifying Data Visualization for Marketers

Posted: 16 Jul 2014 07:05 AM PDT

Posted by Annie Cushing

I presented on wrangling and demystifying the data visualization process for marketers at MozCon this year, and it turns out there was far more to talk about than could fit into that half-hour. For the sake of those who couldn't make it and those who could but want to learn more, I pulled together this overview of my presentation, offering more detail than I could in the slides.

To see all of the links shared in this post, check out my MozCon Bitly bundle.

You may want to open the SlideShare file in another tab or browser window, so you can easily toggle between the post and the SlideShare.

I'm going to go through the presentation slide by slide to bring the narrative to print.

Slide 3

I have a confession: Although it's probably safe to say I'm a fairly advanced Excel user — at least among marketers — until recently I had no real charting strategy. In fact, I signed up to do this presentation partly to force me to carve out a strategy, particularly with Google Analytics data.

Slide 4

In this presentation I have focused on Google Analytics data for a couple reasons:

  1. If you can wrangle Google Analytics, other marketing data is a walk in the park.
  2. It has naming conventions that map beautifully to Excel, making it an ideal tutor.

Slide 5

My approach may seem a bit Karate Kid-esque, but if you can grasp the interplay between Google Analytics and Excel, you'll never be left wondering how to visualize your data.

Although there are many aspects to data visualization, I focus primarily on charting.

Slide 6

In Excel there are two components to charts that are critical to understand: data series and categories. They are always used together.

Think of categories as buckets for your data and data series as the data itself.

Slide 7

If you dumped a pile of Legos in front of a group of kids and told them to organize them by color into their corresponding, labeled containers and then count them, the containers would be categories. And the data series would be the count of Lego bricks.

Slide 8

First let's peek under the hood on a PC by cracking open the Select Data Source dialog. You get to it by right-clicking on your chart and choosing Select Data.

Slide 9

Excel for Mac also has data series on the left and categories on the right. And that's about all they have in common.

Slide 10

But, as with most features in Excel for Mac, the functionality of the Mac's Data Source dialog is far inferior to that of the PC.

Slide 11

This sort option is helpful if you have a stacked chart and want to sort the individual data series. I like to put the larger series on the bottom and smaller ones on the top. But if you have a stacked chart on the Mac and you want to reorder the data series, you actually have to delete the series you want demoted and manually add it back in.

It's kind of like that game, Hand on Hand, you might have played as a kid where kids go around in a circle putting their right hands in the middle, followed by the left hands. Then they go around the circle moving the bottom hands to the top of the pile as fast as possible.

Although in this case, you're moving the data series to the bottom of the pile.

Slide 12

To move the Sessions data series to the bottom of the pile, first select it from the Series list.

Slide 13

Then click the Remove button to delete it from the list.

Slide 14

Then click the Add button to add it back to the list of data series.

Slide 15

Click the data selector button to the right of the Name field and select the series name, as directed in the screenshot.

Slide 16

Click the data selector button to the right of the Y values field and click-and-drag over the values. If the column is long, just click the first cell and press Ctrl-Shift-Down Arrow (Mac: Command-Shift-Down Arrow) to select the entire column without scrolling. (We are nothing if not efficient.)

Slide 17

And finally you need to click-and-drag over the category axis labels. Which brings us to the Mac's other issue ….

Slide 18

In the PC version, there's one place for the category axis labels. On the Mac you have to choose the axis labels for each series. It's counter-intuitive.

Slide 19

Categories end up along the horizontal axis — or the vertical axis for horizontal bar charts.

Slide 20

The data series ends up in the legend and is usually a metric (from GA). But there are a couple exceptions, which we'll get to in a minute. The categories populate to the horizontal axis label or vertical axis label with the bar chart.

Slide 21

Transition to Google Analytics.

Slide 22

The two major players in Google Analytics – that we'll be mapping to Excel – are dimensions and metrics. They're (practically) inseparable.

Slide 23

Dimensions are the buckets your data is broken up into. These come into Excel as text – even if they're values – like you get with the Days to Transaction dimension (which you can get from Conversions > Ecommerce > Time to Purchase). They are always the far-left column of the table.

  • Add a secondary dimension in any report (standard or custom).

  • Create a custom flat table with two dimensions. Learn how in this post.
  • Use the API. This is the only option that will allow you to use more than two dimensions. You can pull up to seven dimensions in one API call.

Slide 24

Metrics are anything that can be measured with a number.

Slide 25

If you're in a custom report (or have clicked the Edit link at the top of most standard reports), metrics always show up to a party in blue.

Slide 26

And dimensions show up as green.

You can learn more about custom reports from the video tutorial I created to help marketers.

Now it's time to marry Google Analytics and Excel.

Slide 27

In most cases dimensions in Google Analytics map to categories in Excel.

Slide 28

And metrics map to data series in Excel.

Slide 29

I'm going to break this down systematically, based on the number of dimensions and metrics you're wanting to visualize.

Slide 30

Dimensions: 0

Metrics: Multiple

You want this if you want to know aggregate numbers, e.g, sessions for the month, or revenue, or goal completions.

