Posted by evolvingSEOThe perception of success
The following screenshot is from an actual blog post. Based upon what you see here, would you call it successful?
I think it depends on perception.
The optimist might see this… Continue reading →
Posted by shannonskinner
One of the biggest challenges for SEO is proving its worth. We all know it’s valuable, but it’s important to convey its value in terms that key stakeholders (up to and including CEOs) understand. To do that, I put together a process to calculate an estimate of ROI for implementing changes to keyword targeting.
In this post, I will walk through that process, so hopefully you can do the same for your clients (or as an in-house SEO to get buy-in), too!
There are quite a few parts to this recipe, and while the calculation part is pretty easy, gathering the data to throw in the mix is the challenging part. I’ll list each section here, including the components of each, and then we can go through how to retrieve each of them.
There has been quite a lot written about keyword research, so I won’t go into too much detail here. For the Keyword data list, the important thing is to get whatever keywords you’d like to assess into a spreadsheet, and include all the information listed above. You’ll have to select the preferred URLs based on what you think the strongest-competing and most appropriate URL would be for each keyword.
For the Preferred URLs list, you’ll want to use the data that’s in your keyword data under the preferred URL.
Once you have the list of de-duplicated preferred URLs, you’ll need to pull the data from Open Site Explorer for these URLs. I prefer using the Moz API with SEOTools. You’ll have to install it to use it for Excel, or if you’d like to take a stab at using it in Google Docs, there are some resources available for that. Unfortunately, with the most recent update to Google Spreadsheets, I’ve had some difficulty with this method, so I’ve gone with Excel for now.
Once you’ve got SEOTools installed, you can make the call “=MOZ_URLMetrics_toFit([enter your cells])”. This should give you a list of URL titles, canonical URLs, External & Internal links, as well as a few other metrics and DA/PA.
For the Where the competition sits list, you’ll first need to perform a search for each of your keywords. Obviously, you could do this manually, or if you have exportable data from a keyword ranking tool and you’ve been ranking the keywords you’d like to look at, you could use either of these methods. If you don’t have those, you can use the hacky method that I did–basically, use the ImportXML command in Google Spreadsheets to grab the top ranking URLs for each query.
I’ve put a sample version of this together, which you can access here. A few caveats: you should be able to run MANY searches in a row–I had about 850 for my data, and they ran fine. Google will block your IP address, though, if you run too many, and what I found is that I needed to copy out my results as values into a different spreadsheet once I’d gotten them, because they timed out relatively quickly, but you can just put them into the Excel spreadsheet you’re building to make the ROI calculations (you’ll need them there anyway!).
From this list, you can pull each URL into a single list, and de-duplicate as explained for the preferred URLs list to generate the Strength of the Competition list, and then run the analysis you did with the preferred URLs to generate the same data for these URLs as you did for the preferred URLs with SEOTools for Excel.
Once you’ve got these lists, you can use some VLOOKUP magic to pull in the information you need. I used the Where the competition sits list as the foundation of my work.
From there, I pulled in the corresponding preferred URL and its Page Authority, as well as the PAs and DAs for each URL currently ranking 1-10. I then was able to calculate an average PA & DA for each query, and could compare the page I want to rank to this. I estimated the chances that the page I wanted to rank (given that I’d already determined these were relevant pages) could rank with better keyword targeting.
Here’s where things get interesting. You can be rather conservative, and only sum search volumes of keywords you’re fairly confident your site can rank, which is my preferred method. That’s because I use this method primarily to determine if I’m on the right track–whether making these recommendations are really worth the time to get implemented. So I’m going to move forward assuming I’m counting only the search volumes of terms I think I’m quite competitive for, AND that I’m not yet ranking for on page 1.
Now, you want to move to your analytics data in order to calculate a few things:
I’ve set up my sample data in this spreadsheet that you can refer to or use to make your own calculations.
