Majestic SEO’s new metrics are named Flow Metrics and are grouped into two categories; trust flow and citation flow.

• Citation Flow is a number of predicting how influential a URL might be based on how many sites link to it.
• Trust Flow is a number predicting how trustworthy a page is based on how trustworthy sites tend to link to trustworthy neighbors.

What Do Flow Metrics Look Like?

Let’s compare Search Engine Land to Search Engine Roundtable to Google:

Keep in mind, the values for citation and trust flow are between 0 and 100, where Google is a 99 on both, Search Engine Land has a citation flow of 75 and trust flow of 64 and Search Engine Roundtable has a citation flow of 69 and trust flow of 43.

Let’s visualize how this looks on a chart. Majestic SEO explains that links with high Citation flow end up more to the right and links with more Trust flow end up higher on the chart.

Search Engine Land:

Search Engine Roundtable:

Google:

I asked Branko Rihtman (aka @neyne) what he thinks of the new metrics and he was very pleased. He said it is a huge improvement over Majestic SEO’s AC rank metric and says these new metrics “correlates to PageRrank much better than anything” else. Overall Branko feels “it provides a much better measurement of perceived quality.”

For more details, see Majestic SEO’s blog.

“Let’s say this is your first day at Urbanspoon and I show you the following data. We’ve just launched an A/B test of that I’d like you to evaluate. [The example can be almost anything you want to test different results for – from almost any search element, PPC campaigns, email subject lines etc. In this case, I’m using a PPC example.] Imagine you are running two different ads on a campaign with 50 kewords. We’ve been running Ad A for a while and have 17,235 impressions and 272 clicks. I started running Ad B last week and that has received 41 clicks on 2,253 impressions. What would you do?”

I’m looking for an answer that goes beyond demonstration of pre-algebra skills and rudimentary familiarity with a calculator.

Obvious answers include splitting up 50 keywords into different groups, looking down stream to see differences in conversion rates, and technical answers around quality score. But what I’m really looking for is a theoretical understanding of statistics and the interplay between sample sizes, variability and confidence intervals.

Answers to the above theoretical question usually fall into one of three buckets:

1. I’d run as more Ad B’s so our impressions are equal and then compare the click through rates.  #FAIL
2. I’d run the Ads longer, you need at least 3 weeks of data to make a decision.  #FAIL
3. Ad B is better b/c the click through rate is higher.  #FAIL “and thanks for taking the time to talk with me, our HR department will be in touch . . .”

Turns out, you don’t need to have an equal number of impressions or a set amount of time to run this analysis. It’s actually a fairly simple concept that can ultimately then be mathematically defined:

The greater the difference between your A and B samples (drawn randomly from the same pool) the smaller the size your test needs to be in order to confidently assert that one performs better than another. Example – if we wanted to test if men were taller than women and we measured 100 men and 100 women  and the men averaged 7 feet tall and the women averaged 4 feet tall, you’d be fairly confident saying that men are taller than women.

Conversely, if the difference was 3 inches instead of 3 feet, you’d probably want to measure more men and women before confidently asserting men are taller than women.

In fact, it’s possible that your sample was misleading – as a population women are really taller than men, but your sample didn’t bear that out. This level of confidence can be mathematically expressed as a percentage – I’m 95% certain that A is better than B. (Meaning there is a 5% chance, or 1 out of every 20 times where you’ll unwittingly pick the underperformer.) The greater the level of confidence you want, the larger the sample size you need.

All of this can be calculated with innumerable free online tools. Larger sophisticated systems like Adwords and big ESPs build this statistical testing in to their testing methodology – but it’s easy for do-it-yourselfers too.

I like a tool called AB Tester, which allows you to measure up to three alternatives compared to a benchmark:

In the results above, I’ve done the analysis for our question . . . The “Confidence” column tells me there’s a 79.19% chance that B is better than our control A.

Watch how this Confidence grows when we add a zero to each column – keeping the CTR the same but increasing the sample size:

By increasing the size of the test tenfold, now there’s only 0.5% that A is really better than B.

Let’s go from theoretical to real. Here are results from an email test we did for our Hawaiian getaway promotion to Ludobites 9. (It’s over now, sorry.)

The first data column is sends, then delivered, then opens, then clicks. Assume we want to test three different content types to three different cities (now admittedly this is not a random sample – maybe people in San Francisco respond differently to content . . . )

Take the data from the 3/6 send and plug it in to A/B Tester. Note I’m comparing the CTR from Opened emails to isolate content as an impact to click through rate. Also note that while the sample sizes are similar, they don’t have to be the same.

My best performer here is the San Francisco content at a 5.5% CTR. I use that as a control and plug the other two into AB Tester:

This tells me there’s a 3.3% likelihood that the LA content might really outperform the winner (San Francisco). Additionally, there’s a 23.5% chance Seattle content is better than our “winner”. More testing necessary . . . .

Germinating your Keyword Seeds

Using the example of Artisan Construction Services again (thanks!), we’ll walk through the process that I use to grow a single seed.

In this case, let’s use “deck”. To begin with, I want to think of the types of decks my client might build:

• Composite
• Wood
• PVC
• Hardwood
• Cedar

Then I am going to use what we learned about the client already to develop a list of verb modifiers:

• Build
• Building
• Builder
• Replace
• Replacement
• Replacing
• Construct (wait, I’m not going to use that one, see below)
• Constructing (not this one either)
• Construction

I was able to generate the list above based on conversation with the client and a working knowledge of the deck building industry. If you’re not familiar with your client’s industry, you may want to research this more – look at forums, the discussions part of Google Search, and Yahoo Answers, for example.

Categorize Keywords By Intent

After I build my list of modifiers, I go back and think about intent. I’m not going to use “construct” or “constructing” as modifiers because I think the intent behind them is DIY. Construction probably has some DIY in it as well, but it might glean several useful keywords. I’ll make a note to filter those after the fact.

By the way, don’t go above 10 total modifiers; it gets to be too much to work with.

Since the focus of this post is how-to research, I won’t go into all the modifier sets I’d use, but normally, after I did this set, I’d go back and do one for decking and maybe also for specific brands (Azek, Nichiha, etc.) When you do this for yourself, keep in mind that de-duplication will be important (see below).

Concatenation

(Yes, it is a word, and a useful one for search!)

Next, I use a tool like the one found here to concatenate (that means put together) the keyword seed with the modifiers. I prefer not to use the “concatenate” function in Excel, because sometimes it is limiting. I also find this formula to be infinitely easier to remember and manipulate:

Let’s say you have the following cells:

You want to get the phrase [build composite deck] out of this. Use the formula A1&“ ”&B1&“ ”&C1.

The &“ ”& just adds a space between the two cells’ values. If you want to lock down a value, as in the list below, so that you could get the phrase [building wooden deck]:

Use the formula the same way as above, but lock out C1 with dollar signs: A2&“ ”&B2&“ ”&$C$1.

You can “lock out” either a Row or a Column value.

Ok. If you haven’t already clicked to open the spreadsheet I mentioned above, do it now. This will show you my initial list of keywords based on the modifiers I listed above.

Notice that because I didn’t use all 10 possible modifiers, my actual keyword list ends at line 40. Just don’t use the incomplete keywords at the bottom.

Getting Search Frequencies

Go to the Google Keyword Tool. Login if you have an Adwords account; it saves you from having to enter the captcha for each search.