Slide 31

I hate to start on a downer, but you need the API to do this. The GA interface requires at least one dimension.

Slide 32

As with most things, if you prod enough, you'll discover hacks and workarounds. But the name of the game here is to come up with a dimension that will only have one bucket. Going back to the Legos analogy, it would be kind of like saying, "Put all the plastic Legos in this bucket and count them."

Slide 33

Workaround: Set dimension to something that will encompass all of your data, meaning you'll only have one row in the report. One example of that would be the User Defined dimension (under Audience > Custom > User Defined).

As you'll see in the screenshot, all of the values are consolidated as (not set) since this profile (now called view) doesn't use the User Defined dimension.

Slide 34

If you're still using the User Defined dimension (and, therefore, have multiple rows reporting), you really need to update.

If you're using classic GA, you should be using custom variables and custom dimensions if you're using Universal.

Slide 35

Another option is to use the Year dimension with a custom report. This is ideal if you are gathering data for a single month. You can aggregate data beyond one month, as long as the date range you choose doesn't straddle more than one year.

Slide 36

Here's what the custom report looks like under the hood. Learn how to  create custom reports in Google Analytics in a video tutorial I did.

Slide 37

You can access this report  here while logged in to Google Analytics.

Slide 38

This data isn't conducive to charting, but you can sexy up a table with sparklines and conditional formatting.

Slide 40

Dimensions: 1

Metrics: 1

An example of this might be revenue segmented by country or bounce rate segmented by device category.

Slide 41

Pie Chart Basics

Here are some highlights about the pie chart:

  • They use angles to show the relative size of each value.
  • You should put data in descending order to put the most significant data point at 12:00 and radiate clockwise.
  • Avoid 3D pie charts. They distort data.
  • Data points must add up to 100%. So you can't take traffic from 5 of your 8 campaigns and chart them.
  • Microsoft says no more than seven categories; I say no more than five.
  • None of the values in your data series can be negative.
  • Learn more

Pie Chart Tricks

Ways to trick out your chart:

  • You can grab a piece of the pie to isolate it and drag it out slightly to draw attention to it. This is called exploding pie pieces.
  • You can also change the values to percentages in the data labels or even add the categories, thereby negating the need for a legend.

Slide 42

Donut Chart Basics

Here are some highlights about the donut chart:

  • Donut charts show data in rings, where each ring represents a data series
  • It uses the length of the arc to indicate the size of the value.
  • You should put data in descending order to put the most significant data point at 12:00 and radiate clockwise.
  • Data points must add up to 100%. So you can't take traffic from 5 of your 8 campaigns and chart them.
  • Microsoft says no more than seven categories; I say no more than five.
  • None of the values in your data series can be negative.
  • Learn more

Donut Chart Tricks

Ways to trick out your chart:

  • You can put the title or the value you want to highlight in the center. 

  • I don't recommend using the donut chart for multiple series or dimensions. They're more difficult to interpret. 

  • Like the pie chart, you can pull one out to draw attention to it.
  • You can use a donut chart to create a speedometer chart.
  • You can fill it with an image that resembles the surface of a donut to make it look like a … Okay, yeah, never mind …

Slide 43

Column Chart Basics

  • Should sort in descending order.
  • The axis should start at 0.
  • Categories don't have to add up to 100%
  • Learn more

Column Chart Tricks

  • You can add a trendline to make trends stand out.
  • Consider going totally minimalist with the techniques I demonstrate in this video tutorial. (You can skip to the 15:53 mark.)
  • Don't be afraid to move the legend around.
  • Excel's default axis tends to be dense. I typically double the Major Unit, so if the major unit is set to 100, I typically up it to 200. Learn more about the major unit from the Microsoft site. (But I also show how in the above-mentioned video tutorial.
  • You can use a column chart to create a bullet graph to show current data vis-à-vis goals or projections.
  • You can use a column chart to create a waterfall chart.
  • You can add a target line to your chart.
  • If you have many categories to chart, you can use a scrollbar.
  • You can use a column chart to create a thermometer chart.
  • Just remember safety first when working with column charts.

Slide 44

Bar Chart Basics

  • You need to sort your data in ascending order to put the longest bars at the top.
  • Bar charts are good for categories with longer labels.
  • You shouldn't use bar charts if your dimension is time based (date, month, etc.).
  • Learn more

Bar Chart Tricks

  • You can use all of the tricks (except the last two) listed in the Column Chart Tricks list.

Slide 45

Radar Chart Basics

  • Category labels are at the tip of each spine.
  • You can use a fill with your radar charts.

Radar Chart Tricks

  • Radar charts can be compelling when you compare multiple entities at once. For example, I saw a set of 50 radar charts that compared metrics like crime rates for different types of crime for each state.
  • If you don't want the axis labels to show, you can set the number formatting to ;;; to hide them altogether. You can then include an annotation on your chart that lets viewers know the intervals. 

Slide 46

Notes about the Heat Map

Learn how to create a heat map in this video tutorial I did.

Slide 47

And now let's look under the hood at a typical chart that uses 1 dimension and 1 metric. Let's say we have this table of analytics data ….