Each of the assumptions can be adjusted depending on the actual site data, or using estimates. I’m using very very generic overall CTR estimates, but you can select which you’d like and get as granular as you want! The main point for me is really getting to two numbers that I can stand by as pretty good estimates:
This is because, for higher-up folks, money talks. Obviously, this won’t be something you can promise, but it gives them a metric that they understand to really wrap their head around the value that you’re potentially brining to the table if the changes you’re recommending can be made.
There are some great tools for estimating this kind of stuff on a smaller scale, but for a massive body of keyword data, hopefully you will find this process useful as well. Let me know what you think, and I’d love to see what parts anyone else can streamline or make even more efficient.
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Continue reading → Posted by iPullRank
We all know that creating and promoting content can be a ton of work (not to mention expensive). So how do we know whether it’ll be worth it? In today’s Whiteboard Friday, MozCon 2014 speaker Mike King shows you several w… Continue reading →
Posted by Bill.Sebald
In rock n’ roll music, stealing is expected. Led Zeppelin 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.
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!
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.
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!
Next, step it up with more quantifying data.Time to trim the fat.
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.
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. Topsy, Social 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.
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.
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 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:
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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Continue reading →
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.
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.
In this presentation I have focused on Google Analytics data for a couple reasons:
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.
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.
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.
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.
Excel for Mac also has data series on the left and categories on the right. And that�s about all they have in common.
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.
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.
To move the Sessions data series to the bottom of the pile, first select it from the Series list.
Then click the Remove button to delete it from the list.
Then click the Add button to add it back to the list of data series.
Click the data selector button to the right of the Name field and select the series name, as directed in the screenshot.
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.)
And finally you need to click-and-drag over the category axis labels. Which brings us to the Mac�s other issue �.
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.
Categories end up along the horizontal axis � or the vertical axis for horizontal bar charts.
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.
Transition to Google Analytics.
The two major players in Google Analytics � that we�ll be mapping to Excel � are dimensions and metrics. They�re (practically) inseparable.
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.
Metrics are anything that can be measured with a number.
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.
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.
In most cases dimensions in Google Analytics map to categories in Excel.
And metrics map to data series in Excel.
I�m going to break this down systematically, based on the number of dimensions and metrics you�re wanting to visualize.
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.
I hate to start on a downer, but you need the API to do this. The GA interface requires at least one dimension.
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.�
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.
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.
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.
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.
You can access this report here while logged in to Google Analytics.
This data isn�t conducive to charting, but you can sexy up a table with sparklines and conditional formatting.
Dimensions: 1
Metrics: 1
An example of this might be revenue segmented by country or bounce rate segmented by device category.
Here are some highlights about the pie chart:
Ways to trick out your chart:
Here are some highlights about the donut chart:
Ways to trick out your chart:
Learn how to create a heat map in this video tutorial I did.
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 �.
If we create a column chart from this table, this is what it�s going to look like (with some cleanup).
Now if we look at the data source this is what we�ll see �.
The mediums show up over here in the categories �
And the sessions values show up here in the data series �
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.
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
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.
Learn all about combination charts in this post I wrote on the Search Engine Land site.
Self-explanatory as they follow the same dialog as slides 46 � 52.
Same as Slide 60.
Same as Slide 58
Self-explanatory.
Things get more complicated when you want to chart two dimensions. There are three ways to get 2 dimensions:
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).
Here�s an example of what a clustered column chart might look like.
We now have a dimension in the legend � or category in Excel.
Using the Switch Row/Column button �.
This is what the chart would now look like. Notice we now have three data series and two categories.
Now let�s take a peek under the hood.
Again, here you see we have dimensions, not metrics, in the data series. The metrics should be included in the chart title.
And now the Device Category dimension is in the category area.
Your chart options are the same as when you had one dimension and multiple metrics. These options are not exhaustive.
The data in this table is in report format. If only marketing export data came in this format. (It doesn�t.)
This is how marketing data actually comes out of different marketing tools. It�s called tabular format.
Just as in a database, rows in tabular data are called records.
Columns are called fields.
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 �
Gross, I know. I�m a child.
Here�s what it actually looks like. A royal mess.
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 …
Pivot tables sound scary and intimidating but not if you think about what pivot means.
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.
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.
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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