Cut and paste the first section of keywords from your list into the keywords field. Make sure the box that says “only show ideas closely related to my search terms” is NOT checked. Click the box on the left for “exact match” (and turn off “broad”).

Google Keyword Tool Settings

After you click “Search” and get a list of results, don’t even look at them. Just click the “download – all – CSV for Excel” link.

Repeat these steps for each modifier, but when you get the CSV’s for the other modifiers, paste them into the original Excel sheet so that you have one big list of keywords in one Excel sheet.

Once you have your full list, you need to de-duplicate.

Steps to De-duplicate

1. Make sure all of your data is in the same format. I like to find and replace (CTRL-h) all of the brackets with nothing. Find=[, Replace=nothing (leave it blank). Then repeat with “]”.
2. Sort by global monthly searches ascending. Delete any rows where global monthly searches are “0” or “-“.
3. Now sort by Keyword A-Z.
4. Go to Data – Remove Duplicates. Specify (check) only the “Keyword” Column. If you’re using an older version of Excel, it’s a little harder to do this – see these instructions to de-duplicate.

Removing 0-Value Keywords

Now we’re going to look for keyword patterns. The first obvious one I see is “how to”. Anyone using the word “how” is not interested in paying a contractor (at least not at this time).

1. Use the following formula in Column E: =FIND(“how”,A2). This will “find” anywhere in A2 that has the text string “how” and return the character position it starts at. Trust me, this is useful.
2. Now copy/paste the formula all the way down your list.
3. Select Col E and copy it.
4. Paste special for values only in Col F.
5. Sort Col F. ascending.

This brings all of the keywords with “how” to the top of the list. Simply delete out these rows, and then look for another pattern.

Hint: to easily look for another pattern, just select all of Col E and find/replace the string you’re looking for (example “how”) with the next one you need (example “design”).

When you think you’ve removed as much as you can, you will probably be down to under 100 keywords. These are the ones you really care about. Take just a minute to scroll through the completed list to make sure you didn’t miss anything and delete it.

Rinse and repeat as necessary. It takes a lot less time to do it than to explain it. You may also enjoy these easy PC/Excel shortcuts:

Shift+space bar = select an entire row

CTRL+- (that’s the minus sign) = delete an entire row

CTRL+h = open the find and replace window

ALT+e, then s, then v = copy and paste special for values only

Next time: Categorizing and mapping keywords.

Photo (http://www.women24.com/HomeAndAway/Gardening/Growing-seedlings-20090216) from Abalimi Bezekhaya.

Feeds define the data that may be changing regularly, like e-commerce inventory, store locations, etc. Readers may be able to generalize these terms to apply to existing rules-based tool if one is currently in use. First, a word about where this approach fits in to the broader picture.

The Broader Landscape of Managing Keywords & Negatives

There are many approaches for discovering keywords and adding them to a PPC Account, and many tools to support the various approaches. This article presents a rules-based approach and some tools that support it.

A good rules-based approach such as this often separates homegrown PPC Accounts from their more-mature competitors. This approach could be part of an overall strategy for managing Keywords and Negatives throughout the lifetime of a PPC account.

A Rules-Based Approach

A good rules-based approach is part of an overall Keywords and Negatives strategy. Most advertisers know how to manually add Keywords, many Advertisers know how to effectively use Negatives, and even How to Use Search Term View.

Quite often, efforts to add keywords in this way end up mostly manual, and occur something like: “Well, what about THIS keyword, or THAT one? Yeah, they would be good… But do don’t we have to go back and add that every where else, too?”

Good advertisers will often take that one step further and actually measure the value of their efforts by monitoring the value of those keywords over time. Some Advertisers know how to use other keyword research and generation tools such as SpyFu, Compete.com, or Wordstream.com and the like.

Some advertisers mine their SEO search queries for PPC keywords (and vice-versa), and some advertisers define rules for their automated tools that automatically add keywords based on performance (e.g.: for every broad match keyword with more than 100 clicks in a month, add a phrase match for the same keyword), or remove keywords based on lack of traffic (e.g.: remove keywords that have zero impressions after 3 months of eligibility). There are many components to managing Keywords and Negatives.

These are all healthy, productive components of a complete Keywords and Negatives strategy. A good rules-based approach helps advertisers leverage the good ideas by building them out across accounts.

• Dictionaries: Define the various groups of words that are relevant to your account
• Grammar: Define how to combine Dictionaries into actual Keywords, within your Account structure of Accounts, Campaigns, and AdGroups
• Feeds: Define keywords that change over time, as with available inventory, prices, etc.

Dictionaries

Consider a mythical PPC client, the ACME Bike Store. A previous Search Engine Land article demonstrating Stone Temple’s PPC Permutator uses a similar example. Today’s article builds on the ideas in the Permutator article. ACME’s Grammar might look like this:

That is to say, the Dictionary defines [terrain], [bike], and [product], each as a set of tokens that can be used in a Keyword Grammar to create Keywords.

Grammar

ACME’s Grammar might look like this:

That is to say, the Grammar defines meaningful ways to combine the tokens defined in the Dictionary, to create Keywords. For example, the combination of the Dictionary and Grammar so far would produce:

The list expands quickly from here. This is one of the values of this approach: Generate a lot of useful Keywords for a minimum of creative effort. However, this benefit can also be a drawback.

It is not productive to produce an excess of keywords that generate zero impressions. It can actually be counter-productive, and negatively impact Quality Score. Managing Zero Impression Keywords and Quality Score are both integral parts of any complete PPC effort, and beyond the scope of this entry.

Regardless, there is no need to over-tax those processes by wantonly adding keywords.

Grammar With Match Types

A Grammar can also define the Match Types for each Grammar entry. By defining the Match Types for each entry, the Advertiser can avoid repeating the entry for each Match Type wanted. The above Grammar definition might now look like this:

Advertisers should choose the right combination of Match Types to create a list that is full of keywords that are specific enough, but not so specific that the Grammar generates an excess of Keywords with zero traffic.

That brings up another good practice beyond the scope of this article, which would be to devise a system for automatically expanding Keywords on new Match Types when they get enough search volume. Another day…

Grammar With Modified Broad Match Support

Advertisers may choose to support “Modified”, as in the example below. One way to do that is to include the “+” sign wherever it might apply in the Grammar definition for the Keyword. Keywords being added as “Modified” retain the “+” in the output, and any output for other Match Types simply remove them.

This way, the Grammar is expressive enough to address the requirements of “Modified”, without requiring additional entries. That is one worthy goal when designing these tools; minimize the amount of effort required of the Advertiser, by maximizing the expressive power and flexibility of the Grammar Syntax.

For the sake of simplicity, the remainder of this article will use examples without the “+”.

Grammar With Negative Match Types

Similarly, a Grammar can define Negative Keywords and Negative Match Types. This helps to craft Airtight AdGroups, which is a highly recommended best practice.

For example, an Airtight AdGroup approach would prevent searches for “mountain bike” from matching for the AdGroup containing the Keyword “mountain bike tires.” To handle that case, add a Negative Exact Match “mountain bike” to the “mountain bike tires” AdGroup.

Grammar With Account Structure

It will be necessary to specify where to put all of these Keywords in the PPC Account. This is one of the final pieces needed for a complete Keyword Grammar: Account, Campaign, and AdGroup.

In addition to naming the structure elements, using a good naming convention will be valuable for many reasons; one convention to consider would be to name the group from the Dictionary, then embed the actual token used within parenthesis.