Slide 48

If we create a column chart from this table, this is what it's going to look like (with some cleanup).

Slide 49

Now if we look at the data source this is what we'll see ….

Slide 50

The mediums show up over here in the categories …

Slide 51

And the sessions values show up here in the data series …

Slide 52

Which populates to the legend. But you can delete the legend when you only have one metric (or data series). You'll then want to include the metric in the chart title.

Slide 53

And the mediums populate the horizontal axis labels.

A little piece of Excel trivia: The Select Data Source dialog still says Horizontal Axis Labels, even for bar charts where the labels are on the vertical axis. #pedantic

Slide 54

Example of 1 dimension and multiple metrics: Sessions, goal completions, and revenue broken down by Device Category (mobile, tablet, desktop)

BTW, the Device Category dimension is one of the most important in Google Analytics. By itself it's pretty useless, but in the context of other data, it's very useful. You should be segmenting all of your data by it.

Slide 55

Notes about the Clustered Column Chart

  • Clustered column charts are good for showing comparisons (e.g, sessions vs revenue for each month or ROI vs Margin by campaign (or keyword).
  • You could transform the clustered column chart into a combination chart by adding a line chart on the secondary axis that adds a percent value.

Slide 56

Notes about the Stacked Column Chart

  • The stacked column chart is good for showing how each data series contributes to the whole.
  • An example might be revenue broken down by medium.
  • If you want to order the columns by overall height, you can create a total column for the series. You just won't chart that column.

Slide 57

Notes about the Clustered Bar Chart

  • All of the notes in the above-mentioned stacked column chart.
  • Like the [single] bar chart, the clustered bar chart is better for categories with long labels.
  • You can hack the clustered bar chart to create a double-sided bar chart. You can view a video tutorial I did on how to do this.

Slide 58

Notes about the Stacked Bar Chart

  • If you want to sort the bars so that the longer bars are on top, create a totals column and sort it in ascending order.
  • You shouldn't use the stacked bar chart if your dimension is time oriented (date, month, etc.).

Slide 59

Notes about the 100% Stacked Column Chart

  • Use the 100% stacked column chart when you are working with percentages.
  • The data series must add up to 100%.
  • For example, if you wanted to see what percentage of social referrals came from desktop, tablet, and mobile devices.

Slide 60

Notes about the 100% Stacked Bar Chart

All of the notes under the 100% stacked column chart apply here.

Slide 61

Notes about the Radar Chart

  • Category labels are at the tip of each spine.
  • You can use a fill with your radar charts.
  • Radar charts can be compelling when you compare multiple entities at once. For example, I saw a set of 50 radar charts that compared metrics like crime rates for different types of crime for each state.
  • If you don't want the axis labels to show, you can set the number formatting to ;;; to hide them altogether. You can then include an annotation on your chart that lets viewers know the intervals. See the screenshot under the Slide 45 note above.

Slide 62

Notes about the Combination Chart

Learn all about combination charts in this post I wrote on the Search Engine Land site.

Slide 63 – 69

Self-explanatory as they follow the same dialog as slides 46 – 52.

Slide 71

Notes about the Line Chart

  • In a line chart, category data is usually distributed evenly along the horizontal axis and value data is distributed evenly along the vertical axis.
  • Line charts can show continuous data over time, so they're ideal for showing trends in data at equal intervals, like months, quarters, or fiscal years.
  • You can add markers and set the lines to none to use them in ranking charts.
  • Avoid using stacked line charts. It's not always apparent that the data series are stacked. If you want to stack, use an area chart instead.
  • You can add interesting line markers like the ones I created in this video tutorial to replicate the charts in Moz's tool set

Slide 72

Notes about the Stacked Area Chart

  • Ideal for showing stacked data series over time, especially if you want to demonstrate a fluid trend. Stacked column charts should be used if you want to keep each of the categories more disparate.
  • You should order the data series so that the larger series are at the bottom of the stack with the smaller series being clustered together at the top because people's eyes naturally travel from the horizontal axis upward with stacked area charts.
  • If you keep the gridlines, make them significantly lighter. A light gray works well.
  • Make sure you have adequate contrast between contiguous data series. Sometimes Excel puts two colors next to each other that blend.
  • If you have smaller data series that are difficult to see, use stronger colors to make them easier to view.
  • If you have all larger data series and you want to add some finesse, give your data series a line (what would be called a stroke in graphic design programs) that's slightly darker than the fill.
  • You can create a combination chart with a stacked area chart. Just don't use a line chart for the other style. I like to use a chart style that stands out from the area chart, such as a column chart. You may want to increase the transparency of its fill so that you can easily see through to the stacked area chart.

Slide 73

Notes about the Clustered Column Chart

  • You use the clustered column chart to show comparisons between data series (as opposed to how they contribute to the whole).
  • The clustered column chart is especially effective for showing year-over-year data. The categories would just have the name of the month (I abbreviate to three letters, which you can learn how to do in this tutorial), and one column would be used to show data from one year and the other colored column would indicate the previous year. To show the month from each year as a disparate data series, you would have to make each year a separate column in your data.
  • You can add a line chart on the secondary axis that highlights the percent change between values.
  • You can play with the gap width and overlap settings to adjust the series. You get to those by selecting a column, pressing Ctrl-1 (Mac: Command-1), and navigating to the Series Options (Mac: Options) area of the Format Data Series dialog.
  • Excel doesn't provide the option to add a data label that indicates the total of all the data series for each column. You can hack one by adding a total column that you include in the clustered column but then change to a line chart. From there, remove the line and add data labels above the line.

