Category Archives: SEO Tips

Posted by randfish

Last night at the SEOmoz meetup in Avi Wilensky‘s incredible office space, a frequent topic of discussion both during the presentations/Q+A and in small group networking before and after was the propensity for Google (and Bing) to bias towards exact match domains in the rankings.

How big an issue is exact-match domains? Let’s look at some data from our correlation analysis from SMX Advanced earlier this year:

Just by itself, exact match is remarkably high in correlation to rankings. No other on-site/on-page factor we examined even came close. Granted, that’s not causation, and it could be other factors influencing those impressively high rankings. Let’s get a bit deeper and more granular around the issue:

Holy what?! Actually, this probably isn’t very surprising to most SEOs. The second highest correlation we found of anything – links, on-page elements, URL factors, keyword usage, third-party metrics  (excluding only Page Authority scores, which are specifically designed to predict Google rankings) was exact-match .com domain names. Yeah – it’s powerful stuff.

We can also look at the raw prominence (less interesting for determining what might help a page/site rank, but useful for this application:

That’s saying that more than 1/4 and nearly 1/3 SERPs contain an exact match domain in the top 10. The only thing more prominent?

No surprise it’s keyword-in-the-domain matches (but not necessarily exact). So, in the first pie chart set, we’d say that for the query "org chart" only orgchart.* type domains would count. In the second, something like myorgcharts.com, greatorgcharts.net, etc. would fit the pattern. These appear in around half of all SERPs on both engines.

The question is, with search results in so many sectors becoming so overrun with obviously over-SEO’d, spammy, manipulative and sometimes, downright poor quality exact-match domains, is Google bound to take action?

Blueglass’ Chris Winfield argued that Google is bound to giving outsized benefit to exact match domains because of the brand intent behind so many queries. Since a search for "Alaska Airlines" or "MSN" or "NY Times" is likely to want exactly those websites in the first position, Google’s overcompensating in the broader algorithm by biasing towards these exact matches. Many in the audience agreed (and I personally find this viewpoint credible, too).

Interestingly, some of the more experienced, ear-to-the-ground SEO types indicated that they’d heard (or believed) that Google would soon be taking action against exact match domains. One person, who wasn’t at the event, but whom I trust a great deal (and will remain anonymous) indicated they thought the next 6 months would bring about this shift.

Personally, I’d welcome it as both a searcher and an SEO. I think Google’s relied on exact match for far too long, and it would give them a substantive quality boost over Bing to have more subtlely in the domain matching algo. But, as always, I’m curious to hear what you think – is this really a weakness/problem? Should Google take action? Do you think they will (particularly given the poor track record of improvements like this in the past year or so)?

A completely unrelated p.s. Linking to Twitter profiles (as I did at the start of the post) is curious. Notice that the URLs if you’re logged into Twitter look like http://twitter.com/#!/aviw instead of http://twitter.com/aviw. Now,this could be an incredibly dumb move, but actually, both Twitter and Facebook are using Google’s new AJAX crawling protocol. Not as SEO-ignorant as they look, eh?

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Posted by JoannaLord

Are you an SEOmoz fan? Do you love our software? Do you dream of introducing Roger to your friends? Well, what if you could do all that and make money? Too awesome to believe, huh? Well now you can! We are excited to present our new affiliate program, which has higher payouts, an easier management platform, and better service all-around.

For years we have been struggling to really let the potential of our promoters and evangelists shine through. Well enough of that.

We have moved to the HasOffers platform, another Seattle-based startup that is quickly earning a reputation as an industry leader. With HasOffers our affiliates will now enjoy more visibility into their account, top-notch reporting, and an easy to navigate platform for better usability.

Now let’s talk money! Like I said, we are paying out big for your help in promoting our software. It’s a win-win…you get to share software you love and make money while you’re at it! Here is what the payouts look like.

              (Wowzers is right! These are the highest affiliate payouts in our industry!)

Got questions? We have some answers!

How long does my cookie last?
We are giving a generous 60-day cookie, to make sure you get the credit you deserve!

What kinds of tracking does HasOffers provide me?
Our new program offers real-time tracking. This means when they convert…you know! Please note the tracking will begin on the click, not the impression.

What kind of resources are available to me?
We have over 25 different creatives in there for you to get started with. This includes a variety of themes as well as sizes. We are also going to be adding to this regularly, based on affiliate feedback and needs. In addition to a plethora of creatives, we provide you a variety of optimized landing pages to help your traffic better understand what SEOmoz PRO is all about and purchase with confidence!