Example: Group(token), as in the table below.

The Grammar definition above defines a new AdGroup for each token defined in the Dictionary for [terrain] and [product], e.g.:

• Terrain(mountain) Product(tire)
• Terrain(mountain) Product(tube)
• Terrain(road) Product(tire)
• etc.

Defining a naming convention enables the Advertiser to report or take action in bulk based on the name of the Account Structure elements, such as Campaign and ADGroup. Example: generate a report showing the performance for all “Product” AdGroups, vs. other AdGroups to find out if customers convert differently when they have a specific product in mind.

Advanced & Optional Considerations For Grammars

There are some optional components to include in this approach. Named Sets allow advertisers to define specific groups within the Dictionary. For example, a Grammar Entry for [product:tire], which might output the set of keywords {tire, tires}.

Similarly, advertisers could implement Named Sets with Magic Words. The “Each” Magic Word directs the Grammar to expand the Grammar Entry once for “each” token in the Dictionary. Advertisers could consider more complex rules, such as [product:singles]. This could expand one line for each of the Dictionary entries in the “product” group that are single words (vs. plurals).

One worthy enrichment is the “Other” Magic Word. This is really useful for Negatives, allowing the addition of every “Other” token the in Dictionary as a Negative. For example, add a Manufacturer(Puffy) AdGroup, and add a {Manufacturer:Other} Keyword as a Negative Phrase Match to prevent this AdGroup from picking up searches from any other Manufacturer.

Advertisers can enrich the Dictionary with whatever meta-data works in a given application, and use Syntax in the Grammar, like Named Sets and Magic Words, to consume that meta-data. The simplest version of these special cases that extend the capabilities beyond the Dictionary is the use of “raw text.”

Grammar Special Case: “Raw Text”

Grammars should be capable of using “raw text” to create names, as with “ACME Bikes” to define the Account Names in the Grammar above. Similarly, Advertisers could use “raw text” in a Keyword definition to combine with Dictionary tokens, or in even in lieu of them.

A good time to use this is when starting with an existing keyword list. Just add the current Keywords do the Grammar, and then continue to add new definitions using the Dictionary. This way the Grammar describes an entire Account structure, which can be quite valuable.

Feeds

Use Feeds to define data that changes over time, such as available inventory. For example, ACME carries Puffy, Hwinn, and sometimes Sianchi bikes. A Feed might contain the following data:

Using Feeds In A Grammar

In order for a Grammar to consume the Feed above, the tool needs to understand a Syntax to express it. One could use curly-braces, e.g.: {Manufacturer}, {Category}, etc.

If Named Sets and Magic Words are implemented, consider enabling them to work with Feed elements as well. This provides the ability to expand Feed-sourced elements in a Grammar, like so:

Separate Grammar & Feed

Separating Dictionaries and Feeds provides the flexibility to adjust one while still leveraging the other. For example, an Advertiser can add a new Bike synonym to the Dictionary.

The next time the Account is generated, the Grammar will expand the new Bike-derived keywords for everything in the feed. Another example would be to add or remove a particular Manufacturer. The next time the Account is generated, the Manufacturer would be added or removed, respectively.

One note on scalability and the underlying implementation. Depending on the application, Advertisers may choose to expand the Dictionary first, or expand the Grammar first. Either way, the results should be deduped. Consider which approach will minimize the computing resources needed to address the exponential growth of the output.

Finally, it is worth noting that a similar approach, and much of the same data, can be used to produce Ads, as well as much of the rest of the information required to build a complete PPC Account.

A similar approach works for adding Geo-Targeting, with {lat} and {lon} for each {Location} provided in the Feed, and consumed in a Campaign Grammar supporting geo-targeting settings. The approach can be expanded to include whatever is needed in a given environment.

Build Your Own, Or Use An Existing Tool

This rules-based approach to Keyword generation, using Dictionaries, Grammars, and Feeds, provides a practical means of powerfully expressing new Keyword ideas into massively scaled PPC Account buildouts. The approach was presented generically in order to provide a basis for Advertisers to incorporate the ideas into tools and processes.

There are also commercial PPC tools available that already incorporate their own versions of some of the same concepts. Kenshoo offers “Real Time Campaigns”. Enterprise Travel advertisers have been using similar home-grown systems for almost a decade now. Of course Stone Temple offers The Permutator for free, and we have our own internal tools we use during Client engagements (Disclaimer: The Author is associated with Stone Temple Consulting.)

Stone Temple has built accounts with 10s of millions of Keywords, Negatives, and Ads for a client with over 800 locations throughout the United States, and uses the same approach to manage Keywords with the changing inventories of various e-commerce clients’.

These ideas can be implemented readily using home-brew or off-the-shelf tools, or by engaging with Consultants who have access to them. On a personal note, I eagerly anticipate the day when Search Engines adopt these ideas and make them available to Advertisers.

How would a rules-based Keyword Generation approach impact your PPC efforts?

To begin with, you should have three lists of keyword-types (I call them seeds):

Elements to include in keyword research

1. Seeds most important to your clients (note that these may include jargon and industry-specific terms that need further research)
2. Seeds that accurately describe the business (these would be your own layman’s terms for what this client does)
3. Seeds that are not relevant or core to your client’s business

I like to refer to these as seeds because they are a seed of an idea that could grow into giant “trees” of information and possibilities.

There’s no need at this point to distinguish between “deck” and “decking” for example, and this is a mistake SEOs often make; trying to narrow the field too much too early.

Let’s dive into each of these a little more deeply using an example of a client I did work for: Artisan Construction Services.

Note that all of these lists have far more than 2-3 keywords on them, but for purposes of example, I’ve simplified them. This client is a local (to Raleigh, NC) remodeling company that specializes in building decks and screened porches and remodeling kitchens and bathrooms. (Those are my own words for List Two).

The owner of the company, when asked to describe the product in his own words, said:

“We provide decking, siding and window replacement, and interior remodeling.”

Seeds most important to the client (based on the above description and the keywords he mentioned) are decking, siding, windows and interior remodeling. This would be List One above.

Seeds that aren’t relevant (List Three above) are things the client prefers not to do or sub-contracts out, such as roofing (says he can never do it as cheaply as professional roofers), plumbing (he hates it) and highly specialized design work like tile inlays. He’s also not a licensed electrician. So these are keyword seeds to avoid.

Example of Keyword Seed Lists

List One

List One is based on jargon, and requires further research. The first thing I do with keywords like this is to look at competitors’ websites. I’ve gotten a list of competitors from the client that I’ll research, and I’ll also put these terms into Google or Bing and look at the sites that come up in the results (I’ll localize to Raleigh, NC so that I’m getting the most accurate set of competitors).

Reviewing these sites will give me more seeds to research based on that jargon. In this case, I found specific types of decking, such as composite and pressure-treated, and I found that many competitors also refer to screened porches as sunrooms or patios (which are slightly different, but may cover more potential customers).

One additional thing the client told me is that customers often aren’t sure of what they want until they call him in for an estimate, so I’m keeping this in mind. Also during my research, I found another competitor in search that wasn’t mentioned as a major competitor. I’ll put this on a list of things to ask the client about in our next meeting.

Example of List One Expansion based on Competitors research

Next, I’ll look at how customers are actually referring to the different products and services.

I’ll use the “related searches” area at the bottom of Google’s SERPs, Google Insights to look at trends, and the “Discussions” search option (click “More” under “Search” on the left side of a Google SERP page).