Slide 74

Same as Slide 60.

Slide 75

Same as Slide 58

Slide 76 – 77

Self-explanatory.

Slide 78

Things get more complicated when you want to chart two dimensions. There are three ways to get 2 dimensions:

Slide 79

So here we have two dimensions (Device Category and User Type). I picked these dimensions to demonstrate because they have a finite number of options. I LOVE the device category dimension and use it frequently to segment my data in Google Analytics.

Note: When you chart two dimensions, you can only use one metric (or data series in Excel).

Slide 80

Here's an example of what a clustered column chart might look like.

Slide 81

We now have a dimension in the legend — or category in Excel.

Slide 82

Using the Switch Row/Column button ….

Slide 83

This is what the chart would now look like. Notice we now have three data series and two categories.

Slide 84

Now let's take a peek under the hood.

Slide 85

Again, here you see we have dimensions, not metrics, in the data series. The metrics should be included in the chart title.

Slide 86

And now the Device Category dimension is in the category area.

Slide 87

Your chart options are the same as when you had one dimension and multiple metrics. These options are not exhaustive.

Slide 88

Slide 89

The data in this table is in report format. If only marketing export data came in this format. (It doesn't.)

Slide 90

This is how marketing data actually comes out of different marketing tools. It's called tabular format.

Slide 91

Just as in a database, rows in tabular data are called records.

Slide 92

Columns are called fields.

Slide 93

And the column headings are called field names. But if I were to select two dimension columns and one metric and select a chart, here's how Excel digests the data …

Slide 94

Gross, I know. I'm a child.

Slide 95

Here's what it actually looks like. A royal mess.

Slide 96

Excel requires that data be in a report format in order to chart two dimensions. And the one metric (sessions, revenue, impressions, whatever) goes into the green area. There's only one way to corral an export with two dimensions and one metric into report format ...

Slide 97

Pivot tables sound scary and intimidating but not if you think about what pivot means.

Slide 98

When a soldier pivots, s/he very simply goes from standing facing one direction to turning at a 90 degree angle. That's what a pivot table does. By moving one of your dimensions into the Columns field (Mac: Column Labels field), Excel puts that dimension's values across the top of your data. 

Once you have your data in report format, and you can chart it. You typically want to put the dimension with fewer values into the columns area.

Learn how to create pivot tables in this comprehensive video tutorial I did.

Slide 99

Although pivot tables come with a lot of junk in the trunk, you can see the pivot table puts the data into report layout, which Excel can then use to chart the data. If you're on a PC, you can create a pivot chart. If you're on a Mac, you can create a static chart from the pivot table because Excel for Mac still doesn't support pivot charts. Still. Ridic.

Slide 100

Now you're ready to look at GA data — nay, all marketing data — with a more strategic eye… And spend less time tooling around in Excel trying to figure out how to visualize your data!


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Content Marketing Show: Key Takeaways

Content Marketing Show: Key Takeaways

Link to White.net

Content Marketing Show: Key Takeaways

Posted: 17 Jul 2014 02:30 AM PDT

Today we are at the Content Marketing Show in London. Our very own Digital Consultant Charlie will be taking to the stage later this afternoon to share his knowledge, but before then we will be blogging the key takeaways from the sessions…

 

Can a brand ever truly be social – Stephen Waddington

Brands want to join the social media party. They try and adopt different personalities to “fit in” to the party.

  • Nice, but dim (Brands trying to associate themselves with unrelated events such as World Cup or Mother’s Day)
  • Nutters (Those who might associate themselves with serious topic and trying to make it a joke)
  • Automation (Big risk as the conversations do not flow)

Therefore it’s a good idea to improve the content that you share on the internet, including when working with your clients.

Social media is intrinsically human, so automate with extreme care.

 

How to use data for your content strategy – Jojo

Most data is already available (and free) so use it to tell your story. Tools to use for data include:

  • Facebook Insights
  • Google Analytics
  • Google Trends

It is important to present data in an interesting way. Try using these tools:

  • Gephi (can show network analysis)
  • CartoDB (mapping analysis)

 

Content marketing yearbook 2014 – Fergus Parker

Effective content marketing is not simple – it’s complicated! Success isn’t a straight journey as there are lessons to learn on the way.

£4 billion is spent on content marketing in the UK. There is £2.1 billion currently being used ineffectively.

Focus on 7 things to improve:

  • Inspire
  • Educate
  • Emotion
  • Belief
  • Meaning and purpose
  • Relevant
  • Authentic

Produce content for people who already have an audience. And make sure you know what they want.

 

Create an inbound marketing strategy in a boring industry – Jasper Martens

Look at the purchase decision funnel when creating content. Content can be done before the purchase, when people are a customer, and as a retention tool.