What kinds of campaigns are allowed?
We allow a number of different campaigns–website, blog, email, and coupon are just some examples. Currently, we are not accepting incentivized traffic and paid search campaigns require affiliate manager approval.

How often do I get paid?
We work off a 30-day pay period, and then we will be paying on Net 30 after the close of each pay period.

What about all my other questions?
Well friends, this is the awesome part. We have moved this affiliate program in-house because we are serious about making this a top-notch affiliate program. If you have any questions you can contact us directly at affiliate@seomoz.org, and will get back to you speedy as speedy can be!

That about sums it up for now. We are so excited, and urge all of you check out the program and sign up! If you are looking for more information you can read about the new affiliate program in detail or if you are ready to sign up and get promoting, you can join below!

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Posted by Dr. Pete

Since Google released the Canonical Tag in early 2009, we’ve heard a similar SEO horror story replay itself. It boils down to this: "I accidentally canonicalized my entire site to one page, and my site was completely dropped from the index." Although the evidence of rel-canonical going very wrong was overwhelming, I decided it was time to get some firsthand data in an effort to help people both avoid this problem and potentially fix it.

Warning!
The following SEO experiment was conducted by a trained professional (allegedly), and it didn’t turn out to be a very good idea even for him. Kids, don’t try this at home. Seriously.

Experiment Overview

First things first – throughout this post, I’ll refer to the "Canonical Tag," by which I mean the meta directive <link rel="canonical"… /> and not canonicalization in general. On August 23, 2010, I added the Canonical Tag sitewide to my usability blog. Each tag was identical, canonicalizing every page to my home-page:

<link rel="canonical" href="http://www.usereffect.com" />

As much as possible, I made no other content changes during the experiment. Every day, I measured ranking for a couple of critical terms along with Google’s indexed page count (using the "site:" operator).

Stage I – The Decline

The graph below shows indexed pages from the day I put the Canonical Tag in place until the day I removed it, just under 3 weeks later:

Despite a short-term bump in indexed pages, the overall impact was huge, even in a relatively short term. Total indexed pages dropped from 237 to 103 (57%). The lower, light-red line shows the non-supplemental page count (the pages prior to hitting omitted results). I thought this might be worth tracking, but the pattern was very similar. Although canonicalization can be used to remove duplicate content, Google does NOT consider a wrongly canonicalized page to be a duplicate – the page is simply removed from the index.

I’m going to briefly discuss some major milestones along the decline. Each milestone is marked with the date and the number of days that passed after putting the tag in place (e.g. +1 = 1 day after).

Day +1 (Aug 24) – SEOmoz Canonical Warning
Just over a day past turning the Canonical Tags "on," I noticed a handful of Rel-Canonical warnings in the SEOmoz campaign manager under the "On-page" tab. If you have no Canonical Tag or a self-referencing tag, you should see this:

Keep in mind that an unchecked box may be fine – obviously, some Canonical Tags will point to different URLs. If you start seeing this in huge volumes, though, you may have a problem. Unfortunately, Google Webmaster Tools shows no errors for bad canonicalization.

Day +3 (Aug 26) – Top Page #1 De-indexed
Although indexation actually showed a bump around this time, my most trafficked page, with the #1 spot on Google for a solid 2-word phrase, was de-indexed. My home-page took its place in the rankings for that phrase. This demonstrates a critical point. With many SEO problems, strong pages are buffered a bit due to their "authority", link profile, etc. In this case, since high authority means more frequent crawling, the top pages on my site were the first to be affected. By the time you notice the damage of a bad sitewide canonicalization, your top pages may have been de-indexed for weeks.

Day +12 (Sep 4) – Top Page #2 De-indexed
Just over a week later, I noticed that my 2nd top page had disappeared from the index, also for a pretty competitive keyphrase. My home-page took its place, but unfortunately the ranking dropped from #1 to #9. Unfortunately, I wasn’t monitoring this page from the start, so it was probably de-indexed earlier.

Day +19 (Sep 11) – Major Traffic Loss
The de-indexation by itself was starting to worry me at this point, especially for the top pages, but by the 2nd week I was starting to also see significant loss of search traffic:

The graph covers 4 weeks, including the week before the canonicalization. It was about this time that I lost my nerve and decided I’d had enough. So, I set about reversing the process.

Stage II – The "Recovery"

On September 11th, I removed the sitewide Canonical Tag. I continued collecting data until October 14th. Here’s the graph of Google’s indexed pages during the recovery:

There was a fairly quick bump in indexed pages, followed by a couple of leveling-off periods. The total count (149 on the last day) never regained the original indexation count of 237, even after a full month, but some of that content may have been duplicated.