Based on what I found here, I’ve learned that many people are asking what the differences are between screened porches and sunrooms, as well as that they’re sometimes referred to as lanais or three-season porches. I’ll add these seeds to my research.

I also learned that many people are interested in enclosing an existing deck into a screened porch, or “winterizing” a screened porch. More seeds for my research.

To review, I’ve taken the keyword seeds [screened porch], [patio], and [sunroom] and added:

• enclosing deck
• winterizing porch
• lanai
• three-season porch

These are all things that my client’s customers are looking for that his competitors aren’t servicing. They should be easy wins.

Example of List Two expansion based on Google "Discussions"

List Two

I can research List Two in much the same way I did List One. I’ll add these seeds to the research as well.

List Three

List Three is a little different from the others. I won’t add these as seeds to my research, but I will save them for the elimination and refinement process later.

This is where instinct and experience becomes particularly useful, as it’s likely that I can take any list of keywords to avoid and expand it on instinct.

For example, based on what I know of this client, he already wants to avoid roofing, plumbing, tile, and electrical. But here are a few more statements I jotted down at our meeting that give me more clues:

• “I’m not the best priced contractor out there, because I don’t hire any undocumented workers and I pay my taxes. But I am very experienced and my clients are always happy with my work.”

Now I know I need to avoid [cheap], [free], [low-cost], [best priced], and other keywords like that. [Quality], [experience] and [ethical] are possible modifiers that are allowed.

• “I prefer to work with composite materials rather than pressure-treated lumber for decks. It’s much higher quality and creates a nicer finished product.”

So it’s a good idea to focus on any searches asking for the differences between those materials. Also I’ll probably weight the research more heavily to different types and brands of composite materials.

Another note I’m jotting down from this statement is to suggest the client create a page that discusses the pros and cons of composite vs. pressure-treated materials.

• “A lot of customers get a quote from a company like SEARS home improvement when they’re thinking about doing a remodeling project. This makes it tough for me because the materials that SEARS uses are limited to less-expensive ones. It helps me a lot if I can get a sense of a client’s budget beforehand; a single project can vary by thousands of dollars depending on the materials used. But of course, nicer materials create a nicer finished project.”

I’m not exactly sure what I could take from this, but there are likely to be a lot of keywords related to home improvement and/or SEARS.

I’ll be careful of those keywords and use something like Google Insights to determine if those trend higher at a certain time of year. I might even put them into a tool like ComScore to see if I can determine if people who search for [home improvement] related terms are in a lower income bracket. Of course, I also know I’ll have to avoid any keywords having to do with the television program of the same name.

Example of expansion of List Three based on notes from the client meeting

This is just the tip of the iceberg for keyword research. The proverbial “rabbit hole” can get very deep sometimes, so it’s important to make good decisions about which keywords to expand and which to keep at surface level.

I’m sure at this point, you’re wondering why I haven’t mentioned Google’s Keyword Frequency Tool. Researching search frequency can be very useful, especially in determining how far to expand a certain keyword seed. For example, I found almost immediately that [lanai] has very low search frequency. So I didn’t spend a lot of time on it.

Conversely, I found that [enclosing deck] is actually quite large, especially when viewed through Google Insights in the spring and summer months, localized to North Carolina.

Ultimately, I’ll put all of these keyword seeds into the Google Keyword Tool to find the most highly searched combinations of keywords and an overall estimate of the search frequency of one service (decks) over another (window replacement). This will help me guide the client on what content should be created for the website.

I prefer to do most of the research in the manner discussed above, and then use search frequency to refine, categorize and prioritize it. I have certain tools and formulas that I use to do that. Next time, I’ll give you these tools and explain how to refine what you’ve found and present it to your client.

Thanks to Artisan Construction for allowing me to use them as an example.

It is possible to properly attribute blekko search traffic referrals by implementing advanced Web analytics configuration techniques.

Is the extra effort worth the trouble? Certainly investors are betting on blekko. In late September, blekko received an addition $30 million investment, half of which was from Russian search engine Yandex.

Why Web Analytics Systems Can’t See Blekko’s Search Keywords

The underlying difficulty many Web analytics systems encounter in recognizing blekko as a search engine is due to a reliance on search engines to specify the search keywords in the request for the destination webpage by using parameters in the referrer URL.

Unfortunately, blekko deviates from what has historically been the de facto standard for transmission of search query information. In the absence of advanced tool configuration, traffic from blekko will show up in a referring sites report instead of an organic search traffic report.

While this scenario is better than nothing, it makes keyword analysis difficult. Knowledge and analysis of search query keywords is a key element of search marketing as a user’s search keywords express their intent when selecting a site from a listing of search results.

In the following discussion, Google Analytics will be used to illustrate solutions, but the same data transformation logic can be applied to other tools as well. The technically inclined are encouraged to read on. Everyone else might want to pass this article on to their technical staff for analysis and implementation.

Deconstructing & “Fixing” The Blekko Search Referrer

A typical blekko referrer URL contains blekko’s domain name, the parameter ws and the user query. Spaces between multiple query words are replaced with plus signs.

In the following example, the user’s search keywords stop+blekko+indexing follow http://blekko.com/ws/ in the search engine results page URL, visible in the browser address bar.

Figure 1: A blekko search result with user keywords in the result URL

Unfortunately, this isn’t in the form most Web analytics systems were designed to recognize when processing organic search traffic.

The URL piece ws/ needs to be expressed as a parameter, in the format name = value, i.e. ?ws=stop+blekko+indexing.

Thus: http://blekko.com/ws/stop+blekko+indexing needs to be transformed into http://blekko.com/?ws=stop+blekko+indexing

Two Possible Solutions To Solving The blekko Attribution Problem

There are two potential solutions to proper recognition of blekko as a search engine by Web analytics systems. The first approach is to modify the raw data sent to the Web analytics data collection system. The second is to transform the data collected by the Web analytics system before it is processed for reporting.

Option 1: Modify The Search Referrer Sent To Google Analytics

The blekko referrer URL needs to be transformed into the de facto standard format which includes the user query using URL parameters, e.g. ?query=keywords, a format recognized by most Web analytics systems. JavaScript is well suited to this type of task.

The HTML standard doesn’t allow direct manipulation of the document (page) referrer, but the referrer sent to the Google Analytics data collection system can be changed by assigning the modified value to a special parameter, _setReferrerOverride.

In the asynchronous Google Analytics tracking code which follows, the code in bold will transform blekko referrer URLs and assign the result to _setReferrerOverride.

The code:

• assigns the page referrer to the variable blekref
• tests to see if blekref contains the string http://blekko.com/ws/
  • if so, tests to see if http://blekko.com/ws/already contains URL parameters, which start with a ?
    • if so, substitutes the ? with a & which indicates successive URL parameters
  • substitutes ws/ with ?ws=
  • places the modified URL in the Google Analytics variable _setReferrerOverride

<script type="text/javascript"> var _gaq = _gaq || []; _gaq.push(['_setAccount','UA-xxxxx-1']); var blekref = document.referrer ; if (~blekref.indexOf('http://blekko.com/ws/')) { if (~blekref.indexOf('?')) { blekref = document.referrer.replace('?','&'); } blekref = blekref.replace('/ws/', '/?ws='); _gaq.push(['_setReferrerOverride',blekref]); } _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); </script>

Note: this code will need to be modified if blekko changes their URLs, such as using www.blekko.com in addition to blekko.com.