Create relevant content by answering questions that consumers have. It is important to be consistent and invest. Big ideas may have risk, but could also have big results.

Great content is shared, so be prepared and make sure the piece is hosted somewhere that can handle a lot of traffic in a small amount of time.

Data can be used to create interesting content that can be used by news services to support their reports. This can increase your visibility.

 


Alex Johnson taking over now, ready for the second session of the day at the Content Marketing Show. You can catch me on twitter @alex_cestrian

Why do we share stuff? – Emma Dunn – Caliber

  • Social Currency – stuff that makes them look cool/smart/in the know
  • Usefulness
  • When things are unexpected/surprising
  • Emotional triggers – amusement, anger, surprise
  • Stories – people love to share a good story, tap into emotional identity

 

What can being a poker player teach me about content marketing?  – Andrew Tipp

1. Data – Be thoughtful, analytical- data can tell a story

Everything should begin and end with data (don’t be a content marketing cowboy!)

2. Tells – Look for insight and clues to make decisions from incomplete information

3. Expectation – Make content decisions with a positive expectation value – look at the long term goals

18-24 month strategies work well in looking at long term value

4. Research your opponents – Analyse what they are doing better than you!! What is their strategy/how are they gaining coverage?

5. Strategy - Be flexible in your strategy – different strategies for different campaigns

6. Winning – Is it all about the big win? – its all about picking up the small incremental win – most wins should be from the ‘bread and butter content’

7. Losing – Avoiding big losses is as good as creating big wins – test to make sure you’re not losing out

8. Folding – Know when to hold ‘em and know when to fold ‘em – don’t persist with content concepts that aren’t winning!

 

How a journalistic approach and a magazine mindset improves brand content – Steve Masters

  • The journalist mindset is all about finding a good story
  • The magazine mindset is about variety
  • Interviews improve storytelling – quotes bring passion, kudos and weight
  • Ask experts to give you quotes and include references and also contradictions
  • Conversation is key – interviews are not an inquisition
  • Listen for soundbites – they won’t come ready-made

 

How do you measure Content Marketing? The $44bn question – Andrew Davies

  • Content Marketing is now a $43.9bn industry
  • The big question is proving the value of content marketing
  • 3 steps: Content Performance -> Audience Performance -> Business Performance
  • Measure your audience – build personas and measure
  • Don’t forget the individual
  • At this point, Andrew Davies offends northerners [ed. is a northerner...grrrr!]
  • From here it gets a bit confusing and very detailed, so I’d recommend you check out Andrew Davies’ slide deck – we’ll add a link here once its up online

Right, it’s now time for lunch at the Content Marketing Show, we’ll be back in a couple of hours with takeaways from the afternoon sessions, live from the Institute of Education in Central London… over and out!

Coming up….

4:30 Raph Goldberg The Hero's Journey: using archetypes in video marketing
Wes West Making animation for the web
Nichola Stott Getting Past the Buying Objection with Problem-Solving Content
Jess Collins Types, tripe and how to get it right (types of content & how to make boring content inspiring)
 
16:30 Marcin Chirowski How to organise successful international bloggers event
Chelsea Blacker Motivational Content Stories For the Down Trodden
Charlie Williams Gateway-drug content strategy elements you should use
Lisa Myers Running and motivating a creative content team

 

The post Content Marketing Show: Key Takeaways appeared first on White.net.

Competitor Analysis: Identifying your online competitors

Posted: 17 Jul 2014 12:30 AM PDT

Do you conduct any competitor research for the industry that you work in? If the answer is yes, then great. If it is no, then you are not the only one!

In my opinion, competitor research is one of the most underrated pieces of work completed. Often even if it is done, it is not used, and gets left on the desk, put in a drawer or, if sent electronically, not even read.

Why dammit, why?

Why dammit, why - Jackie Chan

This piece of research is essential to your online marketing plan, your strategy, and your business! It is key to understanding what is going on and what is required for your business to succeed or, at the very least, keep it afloat. Yet, so many people just don’t seem to care, or view it as a pointless task.

Well, over the next four posts I am hoping to change your mind. I want to show you what you can uncover with competitor research, and how it can all come together to influence your search marketing plan.

In these posts I am going to be discussing:

  • Identifying competitors based on search terms
  • Finding keyword & content opportunities
  • Understanding what content performs well
  • What coverage your competitors are getting, and why

But first I am going to start with identifying your online competitors.

Online is different to Offline!

If you have got this far, then you either don’t normally conduct competitor analysis or you want to know how and why to do it.

To start with, you need to ask yourself a few questions. Who are my competitors? What search terms are they visible for? Are those search terms of value to you? What are your competitors ranking for and should you be? How much money will it cost me to buy that traffic through paid search?

Luckily, there are tools available to help you do this. Some are paid, as you would expect, but they are worth the money if you are going to be constantly monitoring the landscape – which you should be!

So how do you understand who your online competitors are within search and what their visibility is? Well here is what I do in 10 steps…

*To complete these steps you will need paid access to both SEMrush & Linkdex.