Unfortunately, while indexation seemed to jump in the first few days, regaining status for my top pages took a while longer. Below are a few milestones, measured from the day I removed the sitewide Canonical Tag.

Day +18 (Sep 29) – Resubmitted XML Sitemap
For the purposes of the experiment, I tried to let recovery proceed on its own, but after a couple of weeks of not regaining my top pages, I started to get itchy. My first step was an easy one, resubmitting my XML sitemap via Google Webmaster Tools.

Day +21 (Oct 2) – Resubmitted Partial XML
Knowing that a basic resubmission probably wouldn’t accomplish much, I created a 2nd XML sitemap with just my Top 3 pages and submitted that separately. I didn’t have high hopes, but I figured I’d try to kick-start the crawlers.

Day +24 (Oct 5) – Added Unique Canonical Tags
Since the top affected pages were all blog posts, I decided to add back in Canonical Tags, but this time proper tags pointing to the correct, individual pages. My hope was that a good Canonical Tag might offset a bad one, or at least get the crawlers’ attention.

Day +26 (Oct 7) – Submitted Reconsideration Request
Finally, almost 4 weeks after removing the Canonical Tag, I got a bit desperate. I submitted my first Google reconsideration request in quite a while. I’ll talk about that a bit more later.

Day +27 (Oct – Top Page #1 Re-indexed
Just a day after filing for reconsideration, my Top page regained its #1 spot and kicked out the home-page. Given the timing, I doubt this had anything to do with the request, but the re-implemented Canonical Tags may have helped.

Day +28 (Oct 9) – Top Page #2 Re-indexed
The next day, my #2 page regained its status. This was more important in a way – while the #1 page was just replaced by the home-page in the rankings, the #2 page had fallen off the rankings entirely. Not only was the page re-indexed, but it immediately regained its ranking position. After 4 full weeks, I finally saw some light at the end of the tunnel.

Stage III – The Pleading

Consider this a bit of an epilogue (as if this post wasn’t already long enough). I thought our readers might enjoy seeing my reconsideration request. If nothing else, it’s honest:

I did something bad. Let’s get that out in the open. In late August, I rel-canonicaled my entire site (www.usereffect.com) to the home-page. Here’s the thing – I did it on purpose. "Why would you do something that stupid on purpose?" you might ask. Fair enough.

Full disclosure – I write for a well-known SEO blog (SEOmoz.org). For months, we’ve been hearing horror stories from people who accidentally rel-canonicaled their site to one page. The problem is, they usually didn’t know when it started (since it was accidental) and they didn’t have much data. So, I decided to collect some. I wasn’t trying to mess with Google – I just wanted to get some good data for business owners to help them avoid a costly mistake.

The good news is that my experiment was wildly successful. Within 3 weeks my Google index was chopped in half and my most prominent pages were replaced in the SERPs with the home-page. I decided I made my point and reversed the tags on September 11th (probably not the best choice of dates, in retrospect).

Almost a month later, and some of my key pages are still gone from the index. These are strong pages with good, natural link profiles. I’ve resubmitted my XML sitemap, submitted a focused sitemap with just those pages and have added new rel-canonicals self-referencing those pages. So far, nothing.

So, embarrassing as it is, I have no option left but to beg the forgiveness of you, the Google Gods. You who are mighty atop your Mountain View, each one better looking and more brilliant than the last, I beseech thee – please look with pity on this mere mortal and grant your bounty upon the following pages that have provoked your disfavor:

[short list of URLs]

Yours in humility,

Dr. Peter J. Meyers ("Dr. Pete")

Lessons Learned

I think the lesson here is pretty straightforward – don’t do this. Of course, you’d never canonicalize your entire site to one page on purpose, but with today’s sitewide headers and CMS systems, it’s shockingly easy to write a header tag that affects your entire site, even across 1000s of pages. I’m not bashing the Canonical Tag as a tool – I think it has some very strategic uses. The problem is that it is one of those rare cases where you can effectively destroy your SEO efforts by changing just one line of code.

With just one 57-character tag, I lost ranking on my most competitive terms and cut my indexed pages and search traffic by more than half. The Canonical Tag is a powerful tool, but use it wisely and plan carefully.