As with any code changes, do be sure to consult current Google documentation and fully test changes before putting them in production. JavaScript errors in a page can stop successive JavaScript from being executed. No warranties are provided, implied or otherwise!

Tell Google Analytics How To Recognize blekko As A Search Engine

Once blekko’s referrer string has been transformed into the standard query parameter = keywords format, the second and final step is to add blekko to Google Analytics’ list of known search engines.

This also has to be done within the Google Analytics tracking code inserted in each page of the website.

The syntax to add a search engine is of the form:

_gaq.push(['_addOrganic','blekko','ws']);

where blekko is a string in the referrer domain and ws is the query parameter name.

This has to be inserted somewhere before the ['_trackPageview'] call, i.e. from the above example we can change

_gaq.push(['_trackPageview']);

to

_gaq.push(['_addOrganic','blekko','ws'],['_trackPageview']);

A more in depth discussion on working with the _addOrganic configuration option can be found in an article on Google Analytics search engines list customization.

Tracking Page Speed In Google Analytics?

During testing, it emerged that Google Analytics would ignore the value of _setReferrerOverride if _trackPageLoadTime was specified.

Using the Google Analytics debugger, it became clear that Google was deprecating _trackPageLoadTime so the message is simple: remove _trackPageLoadTime from existing tracking code to avoid unforeseen consequences.

Option 2: Transforming Collected Data

Should sites not be able to modify their Google Analytics tracking code, or should their Web analytics system not provide a referral override option, there is a second approach which should work as well. The technique involves transforming the collected data before reports are created.

In Google Analytics, Profile filters perform this task. Filters are a wonderful, but underutilized, Google Analytics feature and this is a great example to show off their potential. The process is:

1. Identify search referrals from blekko
2. Update the medium, source and term campaign fields accordingly.

Unfortunately, Google Analytics only allows a profile filter to update one field at a time, so it will be necessary to create multiple filters.

To do this, enter the Google Analytics administration area and create new advanced custom profile filters as illustrated below. Filters can be created at the account level, making it easy to quickly apply the filters to multiple profiles.

Figure 2: Blekko set campaign medium filter.

The above filter selects referral traffic from Blekko which contains /ws/ in the URL and changes the Google Analytics Campaign Medium from referral to organic (required for blekko traffic to appear in the organic search report).

Currently, it isn’t strictly necessary to include the Field B requirement, but it may be helpful in order differentiate from paid search should Blekko offer it in the future. Note that the values referral and organic must be in English, even if the Google Analytics interface is in a different language.

Figure 3: Blekko set campaign source filter

Google Analytics will automatically identify blekko.com as the source of traffic, however, some may want to see the source report as “blekko” instead of “blekko.com” in their reports, so as to be consistent with the other search traffic sources. The above filter, which is optional, will do just that.

Figure 4: Blekko set campaign terms filter

The set campaign terms filter captures the user’s search query and places it in the Campaign Term field, the source of the keywords which appear in the Google Analytics organic search traffic report.

The key is in the regular expression specified in Field A:

^http://blekko\.com/ws/(.+)\??

It says, select referrers which start with http://blekko.com/ws/, capture everything that follows up to a question mark, which may or may not be present, and put the result in the campaign terms field.

Astute readers might note there is still one problem remaining: multiple keywords will still be separated by one or more plus signs which should be converted to spaces.

Unfortunately, it doesn’t seem that Google Analytics supports global (multiple) substitution using filters, but in the spirit of crowd sourcing, a reader may want to chime in to improve this last step.

One work around might be to create multiple substitution filters which will replace a single + with a single space:

Figure 5: Filter to substitute one plus sign, +, in blekko search keywords, with a space

Once the profile filters are created, they need to be activated by apply them to each Google Analytics profile.

The order in which the filters are applied is not important, with the exception of the plus sign substitution filters, which must come after the set campaign term filter. A Google Analytics best practice is to keep at least one profile without any filters as once data has been transformed by filters as there isn’t any way to revert filter changes.

An unadulterated profile is also helpful in troubleshooting any issues which may arise. New filters can (and should) be tested on a new “throw-away” profile before being applied to production profiles.

A Note About blekko’s Slash Tags

One of Blekko’s distinguishing features is the ability, encouraged by blekko, of users to further restrict their search queries using search operators and filters, what blekko calls slashtags. As the slash tags do condition the user’s query, and to a degree express the user’s intent, neither of the approaches illustrated above attempts to remove slashtags, if present, from the search query keywords.

For our purposes here in Link Week, I’ll be reviewing the Site Explorer, Backlink History, Neighbourhood Checker, and Clique Hunter.

Basic Tool Functions

First up is the Site Explorer, which takes your URL and returns tons of information: Summary, Top Backlinks, Referring Domains, and Top Pages. Each section is represented on the results page, but you can click on the tabs and get more in-depth info if you like. You can choose the Fresh Index or the Historic Index and run the report on either the domain, the subdomain, or the page itself.

My favorite tool, the Backlink History, also shows up here in the results page but not in its own tab. Now, a note on the Fresh vs. Historic Index: the Fresh index gives you information from a rolling 30 day period and is more, um, fresh, while the Historic index gives you everything in Majestic’s history for a site.

The Domain Information gives you your Referring Domains and External Backlinks, so you can quickly tell if you have a lot of sitewides (if you have 10k backlinks and 10 referring domains, yes, you have a lot of sitewides.)

Your backlinks and referring domains are broken down into educational and governmental and the following information is displayed: Referring IP Addresses, Class C Subnets, Indexed URLs, Images, Nofollow Links, Redirects, Frames, and Deleted Links. You’ll also see a number called the Majestic Million which is a list of the top one million domains as rated by Majestic SEO.

The Backlink History shows your 2 charts: 1 is your external backlinks discovery over the past 12 months and the other is your referring domains discovery over the past 12 months. You can click on these two charts in order to go directly to the Backlink History tool and get more information.

The Top Backlinks area shows the Source URL, Anchor Text, Target URL, and Last Crawl Date.

The Referring Domains area lists the top domains that link to your URL.

The Top Pages area lists your top pages: Title, URL, AC Rank (which is a measure of how important a page is based on the number of unique referring external root domains), Date, External Backlinks, and Referring Domains.

How To Interpret The Link Data

This is a wealth of information for analyzing a backlink profile. If you’re analyzing your own site, I see numerous uses:

• Identifying the top pages that link to you and making sure these links stay up. It’s tricky to watch all your backlinks if you have a lot of them, but if you’re too overwhelmed, just identify the top ones and track those.

• Identifying your site’s top pages so that you can continue to build good links to these in particular (and go through your site to ensure that your top pages link to other pages that you think are critical but aren’t viewed as top ones just yet.)

• Identifying pages of lower importance so that you can work on their content in hopes of generating more backlinks.

For competitive analysis, this information can easily be used to see where you stand compared to others in your niche.

For potential link partners, use this data to identify the top pages on a site and go after a link on that page. With so much information, there are truly countless ways of using this data to help better your link building efforts.

Note: I’d love to include a screenshot here but the information you get takes up a large amount of space and I don’t think I can do it justice. Since you can see the Site Explorer for free, even if you can’t get all the detailed data, I’d suggest checking it out for yourself.