  1. Firstly, head over to SEMrush, type in your domain, choose the country that you want to analyse (SEMrush currently has 22 countries), and hit search. This will return a lot of data, but at this point you are purely focusing on the organic keywords and competitors, which you will find if you scroll down the page.
    SEMrush - Competitor Analysis
  2. What you need to do now is to download all the organic keywords from the top 10-20 organic competitors. You can obviously choose more or less depending on the market that you are researching. To do this, simply click on the ‘Organic Competitors’ full report, then click on the competitor of choice. This will provide you with a list of keywords that you can simply download into Excel format. Go ahead and do this for your chosen number of competitors.
    SEMrush - Competitor Keywords
  3. Now you have all the keywords, you need to merge them into a single spreadsheet, keeping all the data, and de-dupe them.
  4. Now that you have a single list, you will need to spend some time going through the keywords and removing any that are unnecessary. Terms that include brand, jobs, recruitment, sales and anything else that isn’t relevant to your business and market, need to be removed. This will give you a much more accurate list of terms.
  5. Once you have completed your list in Excel, you will need to import this data into Linkdex, keeping the Term, Search Volume and CPC data found in SEMrush. To do this, simply go to the keyword rankings function within Linkdex and bulk upload using their import tool. Choose the correct headings and let it gather ranking data for those terms.
  6. Whilst that is happening, head over to the new ‘visibility’ feature that has recently been released by Linkdex. This feature is similar to that of SEMrush in that it tracks millions of keywords, but it also allows you to do some of your analysis side-by-side.
  7. Once you are in the new feature, you need to start entering the competitors that you identified in SEMrush. Once complete you will start to see the table populate with terms that each domain is visible for.
    Linkdex - Competitor Visibility
  8. The next step is pretty time-consuming, but is required. You will need to go through each competitor and add any keywords that are not currently in your list, but that are relevant to you. You may have to go and get the search volume and CPC data for these extra terms. This can be done by heading over to the keyword planner and adding in the terms as exact match and returning the data.
  9. By time you have done this, you should have a very comprehensive list of search terms that you and your competitors are competing for.
  10. Still in Linkdex, head over to the dashboards and create a ‘Competitor Detective Pro’ widget that looks at all of the keywords that you have added into Linkdex for checking. Once you have set this up and clicked OK, wait for the data to load and voila! Here are your competitors based on all the terms within the market, along with rankings by position, estimated traffic volume and how much that traffic is worth if you paid for it through PPC.
    Linkdex - Competitor Detective

So there you have it, a list of your online competitors who are targeting the key phrases within your industry, along with ranking data, estimated volumes and how much it would cost. This data can be useful to understand where you currently sit in the search landscape vs your new found competitors. It will also likely throw up some competitors that you may not have thought were competing on similar terms. All this data can form part of your strategy going forward and inform the next steps.

In my next post I will talk about how you take this data and find new opportunities that your competitors are already taking advantage of.

Are you conducting any competitor analysis for your clients? Do you follow a similar process, or are you doing something completely different? I’d really like to hear your comments on my thought process and what you would do differently in the comments below or over on twitter @danielbianchini.

Flickr Image Credit.

The post Competitor Analysis: Identifying your online competitors appeared first on White.net.

Seth's Blog : The special problem

 

The special problem

Yes, it's possible that your particular challenge is unique, that your industry, your job situation, your set of circumstances is so one-of-a-kind that the general wisdom doesn't apply.

And it's possible that your problem is so perfect and you are so stuck that in fact there's nothing out there that can help you.

Possible, but not likely.

When you complain that you need ever more specific advice because the general advice just doesn't apply, consider looking for your fear instead. As Steve Pressfield has pointed out, the resistance is a wily adversary, and one of the clever ways it will help us hide from the insight that will lead to forward motion is to play the unique, this-one-is-different card.

We can learn by analogy, if we're willing to try and fail, and mostly, if we're willing to get unstuck.

The first step is acknowledging that our problem isn't that special.

       

 

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miercuri, 16 iulie 2014

Mish's Global Economic Trend Analysis

Mish's Global Economic Trend Analysis


Yellen Yap: Silliness, Outright Lies, and Some Refreshingly Accurate Reporting

Posted: 16 Jul 2014 09:45 PM PDT

Yellen Yap Silliness

The spotlight on Fed Chair Janet Yellen is rather amusing given she is more disingenuous than former Chair Ben Bernanke. Some of the headlines are downright silly. For example, Bloomberg reports Dollar Rises to Highest in 3 Weeks on Yellen Comments.
The dollar reached the strongest in a month versus the euro as wholesale prices in the U.S. rose more than forecast and the Fed saw modest to moderate growth in June. New Zealand's dollar slumped the most in seven weeks after inflation accelerated slower than expected and a gauge of dairy prices dropped to its lowest since 2012. South Korea's won slid to the weakest since April.

Yellen testified before the House Financial Services Committee today that she's not seeing "alarming warning signals" in markets.