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Posted by randfish

Yesterday, Perfect Market, a company that "helps publishers create value from their online content with little effort and no risk¹" released a study that’s been getting quite a bit of attention. The study analyzes the relative traffic value per visit of several types of content, coming to the conclusion² that "while the Lindsay Lohan sentencing and other celebrity coverage drove significant online traffic for major news publishers, articles about unemployment benefits, the Gulf oil spill, mortgage rates and other serious topics were the top-earning news topics based on advertising revenue per page view."

Coverage included the New York Times’ Traffic Bait Doesn’t Bring Ad Clicks, Columbia Journalism Review’s Celebs are Loud, but Hard News Pays, Nieman Journalism Lab’s Public Interest News Can Be More Valuable to Publishers than Traffic Bait and Search Engine Land’s Hard News Pays More than Chasing Search Trends.

I’m worried for a few reasons:

1. What’s the branding value of those stories? Do they drive up awareness of the publications that authored them? Do they increase return visits?
2. What other actions do those visitors take? Are they more likely to subscribe to an RSS feed? To share those stories on social networks? To get email notifications?
3. Do these stories drive links that then help other, lower link-earning content rank well in search engines? The goal of linkbait, after all, is often to drive branding, links and sharing rather than being directly monetizable. Plenty of consultants on viral content creation even recommend removing ads to drive up sharing and linking activities.

Granted, from a personal perspective, I love the idea that writing about celebrity gossip and other "soft news" isn’t profitable and therefore might be less prevalent in the future. It’s purely opinion, but I suspect that many share my sentiment that the United States’ major media outlets are far too focused on shallow reporting of topics (like those mentioned in the Perfect Market analysis) that deserve far less attention than, say, understanding what caused the mortgage crisis, who’s spending money on elections and why, the success other nations have had in dealing with crime, poverty, drugs, multiculturalism, etc.

However, anytime a skin-deep, single-metric analysis like this makes its way into major publications, it has an effect on content publication that’s not necessarily positive. If executives, editors and journalists start using singular metrics rather than deep analyses of data to make decisions, their publications will suffer and their content and marketing budgets will be misallocated.

If Perfect Market (or another source) could show:

• The value of the links brought in from those stories
• The branding impact of the visits generated
• The value of sharing activities from those visits

I’d be far more inclined to agree with the conclusions the press is reporting.

If you can’t fully/accurately analyze the true lifetime value to your publication of so-called "bait" (and I don’t just mean celebrity-obsessed soft news, but a broader group of creative, traffic-driving pieces), that’s OK. Just don’t presume a single metric like "ad click value" combined with "page views" will give you the whole story. The web is all about providing data, and you’re cheapening your own value when you cut corners to this extent.

BTW – I don’t mean to cast all the blame on Perfect Market – they did some reasonable data analysis and shared the findings. I wish it had included a bit more caveats, but their job is promoting their work. I’m more concerned with how the media treated the story – reporting, exaggerating and not bothering to dig deeper. Just look at the opening lines of the NYTimes piece³:

Sure, articles about Lindsay Lohan’s repeat trips to rehabilitation and Brett Favre’s purported sexual peccadilloes generate loads of reader traffic, but do they actually make decent money for the Web sites that publish them? According to a new analysis, no.

That’s not what the analysis showed. It showed one metric and it’s impact, but it didn’t explore the overall value of the page views, visits and CLTV (Customer Lifetime Value) of the stories it examined. Let’s hope the publishers do a more thorough job and that we, as content creators & marketers, think carefully about how to value the content we create and the traffic we attract.

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Posted by Tom_C

1) Regex for Counting " " and "/"

Regex is awesome. I don’t claim to be amazing at it but there are a few common regex strings I use all the time in my analysis.

Length of Keyword

To quickly filter your keywords report by the length of keyword, I use some regex to count the number of spaces in the keyword like this:

^([^ ]+ ){5,50}[^ ]+$

The above regex searches for keywords that have between 5 and 50 spaces in them. You can also search across a single number as shown below. This image is a search for all keywords with 6 spaces in them for the distilled site (i.e. 7 words):

Depth of Page

Very similar to the above regex, but when I’m looking at top landing pages I use regex like this to count the number of slashes in a URL:

^/([^/]+/){3}[^/]*$

Note that because I’m not a full regex ninja this actually counts those URLs that have 4 slashes in (i.e. n+1). So the following image is showing all traffic to those pages with 5 slashes in them:

Note how useful this search is? Pretty much all of these pages are low quality like pagination or blog pages that have multiple categories assigned. For large sites if you construct the regex correctly this can be a great way to analyse where traffic is landing on the site and identify low quality pages to remove from the index.

If you’re new to regex – this is my goto guide for using regex in Google Analytics (PDF).