Next up is the Backlink History, which has been something I have relied upon many times (especially when clients are trying to convince me that they know their competitors aren’t matching/beating their own link building efforts.) You can again use either the fresh or historic index here and you can compare up to five domains. Currently, subdomains are ignored.

This tool shows backlink discovery by month, so let’s get a screenshot of a few SEO sites to compare (Disclaimer: I write for the three sites used here):

What you see here is the default view, which is a set of two graphs: backlinks discovery and referring domains discovery in non-cumulative view.

You can view the charts in Monthly, Cumulative, or Normalized forms, see them as a Spline, Line, Column or Area chart, and choose whether you want to see data from 1 year, 2 years, 5 years, or all time.

Majestic SEO advises that you use the Normalized view and/or compare domains in order to get the most comparable data down at the bottom of the page. In these charts, you’ll see how you compare to your competitors.

It’s interesting to compare the two charts as well because of the difference in referring domains and backlinks discovery (and for those of you who are new to link building concepts, referring domains would show a value of 1 even if there were 150 links to a site on that same site, but it would show a value of 150 if you were looking at backlinks.)

According to the chart in the screenshot, Search Engine Land has traditionally built more backlinks than the other two but the referring domains numbers between SEL and Search Engine Journal are quite close. (Poor SEO Chicks.) This would make me suspect that Search Engine Land is generating more instances of multiple links on the same domain. In any case, this is a seriously great tool to use to see how your backlinks/referring domains compare with others.

Now we’ll turn to the Neighbourhood Checker, which accepts either a domain or an IP address. As usual, you can choose either fresh or historic data. This tool shows you the most backlinked domains hosted on any IP or subnet so that you can get a better idea of potentially dangerous cohosted sites.

When you enter your site or IP, you get two lists: one that shows what is cohosted on your IP, and one that shows what is cohosted on your subnet. Here’s an edited screenshot, as I don’t want to rile anyone up:

The data shown lets you check the site out by visiting it, going to the Site Explorer, or seeing its Backlink History, so checking out what’s cohosted is pretty simple. You’re shown the location of the IP, referring domains, and external backlinks to each domain listed.

I would recommend using this tool in conjunction with others that give you a fuller picture, of course, since with the rise in spammy sites over the past decade, it’s truly difficult to run a report like this and not find something that might seem a bit dangerous at first glance. There are many ways in which this data could be interpreted, so I’ll leave that up to you.

Last but not least is the Clique Hunter which allows you to enter up to ten unique root domains (with a minimum of two) and shows you a representation of the main sites that link to the sites you’ve entered, therefore identifying your cliques.

The thickness of the line indicates that more links come from that domain to the domain it points to, so as you can see in the screenshot below, there are lots of links on blogspots that point to Search Engine Land.

Further down, you can better filter your results by increasing your minimum number of matches, number of results shown, and the depth of analysis. You can then sort the data by about a dozen metrics, ascending or descending, then sort further (like you’d do with a spreadsheet.)

You’ll see a list of domains along with the number of linked domains to those sites, matches, Alexa rank, and numbers for your queried domains. For each domain listed, you can visit the site, view the backlink history, see it in Site Explorer, or create an exportable CSV report. There are also links at the bottom that will allow you to view the queried domains in summary form on the Bulk Backlink Checker and the Backlink History.

Interestingly, this tool was inspired by Aaron Wall’s Hub Finder (my favorite of his set.) Just as with the Site Explorer, there is a multitude of great information here and I can see tons of ways to use it for link building but the main one is simple:

Identify the sites that link to your competitors but not to you, yet. Pursue links on those sites.

It’s worth mentioning that Majestic has a cool plugin that works in either IE (gasp) or Firefox. It lists Majestic SEO as one of your search engines so you can simply choose it and go to the Site Explorer page for a site.

Link Tool Takeaways

So there you have it…another amazing suite of tools that can ramp up your link building efforts, complete with a blog, video tutorials, and a constantly updated index.

Overall, Majestic is extremely easy to use and is quite intuitive, so even for a true beginner, I can’t see these tools being overwhelming. The amount of data could potentially overwhelm someone but honestly, that’s not a bad thing in my opinion. I’d rather have more than less. With the different packages available, even a small or solo operation would be well-advised to use this system.

Majestic also has a great blog that is searchable and contains posts about latest updates, along with tons of training posts and videos. If you can’t figure out how to use one of their tools, it’s not for their lack of trying. I actually found it all quite straightforward and intuitive though, and even for a novice tool user, I don’t think you’ll be stumped.

Finally, there is the API: I did not test this but it’s worth noting as it enables you to access key functions and gives you a development area where you can test applications without affecting your subscription resources (which include set numbers of reports you can run a month, for example.)

While we’re on the subject of reports, there are two types: standard and advanced. All subscription plans provide access to standard reports, which can be run for any website. A standard report gives you what Majestic believes is the key set of information including an overview, backlinks information, and anchor text information. Advanced reports gives you absolutely everything you could possibly want to know about a domain. These reports are also downloadable so that you can further analyze the data.

To sum it up, this is a massively powerful set of tools if you’re interested in link building. It’s intuitive, fast, and due to the different tiers of pricing, there’s an option for everyone. If you need less link-focused tools this may not be the suite for you, because there are no rank checkers or code validators, but for link building, it’s seriously top notch.

If you click on the SEO Tools link on the topnav, you’ll see a list of both free and paid tools. However, other pages on the site did lead me to a few other tools not specifically listed there, so I’ll do my best to give you a comprehensive list of what’s out there with regards to link building tools, since that’s the focus of this review.

Listed on the nav bar, we have the following tools that I have reviewed for link building purposes:

• Local Rank
• Hub Finder
• Competitive Research

Other particularly interesting public link tools I found through digging on the site include:

• Link Harvester
• Link Suggestion Tool
• Link Popularity Comparison Tool

There are also free Firefox add-ons (SEO for Firefox is included in this review), keyword tools, analytics tools, PPC tools, a rank checker, a site health check, and more. That’s one thing that makes this suite of tools so valuable; you don’t get tools that are only built for one aspect of marketing.

SEO For Firefox Plugin

First up is the free plugin SEO for Firefox. The SEO for Firefox plugin is truly one of the most indispensable tools out there in my opinion. All of my link builders use it and it’s my go-to quick analysis tool.

PageRank is the only thing that is automatically pulled into the tool, so if you want other metrics, you’ll have to customize it. There is some very helpful advice from Aaron about how to configure the extension to use the least resources so I’d highly recommend reading his instructions.

This extension can easily be turned on and off so that it’s not running nonstop, which I really like. Firefox is slow enough. Obviously, there is a lot of link data here. Metrics are as follows (listed in as small a space as possible since there are so many):

PR, Google Cache Date, Traffic Value, Age, del.icio.us, del.icio.us Page Bookmarks, Diggs, Diggs Popular Stories, Stumbleupon, Twitter, Y! links, Y! edu links, Y! gov links, Y! Page links, Y! edu page links, OSE links to page, OSE links to domain, Blekko domainlinks, Technorati, Alexa, Compete.com rank,, Compete.com uniques, Cached, Dmoz, Bloglines, Page blog links, wikipedia, dir.yahoo.com, BOTW, Business, Majestic SEO linkdomain

You can doubleclick a metric in the popup window and be taken to the source for further digging. I didn’t get data for many of the metrics, however, but issues like this are discussed in the instructions, as some of the data sources might not always be reliably pingable.