"My general assessment at this point is that threats to financial stability are at a moderate level and not a very high level," she said. While some asset values "may be on the high side and there may be some pockets where we see valuations becoming stretched," in general "price equity ratios and other measures are not outside of historical norms."
US Dollar Weekly Chart



click on chart for sharper image

Questions of the Day

  1. Did the US dollar rise because of what Yellen said or in spite of what Yellen said?
  2. Was the move based on wholesale prices or other economic data, as opposed to anything Yellen said?
  3. What about none of the above? Was this a random fluctuation not attributable to anything?
  4. Even if the move in the US dollar could reasonably be attributed to Yellen, was the move worth mentioning in a headline?

Yellen Yap Lies

Let's move from silliness to outright lies, and an excellent post by Yves Smith on Naked Capitalism that discusses the lies (whoppers).

In response to the New Yorker article The Hand on the Lever regarding "How Janet Yellen is redefining the Federal Reserve", by Nicholas Lemann, Smith accurately assesses the setup in her coverage Yellen Tells Whoppers to the New Yorker
A Nicholas Lemann profile of Janet Yellen in the New Yorker, based on interviews with her, is creating quite a stir, and for many of the wrong reasons. The article verges on fawning, but even after you scrape off the treacle, it's not hard to see how aggressively and consistently the Fed chair hits her big talking point, that's she's on the side of the little guy.

In fact, as we'll discuss, Yellen's record before and at the Fed shows she's either aligned herself with banking/elite interests or played two-handed economist to sit out important policy fights. Even if she actually harbors concern for ordinary citizens, she's never been willing to risk an ounce of career capital on it.

The article is also generously larded with standard defenses of the Fed, that it lacked the power to do much of anything about dodgy mortgage lending in the runup to the crisis. In fact, the Fed was so firmly in denial that even in 2007, Fed officials were convinced that banks were victimized by subprime borrowers, which is hardly a pro-intervention stance.

And why did the Fed take so little interest? The real reason was that prior to the crisis, if borrowers bought more costly housing than they could afford, the losses fell mainly on them. There was enough of an equity cushion on average that the banks came out at worst only mildly dented.

In other words, readers are supposed to take Yellen's claims at face value, when the Fed's policy of saving banks by goosing asset prices and convincing itself that ordinary people would benefit because the "wealth effect" would lead to more consumption. The result has been widening income and wealth disparity and corporate profits at record levels as a percent of GDP, meaning workers are getting less than they've ever gotten.

At the Fed, Yellen is given more credit than she deserves for sounding some mild concern about rising housing prices. She's also been cited as the best forecaster on the FOMC, but given how the FOMC failed to see the crisis coming, her "success" is tantamount to declaring her the winner of a height competition among peanuts.

In other words, Yellen was in the center to center-right of the Democratic party technocratic elite of the 1990s and never departed from conventional thinking. She's now trying to rewrite her record by making pious statements and getting her interlocutors to focus on what she presents as her character in the hope that they won't bother looking at her actions.

Yellen's contention that she's really out to help little people would be far more credible if she acknowledged her past anti-middle class policy positions and claimed that she'd made a Pauline conversion. But her institutional and political loyalties preclude that. 
Bingo

The Fed is the number one cause of widening income and wealth disparity and corporate profits at record levels, as I have pointed out on numerous occasions.

It is very refreshing to be on the same side of the debate as Yves Smith, possibly because her article did not go into solutions. I advocate free market solutions while Yves tends to advocate more intervention.

Regardless, Yves did an outstanding job bashing the disingenuous nature of Yellen. Her article merits a read in entirety.

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

"Treasury Bond Undervalued" Says Hoisington Second Quarter Review; Path to Fiscal Ruin

Posted: 16 Jul 2014 11:42 AM PDT

The Hoisington Investment Management, Second Quarter 2014 Review and Outlook makes the case "Treasury Bond Undervalued".
Thirty-year treasury bonds appear to be undervalued based on the tepid growth rate of the U.S. economy. The past four quarters have recorded a nominal "top-line" GDP expansion of only 2.9%, while the bond yield remains close to 3.4%.  

To put the 2.9% change in nominal GDP over the past four quarters in perspective, it is below the entry point of any post-war recession. Even adjusting for inflation the average four-quarter growth rate in real GDP for this recovery is 1.8%, well below the 4.2% average in all of the previous post-war expansions.

Fisher's Equation of Exchange

Slow nominal growth is not surprising to those who recall the American economist Irving Fisher's (1867-1947) equation of exchange that was formulated in 1911. Fisher stated that nominal GDP is equal to money (M) times its turnover or velocity (V), i.e., GDP=M*V. Twelve months ago money (M) was expanding about 7%, and velocity (V) was declining at about a 4% annual rate. If you assume that those trends would remain in place then nominal GDP should have expanded at about 3% over the ensuing twelve months, which is exactly what occurred. Projecting further into 2014, the evidence of a continual lackluster expansion is clear. At the end of June money was expanding at slightly above a 6% annual rate, while velocity has been declining around 3%. Thus, Fisher's formula suggests that another twelve months of a 3% nominal growth rate is more likely than not. With inflation widely expected to rise in the 1.5% to 2.0% range, arithmetic suggests that real GDP in 2014 will expand between 1.0% and 1.5% versus the average output level of 2013. This rate of expansion will translate into a year-over-year growth rate of around 1% by the fourth quarter of 2014. This is akin to pre-recessionary conditions.