2) Check Your Analytics Code Is Correctly Installed

This is a super easy one, but definitely one worth running on any new site you take a look at. SiteScan will crawl your site and check for the analytics code which is pretty nifty. It even intelligently checks for the old and new versions of the GA code. Nice. Unfortunately the free version only checks 100 pages but it’s definitely a solid resource for smaller sites:

Another quick check for correctly installed Google Analytics is to look for referrals from your own domain. Any referral from your own domain indicates that there are pages not correctly tagged (and will even show you which ones!). Nice.

3) 5 Ways to Segment your Funnel

Segmenting your funnel is not something you can do natively in Google Analytics which annoys the hell out of me. I’m hopeful that Google will be adding this feature sometime in the near future. In the meantime, there’s a few ways to segment your funnel:

• ROI Revolution have a method for segmenting on the fly - Doesn’t do custom date ranges
• LunaMetrics have a way of segmenting the funnel using goals. – Won’t let you run across historical data
• Analytics Ninja has a way of segmenting using advanced segments. – Maximum 3 steps at a time
• I give a method using regex and excel here. - takes a bit longer and uses regex and excel
• Paditrack is a tool for doing this - requires some setup and is fairly basic  but gives pretty reports

Why do you care about segmenting your funnel? Well I give a detailed run-down of why this is important over here but hopefully this image should explain itself (the output of segmenting the funnel using my method):

4) Track SEO Variables In Google Analytics

This is a nifty use of custom variables which I recently started using on a few sites. Imagine you’re running a hotels reviews website. Some of your reviews have 100s of reviews and are lovely content-rich pages. But some of your hotels are awaiting their first review. In that case, your hotel page might be very light on content and might only have the name and address of the hotel on the page (which is duplicated on 100s of other sites). Wouldn’t it be nice to be able to segment your Google traffic by how many reviews your hotel page had? Well using page level custom variables this is as easy as the following code:

_gaq.push(['_setCustomVar',          1,                   // This custom var is set to slot #1. Required.          'Num_Reviews',       // The name of the custom variable. Required.          0,                   // Sets the value of "Num_Reviews" to 0. Required.          3                    // Sets the scope to page-level.  Optional.         ]);

You don’t have to limit yourself to just using this for number of reviews, you could look at other factors that you think might be affecting your pages ability to rank and pass those into GA. For example, you could pass the length of the description of a page. Or the number of tweets it has or anything you can think of really!

Learn more about page level custom variables over here.

5) Track Form Abandonment

This one comes from a blog post Duncan wrote a little while back, but I love how simple this is to use and how useful the insight is. Basically, using jquery it becomes very easy to track how far through a form people get. The idea was prompted by Sam’s post from some time ago, but uses events instead of virtual page views.

You should read the full write-up on Duncan’s post but the code looks something like this:

1.  $(document).ready(function() {   2.      var currentPage = jQuery.url.attr("path");  3.      $(':input').blur(function () {  4.          if($(this).val().length > 0){  5.              pageTracker._trackEvent("Form: " + currentPage, "input_exit", $(this).attr('name'));  6.          }   7.      });  8.  });  

Bonus!

While writing this post, one of Dave Naylor’s gang posted about a new interface for in-page analytics which replaces the old site overlay. I’m quite excited about this, I think it paves the way for all kinds of cool things (not least of which is heatmaps as David points out…)

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Posted by randfish

As many of you likely noticed, Linkscape updated its index on Thursday night. New data is now available in the SEOmoz Firefox and Google Chrome toolbars, Open Site Explorer, the classic Linkscape tool and many of our other SEO tools.

This update was, sadly, just over 2 weeks off schedule, primarily due to some hardware failures at Amazon’s EC2 where we run processing on our large link graph to produce the metrics and views for the API.  Although we’ve encountered issues like this in the past, this was one of the larger failures and meant processing had to be restarted several times to update the index. You might be able to read more about the technical details in the near future on our nascent and geektacular Dev Blog.

Index Stats

• Pages: 41,219,038,886 (41 Billion)
• Subdomains: 436,693,488 (436 Million)
• Root Domains: 99,649,652 (99 Million)
• Links: 402,521,240,277 (402 Billion)

 I made some graphs showing a few interesting trends over the past few months in the web’s adoption/use of certain protocols.

The chart above shows how using rel=nofollow on internal links is slowly becoming less popular (though it’s still a majority of use).