You can use SEO for Firefox as an on-demand service, like I do (which means that you can easily just rightclick, go to SEO for Firefox, and look up the page) or you can run it all the time and you’ll see the data at the bottom of the SERPs like this:

Again, the data you see is clickable and leads you to the source for more information.

Another handy part of this tool is that you can highlight nofollow links and external links and see images’ alt attributes through SEO XRay, which is a feature on the extension that gives you even more cool information such as is seen in the following screenshot:

How would you use this to help build links?

For starters, it tells you just about everything you need to know about a site that you’re thinking about getting a link on, and it’s a great way to do some competitive analysis. It’s not a tool to show you where to build links, but it’s a great analysis tool to firm up your decision.

As always though, don’t let metrics alone be the deciding factor in whether or not to pursue a link. Some sites may show low metrics for certain things (like the dreaded PageRank) so use caution when evaluating a site strictly by numbers.

Pros

• I love the on-the-fly capability of this, as I hate running tools that slow down my browser.
• I like that you can either see the desired data by clicking and grabbing it on the page, or by viewing it in the SERPs.
• For a quick overview on a site, I don’t know of anything that I find more useful.

Cons

• If you aren’t using Firefox, you’re out of luck.
• Not all of the data sources return information all the time.

Local Rank

Next we have Local Rank. Local Rank is a Member’s Only tool. The tool runs on the premise that sites that are well linked to from top results might get a rankings boost in Google for certain keywords.

Fields are as follows:

• The Keyword field is where you enter the keyword…pretty self-explanatory.
• The Scan First (x) Results lets you choose from the default 100 to 1000 results. This tells the tool how many results to scan.
• The Find LocalRank links pointing at the top search results field lets you choose from the default 10 to 50 results and tells the tool how many top results to find links to.
• The Region/Country field lets you choose where you want your query to be run, with Google.com as the default. From what I can tell, there are choices for just about every country-specific Google.

When you submit your query, you’ll see results like the following:

The tool lists the URLs found for the initial query along with crawl dates and both Yahoo and Majestic link counts.

It then uses a re-ranking algorithm to identify a list of sites that have are frequently cited from those sites. These are your likely link targets and they’re listed in the matrix below the initial results set. There is also a link to the Competitive Research Tool which is quite handy, and the matrix can be exported.

Here’s a screenshot:

Each URL has associated numbers running across the top, along with checkmarks that indicate that the site to the right of it is linking to the site at the top of the matrix. (Note: I did have to ask Aaron to clarify this one.)

Pros

• The Yahoo and Majestic link count data is pretty cool and takes you to the source in case you want to do any extra digging.

Cons

• While I was able to see the Yahoo data from the links in the results listed above the matrix, all of the non-URL results that I clicked on in the results matrix sent me to a Yahoo site explorer page with no results.

Competitive Research Tool

Now we’ll turn to the Competitive Research module which is a tool that’s only available to members. Enter in a URL or keyword, choose a datacenter, and you’re provided with competitive information including organic and pay per click.

Since I also do PPC work, this is a particularly cool tool for me because I could use it to help make recommendations about where to put the marketing efforts.

If you put in a URL you’ll just get data for that one in particular, and if you input a keyword, you’ll see a list of URLs along with corresponding numbers but the results available are a bit different, so it’s worth playing around with, using both URLs and keywords, in order to get a full picture.

You can either enter a URL or a keyword for this one. For the first example, I’ve entered the keyword “umbrellas” as it’s looking a bit rainy outside today. You need to make sure to hit the Keyword button of course (and obviously the Domain button if you’re entering a URL) and then choose a Google datacenter.

The keyword report shows you CPC, Competition, and Ave. Volume for your keyword and several related ones.

To get even more specific domain-related information, choose the Domain option. For this example I’ll use the New York Times since they are a well-known site and certainly can’t complain about being outed anywhere for anything.

Here you’ll see the following: Domain, Rank, Organic Keywords, Organic Traffic, Organic Cost, Adwords Keywords, Adwords Traffic, and Adwords Costs for the domain summary itself.

Next, you’ll see the following for fields for keyword-specific organic data: Keyword, Pos, Traffic %, Costs %, Costs CPC, Volume, Total Keyword value, Value of #1 Ranking, Current Position Value, Upside Potential, Upside Potential %, and URL. That’s a plethora of great data.

The data doesn’t stop here, however.

We also get tons of Adwords-specific information about the keywords (and quite honestly, I will just bore you if I continue to list all the fields so suffice it to say, you’ll be buried under information here), along with competitors’ information (again in both organic and Adwords-specific formats), then Potential Ads/Traffic Buyers listed in both Organic to Adwords Competition and Potential Ads/Traffic Sellers listed in both Organic to Adwords Competition.

Wow. For all of the sets of data, you have the option to get even more information. This tool alone could take up an entire review and I doubt I’d still cover it all.

So how would you use this for link building?

Tons of ways, I’m sure – but I’ll cover just a few ways I’d use the data.

1.  Just as it’s good to use organic data in conjunction with PPC data in order to maximize your outlay and resources, it’s good to use both of these to help decide where to hit links the hardest. By showing you what your URL ranks for in Google alongside the traffic percent and cost per click of that keyword, you can decide which anchors to go after.

2.  By using a keyword, you can see a list of related competing keywords alongside their cost per click and current value. Depending upon my budget, I’d make decisions on where to spend the most money to get the best results.

3.  Figure out how your competitors are doing in organic and paid search in order to better tailor your link efforts.

Pros

• This data is fantastic and the tool is easy to use. I love that it combines PPC and organic in a way that is applicable to link building. Usually, you don’t get that in one tool.
• The report runs quite quickly which is always nice.
• I like that you can either do this by keyword or by domain, depending upon your needs.

Cons

• Honestly, I can’t think of any, unless you happen to be a person who hates great data.

Hub Finder

The Hub Finder is another member’s only tool that finds instances of co-occurring links. It runs on the premise that a site that links out to several competitors in the same field should have editorial integrity and be willing to link to you as well. You can plug in between two and ten of your competitors in order to identify sites that link to as many of them as you specify.

So let’s get started with the fields and what to do with each one. Straightforward for many of you, but I’m such a literalist that I did have to ask Aaron a few questions in order to understand what to do with two of the fields.

• The Subject can be used to pull a list of relevant sites to query from either Google or Yahoo. You can also just enter your own URLs.
• The Results field is a dropdown list of 1 to a default value of 20 that simply specifies how many results you’d like to see when you run the tool.
• The API field is used to specify whether you want to use the API or Yahoo, Google, or both of them at the same time.
• Min Match tells the tool how many minimum matches to show, meaning that if you want to only see sites that link to all 5 of your competitors, you’d choose 5. If you wanted to see sites that linked to just 2 of your 10 competitors, you’d choose 2. Nice and straightforward stuff, even for me.
• The Depth field is used to specify how many top inbound links you want to search through for each domain, ranging from the default of 50 to 250.
• The optional Include This Site field is one that I did have to ask about, as I wasn’t sure what the purpose was. The answer made perfect sense: this field is used to force the inclusion of one URL in particular. I’ll share Aaron’s example here:

“an example…a site competing against one of my sites rebranded to a new URL. so I found links to them that were on any page that had links to other top sites in that vertical. figuring some of those are resources pages that might be willing to list more sites in the niche, I can then manually contact them & let them know about the updated link or broken link or whatever & then also pitch the link I want promoted as well.”