An Alternative View of Debt

The perplexing fact is that the growth rate of the economy continues to erode despite six years of cumulative deficits totaling $6.27 trillion and the Federal Reserve's quantitative easing policy which added net $3.63 trillion of treasury and agency securities to their portfolio. Many would assume that such stimulus would be associated with a booming economic environment, not a slowing one.

Readers of our letters are familiar with our long-standing assessment that the cause of slower growth is the overly indebted economy with too much non-productive debt. Rather than repairing its balance sheet by reducing debt, the U.S. economy is starting to increase its leverage. Total debt rose to 349.3% of GDP in the first quarter, up from 343.7% in the third quarter of 2013.

It is possible to cast an increase in debt in positive terms since it suggests that banks and other financial intermediaries are now confident and are lowering credit standards for automobiles, home equity, credit cards and other types of loans. Indeed, the economy gets a temporary boost when participants become more indebted. This conclusion was the essence of the pioneering work by Eugen von Böhm-Bawerk (1851-1914) and Irving Fisher which stated that debt is an increase in current spending (economic expansion) followed by a decline in future spending (economic contraction).

In concert with this view, but pinpointing the negative aspect of debt, contemporary economic research has corroborated the views of Hyman Minsky (1919-1996) and Charles Kindleberger (1910-2003) that debt slows economic growth at higher levels when it is skewed toward the type of borrowing that will not create an income stream sufficient to repay principal and interest.

John Maynard Keynes (1883-1946) correctly argued that the severity of the Great Depression was due to under-consumption or over-saving. What Keynes failed to note was that the under-consumption of the 1930s was due to over-spending in the second half of the 1920s. In other words, once circumstances have allowed the under-saving event to occur, the net result will be a long period of economic under-performance.

Implications for 2014-2015

In previous letters we have shown that the largest economies in the world have a higher total debt to GDP today than at the time of the Great Recession in 2008. PSRs [Personal Savings Rates] also indicate that foreign households are living further above their means than six years ago. According to the OECD, Japan's PSR for 2014 will be 0.6%, virtually unchanged from 2008. The OECD figure is likely to turn out to be very optimistic as the full effects of the April 2014 VAT increase takes effect, and a negative PSR for the year should not be ruled out. In addition, Japan's PSR is considerably below that of the U.S. The Eurozone PSR as a whole is estimated at 7.9%, down 1.5 percentage points from 2008. Thus, in aggregate, the U.S., Japan and Europe are all trying to solve an under-saving problem by creating more under-saving. History indicates this is not a viable path to recovery.

Japan confirms the experience in the United States because their PSR has declined from over 20% in the financial meltdown year of 1989 to today's near zero level. Japan, unlike the U.S. in the 1940s, has moved further away from financial stability. Despite numerous monetary and fiscal policy maneuvers that were described as extremely powerful, the end result was that they have not been successful.

With U.S. rates higher than those of major foreign markets, investors are provided with an additional reason to look favorably on increased investments in the long end of the U.S. treasury market. Additionally, with nominal growth slowing in response to low saving and higher debt we expect that over the next several years U.S. thirty-year bond yields could decline into the range of 1.7% to 2.3%, which is where the thirty-year yields in the Japanese and German economies, respectively, currently stand.

Van R. Hoisington
Lacy H. Hunt, Ph.D.

Reflections on Keynesian Analysis

Unlike the Hoisington authors, I have no praise at all for Keynes. That said, Hoisington politely blasts Keynes in this snip: "Keynes failed to note was that the under-consumption of the 1930s was due to over-spending in the second half of the 1920s. In other words, once circumstances have allowed the under-saving event to occur, the net result will be a long period of economic under-performance."

I like the discussion on personal savings rates, especially this comment: "In aggregate, the U.S., Japan and Europe are all trying to solve an under-saving problem by creating more under-saving. History indicates this is not a viable path to recovery."

Bingo.

Path to Fiscal Ruin

The OECD predicts Japan's PSR for 2014 will be 0.6%, but Hoisington points out that assessment is "likely to turn out to be very optimistic as the full effects of the April 2014 VAT increase takes effect. A negative PSR for the year should not be ruled out."

The US and Japan are both on the path of fiscal ruin. It appears highly likely Japan will get there first thanks to a big head start followed by Abenomics.

Yesterday, in Corporate and Government Bonds: Where to From Here? and in sharp contrast to all of those who see a US treasury bond bubble, I stated: "The worry about US government bonds is, for the time, overblown."

Hoisington provides much analysis that shows why my statement is correct. There is much more in the article and it merits a full read. Ignore any positive references to Keynes,  but accept all of the negative ones, and the article reads perfectly.

Personal Update

I am in Glacier National Park, Montana (to be more precise, just outside the park). There is no phone or internet in the park. It can take 1 hour to do an email on the park satellite Wi-Fi.

Liz and I went on a 10 mile round-trip hike to Iceberg Lake. It has about a 1,200 foot elevation change, all up on the way to the lake, all down on the way back. Here are a couple of images.

Iceberg Lake



Iceberg Lake Closeup



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