This chart’s telling us that rel canonical use has barely grown from June to October (as a percent). In this index, 5.42% of the pages we saw used rel=canonical tags. The datapoint from May (when rel=canonical was on 5.50% of pages we saw) is curious, but I suspect it has more to do with which pages we were choosing to crawl and index vs. an actual shift in usage. It’s a good reminder, though, that unless we see large, sustained shifts across indices comprised of relatively similar URLs, we shouldn’t jump to conclusions. 

The next scheduled update for Linkscape is Nov. 12th (see the Linkscape Calendar page on our API Wiki) and we hope to be in much better shape with hitting that deadline.

Linkscape is also undergoing some serious upgrades over the next 3 months. With our web app launched (and regular upgrades on track), 4 of our 10 engineering folks (Phil, Chas, Bryce & Ben) are going to be working to make Linkscape fresher, faster, more comprehensive and higher quality by January. Expect to see some incremental improvements between now and then, which we’ll report here on the blog.

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Posted by rishil

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

I typically run a couple of a hundred queries a week around SEO long tails to try and capture SEO posts that may not be on my radar, but worth a read. The nature of these change from time to time based on my current interests – after all, I started out as a Small Business SEO and now work at advising Big Brands on SEO. As may be apparent, due to changing interests, many of these queries are around Big Business (or Enterprise) level key phrases. One of these being around budgeting and estimating Return on SEO Investment (aka SEO ROI).

Surprisingly, there aren’t many resources available to businesses to realistically budget their SEO spend, for many reasons, one of the bigger ones in my opinion the difficulty in calculating life time value of a position. After all, it’s not like SEO rankings are like PPC, where every click has a direct attributable and potentially a onetime cost (of coursed based solely on a last click metric. Under other models such as a weighted average model the PPC click has a higher lifetime value than the last click model). So imagine my surprise that that one of these keywords “SEO Budgeting” led me to an eHow article. Now for those of you who may not be familiar with the content farm nature of eHow, you may want to visit my post on content farms.

The article (which I refuse to link to) was written by a company that ,surprise surprise, offers Search Marketing services. Now I don’t want to blast that company – what they do is none of my business. But the information they supply to the general public is. So with the hope that SEOmoz ranks higher for this keyphrase, I would like to dissect their advice and point out some better resources for the SEO planner. 

Difficulty:

The level of difficulty indicated is Easy. I wouldn’t say it’s easy to decide how much money needs to be spent on SEO. There are so many assumptions that need to be made – take for example some of the top line questions you should be asking:

• What is your sites SEO Swot Analysis?
• What are you trying to rank for?
• What is your average return on your current SEO?
• What level of budget do you have available?

I would give difficulty level between Moderate and Difficult, depending on the size of the business to the breadth of keywords that the site is trying to rank for.  

Ways to Gauge Budget:

Once again, in my view this advice fails. The three things that have been advised are:

• base your campaign on a past successful campaign. If you had a past successful campaign, you probably not going to be looking for SEO budgeting advice, considering that you have already run successful SEO.
• “designate a target percentage of total sales you wish to achieve though SEO marketing and work backwards to make this figure a reality.” This is a feasible way to predict future budgeting ONLY if you have run a campaign previously. Ideally you should base your budgets on the opportunity available with reference to your available spend and ROI.

What portion of your budget should SEO be made up off:

Sorry, but that is utter rubbish. Where did those figures come from? Very poor advice. SEO, like any other marketing strategy should be run by either branding decisions where you work with maximum exposure possible) or on measurable ROI.

To explain that a bit further – take for example you have an opportunity to rank for a pot of 4 keywords:

If you had to choose, you would probably go for Green and Yellow widgets, if traffic was your goal right?   Now look at the details below:

A Bit more depth – you have evaluated the cost of acquiring links, writing content etc for those specific keywords, and worked out the cost of ranking that keyword. Now if you had the full £9,500, you would probably go for all those keywords. However, budgets are finite, so you may not have the full £9,500 to spend. So what else do you need?   You need to work out the revenue on those keywords:

Remember, every keyword has its own revenue potential.  Just because a keyword has high volume, doesn’t mean that its value in proportion will be the same as a low volume keyword, in fact, the returns on volume are normally diminishing. Which is why you may want to add one more metric, ROI:

Isn’t that an interesting metric? The keyword with the highest volume has the lowest return, while the third lowest keyword has the highest return. Taking your keyword evaluations this far help you nail down the most profitable keywords that you should be aiming for, which in return gives you a view of getting the right budgets put together.

OK so the big questions are HOW do I work out those figures above?