• The Link Type field is used to select either Domain or Page values.
• The Application field is used to select either Sort By or Must Link. Options are Sort By and Must Link.

So let’s get started, using a nice SEO example with the major SEO sites listed, forcing the include for the SEO Chicks site.

The results are mapped with a number corresponding to each competing URL, along with Xs that denote which sites have co-occuring links. There’s a highlight function that is activated if you click on one of the numbers that denotes a URL so you can more easily see specific instances of co-occurrences.

The results set also contains links to the following:

• Whois (W)
• Archive.org (A)
• Google cache (H)
• DMOZ (D)
• Yahoo Directory (Y)

You’ll see the IP address for each URL and be able to click through to the sites themselves. The report is also downloadable which is always handy.

Pros

• There is a lot of documentation for the Hub Finder, which was helpful.
• I love the results report, which makes it very easy to quickly see which sites link to competing URLs but not your own.
• It uses Yahoo mainly, not Google. Hooray!
• In addition to loving the results report, I love the results, period. Finding co-occuring links is a great way to find new opportunities for your own site.
• The report is downloadable.
• The report runs quickly.

Cons

• Error checking messages are non-existent on this tool. If you do something wrong, you just won’t see results and you may not understand that it is because you have misused the tool. I had to ask a few questions in order to understand how to use this properly, even after reading the instructions. Once you understand how exactly to use it, it makes sense of course, but I do think that error messages would be helpful to users.
• Some functionality just didn’t make sense to me. For example, if you choose the default Sort By in the Application, you will see results that don’t necessarily link to the site that you may have put into the Include This Site field. If you choose the Must Link, you’ll only see results that link to that site at a minimum. To me, this is a bit confusing since I’d assume that including a site would take care of this.
• The example image used in the instructions page had a field in it that was not present in my current version.

Bottom line: I’ve used this tool before (long enough ago that I forgot how it worked, oddly enough) and always been a fan of it. I love the idea behind it and the fact that it’s a great source for new links. Error checking messages are something I seem to need due to being a bit of a clumsy user, but as I said, once you understand what to do, the tool is straightforward.

Wrapup

My favorite tool in this suite was the Hub Finder. It’s straightforward and applicable to the way I build links. I also rely upon the SEO for Firefox plugin but that’s a free tool. I could also use more error messages as I have said, due to being a bit spastic and clicking without reading things carefully.

I would probably not purchase a membership strictly for the tools. However, the forum alone is worth the price. Heck, the monthly wrapup that Aaron does is worth the price! I do keep up on what’s going on but his October wrapup, available only to members, alerted me to tons of articles that I had not seen. If you’re busy and can afford the cost, I’d become a member for that alone as it honestly did cover just about everything that happened. Participants in the forum seem to be extremely open and helpful and just in skimming a few topics, I learned a lot.

There are also training videos, tons of archived articles, downloadable strategy guides, and quick start checklists. If you’re just starting out in SEO, I’d say this program would definitely benefit you. If you’re a seasoned SEO and want to get the same kind of information that you tend to get only at bars after a conference, this is what you’re looking for.

Majestic SEO is a popular tool used by SEOs for competitive link analysis. The main alternatives to the tool include SEOmoz’s LinkScape, RankAbove’s SEO Drive and the soon defunct Yahoo Site Explorer, amongst others.

The OpenApps feature allows developers and third-parties to build modules on the data. It allows developers to build modules to let their users request data from the Majestic SEO toolset. It is available to “all Majestic SEO subscribers on all plans,” while the commercial API is restricted to full reseller licensees. There is also no service level commitment to use the Open App method.

For more details on how to access the Majestic SEO OpenApps, see the blog and the developer area. Finally, for an example tool, see this page.

Related Stories:

• Enterprise SEO Tools, Part 2: The Cloud
• Yahoo Shutting Down Site Explorer This Year
• Link Building Tool Review: SEOmoz PRO
• Worthy Alternatives To The Useless SEO Data Provided By Search Engines
• SEOMoz’s New Toolbar & Linkscape Doubles In Size

We use the PPC permutator to help us rapidly expand keyword lists from simple ideas. Let’s work with an example. Consider the following keyword ideas related to a hypothetical bicycle shop. Using the tool, we take three simple ideas and generate 48 keywords.

• Terrain: mountain, road, touring
• Bike Synonyms: bike, bicycle, cycle
• Product: tire, tires, tube, tubes

We enter them in the tool, like this:

Push-Button List Generation

The tool built an extensive list of keywords from a few keyword ideas. If you can imagine typing out a list like this, the time savings might be an obvious benefit.

The more valuable benefit in my opinion is that it lets the campaign manager remain in “creative” mode without having to task-switch to “manual labor / typing” mode. The tool does the work of generating the list as the output. The campaign manager generates more ideas as the input. While this is valuable all by itself, there is more.

Change Input Easily

If we decide we also want to generate keywords like “bike tire” (i.e.: combining only the last two keyword ideas, skipping the first), then we just check a box and the tool makes it happen. Likewise, if we start selling “fixie” bike tires, we can simply add “fixie” to the list. The tool will generate the new keywords.

So another benefit is that it lets us change input easily without paying the penalty of having to produce the list again. We just refine the input and click a button.

Now if you have been doing PPC for some time, using tools to generate keyword lists may not be a new idea. Tools are available online, and you may have one of your own. Before you go, there is more. What if you want to place these keyword ideas into campaigns and ad groups?

Organize Keywords Into Campaigns & Ad Groups

In PPC it is important to break long lists of keywords into tightly themed ad groups. The key benefits of putting keywords in separate, tightly themed ad groups are clear: We can write targeted ads pointed at targeted landing pages.

We also get some sanity and manageability: ad level URLs, default ad group bidding, etc. The PPC permutator can help.

Let’s build ad groups based on the “root” idea of each keyword. We want separate ad groups for the terrain types, and for the tire type, but do not want separate bike synonym ad groups because we believe they all perform similarly and we want them in the same ad group. (By the way, if we wanted to put the bike synonyms into separate ad groups, we could do that too. This is just an example.)

This is one feature that is not always found in a tool of this sort. We can add some “meta data” about the “root” of each word, and the tool carries that information forward for us to use. First, we add the roots for each word:

Then we use that information to help us generate campaigns and ad groups in the output:

We have some options here. We could have changed the “sequence” of the ad group names if we wanted “Product(Tire) Type(Mountain)” rather than “Type(Mountain) Product(Tire)”, for example.

We also could have chosen to use the bike synonym root and generated ad group names like “Product(Tire) Type(Mountain) Bike(Cycle)”. We could use a different naming convention and change the separator to “+”, e.g.: “tire+mountain+cycle”. The tool helps us organize keywords into campaigns and ad groups according to our needs.

Define Match Types

In PPC, we also want to produce keyword lists with match types. Once again, the tool is ready to do the heavy lifting for us. Of course, in practice we would not blindly add “everything on all match types” — this is just an example.

The tool also works with AdWords’ Broad Match Modifier. We just add the “+” to the original words where we want it, and the tool caries it forward (and removes it for non-broad-match instances of the keyword).

Conclusion

The PPC permutator is one of several tools we use around our PPC shop. The primary benefits we see are:

• Push-button list generation
• Change input easily
• Organize keywords into campaigns and ad groups
• Define match types

If you would like a copy of the PPC permutator, feel free to send me an email. You could also adapt these ideas to your own tools. Have fun out there.