To start with, you need to work out potential traffic, by estimating it.  Kate has done a brilliant job of showing you how to predict your traffic. I dont think I need to reguritate those examples here . 

The second step is to work out conversion / return – now there isn’t an easy way to do this, but you have three routes – either grab your current conversion by “generic” keywords, and use an estimation (this will probably NOT give you’re the variant ROI’s as in my example above) or segment those generic keywords into behavior patterns. By this I mean you could breakdown your current generic keywords into the type of keyword, and then break that down a step further into the length of the query – rule of thumb experience says that the longer the tail, the closer the user is to the purchase journey. This means you could apply a higher average conversion rate to longer tail – but using your data segmented into keyword type and then into tail, will allow you to get a rough estimate how that mechanic works currently for our site. You can use those figures to estimate the sales.

However the two examples above require you to have some history. What would you do if you didn’t have any history? I would use an isolated PPC test to judge the conversion value of any pot of keywords.  The isolated tests will probably provide you with usable variants of values that you can use for conversion.  Rand covered this idea way back as part of his Head Smacking tips – Using PPC as an indicator of Success. 

How do you work out the cost of ranking for any single keyword? This is probably one of the more difficult questions to answer. However, some of the key indicators of cost of any given keyword can be derived from working backwards from your Competitor audits. I think the biggest variable outlay for any SEO ranking is the cost of acquiring anchor based text links for the given keyword. The second variable is content rewrite or content enhancement, which probably is lower than the link cost. To these variable costs, I would proportion other SEO “fixed” costs such as any related fees, overall other developments impacting SEO, time of staff spent on SEO improvements etc. If anyone has any real good ideas to estimate the cost of ranking, please share!

Summary

Unfortunately, if you kept up with my ramblings so far, you will notice that there isn’t a fixed art of estimating the budget, but there are some arbitrary estimates that you can use to work out the portioning of spend to SEO. The key thing in my opinion, is to invest in ROI while staying within your allocated budgets.  

Disclaimer

This is my methodology or route to working our SEO Budgets, please make sure that whatever decision you make is best catered for your individual example. Also, the examples given are very simple views of setting keyword base targets – carrying out this exercise for 1000′s of keywords isnt exactly easy, but it is worth the time and effort.

Resources:  

• Jamie wrote a very useful post on convincing your boss to spend money on CRO. He includes a link to a very useful spreadsheet, which you can easily adapt to work out SEO ROI instead of CRO ROI.
• This is a very useful PPC ROI calculator, which yet again be adapted to work out your SEO ROI figures…
• Gab specializes in SEO ROI, and he gives us 8 steps to forecast ROI from SEO.
• The SEO Slingshot blog has a two part article on evaluating the Value of SEO
• Last but not least, Aaron’s SEOBook blog looks at the Value of Google Rankings in some serious Macroeconomic detail. 

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Posted by Aaron Wheeler

A few weeks ago Danny showed us some of the basics for video SEO, a medium that may not initially seem valuable for SEO purposes. Well, Danny dispelled that illusion swiftly, with a little help from his friend Doc Brown. This week, Danny’s out there alone but still manages to show us that words aren’t all they’re cracked up to be; videos can yield some great SEO value, too. Besides giving us proven and actionable suggestions, Danny also postulates on some experimental and potential ways to optimize images that may prove useful now and in the future.

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Video Transcription

Hello, everybody. My name is Danny Dover. I work here at SEOmoz as the lead SEO. On today’s Whiteboard Friday, I’m going to tell you about the basics of image SEO. We found, when we were doing correlation analysis, that images and specifically the alt text that’s inside of them are a remarkably well-correlated metric for SEO. Besides just being useful for people, images are also, it turns out, useful for search engines. I think part of the reason behind that is that pages that are well developed tend to also have images on them because it helps portray information in a way that textual based content can’t do.

Let me go over some of the important factors with image SEO. Number one, I already mentioned this a little bit, is alt text. Alt text is the text that you provide for an image in case it can’t be displayed. Maybe the image is gone or maybe someone is using a program that can’t display images. This is the text that takes it place. So it makes a lot of sense from an SEO perspective that this metric is going to be important because it’s the information you tell the search engines and other technologies what the image represents. With these, I recommend keeping them below about 140 characters. It’s a rough rule of thumb. Also, have them be descriptive and in line with what you’re trying to target for that page.

Number two is the file name. This works off the exact same principles. The file name is also information you give directly to the search engines and to other technologies to identify what the information is about. I would gander, if you will, that the file name is probably a rougher signal than the alt text. Alt text, from my experience, when it’s there, which is not all the time, in fact, alt text is not included many times which is bad for SEO. But when it is included, it tends to be a clearer signal than a file name which a lot of times is just algorithmically generated by the timestamp, so it’s just a bunch of numbers.

Number three is the surrounding text. I think a lot of people don’t think about this when they’re thinking about image SEO. The text around an image tells a lot about the image itself. This makes sense, right? You’ll see a lot of times where images will be on a blog post and you’ll have a caption describing the image. This is just another signal telling the search engine and other people and technologies what it is this image is about. The surrounding text, and that can either be a caption, like you’ve seen traditionally, or it can just be the paragraphs around the image. A lot of times an image will be used to supplement what the textual information is talking about. So the surrounding text is very important.

Fourth, as with all SEO, inbound links are important. It wouldn’t necessarily be inbound links to the image URL, although it could be, but what I mean in this context is links going to the page that has the image embedded on it. Just like in normal SEO, the anchor text of those inbound links and where they’re coming from and how many of them are all really important factors for image SEO and then SEO in general.

Last is number five which is human categorization. The search engines, especially at the beginning when they were developing this image recognition software, used humans. They would hire people and they’d say, "Label this." Google was semi-famous for creating this game, Google Image Labeler, which I think you can still find online, where it would show you an image of, say, an apple. They would ask you in Family Feud style, which is a game show here in the States, to list words that are associated with that object. You’d say something like apple, and you’d earn points if someone else also said apple. Maybe it’s red, Fuji, or Grandma Smith, or whatever it is. So other words that are associated with the image. And that way they could train their software to start to understand what general shapes and ideas mean within images.

On the other side here, I have some more theoretical things that search engines may be using, while the things on this side are the things that we know they’re using. We’ve heard search engineers talk about this. We’ve seen direct evidence. These are things that I think you should pay attention to but probably just going forward. It’s more just for your knowledge rather than for you to use on your day to day.

The first one is OCR. OCR stands for optical character recognition. It’s a very established software. It comes in a lot of Adobe products. You can get it in lots of places. What it does is it scans an image and can identify characters in it, characters like letters or numbers or spaces or whatever. From that, you can take actual text out of images. Again, this is a very popular software. It seems very likely to me that search engines are using this at least to some degree. It would be very costly for them from a resource perspective to use on every image on the Internet, but it would certainly make sense if they were using it on some or at least playing around with the technology.

Number two is color analysis. It’s very easy from a development perspective to identify at least one color, maybe the primary color, within an image. You pick a pixel and you see what the hex code or whatever it is that you’re measuring that on, it will be based on file type. It’s pretty easy to get a general idea of what the color of an image is. This is helpful from a design standpoint if you’re looking for certain color themes that go with each other or color patterns. Now we’ve seen this actually in the SERPs, so if you go to Google image search, you can see now, and Bing actually had this first, you can go to the image SERPs and you can actually pick to see only images that are of a certain color. Black and white is the obvious one, but then other colors as well.

Number three is file size and type. This one, I think, is more all about the extreme. If the image is ridiculously big, it’s probably not going to get indexed just because the search engines don’t want to spend the resources on that. The exception to that would be if it’s ridiculously well linked to also. It’s about finding these outliers. You probably don’t want to have an image that’s really, really big. It’s probably not going to get indexed. Again, I think what it really comes down to is this is hurtful for users also because they’re going to have to spend time downloading that. If bandwidth is a concern, they’re probably going to click away to begin with. Image size and along with image type, the standard image things are all probably fine for Google.

I’ve heard just a rough rumor here that JPEG is preferred, but honestly GIFs and PNGs and all those other things are probably fine. I would not worry about those aspects. Only worry about it if you’re using obscure file formats, which you shouldn’t be doing to begin with.

The last one on here is the other images on the page. This is twofold. The first part being the other images on the page are likely related to the given image and that’s because they’re on the same page. Right? The other part, and I see this happen a lot especially with bigger clients, is when you put lots and lots of images on one page, like an image gallery, those pages tend to be very hard to get indexed. The reason for that is there’s not a lot unique textual content. A lot of times it’s just overwhelming to users. It doesn’t provide a lot of benefit in a search result.

That’s all the time I got today. I appreciate you listening to this. Please feel free to ask questions in the comments below. Thank you.

Video transcription by SpeechPad.com

Follow Danny on Twitter! Even more to your benefit, follow SEOmoz!

Also, you can follow me, Aaron.

If you have any tips or advice that you’ve learned along the way we’d love to hear about it in the comments below. Post your comment and be heard!

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